Residual Trend Analysis Research Articles

Desertification in northern China from 2000 to 2020: The spatial–temporal processes and driving mechanisms

Desertification is one of the most significant environmental and social challenges globally. Monitoring desertification dynamics and quantitatively identifying the contributions of its driving factors are crucial for land restoration and sustainable development. This study develops a standardized methodological framework that combines desertification dynamics with driving mechanisms at the pixel level, applied to northern China from 2000 to 2020. Using multisource data and employing the Time Series Segmentation and Residual Trend analysis (TSS-RESTREND) method alongside geographical detector, we quantitatively assessed desertification reversion, expansion, and abrupt change processes, along with the impacts and interactions of natural and human factors were quantitatively assessed. Over the past two decades, the proportion of desertified land decreased by 5.60%. Notably, 32.88% of the study area experienced significant desertification reversion, while only 5.86% underwent expansion. Abrupt changes in both reversed and expanding areas were observed, primarily in the central and western regions, with these changes concentrated in the periods of 2009–2011 and 2014–2016. The impacts of various factors in different sub-regions exhibited significant spatial heterogeneity. Increased precipitation, temperature, and evapotranspiration contributed to reversion in the western area, while decreased wind speed influenced the eastern area. Additionally, decreased population density and afforestation activities also promoted desertification reversion. In contrast, decreased precipitation and increased temperature contributed to expansion in the western and eastern areas, respectively, with increased population density exacerbating this process. Overall, the interactions between natural and human factors were enhanced. Future desertification control and ecological engineering planning should focus on the coupling effects of different driving factors and relevant abrupt vegetation changes.

Dominant role of grazing and snow cover variability on vegetation shifts in the drylands of Kazakhstan

Decomposing the responses of ecosystem structure and function in drylands to changes in human-environmental forcing is a pressing challenge. Though trend detection studies are extensive, these studies often fail to attribute them to potential spatiotemporal drivers. Most attribution studies use a single empirical model or a causal graph that cannot be generalized or extrapolated to larger scales or account for spatial changes and multiple independent processes. Here, we proposed and tested a multi-stage, multi-model framework that detects vegetation trends and attributes them to ten independent social-environmental system (SES) drivers in Kazakhstan (KZ). The time series segmented residual trend analysis showed that 45.71% of KZ experienced vegetation degradation, with land use change as the predominant contributor (22.54%; 0.54 million km2), followed by climate change and climate variability. Pixel-wise fitted Granger Causality and random forest models revealed that sheep & goat density and snow cover had dominant negative and positive impacts on vegetation in degraded areas, respectively. Overall, we attribute vegetation changes to SES driver impacts for 19.81% of KZ (out of 2.39 million km2). The identified vegetation degradation hotspots from this study will help identify locations where restoration projects could have a greater impact and achieve land degradation neutrality in KZ.

Spatiotemporal patterns, sustainability, and primary drivers of NDVI-derived vegetation dynamics (2003-2022) in Nepal.

Understanding the vegetation dynamics and their drivers in Nepal has significant scientific reference value for implementing sustainable ecological policies. This study provides a comprehensive analysis of the spatio-temporal variations in vegetation cover in Nepal from 2003 to 2022 using MODIS NDVI data and explores the effects of climatic factors and anthropogenic activities on vegetation. Mann-Kendall test was used to assess the significant trend in NDVI and was integrated with the Hurst exponent to predict future trends. The driving factors of NDVI dynamics were analyzed using Pearson's correlation, partial derivative, and residual analysis methods. The results indicate that over the last 20 years, Nepal has experienced an increasing trend in NDVI at 0.0013 year-1, with 80% of the surface area (vegetation cover) showing an increasing vegetation trend (~ 28% with a significant increase in vegetation). Temperature influenced vegetation dynamics in the higher elevation areas, while precipitation and human interventions influenced the lower elevation areas. The Hurst exponent analysis predicts an improvement in the vegetation cover (greening) for a larger area compared to vegetation degradation (browning). A significantly increased area of NDVI residuals indicates a positive anthropogenic influence on vegetation cover. Anthropogenic activities have a higher relative contribution to NDVI variation followed by temperature and then precipitation. The results of residual trend and Hurst analysis in different regions of Nepal help identify degraded areas, both in the present and future. This information can assist relevant authorities in implementing appropriate policies for a sustainable ecological environment.

