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Recent Advances in Terrestrial Vegetation Productivity with Remote Sensing Techniques

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Environmental Remote Sensing".

Deadline for manuscript submissions: closed (15 September 2023) | Viewed by 22860

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Prof. Dr. Wenquan Zhu

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Institute of Remote Sensing Science and Engineering, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
Interests: vegetation remote sensing; ecological remote sensing

Prof. Dr. Dailiang Peng

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Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
Interests: vegetation remote sensing; agricultural remote sensing; climate change; carbon cycle
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Dr. Zhiying Xie

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Dear Colleagues,

Vegetation productivity is an important component of the terrestrial carbon cycle, which not only reflects the productivity of vegetation communities and characterizes the quality of terrestrial ecosystems but also represents a major factor in determining the carbon source-sink of ecosystems and regulating ecological processes. Therefore, vegetation productivity plays a crucial role in global climate change and the carbon balance, and it has been widely integrated into vegetation–climate interaction studies, land-use evaluation, regional ecological planning, vegetation growth monitoring, crop yield estimation, soil, and water erosion assessments, ecological benefit assessments, etc. Under the interactive influences of climate change and human activities, terrestrial vegetation productivity has changed significantly. It is therefore urgent that dynamic and accurate monitoring of its changes is carried out; the reasons for these changes need to be analyzed for a deeper understanding of their mechanisms.

Remote sensing is an important tool for vegetation productivity estimation, and the development of new remote sensing technologies and methods has generated new opportunities for accurate estimation and application of vegetation productivity. Therefore, with the support of new remote sensing technologies and methods, developing more accurate remote sensing models for vegetation productivity estimation, exploring the interactive effects of multiple factors on terrestrial vegetation productivity, and of terrestrial vegetation productivity on the climate will not only help to deepen the understanding of the “land-atmosphere” carbon cycle mechanism but also provide technical and theoretical support to achieve the goal of “carbon neutrality”.

The purpose of this Special Issue was to introduce new data and methods for remote sensing estimation of terrestrial vegetation productivity, the interactive effects of multiple factors on terrestrial vegetation productivity, and the impact of the feedback mechanism of terrestrial vegetation productivity on climate. Potential topics include, but are not limited to:

New data and models for remote sensing estimation of vegetation productivity;
Driving factors and spatio-temporal differentiations of vegetation productivity;
Quantitative effects of climate change and human activities on vegetation productivity;
Feedback of terrestrial vegetation productivity to climate;
Applications of vegetation productivity in ecological assessment and sustainable development.

Prof. Dr. Wenquan Zhu
Prof. Dr. Dailiang Peng
Dr. Zhiying Xie
Guest Editors

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Keywords

terrestrial vegetation productivity
gross primary productivity (GPP)
net primary productivity (NPP)
vegetation dynamic monitoring
vegetation biochemical and biophysical parameters
sun-induced chlorophyll fluorescence (SIF)
carbon cycle
machine learning and deep learning
climate change

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23 pages, 17216 KiB

Open AccessArticle

Evaluation of Original and Water Stress-Incorporated Modified Weather Research and Forecasting Vegetation Photosynthesis and Respiration Model in Simulating CO₂ Flux and Concentration Variability over the Tibetan Plateau

by Hanlin Niu, Xiao-Ming Hu, Lunyu Shang, Xianhong Meng, Shaoying Wang, Zhaoguo Li, Lin Zhao, Hao Chen, Mingshan Deng and Danrui Sheng

Remote Sens. 2023, 15(23), 5474; https://doi.org/10.3390/rs15235474 - 23 Nov 2023

