Remote Sensing

Research

24 pages, 11743 KiB

Open AccessArticle

A Comparison of Seven Medium Resolution Impervious Surface Products on the Qinghai–Tibet Plateau, China from a User’s Perspective

by Kaiyuan Zheng, Guojin He, Ranyu Yin, Guizhou Wang and Tengfei Long

Remote Sens. 2023, 15(9), 2366; https://doi.org/10.3390/rs15092366 - 29 Apr 2023

Cited by 6 | Viewed by 1890

Abstract

As a vital land cover type, impervious surface directly reflects human activities and urbanization, significantly impacting the environment, climate, and biodiversity, especially in ecologically fragile areas such as the Qinghai–Tibet Plateau (QTP) in China. Thus, precise knowledge of impervious surface information on the [...] Read more.

As a vital land cover type, impervious surface directly reflects human activities and urbanization, significantly impacting the environment, climate, and biodiversity, especially in ecologically fragile areas such as the Qinghai–Tibet Plateau (QTP) in China. Thus, precise knowledge of impervious surface information on the QTP is essential for its ecological protection and social development. In order to improve the application of products and inform further studies, we assessed the accuracy of seven medium resolution (10–30 m) impervious surface products in the QTP, including GAIA, CISC, GlobalLand30 (GL30), GLC-FCS30 (FCS30), GHS-BUILT-S2 (GHSB), ESA WorldCover10 (WC10), and Dynamic World NRT products (DW). The validation set labeled according to domestic GF-1 images was used to calculate the precision, recall, and F1-Score of these products, and two impervious surface vote maps were generated to analyze their spatial consistency. The results showed that CISC and DW had the highest overall quality among the 30 m and 10 m products, with F1-Scores of 0.5701 and 0.5670, respectively. We also validated the accuracy of different data combinations and their intersection and union sets to provide guidance based on the results for data selection in impervious surface studies on the QTP. For results calculated by the strict validation set, which was exclusive of mixed grids, precision decreased slightly while recall increased significantly for all products, indicating that the omissions were mostly mixed pixels with a smaller percentage of impervious surface. In terms of spatial consistency, the maximum impervious surface range voted by the seven products jointly only accounts for 0.82% of the QTP, which is 2,786,800 km² in total. Additionally, the high consistency area (votes > 4), with a distribution concentrated in large cities and dense buildings, only accounts for 15.18% of this maximum range. In summary, each product’s regional accuracy in the QTP was lower than their published accuracy, and they omitted many impervious surfaces, especially those with a background of bare land. Full article

(This article belongs to the Special Issue Remote Sensing in Applied Ecology)

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20 pages, 13600 KiB

Open AccessArticle

Spatiotemporal Variation Characteristics and Dynamic Persistence Analysis of Carbon Sources/Sinks in the Yellow River Basin

by Kun Zhang, Changming Zhu, Xiaodong Ma, Xin Zhang, Dehu Yang and Yakui Shao

Remote Sens. 2023, 15(2), 323; https://doi.org/10.3390/rs15020323 - 5 Jan 2023

Cited by 17 | Viewed by 3156

Abstract

Net ecosystem productivity (NEP) is an important indicator for estimating regional carbon sources/sinks. The study focuses on a comprehensive computational simulation and spatiotemporal variation study of the NEP in the Yellow River basin from 2000 to 2020 using NPP data products from MODIS [...] Read more.

Net ecosystem productivity (NEP) is an important indicator for estimating regional carbon sources/sinks. The study focuses on a comprehensive computational simulation and spatiotemporal variation study of the NEP in the Yellow River basin from 2000 to 2020 using NPP data products from MODIS combined with a quantitative NEP estimation model followed by a comprehensive analysis of the spatiotemporal variation characteristics and dynamic procession persistence analysis based on meteorological data and land use data. The results show that: (1) The total NEP in the Yellow River basin had an overall increasing trend from 2000 to 2020, with a Theil–Sen trend from −23.37 to 43.66 gCm⁻²a⁻¹ and a mean increase of 4.64 gCm⁻²a⁻¹ (p < 0.01, 2-tailed). (2) Most areas of the Yellow River basin are carbon sink areas, and the annual average NEP per unit area was 208.56 gCm⁻²a⁻¹ from 2000 to 2020. There were, however, substantial spatial and temporal variations in the NEP. Most of the carbon source area was located in the Kubuqi Desert and its surroundings. (3) Changes in land use patterns were the main cause of changes in regional NEP. During the 2000–2020 period, 1154.24 t of NEP were added, mainly due to changes in land use, e.g., the conversion of farmland to forests and grasslands. (4) The future development in 83.43% of the area is uncertain according to the Hurst index dynamic persistence analysis. In conclusion, although the carbon−sink capacity of the terrestrial ecosystem in the Yellow River basin is increasing and the regional carbon sink potential is increasing in the future, the future development of new energy resources has regional uncertainties, and the stability of the basin ecosystem needs to be enhanced. Full article