Quantitative analysis of the impact of climate variability and human activities on grassland productivity of the Qilian Mountain National Park, China.

To quantitatively analyze the impact of climate variability and human activities on grassland productivity of China's Qilian Mountain National Park, this study used Carnegic-Ames-Stanford Approach model (CASA) and Integrated Vegetation model improved by the Comprehensive and Sequential Classification System (CSCS) to assess the trends of grassland NPP from 2000 to 2015, the residual trend analysis method was used to quantify the impact of human activities and climate change on the grassland based on the NPP changes. The actual grassland NPP accumulation mainly occurred in June, July and August (autumn); the actual NPP showed a fluctuating upward trend with an average increase of 2.2 g C·m-2 a-1, while the potential NPP increase of 1.6 g C·m-2 a-1 and human-induced NPP decreased of 0.5 g C·m-2 a-1. The annual temperature showed a fluctuating upward trend with an average increase of 0.1°C 10a-1, but annual precipitation showed a fluctuating upward trend with an average annual increase of 1.3 mm a-1 from 2000 to 2015. The area and NPP of grassland degradation caused by climate variability was significantly greater than that caused by human activities and mainly distributed in the northwest and central regions, but area and NPP of grassland restored caused by human activities was significantly greater than that caused by climate variability and mainly distributed in the southeast regions. In conclusion, grassland in Qilian Mountain National Park showed a trend of degradation based on distribution area, but showed a trend of restoration based on actual NPP. Climate variability was the main cause of grassland degradation in the northwestern region of study area, and restoration of grassland in the eastern region was the result of the combined effects of human activities and climate variability. Under global climate change, the establishment of Qilian Mountain National Park was of great significance to the grassland's protection and the grasslands ecological restoration that have been affected by humans.

The impact of global cropland irrigation on soil carbon dynamics

Irrigation can increase crop yields and could be a key climate adaptation strategy. At present, under the background of increasing food demand and continuous expansion of irrigation cropland, there still uncertainties about the soil carbon dynamics under the change of irrigation water volume and irrigated area in view of large-scale spatial heterogeneity. Therefore, this paper uses space-for-time + meta-analysis and a two-step methodology based on the residual trend analysis to quantitatively analyze the relationship between soil organic carbon (SOC) and soil respiration (Rs) in response to fluctuations in irrigation water volume and irrigated land extents. Here we show that the irrigation water volume within 100–1000 mm had a negative impact on SOC, and the impact was correlated with the irrigation water volume. Different levels of irrigation water manifest distinct effects on SOC content across varying soil depths. When irrigation water quantities are less than 700 mm, the impact on SOC content in the 0–30 cm depth layer surpasses that in the 30–200 cm depth layer. Conversely, when irrigation water quantities equal or exceed 700 mm, this pattern is reversed. The overall impact of irrigation on SOC stock at a depth of 0–200 cm was −14.88±6.7%. Tillage, planting intensity, topography, and soil type within irrigated cropland all exert variable impacts on SOC content. Whether these influences are deleterious or beneficial hinges predominantly upon the balance between the augmentation of SOC stock due to heightened carbon inputs from crops and the reduction of SOC through alterations in microbial activity. Mann-Kendall trend analysis showed that from 2000 to 2015, the overall Rs of cropland showed an increasing trend, with an increase rate of 3.67 g/m2/year. The increase of global Rs is mainly driven by climate change factors (temperature, precipitation and solar radiation), while the decrease of Rs in a small number of areas is mainly driven by management practices (fertilizer nitrogen, irrigation, and tillage). Our study further quantifies the impact of irrigation on soil carbon dynamics, thereby offering potential pathways and data support for the advancement of sustainable agriculture.

Spatio-temporal analysis of LAI using multisource remote sensing data for source region of Yellow River Basin