Viewed by 1081

Abstract

Terrestrial carbon fluxes are crucial to the global carbon cycle. Quantification of terrestrial carbon fluxes over the Tibetan Plateau (TP) has considerable uncertainties due to the unique ecosystem and climate and scarce flux observations. This study evaluated our recent improvement of terrestrial flux parameterization in the weather research and forecasting model coupled with the vegetation photosynthesis and respiration model (WRF-VPRM) in terms of reproducing observed net ecosystem exchange (NEE), gross ecosystem exchange (GEE), and ecosystem respiration (ER) over the TP. The improvement of VPRM relative to the officially released version considers the impact of water stress on terrestrial fluxes, making it superior to the officially released model due to its reductions in bias, root mean square error (RMSE), and ratio of standard deviation (RSD) of NEE to 0.850 μmol·m⁻²·s⁻¹, 0.315 μmol·m⁻²·s⁻¹, and 0.001, respectively. The improved VPRM also affects GEE simulation, increasing its RSD to 0.467 and decreasing its bias and RMSE by 1.175 and 0.324 μmol·m⁻²·s⁻¹, respectively. Furthermore, bias and RMSE for ER were lowered to −0.417 and 0.954 μmol·m⁻²·s⁻¹, with a corresponding increase in RSD by 0.6. The improved WRF-VPRM simulation indicates that eastward winds drive the transfer of lower CO₂ concentrations from the eastern to the central and western TP and the influx of low-concentration CO₂ inhibits biospheric CO₂ uptake. The use of an improved WRF-VPRM in this study helps to reduce errors, improve our understanding of the role of carbon flux cycle over the TP, and ultimately reduce uncertainty in the carbon flux budget. Full article

(This article belongs to the Special Issue Recent Advances in Terrestrial Vegetation Productivity with Remote Sensing Techniques)

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25 pages, 17956 KiB

Open AccessArticle

Historical Attributions and Future Projections of Gross Primary Productivity in the Yangtze River Basin under Climate Change Based on a Novel Coupled LUE-RE Model

by Hong Du, Jian Wu, Sidong Zeng and Jun Xia

Remote Sens. 2023, 15(18), 4489; https://doi.org/10.3390/rs15184489 - 12 Sep 2023

Cited by 1 | Viewed by 1281

Abstract

Attributions and predictions of gross primary productivity (GPP) under climate change is of great significance for facilitating a deeper understanding of the global and regional terrestrial carbon cycle and assessing ecosystem health. In this study, we have designed a novel approach to simulate GPP based on the satellite and meteorological data compiling the advantages of the light use efficiency model with regression methods (LUE-RE model), which overcomes the limitation of the satellite-based method in GPP simulation and projection in the future time without satellite data. Based on the proposed method, results show that GPP in the Yangtze River Basin shows a significant increase trend in the historical period. Elevated CO₂ dominates the changes of GPP in the Yangtze River Basin. In the future, with the increase in elevated CO₂ and climate change, the trend of GPP growth is more obvious. The growth slopes under different scenarios are 2.65 gCm⁻²year⁻¹a⁻¹, 12.34 gCm⁻²year⁻¹a⁻¹, 24.91 gCm⁻²year⁻¹a⁻¹, and 39.62 gCm⁻²year⁻¹a⁻¹. There are obvious seasonal differences in the future changes of GPP in the Yangtze River Basin, of which the GPP changes mostly in spring. The spatial patterns show that higher GPP is concentrated in the upper stream, while the low values are mainly concentrated in the middle reaches. This study contributes a new method to project GPP and highlights that stakeholders should pay more attention to the significant GPP increases in spring in the future. Full article

(This article belongs to the Special Issue Recent Advances in Terrestrial Vegetation Productivity with Remote Sensing Techniques)

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19 pages, 15804 KiB

Open AccessArticle

Estimation of Net Ecosystem Productivity on the Tibetan Plateau Grassland from 1982 to 2018 Based on Random Forest Model

by Jiahe Zheng, Yangjian Zhang, Xuhui Wang, Juntao Zhu, Guang Zhao, Zhoutao Zheng, Jian Tao, Yu Zhang and Ji Li

Remote Sens. 2023, 15(9), 2375; https://doi.org/10.3390/rs15092375 - 30 Apr 2023

Cited by 2 | Viewed by 2337

Abstract

The Tibetan Plateau (TP) is one of the most important areas for the study of the carbon budgets of terrestrial ecosystems. However, the estimation of the net ecosystem productivity (NEP) remains uncertain in this region due to its complex topographic properties and climatic conditions. Using CO₂-eddy-covariance-flux data from 1982 to 2018 at 18 sites distributed around the TP grassland, we analyzed the spatial–temporal patterns of the grassland NEP and its driving factors from 1982 to 2018 using a random forest (RF) model. Our results showed that the RF model captured the size of the carbon sink (R² = 0.65, p < 0.05) between the observed and simulated values for the validation samples. During the observation period, the grassland acted as a carbon sink of 26.2 Tg C yr⁻¹ and increased significantly, by 0.4 g C m⁻² yr⁻¹. On a regional scale, the annual NEP gradually increased from the northwest to the southeast, and a similar pattern was also observed in the long-term trends. Furthermore, the moisture conditions, such as the specific humidity and precipitation, were proven to be the main driving factors of the carbon flux in the southeastern areas, while the temperature predominantly controlled the carbon flux in the northwest. Our results emphasize the net carbon sink of the TP and provide a reliable way to upscale NEP from sites. Full article

