Forests

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26 pages, 19147 KiB

Open AccessArticle

Ecological Gate Water Control and Its Influence on Surface Water Dynamics and Vegetation Restoration: A Case Study from the Middle Reaches of the Tarim River

by Jie Wu, Fan Gao, Bing He, Fangyu Sheng, Hailiang Xu, Kun Liu and Qin Zhang

Forests 2024, 15(11), 2005; https://doi.org/10.3390/f15112005 - 14 Nov 2024

Viewed by 417

Abstract

Ecological sluices were constructed along the Tarim River to supplement the ecosystem’s water supply. However, the impact of water regulation by these sluices on the surface water area (SWA) and its relationship with the vegetation response remain unclear. To increase the efficiency of [...] Read more.

Ecological sluices were constructed along the Tarim River to supplement the ecosystem’s water supply. However, the impact of water regulation by these sluices on the surface water area (SWA) and its relationship with the vegetation response remain unclear. To increase the efficiency of ecological water use, it is crucial to study the response of SWA to water control by the ecological gates and its relationship with vegetation restoration. We utilized the Google Earth Engine (GEE) cloud platform, which integrates Landsat-5/7/8 satellite imagery and employs methods such as automated waterbody extraction via mixed index rule sets, field investigation data, Sen + MK trend analysis, mutation analysis, and correlation analysis. Through these techniques, the spatiotemporal variations in SWA in the middle reaches of the Tarim River (MROTR) from 1990–2022 were analyzed, along with the relationships between these variations and vegetation restoration. From 1990–2022, the SWA in the MROTR showed an increasing trend, with an average annual growth rate of 12.47 km² per year. After the implementation of ecological gates water regulations, the SWA significantly increased, with an average annual growth rate of 28.8 km² per year, while the ineffective overflow within 8 km of the riverbank notably decreased. The NDVI in the MROTR exhibited an upward trend, with a significant increase in vegetation on the northern bank after ecological sluice water regulation. This intervention also mitigated the downward trend of the medium and high vegetation coverage types. The SWA showed a highly significant negative correlation with low-coverage vegetation within a 5-km range of the river channel in the same year and a significant positive correlation with high-coverage vegetation within a 15-km range. The lag effect of SWA influenced the growth of medium- and high-coverage vegetation. These findings demonstrated that the large increase in SWA induced by ecological gate water regulation positively impacted vegetation restoration. This study provides a scientific basis for water resource regulation and vegetation restoration in arid regions globally. Full article

(This article belongs to the Special Issue Application of Remote Sensing in Vegetation Dynamic and Ecology)

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17 pages, 7929 KiB

Open AccessArticle

Optimizing Forest Canopy Height Estimation Through Varied Photon-Counting Characteristic Parameter Analysis, Window Size, and Forest Cover

by Jiapeng Huang, Jathun Arachchige Thilini Madushani, Tingting Xia and Xinran Gan

Forests 2024, 15(11), 1957; https://doi.org/10.3390/f15111957 - 7 Nov 2024

Viewed by 772

Abstract

Forests are an important component of the Earth’s ecosystems. Forest canopy height is an important fundamental indicator for quantifying forest ecosystems. The current spaceborne photon-counting Light Detection and Ranging (LiDAR) technique has photon cloud characteristic parameters to estimate forest canopy height, and factors [...] Read more.

Forests are an important component of the Earth’s ecosystems. Forest canopy height is an important fundamental indicator for quantifying forest ecosystems. The current spaceborne photon-counting Light Detection and Ranging (LiDAR) technique has photon cloud characteristic parameters to estimate forest canopy height, and factors such as the sampling window size have not been quantitatively studied. To better understand the precision for estimating canopy height using spaceborne photon-counting LiDAR ICESat-2/ATLAS (Ice, Cloud, and Land Elevation Satellite-2/Advanced Topographic Laser Altimeter System), this study quantified the impact of photon-counting characteristic parameters, sampling window size, and forest cover. Estimation accuracy was evaluated across nine study areas in North America. The findings revealed that when the photon-counting characteristic parameter was set to H70 (70% of canopy height) and the sampling window length was 20 m, the estimation results aligned more closely with the airborne validation data, yielding superior accuracy evaluation indicators with a root mean square error (RMSE) of 4.13 m. Under forest cover of 81%–100%, our algorithms exhibited high estimation accuracy. These study results offer novel perspectives for the application of spaceborne photon-counting LiDAR ICESat-2/ATLAS in forestry. Full article

(This article belongs to the Special Issue Application of Remote Sensing in Vegetation Dynamic and Ecology)

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15 pages, 5676 KiB

Open AccessArticle

The Spatiotemporal Dynamics of Vegetation Cover and Its Response to the Grain for Green Project in the Loess Plateau of China

by Yinlan Huang, Yunxiang Jin and Shi Chen

Forests 2024, 15(11), 1949; https://doi.org/10.3390/f15111949 - 6 Nov 2024

Viewed by 554

Abstract

The Grain for Green Project (GGP) is a major national initiative aimed at ecological improvement and vegetation restoration in China, achieving substantial ecological and socio-economic benefits. Nevertheless, research on vegetation cover trends and the long-term restoration efficacy of the GGP in the Loess [...] Read more.

The Grain for Green Project (GGP) is a major national initiative aimed at ecological improvement and vegetation restoration in China, achieving substantial ecological and socio-economic benefits. Nevertheless, research on vegetation cover trends and the long-term restoration efficacy of the GGP in the Loess Plateau remains limited. This study examines the temporal–spatial evolution and sustainability of vegetation cover in this region, using NDVI data from Landsat (2000–2022) with medium-high spatial resolution. The analytical methods involve Sen’s slope, Mann–Kendall non-parametric test, and Hurst exponent to assess trends and forecast sustainability. The findings reveal that between 2000 and 2022, vegetation coverage in the Loess Plateau increased by an average of 0.86% per year (p < 0.01), marked by high vegetation cover expansion (173 × 10³ km², 26.49%) and low vegetation cover reduction (149 × 10³ km², 22.83%). The spatial pattern exhibited a northwest-to-southeast gradient, with a transition from low to high coverage levels, reflecting a persistent increase in high vegetation cover and decrease in low vegetation cover. Approximately 93% of the vegetation cover in the Loess Plateau showed significant improvement, while 5% (approximately 31 × 10³ km²) displayed a degradation trend, mainly in the urbanized and Yellow River Basin regions. Projections suggest that 90% of vegetation cover will continue to improve. In GGP-targeted areas, high and medium-high levels of vegetation cover increased significantly at rates of 0.456 ×10³ km²/year and 0.304 × 10³ km²/year, respectively, with approximately 75% of vegetation cover levels exhibiting positive trends. This study reveals the effectiveness of the GGP in promoting vegetation restoration in the Loess Plateau, offering valuable insights for vegetation recovery research and policy implementation in other ecologically fragile regions. Full article

(This article belongs to the Special Issue Application of Remote Sensing in Vegetation Dynamic and Ecology)

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20 pages, 7027 KiB

Open AccessArticle

The Role of Climate Change and Human Intervention in Shaping Vegetation Patterns in the Fen River Basin of China: Implications of the Grain for Green Program

by Kaijie Niu, Geng Liu, Cun Zhan and Aiqing Kang

Forests 2024, 15(10), 1733; https://doi.org/10.3390/f15101733 - 29 Sep 2024

Viewed by 836

Abstract

The Fen River Basin (FRB), an ecologically fragile region in China, exemplifies the intricate interplay between vegetation dynamics and both climatic and human-driven factors. This study leverages a 40-year (1982–2022) dataset, utilizing the kernel-based normalized difference vegetation index (kNDVI) alongside key climatic variables—rainfall [...] Read more.

The Fen River Basin (FRB), an ecologically fragile region in China, exemplifies the intricate interplay between vegetation dynamics and both climatic and human-driven factors. This study leverages a 40-year (1982–2022) dataset, utilizing the kernel-based normalized difference vegetation index (kNDVI) alongside key climatic variables—rainfall (PRE), temperature (TMP), and solar radiation (SRAD)—to investigate vegetation variations and their drivers in the FRB, particularly in relation to the Grain for Green Program (GGP). Our analysis highlights significant greening across the FRB, with the kNDVI slope increasing by 0.0028 yr⁻¹ and green-covered areas expanding by 92.8% over the study period. The GGP facilitated the greening process, resulting in a notable increase in the kNDVI slope from 0.0005 yr⁻¹ to 0.0052 yr⁻¹ and a marked expansion in the area of significant greening from 24.6% to 95.8%. Regional climate shifts, characterized by increased warming, heightened humidity, and a slight rise in SRAD, have further driven vegetation growth, contributing 75%, 58.7%, and 23.6% to vegetation dynamics, respectively. Notably, the GGP has amplified vegetation’s sensitivity to climatic variables, with areas significantly impacted by multiple climate factors expanding from 4.8% to 37.5%. Specially, PRE is the primary climatic influence, impacting 71.3% of the pertinent regions, followed by TMP (60.1%) and SRAD (30%). The integrated effects of climatic and anthropogenic factors, accounting for 47.8% and 52.2% of kNDVI variations, respectively, collectively influence 96% of the region’s vegetation dynamics. These findings underscore the critical role of climate change and human interventions in shaping vegetation patterns and provide a robust foundation for refining ecological conservation strategies, particularly in the context of global warming and land-use policies. Full article

(This article belongs to the Special Issue Application of Remote Sensing in Vegetation Dynamic and Ecology)

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24 pages, 8315 KiB

Open AccessArticle

Spatiotemporal Changes in Vegetation Cover during the Growing Season and Its Implications for Chinese Grain for Green Program in the Luo River Basin

by Xuning Qiao, Jing Zhang, Liang Liu, Jinyuan Zhang and Tongqian Zhao

Forests 2024, 15(9), 1649; https://doi.org/10.3390/f15091649 - 19 Sep 2024

Viewed by 710

Abstract

The Grain for Green Program (GFGP) plays a critical role in enhancing watershed vegetation cover. Analyzing changes in vegetation cover provides significant practical value in guiding ecological conservation and restoration in vulnerable regions. This study utilizes MOD13Q1 NDVI data to construct the Kernel [...] Read more.