Introduction: The Leaf area index (LAI) of source region of yellow river basin is an important indicator for environmental sustainability. Most studies focus on the trend of LAI in Yellow River Source Region (YRSR) in accordance with both climate change and human actives. However, quantifying the effect of human activities on LAI is difficult but urgently needed. Specifically, Particle Matter 2.5 (PM2.5) can be an indirect indicator of human activities.Methods: In this study, we explored the potential dependence of LAI on temperature, precipitation, and PM2.5 in different land cover types in YRSR with linear regression and correlation analysis.Results: Over the period of 2001–2020, the climate in the region has been warming and becoming more humid, leading to overall improvements in vegetation. The mean LAI values varied between seasons, with summer having the highest and winter having the lowest LAI. The analysis of the LAI trends revealed that the mean LAI has been steadily increasing, particularly in the eastern region. The correlation analysis showed a significant positive correlation between annual average LAI and both annual precipitation and temperature, indicating that temperature has a greater impact on vegetation growth. The analysis of land cover types showed that most types exhibited a unimodal trend in LAI throughout the year, except for construction land which had two distinct peaks. Human-induced land cover change had a small impact on the overall increase in LAI. Furthermore, the interannual variation of PM2.5 showed a downward trend, with a strong correlation with the trend of LAI. Additionally, multiple linear regression analysis and residual trend analysis showed that climate factors had the strongest impact on LAI.Conclusion: The study highlights the spatiotemporal variations of LAI in the YRSR and its correlation with climatic and human factors. The findings suggest that climate change plays a crucial role in the vegetation growth and LAI in the region.

Effects of precipitation changes on fractional vegetation cover in the Jinghe River basin from 1998 to 2019

Studying the spatiotemporal evolutionary characteristics of vegetation and the effect of precipitation changes is necessary for understanding the regional ecological environment. We used trend analysis, partial correlation analysis, significance tests, and residual trend analysis to analyze the spatiotemporal evolution characteristics and driving factors of fractional vegetation cover (FVC) in the Jinghe River Basin (JRB) from 1998 to 2019. The results showed that vegetation coverage in the JRB significantly improved from 1998 to 2019. FVC showed an increasing trend in 90.64% of the areas in the JRB, and the overall annual change in FVC was extremely significant (p ≤ 0.01). However, precipitation in the JRB showed an insignificant increasing trend; the annual distribution of precipitation developed in a uniform direction and the precipitation centroid tended to move backward. The area with a significant correlation between the FVC change and the precipitation concentration index accounted for the largest proportion (18.47%). Precipitation changes in the JRB generally favored vegetation recovery; however, the effect was limited and non-precipitation factors dominated the changes in FVC. Our study contributes to a more comprehensive understanding of the effects of changes in precipitation patterns on vegetation cover to facilitate regional ecological protection.

Study on the relationship between regional soil desertification and salinization and groundwater based on remote sensing inversion: A case study of the windy beach area in Northern Shaanxi

Soil desertification and salinization are important environmental concerns in arid regions, and their relationship with groundwater change must be further clarified. However, the relationships among soil desertification, salinization, and groundwater are difficult to investigate on a large spatiotemporal scale using traditional ground surveys. In the windy beach area in Northern Shaanxi (WBANS), desertification and salinization problems coexist; therefore, this area was selected as the study area. The feasibility of implementing large-scale remote sensing inversions to identify the degree of desertification and salinization was verified based on measured data, and the degree of influence of groundwater burial depth (GBD) on desertification and salinization was quantified using the geodetector and residual trend analysis methods. The results showed that the GBD in the WBANS presented an increasing trend and the degree of salinization showed a decreasing trend. Moreover, the joint influence of the unique natural environment and anthropogenic activities has led to increases in fractional vegetation cover and considerable improvements in the ecological environment. The intensity of desertification explained by GBD in the WBANS increased significantly (p < 0.01) at a rate of change of 0.0190/year, with high q-values above 0.66 for both Yuyang and Shenmu. The contribution rate of potential evapotranspiration and precipitation to salinization in Yuyang and Shenmu was >97 %, and the contribution rate of GBD to salinization in Dingbian, Jingbian, and Hengshan was 34.78 %, 31.15 %, and 29.41 %, respectively. Overall, the suitable GBD in the WBANS is 2–4 m. The study results provide a reference for research on the inversion, monitoring, and prevention of desertification and salinization dynamics on a large spatiotemporal scale and offer a scientific basis for rationally determining GBD.