(This article belongs to the Special Issue Remote Sensing in Applied Ecology)

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16 pages, 4198 KiB

Open AccessArticle

Identifying the Influencing Factors of Cooling Effect of Urban Blue Infrastructure Using the Geodetector Model

by Yingying Li, Min Xia, Qun Ma, Rui Zhou, Dan Liu and Leichang Huang

Remote Sens. 2022, 14(21), 5495; https://doi.org/10.3390/rs14215495 - 31 Oct 2022

Cited by 12 | Viewed by 2206

Abstract

The urban heat island (UHI) effect has a serious negative impact on urban ecosystems and human well-being. Mitigating UHI through nature-based methods is highly recommended. The cooling effect of urban blue infrastructure (UBI) can significantly alleviate the effects of UHI. Revealing the crucial [...] Read more.

The urban heat island (UHI) effect has a serious negative impact on urban ecosystems and human well-being. Mitigating UHI through nature-based methods is highly recommended. The cooling effect of urban blue infrastructure (UBI) can significantly alleviate the effects of UHI. Revealing the crucial influencing factors of the cooling effect of UBI is of great significance for mitigating the UHI effect. In this study, the water-cooling intensity (WCI) and water-cooling range (WCR) were used to quantitatively analyze the cooling effect of UBI in Hefei city in summer. Furthermore, the influencing factors and their interactions with the cooling effect of UBI were investigated based on the Geodetector model. The results revealed that: (1) The surface thermal environment of the built-up area of Hefei presented obvious spatial differentiation characteristics. (2) There were nine influencing factors that significantly influenced the WCI variation, with the greatest influencing factor of road density. In contrast, only the landscape shape index had a significant effect on WCR variation. (3) The interaction of environmental characteristics, water body characteristics, and socioeconomic characteristics had a significant influence on the cooling effect of UBI, and the interaction relationship between the influencing factors was mutually enhanced. The findings from our research can provide a theoretical reference and practical guidance for the protection, restoration, and planning of UBI as a nature-based solution to improve the urban thermal environment. Full article

(This article belongs to the Special Issue Remote Sensing in Applied Ecology)

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19 pages, 4644 KiB

Open AccessArticle

Characterizing Forest Cover and Landscape Pattern Using Multi-Source Remote Sensing Data with Ensemble Learning

by Yu Wang, Han Liu, Lingling Sang and Jun Wang

Remote Sens. 2022, 14(21), 5470; https://doi.org/10.3390/rs14215470 - 30 Oct 2022

Cited by 5 | Viewed by 2735

Abstract

Accurate information on forest distribution is an essential basis for the protection of forest resources. Recent advances in remote sensing and machine learning have contributed to the monitoring of forest-cover distribution cost-effectively, but reliable methods for rapid forest-cover mapping over mountainous areas are [...] Read more.