The Grain for Green Program (GFGP) plays a critical role in enhancing watershed vegetation cover. Analyzing changes in vegetation cover provides significant practical value in guiding ecological conservation and restoration in vulnerable regions. This study utilizes MOD13Q1 NDVI data to construct the Kernel Normalized Difference Vegetation Index (kNDVI) and analyzes the spatiotemporal evolution and future trends of vegetation cover from 2000 to 2020, covering key periods of the GFGP. The study innovatively combines the optimal parameter geographic detector with constraint lines to comprehensively reveal the nonlinear constraints, intensities, and critical thresholds imposed by various driving factors on the kNDVI. The results indicate that the following: (1) The vegetation cover of the Luo River Basin increased significantly between 2000 and 2020, with a noticeable increase in the percentage of high-quality vegetation. Spatially, the vegetation cover followed a pattern of being “high in the southwest and low in the northeast”, with 73.69% of the region displaying improved vegetation conditions. Future vegetation degradation is predicted to threaten 59.40% of the region, showing a continuous or future declining trend. (2) The primary driving factors for changes in the vegetation cover are evapotranspiration, elevation, population density, and geomorphology type, with temperatures and GDP being secondary factors. Dual-factor enhancement or nonlinear enhancement was observed in interactions among the factors, with evapotranspiration and population density having the largest interaction (q = 0.76). (3) The effects of driving factors on vegetation exhibited various patterns, with thresholds existing for the hump-shaped and concave-waved types. The stability of the kNDVI in 40.23% of the areas showed moderate to high fluctuations, with the most significant fluctuations observed in low-altitude and high-temperature areas, as well as those impacted by dense human activities. (4) By overlaying the kNDVI classifications on the GFGP areas, priority reforestation areas totaling 68.27 km² were identified. The findings can help decisionmakers optimize the next phase of the GFGP and in effective regional ecological management. Full article

(This article belongs to the Special Issue Application of Remote Sensing in Vegetation Dynamic and Ecology)

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20 pages, 39129 KiB

Open AccessArticle

Cold and Wet Island Effect in Mountainous Areas: A Case Study of the Maxian Mountains, Northwest China

by Beibei He, Donghui Shangguan, Rongjun Wang, Changwei Xie, Da Li and Xiaoqiang Cheng

Forests 2024, 15(9), 1578; https://doi.org/10.3390/f15091578 - 9 Sep 2024

Viewed by 719

Abstract

The Maxian Mountains, characterized by high altitudes and abundant vegetation, create a cooler and more humid environment compared to the surrounding areas, and are highly susceptible to climate change. In order to study the cold and wet island effects in the Maxian Mountains, [...] Read more.

The Maxian Mountains, characterized by high altitudes and abundant vegetation, create a cooler and more humid environment compared to the surrounding areas, and are highly susceptible to climate change. In order to study the cold and wet island effects in the Maxian Mountains, air temperature and relative humidity (RH) were analyzed using meteorological station data. Additionally, spatial variations were examined by retrieving Land Surface Temperature (LST) and the Temperature Vegetation Dryness Index (TVDI) from 2001 to 2021. The most pronounced cold island effect was observed in the mountainous area during summer, mainly in May and July. The most significant wet island effect was observed from March to May, with an average relative humidity difference of 24.72%. The cold island area index, as an indicator of the cold island effect, revealed an increasing trend in the summer cold island effect in recent years. The cooling intensity ranged from 5 to 10 °C, with variations observed between 500 and 1000 m. A 30% increase in wet island effects in summer was observed, with a humidification intensity within a range of 500 m. Geodetector analysis identified vegetation cover as the primary factor affecting the thermal environment in mountainous areas. The increase in vegetation in mountainous areas was identified as the main reason for enhancing the cold and wet island effects. The findings emphasize the role of vegetation in enhancing cold and wet island effects, which is crucial for understanding and preserving mountainous regions. Full article

(This article belongs to the Special Issue Application of Remote Sensing in Vegetation Dynamic and Ecology)

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21 pages, 23150 KiB

Open AccessArticle

Analysis of the Influence of Driving Factors on Vegetation Changes Based on the Optimal-Parameter-Based Geographical Detector Model in the Yima Mining Area

by Zhichao Chen, Honghao Feng, Xueqing Liu, Hongtao Wang and Chengyuan Hao

Forests 2024, 15(9), 1573; https://doi.org/10.3390/f15091573 - 7 Sep 2024

Cited by 1 | Viewed by 727

Abstract

The growth of vegetation directly maintains the ecological security of coal mining areas. It is of great significance to monitor the dynamic changes in vegetation in mining areas and study the driving factors of vegetation spatial division. This study focuses on the Yima [...] Read more.

The growth of vegetation directly maintains the ecological security of coal mining areas. It is of great significance to monitor the dynamic changes in vegetation in mining areas and study the driving factors of vegetation spatial division. This study focuses on the Yima mining area in Henan Province. Utilizing MODIS and multi-dimensional explanatory variable data, the Theil–Sen Median + Mann–Kendall trend analysis, variation index, Hurst index, and optimal-parameter-based geographical detector model (OPGD) are employed to analyze the spatiotemporal changes and future trends in the EVI (enhanced vegetation index) from 2000 to 2020. This study further investigates the underlying factors that contribute to the spatial variation in vegetation. The results indicate the following: (1) During the period studied, the Yima mining area was primarily characterized by a moderate-to-low vegetation cover. The area exhibited significant spatial variation, with a notable pattern of “western improvement and eastern degradation”. This pattern indicated that the areas that experienced improvement greatly outnumbered the areas that underwent degradation. Moreover, there was an inclination towards a deterioration in vegetation in the future. (2) Based on the optimal parameter geographic detector, it was found that 2 km was the optimal spatial scale for the analysis of the driving factors of vegetation change in this area. The optimal parameter combination was determined by employing five spatial data discretization methods and selecting an interval classification range of 5–10. This approach effectively addresses the subjective bias in spatial scales and data discretization, leading to enhanced accuracy in vegetation change analysis and the identification of its driving factors. (3) The spatial heterogeneity of vegetation is influenced by various factors, such as topography, socio-economic conditions, climate, etc. Among these factors, population density and mean annual temperature were the primary driving forces in the study area, with Q > 0.29 and elevation being the strongest explanatory factor (Q = 0.326). The interaction between temperature and night light was the most powerful explanation (Q = 0.541), and the average Q value of the interaction between the average annual temperature and other driving factors was 0.478, which was the strongest cofactor among the interactions. The interactions between any two factors enhanced their impact on the vegetation’s spatial changes, and each driving factor had its suitable range for affecting vegetative growth within this region. This research provides scientific support for conserving vegetation and restoring the ecological system. Full article

(This article belongs to the Special Issue Application of Remote Sensing in Vegetation Dynamic and Ecology)

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24 pages, 27069 KiB

Open AccessArticle

Impacts of Intensified Human Activity on Vegetation Dynamics in the Qinba Mountains, China

by Haodong Liu, Maojuan Li, Tianqi Li, Liyang Wu and Hui Zheng

Forests 2024, 15(9), 1561; https://doi.org/10.3390/f15091561 - 5 Sep 2024

Viewed by 582

Abstract

The Qinba Mountain range is a typical climate-sensitive and ecologically fragile region. Monitoring of vegetation dynamics is crucial for ecological protection and achieving sustainable development goals. Various mutation-detection methods, along with slope analysis, hot-spot analysis, and residual analysis, were used to examine changes [...] Read more.

The Qinba Mountain range is a typical climate-sensitive and ecologically fragile region. Monitoring of vegetation dynamics is crucial for ecological protection and achieving sustainable development goals. Various mutation-detection methods, along with slope analysis, hot-spot analysis, and residual analysis, were used to examine changes in the Normalized Difference Vegetation Index (NDVI) during the growing and non-growing seasons over 41 years and to distinguish the relative effects of the drivers. This revealed four key findings. (1) NDVI increased at 0.02 decade⁻¹, with mutation points in 2006 for growing-season NDVI and 2007 for non-growing-season NDVI. (2) The trend in NDVI changed markedly at the mutation point. After the mutation point, NDVI was impacted more by human activity than by climate change. The hot and cold spots of the NDVI trend rate change in location and range in the growing season; in the non-growing season, it shows an obvious north–south distribution. (3) The spatial patterns in the effects of the drivers changed at this point. In the growing season, before this point, climate change and human activity collectively enhanced NDVI in ca. 81.3% of the region; after the mutation point, this value declined to 59.9% of the area, and human activity became the dominant driver in the area formerly dominated by both factors in combination. In the non-growing season, after the mutation point, the areas where both factors promoted vegetation growth decreased by 12.6% and those where climate change alone promoted it decreased by 11.1%, whereas the area affected only by human activity increased by 11.6%. (4) Before this point, human activity contributed >60% to the change in NDVI in the western Qinling region, with climate change contributing >60% in the other areas. After this point, human activity exerted a stronger influence than climate change, contributing >60% to enhancing vegetation growth and >80% reducing it. These findings provide a scientific basis for protecting the Qinba Mountain ecosystem and are essential for achieving sustainable development goals. Full article

(This article belongs to the Special Issue Application of Remote Sensing in Vegetation Dynamic and Ecology)

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21 pages, 13034 KiB

Open AccessArticle

Variations of Terrestrial Net Ecosystem Productivity in China Driven by Climate Change and Human Activity from 2010 to 2020

by Mei Xu, Bing Guo and Rui Zhang

Forests 2024, 15(9), 1484; https://doi.org/10.3390/f15091484 - 24 Aug 2024

Viewed by 744

Abstract

Net ecosystem productivity (NEP) plays an important role in the quantitative evaluation of carbon source/sinks in terrestrial ecosystems. This study used Theil–Sen median trend analysis, the Mann–Kendall method, and the Geodetector model to analyze the variation patterns and its dominant factors of NEP [...] Read more.