(This article belongs to the Special Issue Recent Advances in Terrestrial Vegetation Productivity with Remote Sensing Techniques)

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23 pages, 51384 KiB

Open AccessArticle

NPP Variability Associated with Natural and Anthropogenic Factors in the Tropic of Cancer Transect, China

by Yanyan Wu and Zhifeng Wu

Remote Sens. 2023, 15(4), 1091; https://doi.org/10.3390/rs15041091 - 16 Feb 2023

Cited by 9 | Viewed by 2153

Abstract

The regions near the Tropic of Cancer are a latitudinal geographical zone with typical climatic, topographic, and human landscape features. It is necessary to explore the region’s net primary productivity (NPP) dynamics as it combines complex topography, various vegetation types, and intense human activities. The study sets the transect near the Tropic of Cancer (TCT) and uses the Carnegie–Ames–Stanford (CASA) model to estimate the NPP from 2000 to 2020. After using the RESTREND method, the paper calculates and compares the relative contributions of climate variability and anthropogenic activities to NPP changes. Finally, the geographical detector (Geodetector) model is applied to evaluate how anthropogenic and natural factors affect spatial distribution patterns and NPP changes. The results indicated that the average annual NPP is 820.39 gC·m⁻²·yr⁻¹ during the 21 years. In addition, when the NPP varies, it increases over the entire study area, with a slope of 4.81 gC·m⁻²·yr⁻¹, particularly in the western region. Across the entire research area, 63.39% and 77.44% of the total pixels positively contribute to climate variability and human activities in NPP, with a contribution of 0.90 and 3.91 gC·m⁻²·yr⁻¹, respectively. Within the western, central, and eastern regions, anthropogenic activities have a stronger impact on NPP than climate variability, particularly pronounced in the eastern region. Furthermore, vegetation cover is the dominant factor in the spatial patterns and NPP trends across the TCT and the three regions. In contrast, climate factors are shown to be less influential in NPP distribution than in the western region. The results also demonstrated that the effect of population density and the GDP on NPP gradually rises. Two-factor interaction is much larger than any individual factor, with the dominant interaction factor being vegetation cover with climatic factors. Lastly, the findings revealed that anthropogenic activities positively promote NPP accumulation across the TCT, thus highlighting the importance of human activity-led ecological restoration and ecological protection measures that contribute to regional carbon sequestration and carbon balance. Full article

(This article belongs to the Special Issue Recent Advances in Terrestrial Vegetation Productivity with Remote Sensing Techniques)

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25 pages, 9334 KiB

Open AccessArticle

Assessment of Carbon Productivity Trends and Their Resilience to Drought Disturbances in the Middle East Based on Multi-Decadal Space-Based Datasets

by Karam Alsafadi, Shuoben Bi, Bashar Bashir, Safwan Mohammed, Saad Sh. Sammen, Abdullah Alsalman, Amit Kumar Srivastava and Ahmed El Kenawy