Accurate information on forest distribution is an essential basis for the protection of forest resources. Recent advances in remote sensing and machine learning have contributed to the monitoring of forest-cover distribution cost-effectively, but reliable methods for rapid forest-cover mapping over mountainous areas are still lacking. In addition, the forest landscape pattern has proven to be closely related to the functioning of forest ecosystems, yet few studies have explicitly measured the forest landscape pattern or revealed its driving forces in mountainous areas. To address these challenges, we developed a framework for forest-cover mapping with multi-source remote sensing data (Sentinel-1, Sentinel-2) and an automated ensemble learning method. We also designed a scheme for forest landscape pattern evaluation and driver attribution based on landscape metrics and random forest regression. Results in the Qilian Mountains showed that the proposed framework and scheme could accurately depict the distribution and pattern of forest cover. The overall accuracy of the obtained level-1 and level-2 forest-cover maps reached 95.49% and 78.05%, respectively. The multi-classifier comparison revealed that for forest classification, the ensemble learning method outperformed base classifiers such as LightGBM, random forests, CatBoost, XGBoost, and neural networks. Integrating multi-dimensional features, including spectral, phenological, topographic, and geographic information, helped distinguish forest cover. Compared with other land-cover products, our mapping results demonstrated high quality and rich spatial details. Furthermore, we found that forest patches in the Qilian Mountains were concentrated in the eastern regions with low-to-medium elevations and shady aspects. We also identified that climate was the critical environmental determent of the forest landscape pattern in the Qilian Mountains. Overall, the proposed framework and scheme have strong application potential for characterizing forest cover and landscape patterns. The mapping and evaluation results can further support forest resource management, ecological assessment, and regional sustainable development. Full article

(This article belongs to the Special Issue Remote Sensing in Applied Ecology)

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16 pages, 2077 KiB

Open AccessArticle

Sentinel-2 Images Based Modeling of Grassland Above-Ground Biomass Using Random Forest Algorithm: A Case Study on the Tibetan Plateau

by Xinyue Fan, Guojin He, Wenyi Zhang, Tengfei Long, Xiaomei Zhang, Guizhou Wang, Geng Sun, Huakun Zhou, Zhanhuan Shang, Dashuan Tian, Xiangyi Li and Xiaoning Song

Remote Sens. 2022, 14(21), 5321; https://doi.org/10.3390/rs14215321 - 24 Oct 2022

Cited by 15 | Viewed by 2451

Abstract

Accurate information on grassland above-ground biomass (AGB) is critical to better understanding the carbon cycle and conserve grassland resources. As a climate-sensitive key ecological function area, it is important to accurately estimate the grassland AGB of the Tibetan Plateau. Sentinel-2 (S2) images have [...] Read more.

Accurate information on grassland above-ground biomass (AGB) is critical to better understanding the carbon cycle and conserve grassland resources. As a climate-sensitive key ecological function area, it is important to accurately estimate the grassland AGB of the Tibetan Plateau. Sentinel-2 (S2) images have advantages in reducing mixed pixels and the scale effect for remote sensing, while the data volume is correspondingly larger. In order to improve the estimation accuracy while reducing the data volume required for AGB estimation and improving the computational efficiency, this study used the Recursive Feature Elimination (RFE) algorithm to find the optimal feature set and compared the performance of the Cubist, Gradient Boosting Regression Tree (GBRT), random forest (RF) and eXtreme Gradient Boosting (XGBoost) algorithms for estimating AGB. In this study, ten S2 bands, ten S2-derived vegetation indexes, 218 pieces of AGB field survey data, four types of meteorological data and three types of topographic data were used as the alternative input features for the AGB estimation model. The impurity and permutation importance were used as the feature importance calculation method input to the RFE, and the Cubist, GBRT, RF and XGBoost algorithms were used to construct the AGB estimation models. The results showed that the RF algorithm based on the monthly average temperature (T), elevation, Normalized Difference Phenology Index (NDPI), Normalized Difference Infrared Index (NDII) and Palmer Drought Severity Index (PDSI) performed best (

R^{2}

= 0.8838, RMSE = 35.05 g/m

^{2}

, LCCC = 2.44, RPPD = 0.91). The above findings suggest that the RF model based on the features related to temperature, altitude, humidity and leaf water content is beneficial to estimate the grassland AGB on the Tibetan Plateau. Full article

(This article belongs to the Special Issue Remote Sensing in Applied Ecology)

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16 pages, 3969 KiB

Open AccessArticle

Heterogeneity of Increases in Net Primary Production under Intensified Human Activity and Climate Variability on the Loess Plateau of China

by Xiangnan Ni, Wei Guo, Xiaoting Li and Shuheng Li

Remote Sens. 2022, 14(19), 4706; https://doi.org/10.3390/rs14194706 - 21 Sep 2022

Cited by 13 | Viewed by 2037

Abstract

Regrowth of forests is expected to be an important driver in the large uptake of anthropogenic CO₂ emissions by the terrestrial biosphere. Yet estimates of carbon sink capacity in mid-high latitude regrowth forests still remain unclear. The Loess Plateau (LP), a key [...] Read more.