Net ecosystem productivity (NEP) plays an important role in the quantitative evaluation of carbon source/sinks in terrestrial ecosystems. This study used Theil–Sen median trend analysis, the Mann–Kendall method, and the Geodetector model to analyze the variation patterns and its dominant factors of NEP from 2010 to 2020. The results showed the following: (1) During 2010–2020, the spatial distribution of carbon sinks in China’s terrestrial ecosystems showed a pattern of high in the southeast and low in the northwest. The area with NEP < 0 accounted for 44.74% of the total area, while the area with NEP > 0 accounted for 55.26%. (2) The northwest region belonged to the significant carbon source, while the other regions belonged to significant carbon sinks. (3) The annual average NEP in different sub-regions showed an increasing trend. During 2010–2020, the overall NEP in China showed a trend in shifting from low-level to high-level, indicating that the NEP of terrestrial ecosystems in China increased during the past 11 years. (4) The NEP gravity center in Northeast China showed a trend in moving southward and then northward, while that of the NEP gravity center in East China showed a circular migration trend of ‘northwest-southwest–northeast–southeast’. The gravity center of NEP in Northwest China was moving northeastward. The migration trajectory of the NEP gravity center in Southwest China presented a “Z” shape. The change in the gravity center of NEP in the central and southern regions had a strong circuitous nature, and the overall trend was to migrate southward. (5) The combined actions of climate change and human activities were the main reason for the change in NEP in China’s terrestrial ecosystem from 2010 to 2020. (6) There were obvious differences in the dominant driving factors of NEP evolution in different regions and different periods in the past 40 years. Full article

(This article belongs to the Special Issue Application of Remote Sensing in Vegetation Dynamic and Ecology)

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19 pages, 17938 KiB

Open AccessArticle

Land Surface Temperature May Have a Greater Impact than Air Temperature on the Autumn Phenology in the Tibetan Plateau

by Hanya Tang, Xizao Sun, Xuelin Zhou, Cheng Li, Lei Ma, Jinlian Liu, Zhi Ding, Shiwei Liu, Pujia Yu, Luyao Jia and Feng Zhang

Forests 2024, 15(8), 1476; https://doi.org/10.3390/f15081476 - 22 Aug 2024

Viewed by 715

Abstract

The Tibetan Plateau (TP), with its unique geographical and climatic conditions, holds a significant role in global climate change. Therefore, it is particularly urgent to fully understand its vegetation phenology. Herbaceous plants are widely distributed in the TP. However, previous studies have predominantly [...] Read more.

The Tibetan Plateau (TP), with its unique geographical and climatic conditions, holds a significant role in global climate change. Therefore, it is particularly urgent to fully understand its vegetation phenology. Herbaceous plants are widely distributed in the TP. However, previous studies have predominantly examined the impact of air temperature on the end date of the vegetation growing season (EOS), with less emphasis on the influence of land surface temperature (LST). In this study, the dynamic changes in the EOS from 2001 to 2020 were analyzed by utilizing the Normalized Difference Vegetation Index (NDVI) data published by NASA. Furthermore, the impact of climate change on the EOS was examined, and the dominant factor (air temperature, LST, or precipitation) influencing the EOS was identified. The main findings were as follows: the average annual EOS predominantly occurred between day of year (DOY) 240 and 280, with an advance from the edge of the plateau to the center. The EOS across the entire region displayed a marginal tendency towards delay, with an average rate of 0.017 days/year. Among all vegetation, shrubs showed the most pronounced delay at a rate of 0.04 days/year. In terms of precipitation, the impact of climate change increased precipitation in both summer and autumn, which could delay EOS. In terms of temperature, an increase in summer T_min, autumn air temperatures and summer LST delayed the EOS, while an increase in autumn LST advanced the EOS. Compared to air temperature and precipitation, LST had a stronger controlling effect on the EOS (the largest pixel area dominated by LST). These results could offer new insights for enhancing the parameters of vegetation phenology models across the TP. Full article

(This article belongs to the Special Issue Application of Remote Sensing in Vegetation Dynamic and Ecology)

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22 pages, 10272 KiB

Open AccessArticle

Monitoring of Flash Drought on the Loess Plateau and Its Impact on Vegetation Ecosystems

by Yanmin Jiang, Haijing Shi, Zhongming Wen, Xihua Yang, Youfu Wu, Li Li, Yuxin Ma, John R. Dymond, Minghang Guo, Junfeng Shui and Hong Hu

Forests 2024, 15(8), 1455; https://doi.org/10.3390/f15081455 - 18 Aug 2024

Viewed by 892

Abstract

Flash drought (FD) has attracted much attention due to its severe stress on vegetation ecosystems. Yet to date, the impacts of FD on vegetation ecosystems in different regions have not been fully evaluated and explored, especially for ecologically fragile areas. In this study, [...] Read more.

Flash drought (FD) has attracted much attention due to its severe stress on vegetation ecosystems. Yet to date, the impacts of FD on vegetation ecosystems in different regions have not been fully evaluated and explored, especially for ecologically fragile areas. In this study, we identified the FD events in the Loess Plateau from 2000 to 2023 based on the attenuation rate in soil moisture percentile over time. The evolution process of FD, the driving roles of meteorological conditions and the responses of different vegetation types to FD were explored by vegetation indicators such as solar-induced chlorophyll fluorescence (SIF), SIF_yield, SIF-RCI, etc. The results showed that FD events were predominantly concentrated in wetter areas with dense vegetation, with the highest frequency being 29. Meteorological factors contributed differently to the occurrence and development of FD. The responses of vegetation to FD were not only related to vegetation types (cropland was more sensitive to FD than forest and grassland) but were also significantly influenced by background climate. The SIF_yield anomaly of vegetation was more sensitive than SIF anomaly and SIF-RCI. The results advance our understanding of the formation mechanisms of FD and facilitate the exploration of vegetative photosynthetic responses to FD. Full article

(This article belongs to the Special Issue Application of Remote Sensing in Vegetation Dynamic and Ecology)

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19 pages, 5213 KiB

Open AccessArticle

Revealing the Nonlinear Impact of Human Activities and Climate Change on Ecosystem Services in the Karst Region of Southeastern Yunnan Using the XGBoost–SHAP Model

by Bao Zhou, Guoping Chen, Haoran Yu, Junsan Zhao and Ying Yin

Forests 2024, 15(8), 1420; https://doi.org/10.3390/f15081420 - 13 Aug 2024

Viewed by 1388

Abstract

The Karst region is a critical ecological barrier and functional zone in China. Understanding the spatiotemporal evolution of its ecosystem services and its relationship with human activities and climate change is of importance for achieving regional ecological protection and high-quality development. In this [...] Read more.

The Karst region is a critical ecological barrier and functional zone in China. Understanding the spatiotemporal evolution of its ecosystem services and its relationship with human activities and climate change is of importance for achieving regional ecological protection and high-quality development. In this study, we used the InVEST model and CASA model to evaluate the spatiotemporal evolution pattern of ecosystem services in the study area from 2000 to 2020. The XGBoost–SHAP model was used to reveal the key indicators and thresholds of changes in major ecosystem services in the study area due to climate change and human activities. The results showed significant land use changes in the study area from 2000 to 2020, particularly the conversion of cropland to construction land, which was more intense in economically developed areas. The areas of forest and grassland increased initially but later decreased due to the impact of human activities and natural factors. Habitat quality (HQ) showed an overall declining trend, while soil retention (SR) and water yield (WY) services exhibited significant interannual variations due to climate change. The changes in rainfall had a particularly notable impact on these services; in years with excessive rainfall, soil erosion intensified, leading to a decline in SR services, whereas in years with moderate rainfall, SR and WY services improved. Carbon fixation (CF) services were enhanced with the expansion of forest areas. The XGBoost–SHAP model further revealed that the effects of rainfall and sunshine duration on ecosystem services were nonlinear, while population density and the proportion of construction land had a significant negative impact on habitat quality and soil retention. The expansion of construction land had the most significant negative impact on habitat quality, whereas the increase in forest land significantly improved carbon fixation and the soil retention capacity. By revealing the mechanisms of the impact of climate change and human activities on ecosystem services, we aimed to provide support for the promotion of ecological conservation and sustainable development strategies in the study area, as well as to provide an important reference for areas with geographic similarities to the study area. Full article

(This article belongs to the Special Issue Application of Remote Sensing in Vegetation Dynamic and Ecology)

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22 pages, 14855 KiB

Open AccessArticle

Quantitative Analysis of Vegetation Dynamics and Driving Factors in the Shendong Mining Area under the Background of Coal Mining

by Xufei Zhang, Zhichao Chen, Yiheng Jiao, Yiqiang Cheng, Zhenyao Zhu, Shidong Wang and Hebing Zhang

Forests 2024, 15(7), 1207; https://doi.org/10.3390/f15071207 - 12 Jul 2024

Viewed by 918

Abstract

Elucidating the response mechanism of vegetation change trends is of great value for environmental resource management, especially in coal mining areas where climate fluctuations and human activities are intense. Taking the Shendong mining area as an example, based on the Google Earth Engine [...] Read more.

Elucidating the response mechanism of vegetation change trends is of great value for environmental resource management, especially in coal mining areas where climate fluctuations and human activities are intense. Taking the Shendong mining area as an example, based on the Google Earth Engine cloud platform, this study used the kernel Normalized Vegetation Index (kNDVI) to study the spatiotemporal change characteristics of vegetation cover during 1994–2022. Then, it carried out an attribution analysis through the partial derivative analysis method to explore the driving mechanism behind vegetation greening. The results showed that (1) the growth rate of vegetation cover change from 1994 to 2022 was 0.0052/a. The area with an upward trend of kNDVI accounted for 94.11% of the total area of the study area. The greening effect was obvious, and the kNDVI change would continue to rise. (2) Under the scenario of regional climate warming and humidifying, kNDVI responds slightly differently to different climatic factors, and kNDVI is positively correlated with temperature and precipitation in 85.20% of the mining area. The average contribution of precipitation, temperature, and human activities to kNDVI change in the Shendong mining area were 0.00094/a, 0.00066/a, and 0.0036/a, respectively. The relative contribution rates of human activities and climate change were 69.23% and 30.77%, respectively. Thus, human activities are the main driving factor for the changing of vegetation cover in this mining area, and climate change is the secondary driving factor. (3) The dynamic change in land use presents an increase in forest area under the ecological restoration project. The results of this study can provide a scientific basis for the future ecological construction of the Shendong mining area and help in the realization of regional green sustainable development goals. Full article

(This article belongs to the Special Issue Application of Remote Sensing in Vegetation Dynamic and Ecology)

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21 pages, 10834 KiB

Open AccessArticle

Spatio-Temporal Evolution of Vegetation Coverage and Eco-Environmental Quality and Their Coupling Relationship: A Case Study of Southwestern Shandong Province, China

by Dongling Ma, Qian Wang, Qingji Huang, Zhenxin Lin and Yingwei Yan

Forests 2024, 15(7), 1200; https://doi.org/10.3390/f15071200 - 11 Jul 2024

Cited by 1 | Viewed by 772

Abstract

Propelled by rapid economic growth, the southwestern Shandong urban agglomeration (SSUA) in China has become a crucial industrial hub, but this process has somewhat hindered vegetation growth and environmental quality. Leveraging the functionalities of the Google Earth Engine (GEE) platform, we derived the [...] Read more.