Remote Sens. 2022, 14(24), 6237; https://doi.org/10.3390/rs14246237 - 9 Dec 2022

Cited by 5 | Viewed by 2722

Abstract

Gross primary production (GPP) is a key component in assessing the global change in carbon uptake and in evaluating the impacts of climate change on terrestrial ecosystems. A decrease in the photosynthetic rate due to stomata closing by vegetation could have an impact on GPP. Nonetheless, the uncertainty in long-term GPP patterns and their resilience to drought disturbances has not yet been examined thoroughly. In this study, four state-of-the-art GPP datasets, including the revised EC-LUE algorithm-driven GPP (GLASS-GPP), the EC flux tower upscaling-based GPP (FluxCom-GPP), the MODIS algorithm-driven GPP model (GIMMS-GPP), and the vegetation photosynthesis model-GPP (VPM-GPP), were used to assess GPP characteristics in the Middle East region for 36 years spanning the period of 1982 to 2016. All investigated datasets revealed an increasing trend over the study period, albeit with a more pronounced upward trend for the VPM-GPP dataset in the most recent decades (2000–2016). On the other hand, FluxCom-GPP exhibited less variability than the other datasets. In addition, while GLASS-GPP presented a significant increasing trend in some parts of the region, significant negative trends dominated the other parts. This study defined six significant drought episodes that occurred in the Middle East region between 1982 and 2017. The most severe drought events were recorded in 1985, 1989–1990, 1994, 1999–2001, 2008, and 2015, spreading over more than 15% of the total area of the region. The extreme droughts accounted for a high decline in GPP in the north of Iraq, the northeast of Syria, and the southwest of Iran, where 20.2 and 40.8% of the ecosystem’s GPP were severely non-resilient to drought according to the GLASS and VPM-based GPP responses, respectively. The spatial distribution patterns of the correlations between the SEDI and GPP products were somewhat similar and coherent. The highest positive correlations were detected in the central and western parts of Turkey, the western and northeastern parts of Iran, and north Iraq, which showed anomalous r values (r = 0.7), especially for the SEDI-VPM and SEDI-FluxCom GPP associations. The findings of this study can provide a solid base for identifying at-risk regions in the Middle East in terms of climate change impacts, which will allow for better management of ecosystems and proper implementation of climate policies. Full article

(This article belongs to the Special Issue Recent Advances in Terrestrial Vegetation Productivity with Remote Sensing Techniques)

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21 pages, 8127 KiB

Open AccessArticle

Mapping Soil Organic Carbon Content in Patagonian Forests Based on Climate, Topography and Vegetation Metrics from Satellite Imagery

by Guillermo Martínez Pastur, Marie-Claire Aravena Acuña, Eduarda M. O. Silveira, Axel Von Müller, Ludmila La Manna, Marina González-Polo, Jimena E. Chaves, Juan M. Cellini, María V. Lencinas, Volker C. Radeloff, Anna M. Pidgeon and Pablo L. Peri

Remote Sens. 2022, 14(22), 5702; https://doi.org/10.3390/rs14225702 - 11 Nov 2022

Cited by 6 | Viewed by 2824

Abstract

Soil organic carbon (SOC) content supports several ecosystem services. Quantifying SOC requires: (i) accurate C estimates of forest components, and (ii) soil estimates. However, SOC is difficult to measure, so predictive models are needed. Our objective was to model SOC stocks within 30 cm depth in Patagonian forests based on climatic, topographic and vegetation productivity measures from satellite images, including Dynamic Habitat Indices and Land Surface Temperature derived from Landsat-8. We used data from 1320 stands of different forest types in Patagonia, and random forest regression to map SOC. The model captured SOC variability well (R² = 0.60, RMSE = 22.1%), considering the huge latitudinal extension (36.4° to 55.1° SL) and the great diversity of forest types. Mean SOC was 134.4 ton C ha⁻¹ ± 25.2, totaling 404.2 million ton C across Patagonia. Overall, SOC values were highest in valleys of the Andes mountains and in southern Tierra del Fuego, ranging from 53.5 to 277.8 ton C ha⁻¹ for the whole Patagonia region. Soil organic carbon is a metric relevant to many applications, connecting major issues such as forest management, conservation, and livestock production, and having spatially explicit estimates of SOC enables managers to fulfil the international agreements that Argentina has joined. Full article

(This article belongs to the Special Issue Recent Advances in Terrestrial Vegetation Productivity with Remote Sensing Techniques)