Climatic and Anthropogenic Contributions to Vegetation Changes in Guangdong Province of South China

How to distinguish the relative role of climate change and human activities in vegetation dynamics has attracted increasing attention. However, most of the current studies concentrate on arid and semiarid regions, while the relative contributions of climate change and human activities to vegetation changes remain unclear in warm-humid regions. Based on the normalized difference vegetation index (NDVI) and climatic variables (temperature, precipitation, radiation) during 2001–2020, this study used the Theil–Sen median trend analysis, partial correlation analysis, and residual trend analysis to analyze the spatiotemporal pattern of vegetation trends, the response of vegetation to climate variations, and the climatic and anthropogenic contributions to vegetation dynamics in the warm and humid Guangdong Province of south China. Results showed that the NDVI in most areas exhibited an increasing trend. Changes in climatic variables displayed different spatial variations which, however, were not significant in most areas. Vegetation responded diversely to climate change with temperature as the most important climatic factor for vegetation improvement in most areas, while precipitation was the dominant climatic factor in the southern edge region and radiation was the dominant climatic factor in the central and western regions. Vegetation in most areas was influenced by both climate change and human activities, but the contribution rate of human activities was commonly much higher than climate change. The findings of this study are expected to enhance our understanding of the relative climatic and anthropogenic contributions to vegetation changes in warm-humid regions and provide a scientific basis for future ecological policies and ecosystem management in highly urbanized regions.

Climate impact greater on vegetation NPP but human enhance benefits after the Grain for Green Program in Loess Plateau

Net primary productivity (NPP) has been the subject of numerous studies, but the qualitative and comparative study on NPP that considers human activities, climate change, and their interactions is still insufficient. The Grain for Green Program (GTGP) was established in the Loess Plateau since 1999, could be used as a reference study. The temporal and spatial patterns, change trend, and driving factors of vegetation NPP were analysed in Loess Plateau using data derived from GLASS NPP (the Global Land Surface Satellite Product), meteorology, atmospheric quality, hydrology and land use by correlation and improved residual trend analysis from 2000 to 2020. After GTGP implementation, the climate and human contributions to vegetation NPP were 62 % and 38 %. NPP increased at a rate of 6.3 g C m−2 a−1 from 2000 to 2020, which was higher in 2010–2020 (6.9 g C m−2 a−1) than it was in 2000–2010 (5.7 g C m−2 a−1). The same trend was followed by the contribution of climate change to NPP between 2010 and 2020 (70.6 %) and 2000–2010 (65 %). The GTGP core area was mostly where the vegetation NPP rising zone was located. From the original bare land in the desert area, 4442 km2 of vegetation grew and increased the NPP (68.7 g C m−2). Under the warm–humid climatic trend, the climate factors contributed to NPP more in grasslands (40 %) compared to woodlands (29 %) and precipitation (33 %) was more sensitive than temperature (12.7 %). Consequently, sediment transport, PM2.5 and dusty weather decreased significantly. In conclusion, although climate change has a greater impact on vegetation, human activities have enhanced the impact faster in the first decade, which benefit and profit from the environment finally.

Spatio-temporal dynamics of land use transitions associated with human activities over Eurasian Steppe: Evidence from improved residual analysis

We presented a framework to evaluate the land use transformations over the Eurasian Steppe (EUS) driven by human activities from 2000 to 2020. Framework involves three main components: (1) evaluate the spatial-temporal dynamics of land use transitions by utilizing the land change modeler (LCM) and remote sensing data; (2) quantifying the individual contributions of climate change and human activities using improved residual trend analysis (IRTA) and pixel-based partial correlation coefficient (PCC); and (3) quantifying the contributions of land use transitions to Leaf Area Index Intensity (LAII) by using the linear regression. Research findings indicate an increase in cropland (+1.17 % = 104,217 km2) over EUS, while a − 0.80 % reduction over Uzbekistan and − 0.16 % over Tajikistan. From 2000 to 2020 a slight increase in grassland was observed over the EUS region by 0.05 %. The detailed findings confirm an increase (0.24 % = 21,248.62 km2) of grassland over the 1st half (2000−2010) and a decrease (−0.19 % = −16,490.50 km2) in the 2nd period (2011−2020), with a notable decline over Kazakhstan (−0.54 % = 13,690 km2), Tajikistan (−0.18 % = 1483 km2), and Volgograd (−0.79 % = 4346 km2). Area of surface water bodies has declined with an alarming rate over Kazakhstan (−0.40 % = 10,261 km2) and Uzbekistan (−2.22 % = 8943 km2). Additionally, dominant contributions of human activities to induced LULC transitions were observed over the Chinese region, Mongolia, Uzbekistan, and Volgograd regions, with approximately 87 %, 83 %, 92 %, and 47 %, respectively, causing effective transitions to 12,997 km2 of cropland, 24,645 km2 of grassland, 16,763 km2 of sparse vegetation in China, and 12,731.2 km2 to grassland and 15,356.1 km2 to sparse vegetation in Mongolia. Kazakhstan had mixed climate-human impact with human-driven transitions of 48,568 km2 of bare land to sparse vegetation, 27,741 km2 to grassland, and 49,789 km2 to cropland on the eastern sides. Southern regions near Uzbekistan had climatic dominancy, and 8472 km2 of water bodies turned into bare soil. LAII shows an increasing trend rate of 0.63 year−1, particularly over human-dominant regions. This study can guide knowledge of oscillations and reduce adverse impacts on ecosystems and their supply services.