Propelled by rapid economic growth, the southwestern Shandong urban agglomeration (SSUA) in China has become a crucial industrial hub, but this process has somewhat hindered vegetation growth and environmental quality. Leveraging the functionalities of the Google Earth Engine (GEE) platform, we derived the fractional vegetation coverage (FVC) through the Normalized Difference Vegetation Index (NDVI) and assessed the eco-environmental quality using the Remote Sensing Ecological Index (RSEI). To examine the patterns and shifts in the SSUA, we employed the Theil–Sen median slope estimation, which provided robust estimates of linear trends, the Mann–Kendall trend test to determine the statistical significance of these trends, and the Hurst exponent analysis to evaluate the long-term persistence and predict future changes in the vegetation coverage and eco-environmental quality. Furthermore, to explore the interdependencies between vegetation coverage (VC) and environmental quality, we applied an improved coupling coordination degree model (ICCDM). This model allowed us to assess the co-evolution and synergy between these two factors over the study period, providing comprehensive insights for sustainable urban and ecological planning in the region. The VC and eco-environmental quality improved consistently across most of the SSUA from 2000 to 2020. The dominance of VC had transitioned from being predominantly characterized by relatively high VC to being mainly characterized by high VC. A substantial portion of the SSUA is predicted to experience improvements in its VC and environmental quality moving forward. Furthermore, the coupling coordination relationship between VC and environmental conditions in the southwest of Shandong Province generally exhibited a state of orderly coordinated development. With the passage of time, there was a clear tendency toward expansion in the coupled uncoordinated areas distributed in a network within each regional economic center. Our research unveils the dynamics and spatial-temporal patterns of VC and ecological quality in the southwestern Shandong urban agglomeration (SSUA) and elucidates the coupling and coordination mechanism between these two aspects, which provides theoretical support for understanding the healthy development of vegetation and ecology in urban agglomerations in an industrial context. Full article

(This article belongs to the Special Issue Application of Remote Sensing in Vegetation Dynamic and Ecology)

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21 pages, 27708 KiB

Open AccessArticle

Spatiotemporal Variations of Vegetation and Its Response to Climate Change and Human Activities in Arid Areas—A Case Study of the Shule River Basin, Northwestern China

by Xiaorui He, Luqing Zhang, Yuehan Lu and Linghuan Chai

Forests 2024, 15(7), 1147; https://doi.org/10.3390/f15071147 - 1 Jul 2024

Cited by 2 | Viewed by 1109

Abstract

The Shule River Basin (SRB) is a typical arid area in northwest China with a fragile ecology. Understanding vegetation dynamics and its response to climate change and human activities provides essential ecological and environmental resource management information. This study extracted fractional vegetation coverage [...] Read more.

The Shule River Basin (SRB) is a typical arid area in northwest China with a fragile ecology. Understanding vegetation dynamics and its response to climate change and human activities provides essential ecological and environmental resource management information. This study extracted fractional vegetation coverage (FVC) data from 2000 to 2019 using the Google Earth Engine platform and Landsat satellite images, employing trend analysis and other methods to examine spatiotemporal changes in vegetation in the SRB. Additionally, we used partial correlation and residual analyses to explore the response of FVC to climate change and human activities. The main results were: (1) The regional average FVC in the SRB showed a significant upward trend from 2000 to 2019, increasing by 1.3 × 10⁻³ a^–1. The area within 1 km of roads experienced a higher increase of 3 × 10⁻³ a^–1, while the roadless areas experienced a lower increase of 1.1 × 10⁻³ a^–1. The FVC spatial heterogeneity in the SRB is significant. (2) Partial correlation analysis shows that the FVC correlates positively with precipitation and surface water area, with correlation coefficients of 0.575 and 0.744, respectively. A weak negative correlation exists between the FVC and land surface temperature (LST). FVC changes are more influenced by precipitation than by LST. (3) The contributions of climate change to vegetation recovery are increasing. Human activities, particularly agricultural practices, infrastructure development, and the conversion of farmland to grassland, significantly influence vegetation changes in densely populated areas. (4) The area changes of different land types are closely related to climate factors and human activities. Increased construction, agricultural activity, and converting farmland back to grassland have led to an increase in the area proportions of “impervious surfaces”, “cropland”, and “grassland”. Climate changes, such as increased rainfall, have resulted in larger areas of “wetlands” and “sparse vegetation”. These results provide valuable information for ecosystem restoration and environmental protection in the SRB. Full article

(This article belongs to the Special Issue Application of Remote Sensing in Vegetation Dynamic and Ecology)

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19 pages, 12706 KiB

Open AccessArticle

Disturbance Effect of Highway Construction on Vegetation in Hexi Corridor, North-Western China

by Zhenhua Han, Luqing Zhang, Fenxiang Zhang, Jian Zhou and Song Wang

Forests 2024, 15(7), 1131; https://doi.org/10.3390/f15071131 - 28 Jun 2024

Viewed by 806

Abstract

The ecological environment of the Hexi Corridor in China is fragile owing to its unique geographical conditions. Since the 21st century, the construction of highway projects in the Hexi Corridor has increased with the implementation of China’s western development policy. The impact of [...] Read more.

The ecological environment of the Hexi Corridor in China is fragile owing to its unique geographical conditions. Since the 21st century, the construction of highway projects in the Hexi Corridor has increased with the implementation of China’s western development policy. The impact of highway construction on vegetation along roads is uncertain and requires attention. In this study, the normalized difference vegetation index (NDVI) was obtained based on remote sensing data, and the correlations between the vegetation index, climate changes, and highway construction from 2000 to 2019 were analyzed. The results showed that the NDVI of the Hexi Corridor showed a significant increasing trend, and the spatial difference was obvious, which was largely controlled by climatic conditions. Generally, the vegetation index was negatively correlated with temperature, but positively correlated with precipitation, and the correlation between the NDVI and precipitation was greater than that of temperature. The impact of highways on vegetation is a long-term process. In the eastern region of the Hexi Corridor, where the ecological environment is better, highway construction promoted vegetation development. However, in the western region with high desertification, the damage caused by highway construction to vegetation was serious, and the recovery rate of degraded vegetation was slow after the completion of highway construction. Although the vegetation development in the Hexi Corridor was mainly affected by precipitation, in the western region, highway construction was the main factor controlling vegetation disturbance within a certain range on both sides, and its contribution to vegetation disturbance reached 60%. The research results can improve understanding of the disturbance effect of highway construction on vegetation in arid areas. Full article

(This article belongs to the Special Issue Application of Remote Sensing in Vegetation Dynamic and Ecology)

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22 pages, 5655 KiB

Open AccessArticle

Response of Vegetation Coverage to Climate Drivers in the Min-Jiang River Basin along the Eastern Margin of the Tibetan Plat-Eau, 2000–2022

by Shuyuan Liu, Yicheng Gu, Huan Wang, Jin Lin, Peng Zhuo and Tianqi Ao

Forests 2024, 15(7), 1093; https://doi.org/10.3390/f15071093 - 24 Jun 2024

Viewed by 736

Abstract

Ecological zonation research is typically conducted in the eastern margin of the Tibetan Plateau. In order to enhance the structure and function of regional ecosystems and monitor their quality, it is crucial to investigate shifts in the coverage of vegetation and the factors [...] Read more.

Ecological zonation research is typically conducted in the eastern margin of the Tibetan Plateau. In order to enhance the structure and function of regional ecosystems and monitor their quality, it is crucial to investigate shifts in the coverage of vegetation and the factors that contribute to these shifts. The goal of this study is to assess the spatial and temporal variations in vegetation covering and the partitioning of its drivers in the Minjiang River Basin on the eastern edge of the Tibetan Plateau between 2000 and 2022. The Mann-Kendall test, Hurst index, Theil-Sen median trend analysis, and other techniques were used to look at the features of temporal and geographical changes in regional vegetation coverage as well as potential development trends. The climatic influences leading to the spatial differentiation of vegetation NDVI (Normalized Difference Vegetation Index) were quantified through partial and complex correlation analyses of NDVI with temperature and precipitation. The results of the study showed that (1) the NDVI of the watershed performed well with a stable upward trend, indicating that the vegetation growth was generally good; (2) the spatial analysis showed that the coefficient of variation of the NDVI reached 0.092, which highlighted the stability of the vegetation change in the region; (3) the future development trend of the vegetation coverage in the watershed is low, and there is a certain degree of ecological risk; and (4) the main driver of the vegetation coverage is the non-climate factor, distributed in most parts of the watershed; (5) the climate driver shows localized influence, especially concentrated in the southwest, downstream and part of the upstream areas of the watershed. Full article

(This article belongs to the Special Issue Application of Remote Sensing in Vegetation Dynamic and Ecology)

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21 pages, 6659 KiB

Open AccessArticle

The Spatiotemporal Variation Characteristics and Influencing Factors of Green Vegetation in China

by Xiaodong Zhang, Haoying Han, Anran Dai and Yianli Xie

Forests 2024, 15(4), 668; https://doi.org/10.3390/f15040668 - 7 Apr 2024

Viewed by 1363

Abstract

Green vegetation is one of the main objects of ecological environment restoration and protection, objectively reflecting the quality of regional ecological environments. Studying its spatial distribution characteristics is of great significance to the formulation of ecological environment restoration policies. Based on data on [...] Read more.

Green vegetation is one of the main objects of ecological environment restoration and protection, objectively reflecting the quality of regional ecological environments. Studying its spatial distribution characteristics is of great significance to the formulation of ecological environment restoration policies. Based on data on urban green vegetation in China from 2000 to 2022, this study attempts to analyze the destruction and protection patterns of urban green vegetation in China from the perspectives of total changes in green vegetation contraction and growth and spatial evolution characteristics and trends, and it explores the driving factors affecting the change in green vegetation area. The results show the following: (1) Green vegetation growth and contraction occurred alternately in China from 2000 to 2022. Vegetation contraction showed a “point–line–plane” evolution pattern, forming a contraction stage of point-like aggregation, linear series, and planar spread. Vegetation growth has always presented a frontal pattern. (2) The growth and contraction of green vegetation in China showed a north–south differentiation phenomenon. The vegetation contraction phenomenon spread in the Central Plains urban agglomeration and its surrounding areas and showed an expanding trend. The growth trend is obviously moving northward, mainly concentrated in Inner Mongolia, Ningxia, Gansu, Xinjiang, and other northern provinces, which also coincides with the key ecological restoration policies in northern China in recent years. (3) City scale, economic level, population scale, agro-industrial structure, and water resources content have significant effects on the spatial distribution of green vegetation. Full article

(This article belongs to the Special Issue Application of Remote Sensing in Vegetation Dynamic and Ecology)

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21 pages, 10278 KiB

Open AccessEditor’s ChoiceArticle

Due Diligence for Deforestation-Free Supply Chains with Copernicus Sentinel-2 Imagery and Machine Learning

by Ivan Reading, Konstantina Bika, Toby Drakesmith, Chris McNeill, Sarah Cheesbrough, Justin Byrne and Heiko Balzter

Forests 2024, 15(4), 617; https://doi.org/10.3390/f15040617 - 28 Mar 2024

Viewed by 1914

Abstract

At COP26, the Glasgow Leaders Declaration committed to ending deforestation by 2030. Implementing deforestation-free supply chains is of growing importance to importers and exporters but challenging due to the complexity of supply chains for agricultural commodities which are driving tropical deforestation. Monitoring tools [...] Read more.