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23 pages, 4399 KiB

Open AccessArticle

Impact of the Management Scale on the Technical Efficiency of Forest Vegetation Carbon Sequestration: A Case Study of State-Owned Forestry Enterprises in Northeast China

by Shuohua Liu, Xiefei Liu, Zhenmin Ding and Shunbo Yao

Remote Sens. 2022, 14(21), 5528; https://doi.org/10.3390/rs14215528 - 2 Nov 2022

Cited by 2 | Viewed by 2165

Abstract

Improving the technical efficiency of forest vegetation carbon sequestration is an effective way to accelerate the pace and reduce the cost of carbon neutrality in China. Therefore, it is particularly important to explore the technical efficiency, influencing factors, and optimization paths of forest vegetation carbon sequestration. This work uses a 21-year panel data set (2000–2020) of 87 state-owned forestry enterprises (SOFEs) in Northeast China and combines geographic information system (GIS) and remote sensing (RS) technology. First, stochastic frontier analysis (SFA) was used to quantitatively analyze the technical efficiency of forest vegetation carbon sequestration in different SOFEs during different periods. Then, the individual fixed-effects model was used to examine the factors influencing technical efficiency under the control of climate factors. Finally, the panel threshold model was used to determine the impact of different management scales on the technical efficiency of forest vegetation carbon sequestration. The main results were as follows: technological progress can effectively reduce forestry investment and improve the technical efficiency of forest vegetation carbon sequestration production. There was technological progress in forest vegetation carbon sequestration production during the study period, but the rate of technological progress showed a decreasing trend. Forest management scale, total output value, employee wages, precipitation, and sun duration had a significant positive impact, whereas wood production had a significant negative impact on the technical efficiency of carbon sequestration. The impact of different management scales on the technical efficiency of carbon sequestration is highly heterogeneous. The study established an analytical framework for researching the technical efficiency and optimization of forest vegetation carbon sequestration, providing a theoretical and practical basis for forest management. Full article

(This article belongs to the Special Issue Recent Advances in Terrestrial Vegetation Productivity with Remote Sensing Techniques)

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18 pages, 14128 KiB

Open AccessArticle

Quantifying the Influence of Climate Change and Anthropogenic Activities on the Net Primary Productivity of China’s Grasslands

by Xiafei Zhou, Binbin Peng, Ying Zhou, Fang Yu and Xue-Chao Wang

Remote Sens. 2022, 14(19), 4844; https://doi.org/10.3390/rs14194844 - 28 Sep 2022

Cited by 7 | Viewed by 1994

Abstract

As one of China’s most common vegetation types, grasslands comprise about 27.5% of its terrestrial area and 41% of its carbon storage. Since climate change (CC) and human activities (HA) have a great effect on grasslands, quantifying the contributions of CC and HA on grassland net primary productivity (NPP) is crucial in understanding the mechanisms of grassland regional carbon balances. However, current approaches, including residual trend, biophysical model and environmental background-based methods, have limitations on different scales, especially on the national scale of China. To improve assessment accuracy, modifications to the environmental background-based method were introduced in calculating the CC and HA contributions to the actual NPP (ANPP). In this study, the grassland ANPP in national nature reserves was defined as the environmental background value (PNPP), which was only affected by CC and without HA. The pixel PNPP outside the nature reserves could be replaced by the pixel PNPP in the nature reserve with the most similar habitat in the same natural ecological geographical division. The impact of HA on grassland ANPP (HNPP) could be identified by calculating the difference between PNPP and ANPP. Finally, the contributions of CC and HA to ANPP changes were assessed by the trends of ANPP, PNPP, and HNPP. The results showed that the average grassland ANPP significantly increased from 2001 to 2020. CC contributed 71.0% to ANPP change, whereas HA contributed 29.0%. Precipitation was the main contributor to grassland growth among arid and semi-arid regions, while temperature inhibited productivity in these areas. HA was the major cause of degradation in China’s grasslands, although the effects have declined over time. The research could provide support support for government decisions. It could also provide a new and feasible research method for quantitatively evaluating grasslands and other ecosystems. Full article

(This article belongs to the Special Issue Recent Advances in Terrestrial Vegetation Productivity with Remote Sensing Techniques)

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23 pages, 16367 KiB

Open AccessArticle

The Different Impacts of Climate Variability and Human Activities on NPP in the Guangdong–Hong Kong–Macao Greater Bay Area

by Yanyan Wu, Zhaohui Luo and Zhifeng Wu

Remote Sens. 2022, 14(12), 2929; https://doi.org/10.3390/rs14122929 - 19 Jun 2022