Regrowth of forests is expected to be an important driver in the large uptake of anthropogenic CO₂ emissions by the terrestrial biosphere. Yet estimates of carbon sink capacity in mid-high latitude regrowth forests still remain unclear. The Loess Plateau (LP), a key region of the Grain to Green Program (GTGP), leads in the greening of China, while China leads in the greening of the world. For the sake of global ecological sustainability and accurate global carbon sink evaluation, the detection and attribution of vegetation growth on the LP requires further research after 20 years of ecological restoration. In this study, significant continuous rises (increases of 7.45 gC·m⁻²·a⁻², R² = 0.9328, p < 0.01) in net primary production (NPP) have occurred in the past 20 years. Rapid growth of forest NPP and expansion of forested areas in the southeastern regions has led to vegetation restoration on the LP. Human activities contributed 64.2% to the NPP increases, while climate variations contributed 35.8%. NPP in forests and croplands was dominated by human activities, while grassland NPP was mainly influenced by climate variations on the LP. Meanwhile, a strong El Niño event exacerbated the obstruction of large-scale ecological restoration. These conclusions can provide theoretical support for carbon-cycle assessment and the evaluation of sustainable development. Full article

(This article belongs to the Special Issue Remote Sensing in Applied Ecology)

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15 pages, 2450 KiB

Open AccessArticle

Quantifying the Relationship between 2D/3D Building Patterns and Land Surface Temperature: Study on the Metropolitan Shanghai

by Rui Zhou, Hongchao Xu, Hao Zhang, Jie Zhang, Miao Liu, Tianxing He, Jun Gao and Chunlin Li

Remote Sens. 2022, 14(16), 4098; https://doi.org/10.3390/rs14164098 - 21 Aug 2022

Cited by 23 | Viewed by 2855

Abstract

In the context of urban warming associated with rapid urbanization, the relationship between urban landscape patterns and land surface temperature (LST) has been paid much attention. However, few studies have comprehensively explored the effects of two/three-dimensional (2D/3D) building patterns on LST, particularly by [...] Read more.

In the context of urban warming associated with rapid urbanization, the relationship between urban landscape patterns and land surface temperature (LST) has been paid much attention. However, few studies have comprehensively explored the effects of two/three-dimensional (2D/3D) building patterns on LST, particularly by comparing their relative contribution to the spatial variety of LST. This study adopted the ordinary least squares regression, spatial autoregression and variance partitioning methods to investigate the relationship between 2D/3D building patterns and summertime LST across 2016–2017 in Shanghai. The 2D and 3D building patterns in this study were quantified by four 2D and six 3D metrics. The results showed that: (1) During the daytime, 2D/3D building metrics had significant correlation with LST. However, 3D building patterns played a significant role in predicting LST. They explained 51.0% and 10.2% of the variance in LST, respectively. (2) The building coverage ratio, building density, mean building projection area, the standard deviation of building height, and mean building height highly correlated with LST. Specifically, the building coverage ratio was the main predictor, which was obviously positively correlated with LST. The correlation of building density and average projected area with LST was positive and significant, while the correlation of building height standard deviation and average building height with LST was negative. The increase in average height and standard deviation of buildings and the decrease in building coverage ratio, average projected area, and density of buildings, can effectively improve the urban thermal environment at the census tract level. (3) Spatial autocorrelation analysis can elaborate the spatial relationship between building patterns and LST. The findings from our research will provide important insights for urban planners and decision makers to mitigate urban heat island problems through urban planning and building design. Full article

(This article belongs to the Special Issue Remote Sensing in Applied Ecology)

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22 pages, 13174 KiB

Open AccessArticle

Spatiotemporal Changes and Driver Analysis of Ecosystem Respiration in the Tibetan and Inner Mongolian Grasslands

by Weihua Liu, Honglin He, Xiaojing Wu, Xiaoli Ren, Li Zhang, Xiaobo Zhu, Lili Feng, Yan Lv, Qingqing Chang, Qian Xu, Mengyu Zhang, Yonghong Zhang and Tianxiang Wang

Remote Sens. 2022, 14(15), 3563; https://doi.org/10.3390/rs14153563 - 25 Jul 2022

Cited by 2 | Viewed by 2109

Abstract

Ecosystem respiration (RE) plays a critical role in terrestrial carbon cycles, and quantification of RE is important for understanding the interaction between climate change and carbon dynamics. We used a multi-level attention network, Geoman, to identify the relative importance of environmental factors and [...] Read more.