At COP26, the Glasgow Leaders Declaration committed to ending deforestation by 2030. Implementing deforestation-free supply chains is of growing importance to importers and exporters but challenging due to the complexity of supply chains for agricultural commodities which are driving tropical deforestation. Monitoring tools are needed that alert companies of forest losses around their source farms. ForestMind has developed compliance monitoring tools for deforestation-free supply chains. The system delivers reports to companies based on automated satellite image analysis of forest loss around farms. We describe an algorithm based on the Python for Earth Observation (PyEO) package to deliver near-real-time forest alerts from Sentinel-2 imagery and machine learning. A Forest Analyst interprets the multi-layer raster analyst report and creates company reports for monitoring supply chains. We conclude that the ForestMind extension of PyEO with its hybrid change detection from a random forest model and NDVI differencing produces actionable farm-scale reports in support of the EU Deforestation Regulation. The user accuracy of the random forest model was 96.5% in Guatemala and 93.5% in Brazil. The system provides operational insights into forest loss around source farms in countries from which commodities are imported. Full article

(This article belongs to the Special Issue Application of Remote Sensing in Vegetation Dynamic and Ecology)

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22 pages, 14347 KiB

Open AccessArticle

A Comprehensive Analysis of Vegetation Dynamics and Their Response to Climate Change in the Loess Plateau: Insight from Long-Term kernel Normalized Difference Vegetation Index Data

by Qingyan He, Qianhua Yang, Shouzheng Jiang and Cun Zhan

Forests 2024, 15(3), 471; https://doi.org/10.3390/f15030471 - 2 Mar 2024

Cited by 3 | Viewed by 1514

Abstract

The Loess Plateau (LP) is a typical climate-sensitive and ecologically delicate area in China. Clarifying the vegetation–climate interaction in the LP over 40+ years, particularly pre- and post-Grain to Green Program (GTGP) implementation, is crucial for addressing potential climate threats and achieving regional [...] Read more.

The Loess Plateau (LP) is a typical climate-sensitive and ecologically delicate area in China. Clarifying the vegetation–climate interaction in the LP over 40+ years, particularly pre- and post-Grain to Green Program (GTGP) implementation, is crucial for addressing potential climate threats and achieving regional ecological sustainability. Utilizing the kernel Normalized Difference Vegetation Index (kNDVI) and key climatic variables (precipitation (PRE), air temperature (TEM), and solar radiation (SR)) between 1982 and 2022, we performed an extensive examination of vegetation patterns and their reaction to changes in climate using various statistical methods. Our findings highlight a considerable and widespread greening on the LP from 1982 to 2022, evidenced by a kNDVI slope of 0.0020 yr⁻¹ (p < 0.001) and a 90.9% significantly increased greened area. The GTGP expedited this greening process, with the kNDVI slope increasing from 0.0009 yr⁻¹ to 0.0036 yr⁻¹ and the significantly greened area expanding from 39.1% to 84.0%. Over the past 40 years, the LP experienced significant warming (p < 0.001), slight humidification, and a marginal decrease in SR. Post-GTGP implementation, the warming rate decelerated, while PRE and SR growth rates slightly accelerated. Since the hurst index exceeded 0.5, most of the vegetated area of the LP is expected to be greening, warming, and humidification in the future. In the long term, 75% of the LP vegetated area significantly benefited from the increase in PRE, especially in relatively dry environments. In the LP, 61% of vegetated areas showed a positive correlation between kNDVI and TEM, while 4.9% exhibited a significant negative correlation, mainly in arid zones. SR promoted vegetation growth in 23% of the vegetated area, mostly in the eastern LP. The GTGP enhanced the sensitivity of vegetation to PRE, increasing the area corresponding to a significant positive correlation from 15.3% to 59.9%. Overall, PRE has emerged as the dominant climate driver for the vegetation dynamics of the LP, followed by TEM and SR. These insights contribute to a comprehensive understanding of the climate-impact-related vegetation response mechanisms, providing guidance for efforts toward regional sustainable ecological development amid the changing climate. Full article

(This article belongs to the Special Issue Application of Remote Sensing in Vegetation Dynamic and Ecology)

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14 pages, 14300 KiB

Open AccessArticle

Analysis of Linkage between Long-Term Morphological Spatial Pattern Analysis and Vegetation Carbon Storage of Forests in Hunan, China

by Binglun Li, Longchi Chen, Qingkui Wang and Peng Wang

Forests 2024, 15(3), 428; https://doi.org/10.3390/f15030428 - 23 Feb 2024

Cited by 1 | Viewed by 1140

Abstract

The carbon sequestration of forest ecosystems plays a pivotal role in constraining global warming and mitigating climate change. The landscape pattern of forests is being altered due to the combined effects of climate change and human interference. Furthermore, the relationship between forest pattern [...] Read more.

The carbon sequestration of forest ecosystems plays a pivotal role in constraining global warming and mitigating climate change. The landscape pattern of forests is being altered due to the combined effects of climate change and human interference. Furthermore, the relationship between forest pattern changes and carbon storage distribution in a long time series remains unclear. Therefore, it is necessary to examine the relationship between forest patterns and carbon density, investigating the variations and similarities in the changes in carbon density across different modes of pattern change over time, and suggestions for forest planning were provided from a perspective focused on pattern change to enhance carbon storage. The Google Earth Engine (GEE) platform’s random forest model was used to map the spatial distribution of forests in Hunan Province for 1996 and 2020, followed by analyzing the correlation between the changes in forest patterns using the morphological spatial pattern analysis (MSPA) and carbon density simulated by the model. Results show that the net growth rate ((area in 2020-area in 1996)/area in 2020) of the forest in Hunan increased 26.76% between 1996 and 2020. The importance scores for the decade average temperature, short-wave length infrared band 1 (SWIR-1), and slope were the highest metrics in the model of carbon density, and were 0.127, 0.107 and 0.089, respectively. The vegetation carbon storage in Hunan Province increased by 31.02 Tg, from 545.91 Tg to 576.93 Tg in 25 years. This study demonstrates that vegetation carbon storage is influenced by the pattern type in both newly established and pre-existing forests (p < 0.05). The findings of this study offer empirical evidence to support forest management strategies targeted at enhancing carbon sequestration. Full article

(This article belongs to the Special Issue Application of Remote Sensing in Vegetation Dynamic and Ecology)

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24 pages, 11273 KiB

Open AccessArticle

Analysis of Vegetation NDVI Changes and Driving Factors in the Karst Concentration Distribution Area of Asia

by Shunfu Yang, Yuluan Zhao, Die Yang and Anjun Lan

Forests 2024, 15(3), 398; https://doi.org/10.3390/f15030398 - 20 Feb 2024

Cited by 4 | Viewed by 2579

Abstract

Due to the special nature of karst landforms, quantification of their vegetation dynamics and their underlying driving factors remains a formidable challenge. Based on the NDVI dataset, this study uses principal component analysis to extract comprehensive factors and utilizes an optimized parameter-based geographical [...] Read more.

Due to the special nature of karst landforms, quantification of their vegetation dynamics and their underlying driving factors remains a formidable challenge. Based on the NDVI dataset, this study uses principal component analysis to extract comprehensive factors and utilizes an optimized parameter-based geographical detector and geographically weighted regression models to assess the explanatory capacity of comprehensive factors concerning the spatial differentiation of vegetation change. The results of this study revealed the following: (1) In terms of temporal and spatial vegetation changes, the Asian karst concentrated distribution area (AKC) displayed overall stability and an increasing trend between 2000 and 2020. Notably, the northern (Southwest China) karst region experienced the most substantial vegetation increase, with increased areas exceeding 70%, primarily concentrated in the provinces of Guizhou and Guangxi. In contrast, the southern (Indochina Peninsula) karst region, particularly in Cambodia, Laos, and Vietnam (CLV), exhibited a significant decreasing trend, with decreased areas exceeding 30%. (2) By analyzing the driving factors affecting vegetation change, vegetation changes exhibited distinct spatial differentiations, along with positive and negative effects. Human factors, including human activity intensity, urban economic development, and agricultural economic development (explanatory power and local R² were both greater than 0.2), exerted a more significant impact on vegetation change in the AKC than natural factors such as thermal conditions, water conditions, and soil conditions. This impact was positive in Southwest China but inhibited in the Indochina Peninsula, particularly within the CLV karst area. Notably, the interaction between natural and human factors greatly enhanced their impacts on vegetation changes. These results provide valuable insights into vegetation changes and their driving mechanisms, which are crucial for preserving the stability of delicate karst ecosystems and facilitating vegetation recovery. Full article

(This article belongs to the Special Issue Application of Remote Sensing in Vegetation Dynamic and Ecology)

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23 pages, 30648 KiB

Open AccessArticle

Spatio-Temporal Dynamics of Normalized Difference Vegetation Index and Its Response to Climate Change in Xinjiang, 2000–2022

by Qianqian Zhang, Lei Gu, Yongqiang Liu and Yongfu Zhang

Forests 2024, 15(2), 370; https://doi.org/10.3390/f15020370 - 16 Feb 2024

Cited by 2 | Viewed by 1335

Abstract

Based on the NDVI and climate data from 2000 to 2022, this study systematically investigated the spatial and temporal patterns, trend characteristics, and stability of the NDVI in Xinjiang using the one-way linear regression method, Theil–Sen Median trend analysis, the Mann–Kendall significance test, [...] Read more.