Cited by 11 | Viewed by 2767

Abstract

As two main drivers of vegetation dynamics, climate variability and human activities greatly influence net primary productivity (NPP) variability by altering the hydrothermal conditions and biogeochemical cycles. Therefore, studying NPP variability and its drivers is crucial to understanding the patterns and mechanisms that sustain regional ecosystem structures and functions under ongoing climate variability and human activities. In this study, three indexes, namely the potential NPP (NPP_p), actual NPP (NPP_a), and human-induced NPP (NPP_h), and their variability from 2000 to 2020 in the Guangdong–Hong Kong–Macao Greater Bay Area (GBA) were estimated and analyzed. Six main scenarios were generated based on change trends in the three indexes over the past 21 years, and the different relative impacts of climate variability and human activities on NPP_a variability were quantitatively analyzed and identified. The results showed that the NPP_p, NPP_a, and NPP_h had heterogeneous spatial distributions, and the average NPP_p and NPP_a values over the whole study area increased at rates of 3.63 and 6.94 gC·m⁻²·yr⁻¹ from 2000 to 2020, respectively, while the NPP_h decreased at a rate of −4.43 gC·m⁻²·yr⁻¹. Climate variability and the combined effects of climate variability and human activities were the major driving factors of the NPP_a increases, accounting for more than 72% of the total pixels, while the combined effects of the two factors caused the NPP_a values to increase by 32–54% of the area in all cities expect Macao and across all vegetation ecosystems. Human activities often led to decreases in NPP_a over more than 16% of the total pixels, and were mainly concentrated in the central cities of the GBA. The results can provide a reference for understanding NPP changes and can offer a theoretical basis for implementing ecosystem restoration, ecological construction, and conservation practices in the GBA. Full article

(This article belongs to the Special Issue Recent Advances in Terrestrial Vegetation Productivity with Remote Sensing Techniques)

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12 pages, 40646 KiB

Open AccessTechnical Note

A Radiation-Regulated Dynamic Maximum Light Use Efficiency for Improving Gross Primary Productivity Estimation

by Zhiying Xie, Cenliang Zhao, Wenquan Zhu, Hui Zhang and Yongshuo H. Fu

Remote Sens. 2023, 15(5), 1176; https://doi.org/10.3390/rs15051176 - 21 Feb 2023

Cited by 5 | Viewed by 2080

Abstract

The light use efficiency (LUE) model has been widely used in regional and global terrestrial gross primary productivity (GPP) estimation due to its simple structure, few input parameters, and particular theoretical basis. As a key input parameter of the LUE model, the maximum LUE (Ɛ_max) is crucial for the accurate estimation of GPP and to the interpretability of the LUE model. Currently, most studies have assumed Ɛ_max as a universal constant or constants depending on vegetation type, which means that the spatiotemporal dynamics of Ɛ_max were ignored, leading to obvious uncertainties in LUE-based GPP estimation. Using quality-screened daily data from the FLUXNET 2015 dataset, this paper proposed a photosynthetically active radiation (PAR)-regulated dynamic Ɛ_max (PAR-Ɛ_max, corresponding model named PAR-LUE) by considering the nonlinear response of vegetation photosynthesis to solar radiation. The PAR-LUE was compared with static Ɛ_max-based (MODIS and EC-LUE) and spatial dynamics Ɛ_max-based (D-VPM) models at 171 flux sites. Validation results showed that (1) R² and RMSE between PAR-LUE GPP and observed GPP were 0.65 (0.44) and 2.55 (1.82) g C m⁻² MJ⁻¹ d⁻¹ at the 8-day (annual) scale, respectively; (2) GPP estimation accuracy of PAR-LUE was higher than that of other LUE-based models (MODIS, EC-LUE, and D-VPM), specifically, R² increased by 29.41%, 2.33%, and 12.82%, and RMSE decreased by 0.36, 0.14, and 0.34 g C m⁻² MJ⁻¹ d⁻¹ at the annual scale; and (3) specifically, compared to the static Ɛ_max-based model (MODIS and EC-LUE), PAR-LUE effectively relieved the underestimation of high GPP. Overall, the newly developed PAR-Ɛ_max provided an estimation method utilizing a spatiotemporal dynamic Ɛ_max, which effectively reduced the uncertainty of GPP estimation and provided a new option for the optimization of Ɛ_max in the LUE model. Full article

(This article belongs to the Special Issue Recent Advances in Terrestrial Vegetation Productivity with Remote Sensing Techniques)

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