Quantitative analysis of spatiotemporal changes and driving forces of vegetation net primary productivity (NPP) in the Qimeng region of Inner Mongolia

Vegetation is an essential component of terrestrial ecosystems, and understanding the drivers of vegetation change is of great importance for ecological management. In recent years, vegetation growth has increased under the combined effect of global warming and human activities in Inner Mongolia. The net primary productivity (NPP) was used as an indicator to study the spatial and temporal changes in vegetation in the Qimeng Region (QR). The residual trend analysis method was used to analyze the relative contributions of climate variations (CV) and human activities (HA) to NPP changes across the QR, while their drivers were explored using a geographical detector approach to quantify the driving forces of NPP. The results show that (1) NPP exhibited a fluctuating growth trend from 2003 to 2020, with an overall growth rate of 2.91%/year. (2) Precipitation, GDP and population density were the dominant driving factors for the spatial distribution of NPP. The combined explanatory power of any two dominant factors exceeded the power of any dominant individual factor, and the interaction between climate and human factors had a significant effect on NPP. (3) The change in NPP was influenced by the combined effect of HA and CV, accounting for 37.69% of the total area, with the relative contribution of HA being 51.75%. Finally, the relative contribution of human activities was slightly higher than that of climate change, confirming the initial success of the Grain to Green Program as well as ecological conservation projects. This paper provides a scientific basis for the local government to carry out the conversion of cropland to forest.

Quantifying climate variability and regional anthropogenic influence on vegetation dynamics in northwest India

To explore the spatio-temporal dynamics and mechanisms underlying vegetation cover in Haryana State, India, and implications thereof, we obtained MODIS EVI imagery together with CHIRPS rainfall and MODIS LST at annual, seasonal and monthly scales for the period spanning 2000 to 2022. Additionally, MODIS Potential Evapotranspiration (PET), Ground Water Storage (GWS), Soil Moisture (SM) and nighttime light datasets were compiled to explore their spatial relationships with vegetation and other selected environmental parameters. Non-parametric statistics were applied to estimate the magnitude of trends, along with correlation and residual trend analysis to quantify the relative influence of Climate Change (CC) and Human Activities (HA) on vegetation dynamics using Google Earth Engine algorithms. The study reveals regional contrasts in trends that are evidently related to elevation. An annual increasing trend in rainfall (21.3 mm/decade, p < 0.05), together with augmented vegetation cover and slightly cooler (−0.07 °C/decade) LST is revealed in the high-elevation areas. Meanwhile, LST in the plain regions exhibit a warming trend (0.02 °C/decade) and decreased in vegetation and rainfall, accompanied by substantial reductions in GWS and SM related to increased PET. Linear regression demonstrates a strongly significant relationship between rainfall and EVI (R2 = 0.92), although a negative relationship is apparent between LST and vegetation (R2 = −0.83). Additionally, increased LST in the low-elevation parts of the study area impacted PET (R2 = 0.87), which triggered EVI loss (R2 = 0.93). Moreover, increased HA resulted in losses of 25.5 mm GSW and 1.5 mm SM annually. The relative contributions of CC and HA are shown to vary with elevation. At higher elevations, CC and HA contribute respectively 85% and 15% to the increase in EVI. However, at lower elevations, reduced EVI is largely (79%) due to human activities. This needs to be considered in managing the future of vulnerable socio-ecological systems in the state of Haryana.