Ecosystem respiration (RE) plays a critical role in terrestrial carbon cycles, and quantification of RE is important for understanding the interaction between climate change and carbon dynamics. We used a multi-level attention network, Geoman, to identify the relative importance of environmental factors and to simulate spatiotemporal changes in RE in northern China’s grasslands during 2001–2015, based on 18 flux sites and multi-source spatial data. Results indicate that Geoman performed well (R² = 0.87, RMSE = 0.39 g C m⁻² d⁻¹, MAE = 0.28 g C m⁻² d⁻¹), and that grassland type and soil texture are the two most important environmental variables for RE estimation. RE in alpine grasslands showed a decreasing gradient from southeast to northwest, and that of temperate grasslands showed a decreasing gradient from northeast to southwest. This can be explained by the enhanced vegetation index (EVI), and soil factors including soil organic carbon density and soil texture. RE in northern China’s grasslands showed a significant increase (1.81 g C m⁻² yr⁻¹) during 2001–2015. The increase rate of RE in alpine grassland (2.36 g C m⁻² yr⁻¹) was greater than that in temperate grassland (1.28 g C m⁻² yr⁻¹). Temperature and EVI contributed to the interannual change of RE in alpine grassland, and precipitation and EVI were the main contributors in temperate grassland. This study provides a key reference for the application of advanced deep learning models in carbon cycle simulation, to reduce uncertainties and improve understanding of the effects of biotic and climatic factors on spatiotemporal changes in RE. Full article

(This article belongs to the Special Issue Remote Sensing in Applied Ecology)

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15 pages, 4875 KiB

Open AccessArticle

Spatiotemporal Distribution Patterns and Exposure Risks of PM_2.5 Pollution in China

by Jun Song, Chunlin Li, Miao Liu, Yuanman Hu and Wen Wu

Remote Sens. 2022, 14(13), 3173; https://doi.org/10.3390/rs14133173 - 1 Jul 2022

Cited by 9 | Viewed by 2566

Abstract

The serious pollution of PM_2.5 caused by rapid urbanization in recent years has become an urgent problem to be solved in China. Annual and daily satellite-derived PM_2.5 datasets from 2001 to 2020 were used to analyze the temporal and spatial patterns [...] Read more.

The serious pollution of PM_2.5 caused by rapid urbanization in recent years has become an urgent problem to be solved in China. Annual and daily satellite-derived PM_2.5 datasets from 2001 to 2020 were used to analyze the temporal and spatial patterns of PM_2.5 in China. The regional and population exposure risks of the nation and of urban agglomerations were evaluated by exceedance frequency and population weight. The results indicated that the PM_2.5 concentrations of urban agglomerations decreased sharply from 2014 to 2020. The region with PM_2.5 concentrations less than 35 μg·m⁻³ accounted for 80.27% in China, and the average PM_2.5 concentrations in 8 urban agglomerations were less than 35 μg·m⁻³ in 2020. The spatial distribution pattern of PM_2.5 concentrations in China revealed higher concentrations to the east of the Hu Line and lower concentrations to the west. The annual regional exposure risk (RER) in China was at a high level, with a national average of 0.75, while the average of 14 urban agglomerations was as high as 0.86. Among the 14 urban agglomerations, the average annual RER was the highest in the Shandong Peninsula (0.99) and lowest in the Northern Tianshan Mountains (0.76). The RER in China has obvious seasonality; the most serious was in winter, and the least serious was in summer. The population exposure risk (PER) east of the Hu Line was significantly higher than that west of the Hu Line. The average PER was the highest in Beijing-Tianjin-Hebei (4.09) and lowest in the Northern Tianshan Mountains (0.71). The analysis of air pollution patterns and exposure risks in China and urban agglomerations in this study could provide scientific guidance for cities seeking to alleviate air pollution and prevent residents’ exposure risks. Full article