Based on the NDVI and climate data from 2000 to 2022, this study systematically investigated the spatial and temporal patterns, trend characteristics, and stability of the NDVI in Xinjiang using the one-way linear regression method, Theil–Sen Median trend analysis, the Mann–Kendall significance test, and the coefficient of variation. Meanwhile, the persistence of the NDVI distribution was analyzed by combining the trend results and Hurst index. Finally, partial correlation analysis was used to deeply explore the response mechanisms of interannual and seasonal-scale NDVI and climatic factors in Xinjiang, and the characteristics of multi-year vegetation distribution were comprehensively analyzed with the help of human footprint data. The findings indicate the following: (1) The NDVI of interannual and seasonal vegetation in Xinjiang showed a significant increasing trend during the 23-year period, but the spatial distribution was heterogeneous, and the improvement of the vegetation condition in the southern part of the region was remarkable. (2) The NDVI is relatively stable across the region. Unlike in other regions, in general, it is difficult to maintain the existing trend in NDVI in the study area for a long period of time, and the reverse trend is more persistent. (3) On the interannual scale, both precipitation and temperature are positively correlated with the NDVI, and the influence of temperature (80.94%) is greater than that of precipitation (63.82%). Precipitation was dominantly positively correlated with the NDVI in spring, summer, and the growing season, while it was negatively correlated with it in autumn. Temperature and NDVI were positively correlated, with the greatest influence in the spring. (4) Human activities had the greatest impact on the areas with low vegetation cover and areas with medium–low vegetation cover, and there was a high degree of overlap between the areas where the interannual human footprints and NDVI showed an increasing trend. The percentage of human footprints that significantly correlated with interannual NDVI was 34.79%. In the future, the protection and management of ecologically fragile areas should be increased to increase desert-vegetation cover. Full article

(This article belongs to the Special Issue Application of Remote Sensing in Vegetation Dynamic and Ecology)

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21 pages, 6546 KiB

Open AccessArticle

Response of Vegetation Productivity to Greening and Drought in the Loess Plateau Based on VIs and SIF

by Xiao Hou, Bo Zhang, Jie Chen, Jing Zhou, Qian-Qian He and Hui Yu

Forests 2024, 15(2), 339; https://doi.org/10.3390/f15020339 - 9 Feb 2024

Cited by 7 | Viewed by 1462

Abstract

In the context of global warming, the frequent occurrence of drought has become one of the main reasons affecting the loss of gross primary productivity (GPP) of terrestrial ecosystems. Under the influence of human activities, the vegetation greening trend of the Loess Plateau [...] Read more.

In the context of global warming, the frequent occurrence of drought has become one of the main reasons affecting the loss of gross primary productivity (GPP) of terrestrial ecosystems. Under the influence of human activities, the vegetation greening trend of the Loess Plateau increased significantly. Therefore, it is of great significance to study the response of GPP to drought in the Loess Plateau under the greening trend. Here, we comprehensively assessed the ability of vegetation indices (VIs) and solar-induced chlorophyll fluorescence (SIF) to capture GPP changes at different seasonal scales and during drought. Specifically, we utilized three vegetation indices: normalized difference vegetation index (NDVI), near-infrared reflectance of vegetation (NIR_V), and kernel NDVI index (kNDVI), and determined the drought period of the Loess Plateau in 2001 based on the standardized precipitation evapotranspiration index (SPEI) and the standardized soil moisture index (SSMI). Moreover, the anomalies of VIs and SIF during the drought period and the relationship with GPP anomalies were compared. The results showed that both SIF and VIs were able to capture changes during the drought period as well as in normal years. Overall, SIF captured drought changes better due to water and heat stress as well as GPP changes compared to VIs. Across different time scales, SIF showed the strongest relationship with GPP (meanR² = 0.85), followed by NIR_V (meanR² = 0.84), NDVI (meanR² = 0.76), and kNDVI (meanR² = 0.74), suggesting that SIF is more sensitive to physiological changes in vegetation. Notably, kNDVI performed best in sparse vegetation (meanR² = 0.85). In capture during drought, NIR_V and kNDVI performed better in less productive land classes; SIF showed superior capture as land use class productivity increased. In addition, GPP anomalies correlated better with kNDVI anomalies (meanR² = 0.50) than with other index anomalies. In the future, efforts to integrate the respective strengths of SIF, NIR_V, and kNDVI will improve our understanding of GPP changes. Full article

(This article belongs to the Special Issue Application of Remote Sensing in Vegetation Dynamic and Ecology)

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30 pages, 14594 KiB

Open AccessArticle

Analysis of Factors Driving Subtropical Forest Phenology Differentiation, Considering Temperature and Precipitation Time-Lag Effects: A Case Study of Fujian Province

by Menglu Ma, Hao Zhang, Jushuang Qin, Yutian Liu, Baoguo Wu and Xiaohui Su

Forests 2024, 15(2), 334; https://doi.org/10.3390/f15020334 - 8 Feb 2024

Cited by 1 | Viewed by 1245

Abstract

Subtropical forest phenology differentiation is affected by temperature, precipitation, and topography. Understanding the primary contributing elements and their interactions with forest phenology can help people better comprehend the subtropical forest growth process and its response to climate. Meanwhile, the temporal and spatial variations [...] Read more.

Subtropical forest phenology differentiation is affected by temperature, precipitation, and topography. Understanding the primary contributing elements and their interactions with forest phenology can help people better comprehend the subtropical forest growth process and its response to climate. Meanwhile, the temporal and spatial variations of phenological rhythms are important indicators of climatic impacts on forests. Therefore, this study aimed to analyze both a total area and different forest growth environments within the whole (i.e., coastal site areas (II, IV) and inland site areas (I, III)) as to spatiotemporal patterns associated with subtropical forests in Fujian Province, which is located at the boundary between the middle and south subtropical zones. Considering the asymmetric effects of climate and forest growth, this study chose pre-seasonal and cumulative temperature and precipitation factors and utilized the GeoDetector model to analyze the dominant drivers and interactions within phenology differentiation in Fujian Province. The results show the following: (1) All of the phenological parameters were advanced or shortened over the 19-year observation period; those of shrubland and deciduous broadleaf forests fluctuated greatly, and their stability was poor. (2) The phenological parameters were more distinct at the borders of the site areas. Additionally, the dates associated with the end of the growth season (EOS) and the date-position of peak value (POP) in coastal areas (i.e., II and IV) were later than those in inland areas (i.e., I and III). Among the parameters, the length of the growth season (LOS) was most sensitive to altitude. (3) Precipitation was the main driving factor affecting the spatial heterogeneity of the start of the growth season (SOS) and the EOS. The relatively strong effects of preseason and current-month temperatures on the SOS may be influenced by the temperature threshold required to break bud dormancy, and the relationship between the SOS and temperature was related to the lag time and the length of accumulation. The EOS was susceptible to the hydrothermal conditions of the preseason accumulation, and the variation trend was negatively correlated with temperature and precipitation. Spatial attribution was used to analyze the attribution of phenology differentiation from the perspectives of different regions, thus revealing the relationships between forest phenology and meteorological time-lag effects, the result which can contribute to targeted guidance and support for scientific forest management. Full article

(This article belongs to the Special Issue Application of Remote Sensing in Vegetation Dynamic and Ecology)

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22 pages, 7819 KiB

Open AccessArticle

Identifying and Predicting the Responses of Multi-Altitude Vegetation to Climate Change in the Alpine Zone

by Xin Chen, Tiesheng Guan, Jianyun Zhang, Yanli Liu, Junliang Jin, Cuishan Liu, Guoqing Wang and Zhenxin Bao

Forests 2024, 15(2), 308; https://doi.org/10.3390/f15020308 - 6 Feb 2024

Cited by 3 | Viewed by 947

Abstract

Global climate change has affected vegetation cover in alpine areas. In this paper, we analyzed the correlation between Leaf Area Index (LAI) and climate factors of the Yarlung Tsangpo River basin, and identified their contributions using the quantitative analysis method. The results show [...] Read more.

Global climate change has affected vegetation cover in alpine areas. In this paper, we analyzed the correlation between Leaf Area Index (LAI) and climate factors of the Yarlung Tsangpo River basin, and identified their contributions using the quantitative analysis method. The results show that the vegetation cover in the study area generally exhibited an increasing trend. Grassland in the middle- and high-altitude areas was the main contributing area. Temperature is the dominant climatic factor affecting the change, the effect of which increases with the rise in elevation. The influences of precipitation and radiation had obvious seasonality and regionality. The vegetation illustrated a lag response to climate drivers. With the change in the elevation band, the response time to precipitation was significantly less than that to air temperature in the low-elevation area, while the opposite trend was observed in the high-elevation area. In the future, the LAI of the watershed will show different characteristics at different time points, with the increases in the LAI in autumn and winter becoming the main factors for the future increase in the LAI. This provides a crucial basis upon which to explore hydrological and ecological processes as important components of the Third Pole region. Full article

(This article belongs to the Special Issue Application of Remote Sensing in Vegetation Dynamic and Ecology)

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20 pages, 18390 KiB

Open AccessArticle

Characteristics and Drivers of Vegetation Change in Xinjiang, 2000–2020

by Guo Li, Jiye Liang, Shijie Wang, Mengxue Zhou, Yi Sun, Jiajia Wang and Jinglong Fan

Forests 2024, 15(2), 231; https://doi.org/10.3390/f15020231 - 25 Jan 2024

Cited by 2 | Viewed by 1560

Abstract

Examining the features of vegetation change and analyzing its driving forces across an extensive time series in Xinjiang are pivotal for the ecological environment. This research can offer a crucial point of reference for regional ecological conservation endeavors. We calculated the fractional vegetation [...] Read more.