(This article belongs to the Special Issue Remote Sensing in Applied Ecology)

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17 pages, 5686 KiB

Open AccessCommunication

Long-Term Spatiotemporal Patterns and Evolution of Regional Heat Islands in the Beijing–Tianjin–Hebei Urban Agglomeration

by Hongchao Xu, Chunlin Li, Hao Wang, Rui Zhou, Miao Liu and Yuanman Hu

Remote Sens. 2022, 14(10), 2478; https://doi.org/10.3390/rs14102478 - 21 May 2022

Cited by 15 | Viewed by 2616

Abstract

With the continuous development of urbanization, the urban heat island (UHI) phenomenon is becoming increasingly prominent. Especially with the development of various large urban agglomerations and the shrinking distance between cities, the regional thermal environment has attracted extensive attention. Therefore, we used Modis [...] Read more.

With the continuous development of urbanization, the urban heat island (UHI) phenomenon is becoming increasingly prominent. Especially with the development of various large urban agglomerations and the shrinking distance between cities, the regional thermal environment has attracted extensive attention. Therefore, we used Modis land surface temperature (LST) data and employed least squares, standard deviation and spatial autocorrelation analysis methods to analyze the spatiotemporal patterns and characteristics of summer daytime regional urban heat islands (RHI) in the Beijing–Tianjin–Hebei (BTH) urban agglomeration. Our results indicated that the relative land surface temperature (RLST) in the southeastern part of BTH with a relatively high level of urbanization showed a significant and continuous upward trend. With the continuous development of the level of urbanization in the southeast, the center of gravity (GC) of RHI gradually moved to the southeast, and the development direction of RHI changed from northwest–southeast to northeast–southwest. The area transfer of RHI was concentrated in no change and little change, indicating that the evolution trend of RHI was relatively stable. The high-high aggregation areas were mainly located in the more developed areas in the southeast. In addition, the methods and results of this study can provide reasonable and effective insights into the future development and planning of the BTH. Full article

(This article belongs to the Special Issue Remote Sensing in Applied Ecology)

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15 pages, 5064 KiB

Open AccessArticle

Assessing the Impacts of Tidal Creeks on the Spatial Patterns of Coastal Salt Marsh Vegetation and Its Aboveground Biomass

by Ya-Nan Tang, Jun Ma, Jing-Xian Xu, Wan-Ben Wu, Yuan-Chen Wang and Hai-Qiang Guo

Remote Sens. 2022, 14(8), 1839; https://doi.org/10.3390/rs14081839 - 11 Apr 2022

Cited by 14 | Viewed by 2786

Abstract

The spatial distribution patterns of salt marsh plant communities and their biomass provide useful information for monitoring the stability and productivity of coastal salt marsh ecosystems in space and time. However, the spatial patterns of plant vegetation and its aboveground biomass (AGB) in [...] Read more.

The spatial distribution patterns of salt marsh plant communities and their biomass provide useful information for monitoring the stability and productivity of coastal salt marsh ecosystems in space and time. However, the spatial patterns of plant vegetation and its aboveground biomass (AGB) in a coastal salt marsh remain unclear. This study mapped the spatial distributions of salt marsh communities and their AGB based on image and LiDAR data acquired by an unmanned aerial vehicle (UAV) in the Yangtze River Estuary. The differences in vegetation structure and AGB at regions located at different distances from tidal creeks were also tested. The results show that biomass estimated through a random forest model is in good agreement (R² = 0.90, RMSE = 0.1 kg m⁻²) with field-measured biomass. The results indicate that an AGB estimation model based on UAV-LiDAR data and a random forest algorithm with high accuracy was useful for efficiently estimating the AGB of salt marsh vegetation. Moreover, for Phragmites australis, both its proportion and AGB increased, while the proportion and AGB of Scirpus mariqueter, Carex scabrifolia, and Imperata cylindrica decreased with increasing distance from tidal creeks. Our study demonstrates that tidal creeks are important for shaping spatial patterns of coastal salt marsh communities by altering soil salinity and soil moisture, so reasonable and scientific measures should be taken to manage and protect coastal ecosystems. Full article

(This article belongs to the Special Issue Remote Sensing in Applied Ecology)