Examining the features of vegetation change and analyzing its driving forces across an extensive time series in Xinjiang are pivotal for the ecological environment. This research can offer a crucial point of reference for regional ecological conservation endeavors. We calculated the fractional vegetation cover (FVC) using MOD13Q1 data accessed through the Google Earth Engine (GEE) platform. To discern the characteristics of vegetation changes and forecast future trends, we employed time series analysis, coefficient of variation, and the Hurst exponent. The correlation between climate factors and FVC was investigated through correlation analysis. Simultaneously, to determine the relative impact of meteorological change and anthropogenic actions on FVC, we utilized multiple regression residual analysis. Furthermore, adhering to China’s ecological functional zone classification, Xinjiang was segmented into five ecological zones: R1 Altai Mountains-Junggar West Mountain Forest and Grassland Ecoregion, R2 Junggar Basin Desert Ecoregion, R3 Tianshan Mountains Mountain Forest and Grassland Ecoregion, R4 Tarim Basin-Eastern Frontier Desert Ecoregion, and R5 Pamir-Kunlun Mountains-Altan Mountains Alpine Desert and Grassland Ecoregion. A comparative analysis of these five regions was subsequently conducted. The results showed the following: (1) During the first two decades of the 21st century, the overall FVC in Xinjiang primarily exhibited a trend of growth, exhibiting a rate of increase of 4 × 10⁻⁴ y⁻¹. The multi-year average FVC was 0.223. The mean value of the multi-year FVC was 0.223, and the mean values of different ecological zones showed the following order: R1 > R3 > R2 > R5 > R4. (2) The predominant spatial pattern of FVC across Xinjiang’s landscape is characterized by higher coverage in the northwest and lower in the southeast. In this region, 66.63% of the terrain exhibits deteriorating vegetation, while 11% of the region exhibits a notable rise in plant growth. Future changes in FVC will be dominated by a decreasing trend. Regarding the coefficient of variation outcomes, a minor variation, representing 42.12% of the total, is noticeable; the mean coefficient of variation stands at 0.2786. The stability across varied ecological zones follows the order: R1 > R3 > R2 > R4 > R5. (3) Factors that have a facilitating effect on vegetation FVC included relative humidity, daylight hours, and precipitation, with relative humidity having a greater influence, while factors that have a hindering effect on vegetation FVC included air temperature and wind speed, with wind speed having a greater influence. (4) Vegetation alterations are primarily influenced by climate change, while human activities play a secondary role, contributing 56.93% and 43.07%, respectively. This research underscores the necessity for continued surveillance of vegetation dynamics and the enhancement of policies focused on habitat renewal and the safeguarding of vegetation in Xinjiang. Full article

(This article belongs to the Special Issue Application of Remote Sensing in Vegetation Dynamic and Ecology)

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18 pages, 16249 KiB

Open AccessArticle

Unmanned Aerial Vehicle–Light Detection and Ranging-Based Individual Tree Segmentation in Eucalyptus spp. Forests: Performance and Sensitivity

by Yan Yan, Jingjing Lei, Jia Jin, Shana Shi and Yuqing Huang

Forests 2024, 15(1), 209; https://doi.org/10.3390/f15010209 - 20 Jan 2024

Viewed by 1404

Abstract

As an emerging powerful tool for forest resource surveys, the unmanned aerial vehicle (UAV)-based light detection and ranging (LiDAR) sensors provide an efficient way to detect individual trees. Therefore, it is necessary to explore the most suitable individual tree segmentation algorithm and analyze [...] Read more.

As an emerging powerful tool for forest resource surveys, the unmanned aerial vehicle (UAV)-based light detection and ranging (LiDAR) sensors provide an efficient way to detect individual trees. Therefore, it is necessary to explore the most suitable individual tree segmentation algorithm and analyze the sensitivity of the parameter setting to determine the optimal parameters, especially for the Eucalyptus spp. forest, which is one of the most important hardwood plantations in the world. In the study, four methods were employed to segment individual Eucalyptus spp. plantations from normalized point cloud data and canopy height model generated from the original UAV-LiDAR data. And the parameter sensitivity of each segmentation method was analyzed to obtain the optimal parameter setting according to the extraction accuracy. The performance of the segmentation result was assessed by three indices including detection rate, precision, and overall correctness. The results indicated that the watershed algorithm performed better than other methods as the highest overall correctness (F = 0.761) was generated from this method. And the segmentation methods based on the canopy height model performed better than those based on normalized point cloud data. The detection rate and overall correctness of low-density plots were better than high-density plots, while the precision was reversed. Forest structures and individual wood characteristics are important factors influencing the parameter sensitivity. The performance of segmentation was improved by optimizing the key parameters of the different algorithms. With optimal parameters, different segmentation methods can be used for different types of Eucalyptus plots to achieve a satisfying performance. This study can be applied to accurate measurement and monitoring of Eucalyptus plantation. Full article

(This article belongs to the Special Issue Application of Remote Sensing in Vegetation Dynamic and Ecology)

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22 pages, 5524 KiB

Open AccessArticle

Research on Walnut (Juglans regia L.) Classification Based on Convolutional Neural Networks and Landsat-8 Remote Sensing Imagery

by Jingming Wu, Xu Li, Ziyan Shi, Senwei Li, Kaiyao Hou and Tiecheng Bai

Forests 2024, 15(1), 165; https://doi.org/10.3390/f15010165 - 12 Jan 2024

Cited by 1 | Viewed by 1356

Abstract

The study explores the use of convolutional neural networks (CNNs) and satellite remote sensing imagery for walnut analysis in Ganquan Township, Alar City, Xinjiang. The recent growth of walnut cultivation in Xinjiang presents challenges for manual data collection, making satellite imagery and computer [...] Read more.

The study explores the use of convolutional neural networks (CNNs) and satellite remote sensing imagery for walnut analysis in Ganquan Township, Alar City, Xinjiang. The recent growth of walnut cultivation in Xinjiang presents challenges for manual data collection, making satellite imagery and computer vision algorithms a practical solution. Landsat-8 satellite images from Google Earth Engine underwent preprocessing, and experiments were conducted to enhance the ResNet model, resulting in improved accuracy and efficiency. Experiments were conducted to evaluate multiple CNN models and traditional methods, and the best detection method was chosen through comparisons. A comparison was drawn between traditional algorithms and convolutional neural network algorithms based on metrics such as precision, recall, f1-score, accuracy, and total time. The results indicated that although traditional methods were more efficient compared to CNN, they exhibited lower accuracy. In the context of this research, prioritizing efficiency at the cost of accuracy was deemed undesirable. Among the traditional algorithms employed in this study, k-NN produced the most favorable outcomes, with precision, recall, f1-score, and accuracy reaching 75.78%, 92.43%, 83.28%, and 84.46%, respectively, although these values were relatively lower than those of the CNN algorithm models. Within the CNN models, the ResNet model demonstrated superior performance, yielding corresponding results of 92.47%, 94.29%, 93.37%, and 93.27%. The EfficientNetV2 model also displayed commendable results, with precision, recall, and f1-score achieving 96.35%, 91.44%, and 93.83%. Nevertheless, it is worth noting that the classification efficiency of EfficientNetV2 fell significantly short of that of ResNet. Consequently, in this study, the ResNet model proved to be relatively more effective. Once optimized, the most efficient CNN model closely rivals traditional algorithms in terms of time efficiency for generating results while significantly surpassing them in accuracy. Through our studies, we discovered that once optimized, the most efficient CNN model closely rivals traditional algorithms in terms of time efficiency for generating results while significantly surpassing them in accuracy. In this study, empirical evidence demonstrates that integrating CNN-based methods with satellite remote sensing technology can effectively enhance the statistical efficiency of agriculture and forestry sectors, thus leading to substantial reductions in operational costs. These findings lay a solid foundation for further research in this field and offer valuable insights for other agricultural and forestry-related studies. Full article

(This article belongs to the Special Issue Application of Remote Sensing in Vegetation Dynamic and Ecology)

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21 pages, 10197 KiB

Open AccessArticle

Normal Difference Vegetation Index Simulation and Driving Analysis of the Tibetan Plateau Based on Deep Learning Algorithms

by Xi Liu, Guoming Du, Haoting Bi, Zimou Li and Xiaodie Zhang

Forests 2024, 15(1), 137; https://doi.org/10.3390/f15010137 - 9 Jan 2024

Cited by 4 | Viewed by 1322

Abstract

Global climate warming has profoundly affected terrestrial ecosystems. The Tibetan Plateau (TP) is an ecologically vulnerable region that emerged as an ideal place for investigating the mechanisms of vegetation response to climate change. In this study, we constructed an annual synthetic NDVI dataset [...] Read more.

Global climate warming has profoundly affected terrestrial ecosystems. The Tibetan Plateau (TP) is an ecologically vulnerable region that emerged as an ideal place for investigating the mechanisms of vegetation response to climate change. In this study, we constructed an annual synthetic NDVI dataset with 500 m resolution based on MOD13A1 products from 2000 to 2021, which were extracted by the Google Earth Engine (GEE) and processed by the Kalman filter. Furthermore, considering topographic and climatic factors, a thorough analysis was conducted to ascertain the causes and effects of the NDVI’s spatiotemporal variations on the TP. The main findings are: (1) The vegetation coverage on the TP has been growing slowly over the past 22 years at a rate of 0.0134/10a, with a notable heterogeneity due to its topography and climate conditions. (2) During the study period, the TP generally showed a “warming and humidification” trend. The influence of human activities on vegetation growth has exhibited a favorable trajectory, with a notable acceleration observed since 2011. (3) The primary factor influencing NDVI in the southeastern and western regions of the TP was the increasing temperature. Conversely, vegetation in the northeastern and central regions was mostly regulated by precipitation. (4) Combined with the principal component analysis, a PCA-CNN-LSTM (PCL) model demonstrated significant superiority in modeling NDVI sequences on the Tibetan Plateau. Understanding the results of this paper is important for the sustainable development and the formulation of ecological policies on the Tibetan Plateau. Full article

(This article belongs to the Special Issue Application of Remote Sensing in Vegetation Dynamic and Ecology)

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24 pages, 12632 KiB

Open AccessArticle

Analysis of Spatial and Temporal Changes in Vegetation Cover and Driving Forces in the Wuding River Basin, Loess Plateau

by Hao Zhang, Zhilin He, Junkui Xu, Weichen Mu, Yanglong Chen and Guangxia Wang

Forests 2024, 15(1), 82; https://doi.org/10.3390/f15010082 - 30 Dec 2023

Cited by 2 | Viewed by 1399

Abstract

Vegetation cover in the Loess Plateau region is an important component of ecological protection in the Yellow River Basin, and this study provides a scientific reference for further vegetation restoration. Based on Landsat images and related data, we utilized the dimidiate pixel model [...] Read more.

Vegetation cover in the Loess Plateau region is an important component of ecological protection in the Yellow River Basin, and this study provides a scientific reference for further vegetation restoration. Based on Landsat images and related data, we utilized the dimidiate pixel model and Geodetector method to study the vegetation cover in the Wuding River Basin from 2000 to 2022. The results indicated the spatial and temporal distribution of the vegetation cover and its changes over the study period. Additionally, the driving factors influencing its spatial changes were also uncovered. We also propose a land use shift vegetation cover contribution formula to quantify the effect of land type change on the FVC. The study showed that (1) the overall vegetation cover of the watershed increased significantly, and the FVC showed an increasing trend from 2000 to 2013 and a slow decline from 2013 to 2022, with the gradual transformation of low-graded FVC into a higher graded one. (2) The FVC increased spatially from northwest to southeast, and the trend of future changes is mainly decreasing. (3) The strongest explanatory power for the FVC change is the land use type and its interactive combination with rainfall. (4) The conversion of grassland to cropland contributes the most to the vegetation cover at 1.52%, and the increase in the cropland area is more conducive to the increase in the vegetation cover. Full article

(This article belongs to the Special Issue Application of Remote Sensing in Vegetation Dynamic and Ecology)

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18 pages, 12958 KiB

Open AccessArticle

Spatiotemporal Variation in Vegetation and Its Driving Mechanisms in the Southwest Alpine Canyon Area of China

by Jinlin Lai, Tianheng Zhao and Shi Qi

Forests 2023, 14(12), 2357; https://doi.org/10.3390/f14122357 - 30 Nov 2023

Cited by 8 | Viewed by 1362

Abstract

The Southwest Alpine Canyon Area (SACA), a well-known ecological vulnerability region, plays a very important role in China. Identifying the driving force of the spatial heterogeneity of vegetation and the response of interannual vegetation changes to climate change and human activities would be [...] Read more.

The Southwest Alpine Canyon Area (SACA), a well-known ecological vulnerability region, plays a very important role in China. Identifying the driving force of the spatial heterogeneity of vegetation and the response of interannual vegetation changes to climate change and human activities would be helpful for ecosystem management. Based on the NDVI dataset, the study analyzed the trend of NDVI change from 2000 to 2019 using the Theil–Sen trend analysis and the Mann–Kendal significance test, detected the driving forces of the spatial heterogeneity of NDVI by the means of the geographical detector, and analyzed the relative contribution of climate change and human activities to interannual NDVI changes using residual analysis model. The results showed that, in terms of the spatial distribution, the pattern of NDVI showed that it is higher in the southeast and lower in the northwest region of the SACA. Elevation was the dominant factor influencing the spatial heterogeneity of NDVI, with the explanatory power of 64%, much larger than other factors, and vegetation type, temperature, precipitation, land use type, and soil type were the main factors. In addition, the explanatory power of the dual factor interaction was higher than that of the single factor effect, which showed two kinds of interaction relationships: bivariate enhancement and nonlinear enhancement. In terms of the temporal variation, 85.59% of the study area showed an increasing trend, and only 14.41% of the area showed a decreasing trend. The main factor affecting NDVI changes was human activities, and climate change was the secondary factor, with relative contributions of 71.35% and 28.65%, respectively. The study will promote a better understanding of the complex mechanisms of vegetation changes and provide scientific recommendations for the prevention of vegetation degradation and vegetation restoration in the SACA. Full article

(This article belongs to the Special Issue Application of Remote Sensing in Vegetation Dynamic and Ecology)

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20 pages, 13936 KiB

Open AccessArticle

Quantitative Analysis of Climate Variability and Human Activities on Vegetation Variations in the Qilian Mountain National Nature Reserve from 1986 to 2021

by Xiaoxian Wang, Xiuxia Zhang, Wangping Li, Xiaoqiang Cheng, Zhaoye Zhou, Yadong Liu, Xiaodong Wu, Junming Hao, Qing Ling, Lingzhi Deng, Xilai Zhang and Xiao Ling

Forests 2023, 14(10), 2042; https://doi.org/10.3390/f14102042 - 12 Oct 2023

Cited by 5 | Viewed by 1992

Abstract

Rapid climate variability and intense human activities generate obvious impacts on the Qilian Mountains ecosystem. The time series of fractional vegetation coverage (FVC) from 1986 to 2021 were used to quantify the impact of climate variability and human activities on vegetation variations in [...] Read more.

Rapid climate variability and intense human activities generate obvious impacts on the Qilian Mountains ecosystem. The time series of fractional vegetation coverage (FVC) from 1986 to 2021 were used to quantify the impact of climate variability and human activities on vegetation variations in the Qilian Mountain National Nature Reserve (QMNNR), using 3147 land satellite images based on the Google Earth Engine cloud platform. The contributions of climate variability and human activities to FVC were quantified using multiple regression residual analysis. Partial correlation and correlation methods were used to quantify the impact of temperature, precipitation, and human activity footprints on FVC. The results showed that from 1986 to 2021, the increase rate of FVC was 1.7 × 10⁻³ y⁻¹, and the high vegetation coverage of the FVC was mainly distributed in the southeastern part of the reserve. In contrast, the low vegetation coverage was mainly distributed in the northwest part of the reserve. The Mann–Kendall mutation test found that the year of 2009 was the year of the mutation. The growth rate of FVC from 2010 to 2021 was greater than that from 1986 to 2009. In addition, climate variability and human activities exhibited a remarkable spatial heterogeneity in FVC changes. Climate variability and human activities contributed 49% and 51% to the increase in FVC in the reserve, respectively, and the contribution of human activities was greater than that of climate variability. The warming and humidification phenomena in the reserve were obvious. However, precipitation was the dominant factor affecting the dynamic changes in FVC. This study improves our understanding of the response of vegetation dynamics to the climate and human activities in the QMNNR. Full article

(This article belongs to the Special Issue Application of Remote Sensing in Vegetation Dynamic and Ecology)

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25 pages, 25346 KiB

Open AccessArticle

Quantifying the Spatiotemporal Variation of NPP of Different Land Cover Types and the Contribution of Its Associated Factors in the Songnen Plain

by Nan Lin, Jiaxuan Li, Ranzhe Jiang, Xin Li and Shu Liu

Forests 2023, 14(9), 1841; https://doi.org/10.3390/f14091841 - 9 Sep 2023

Cited by 3 | Viewed by 1942

Abstract

Net primary productivity (NPP) of vegetation is considered an important indicator for ecological stability and is the main object for analyzing the factors influencing the terrestrial carbon cycle. Recent studies have made clear the changes in the NPP of vegetation and its influencing [...] Read more.

Net primary productivity (NPP) of vegetation is considered an important indicator for ecological stability and is the main object for analyzing the factors influencing the terrestrial carbon cycle. Recent studies have made clear the changes in the NPP of vegetation and its influencing factors at various scales. However, the variations in NPP based on different land cover types under various natural conditions, along with their driving factors, remain not well understood. In this study, spatial overlay analysis was used to investigate the link among climatic, soil moisture (SM), and topographic parameters and NPP of various land cover types after analyzing the spatial and temporal trends of NPP in the Songnen Plain from 2001 to 2020. Additionally, the contribution of each influence factor to the NPP of different land cover types was calculated using the elastic net regression model. The elastic net regression model eliminates the multicollinearity among the influencing factors while maintaining the model stability, and the R² of all lands is greater than 0.62, which can effectively quantify the contribution of each influencing factor to NPP. The results show a continuously increasing trend of the overall NPP in the research area over the selected 20 years, and NPP increased most significantly in forest land (FOR). Precipitation (PRE) and NPP showed high correlations in all the different land cover types, while the correlations between NPP and other influencing factors were significantly different. In addition, we found that perennials led to a more significant degree of NPP enhancement, and the effect of topographic conditions on NPP was mainly reflected in differences in moisture conditions due to surface runoff. From the results of the modeling calculations, the cumulative contribution of PRE to NPP ranks first in all land types and is the most vital influencing factor of NPP in the Songnen Plain. SM was an important influence, but the contribution of NPP was greater in land classes with shallow root systems. The results of the study revealed the positive transformation relationship of NPP among land cover types in ecologically fragile areas, which provides a reference for ecological restoration and rationalization of land use structure in zones such as intertwined agricultural and pastoral zones. Full article

(This article belongs to the Special Issue Application of Remote Sensing in Vegetation Dynamic and Ecology)

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23 pages, 20252 KiB

Open AccessArticle

Evaluation of Ecological Quality Status and Changing Trend in Arid Land Based on the Remote Sensing Ecological Index: A Case Study in Xinjiang, China

by Yimuranzi Aizizi, Alimujiang Kasimu, Hongwu Liang, Xueling Zhang, Bohao Wei, Yongyu Zhao and Maidina Ainiwaer

Forests 2023, 14(9), 1830; https://doi.org/10.3390/f14091830 - 7 Sep 2023

Cited by 9 | Viewed by 1591

Abstract

Ecosystems in arid areas are under pressure from human activities and the natural environment. Long-term monitoring and evaluation of arid ecosystems are essential for achieving the goal of sustainable development. The Xinjiang Uygur Autonomous Region (Xinjiang) is a typical arid region located in [...] Read more.

Ecosystems in arid areas are under pressure from human activities and the natural environment. Long-term monitoring and evaluation of arid ecosystems are essential for achieving the goal of sustainable development. The Xinjiang Uygur Autonomous Region (Xinjiang) is a typical arid region located in Northwest China with a relatively sensitive ecosystem. Under the support of the Google Earth Engine (GEE) cloud platform’s massive data collection, the remote sensing ecological index (RSEI) from 2000 to 2020, both in summer and spring, is established, and the variation trend of the ecological quality in Xinjiang is evaluated by coefficient of variation (CV), Sen’s slope analysis, Mann–Kendall trend test (M–K test) and Hurst index. In addition, a partial correlation analysis is processed between RSEI and selected climatic factors, including precipitation and temperature, to find out the mode of correlation between ecological quality and the natural climate. In the last two decades the following has become apparent: (1) The RSEI values of Xinjiang have been relatively low and unstable both in summer and spring, with a trend toward increasing; (2) The distribution characteristics of RSEI levels both in summer and spring have been similar; low levels were concentrated in the desert and wilderness, while high levels were concentrated around the oasis; (3) The ecological quality in Xinjiang has been relatively stable, with a trend of sustained increase both in summer and spring. There was also a small area of sustained decrease around the Junggar Basin and Turpan Basin in summer and a small area of significant decrease in the center of the Taklamakan Desert in spring; (4) In summer, the precipitation has obviously positively correlated in the Southwest. The temperature has obviously positively correlated in the northwestern part; in spring, the precipitation has obviously positively correlated in the Western part; the temperature has obviously positively correlated in the oasis around the Yili River Basin and Tarim Basin. Full article

(This article belongs to the Special Issue Application of Remote Sensing in Vegetation Dynamic and Ecology)

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