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24 pages, 5354 KiB

Open AccessArticle

Grassland Conservation Effectiveness of National Nature Reserves in Northern China

by Siqing Zhao, Xiang Zhao, Jiacheng Zhao, Naijing Liu, Mengmeng Sun, Baohui Mu, Na Sun and Yinkun Guo

Remote Sens. 2022, 14(7), 1760; https://doi.org/10.3390/rs14071760 - 6 Apr 2022

Cited by 6 | Viewed by 3006

Abstract

Grasslands are crucial ecosystem biomes for breeding livestock and combatting climate change. By 2018, the national nature reserves (NNRs) in the Inner Mongolia Autonomous Region (IMAR) had constituted 8.55% of the land area. However, there is still a knowledge gap about their effectiveness [...] Read more.

Grasslands are crucial ecosystem biomes for breeding livestock and combatting climate change. By 2018, the national nature reserves (NNRs) in the Inner Mongolia Autonomous Region (IMAR) had constituted 8.55% of the land area. However, there is still a knowledge gap about their effectiveness in grasslands. Based on a multiyear time series of the growing season composite from 2000 to 2020, we proposed an effectiveness score to assess the effectiveness of the NNRs, using the 250 m MOD13Q1 NDVI data with Theil–Sen and Mann–Kendall trend analysis methods. We found the following: 22 of 30 NNRs were deemed effective in protecting the Inner Mongolian grasslands. The NNRs increased pixels with a sustainable trend 19.26% and 20.55% higher than the unprotected areas and the IMAR, respectively. The pixels with a CV_NDVI < 0.1 (i.e., NDVI coefficient of variation) in the NNRs increased >35.22% more than those in the unprotected areas and the IMAR. The NDVI changes within the NNRs showed that 63.64% of NNRs had a more significant trend of greening than before the change point, which suggests a general greening in NNRs. We also found that the NNRs achieved heterogeneous effectiveness scores across protection types. Forest ecology protection and wildlife animal protection types are the most efficient, whereas wildlife vegetation protection is the least effective type. This study enriches the understanding of grassland conservation and sheds light on the future direction of the sustainable management of NNRs. Full article

(This article belongs to the Special Issue Remote Sensing in Applied Ecology)

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23 pages, 25770 KiB

Open AccessArticle

Spatiotemporal Variations in Satellite-Derived Vegetation Phenological Parameters in Northeast China

by Jinting Guo and Yuanman Hu

Remote Sens. 2022, 14(3), 705; https://doi.org/10.3390/rs14030705 - 2 Feb 2022

Cited by 17 | Viewed by 2465

Abstract

Vegetation phenology does not only serve as a key index of terrestrial ecosystem response to worldwide climate change but also has a major influence on plant productivity and the carbon cycle. In the current research, the change of vegetation phenological parameters was studied [...] Read more.

Vegetation phenology does not only serve as a key index of terrestrial ecosystem response to worldwide climate change but also has a major influence on plant productivity and the carbon cycle. In the current research, the change of vegetation phenological parameters was studied and the impact exerted by climate change on phenological phases in northeast China for 1982–2014 was explored using the latest edition of the Global Inventory Modeling and Mapping Studies Normalized Difference Vegetation Index (GIMMS NDVI3g) dataset. The results showed that the start of the growing season (SOS) slightly advanced, the end of the growing season (EOS) showed a significant delay, and the length of the growing season (LOS) exhibited a significant prolonging at the regional scale. At the different vegetation types scale, there existed diverse responses of vegetation phenological phases to climate change for forest, grassland, and cultivated land. Significant decreasing trends in the SOS occupied 19.1% of the entire research area, whereas pixels with significantly increasing trends in the SOS accounted for 13.1%. The EOS was delayed in most of the study region (approximately 72.1%). As the result of the variations of SOS and EOS, the LOS was obviously enhanced (p < 0.05) in 29.7% of the research area. According to the correlation of vegetation phenology with climate factors, the SOS had a significant negative relationship with the average temperature in springtime, while the EOS was notably negatively connected to summer total precipitation at the regional scale. At the pixel scale, the correlation of phenological parameters with climate variables showed strong spatial heterogeneities. This study contributes to the comprehension of the responses of vegetation phenology to climate change. Full article

(This article belongs to the Special Issue Remote Sensing in Applied Ecology)

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