Quantifying the contributions of climate factors and human activities to variations of net primary productivity in China from 2000 to 2020

Net primary productivity (NPP) plays a vital role in the globe carbon cycle. Quantitative assessment of the effects of climate changes and human activities on net primary productivity dynamics is vital for understanding the driving mechanisms of vegetation change and sustainable development of ecosystems. This study investigates the contributions of climatic factors and human activities to vegetation productivity changes in China from 2000 to 2020 based on the residual trend analysis (RESTREND) method. The results showed that the annual average net primary productivity in China was 325.11 g C/m2/year from 2000 to 2020 and net primary productivity showed a significantly increasing trend (p&amp;lt;0.05) at a rate of 2.32 g C/m2/year. Net primary productivity increased significantly (p&amp;lt;0.05) across 40.90% of China over the study period, while only 1.79% showed a significantly declining trend (p&amp;lt;0.05). The contributions of climatic factors and human activities to net primary productivity increase were 1.169 g C/m2/year and 1.142 g C/m2/year, respectively. Climate factors contributed positively mainly in Sichuan Basin, the Loess Plateau, the Mongolian Plateau, and Northeast China Plain. Positive contributions of human activities to net primary productivity mainly occurred in the Loess Plateau, Central China, and the Greater Khingan Mountains. The effects of climatic factors and human activities on net primary productivity changes varied among sub-regions. In Tropical Monsoon Climate Region and Subtropical Monsoon Climate Region, human activities had greater impacts on net primary productivity increase than climate factors, while climate factors were the dominant factor for net primary productivity recovery in other sub-regions. In addition, during 2000–2020, net primary productivity was dominated by both climate factors and human activities in 49.84% of China, while areas dominated solely by climate factors and human activities accounted for 13.67% and 10.92%, respectively. Compared to changed land cover types, the total net primary productivity as well as the increase of total net primary productivity in China was mostly contributed by unchanged land cover types, which contributed more than 90%.

Potential of GIS and remote sensing in mapping land degradation: catchment of the Manyame River, Zimbabwe

Abstract The Manyame River Catchment area in Zimbabwe, is experiencing severe land degradation mainly due to legal and illegal land husbandry practices. These practices are negatively impacting on the sustainability of the existing ecosystems. The conditions of land can be inferred using its vegetative cover e.g., Normalised Difference Vegetation Index (NDVI). Quantitative data relating to land degradation based on notable physical features such as gullies, for Manyame River Catchment at landscape scales is poor. This study focused on the distribution and magnitude of land degradation in the Manyame River Catchment area. The study mapped out the contours of human induced land degradation using a residual trend analysis (RESTREND) method. In particular, the study used remote sensed data (NDVI and precipitation time series) to analyse the shifts over period 2000 to 2017. The analysis used R statistical software packages (RESTREND and Kendal) and Geographic Information System (GIS) techniques to quantify the degradation trends. The results indicated extracts of those areas which experienced significant human induced land degradation during the study period. RESTREND effectiveness was assessed using Mann Kendal Test. The results of this study can be used by natural resources practitioners in monitoring, assessing and managing environmental changes using GIS tools.

Discrimination among Climate, Human Activities, and Ecosystem Functional-Induced Land Degradation in Southern Africa

Land degradation threatens ecosystems and socio-economic development of Southern Africa. Evaluation of land degradation is widely conducted using a remote-sensed indicator to provide key information for alleviating degradation. However, the commonly used single indicator cannot reveal complex degradation processes. In this study, we conducted an integrated evaluation by utilizing linear regression, residual trend analysis, and sequential regression methods to detect visible, potential human-induced, and functional land degradation in Southern Africa. The results showed that visible, potential, and functional land degradation accounted for 8%, 9.6%, and 21.9% of the entire study area, respectively. In total, 34% (171.96 × 104 km2) of the region exhibited one or more forms of land degradation; 28.9% (146.01 × 104 km2) of the land experienced a single land degradation type, whereas 5.1% (25.95 × 104 km2) exhibited intensified degradation by two or three forms. Land degradation was more severe in South Africa, Angola, Botswana, and Mozambique. Potential degradation (11.76%) and functional degradation (56.88%) may co-exist with vegetation greening. This study suggests that a single indicator assessment underestimates the overall land degradation, and thus integrated indicators and methods are better for a comprehensive assessment. Spatial pattern and degradation process analyses are useful for the formulation of land restoration policies in Southern Africa.

Improved Vegetation Ecological Quality of the Three-North Shelterbelt Project Region of China during 2000–2020 as Evidenced from Multiple Remotely Sensed Indicators

Evaluation of the long-term effect of ecosystem recovery projects is critical for future ecological management and sustainable development. The Three-North Shelterbelt (TNS) is a large-scale afforestation project in a crucial region of China. Numerous researchers have evaluated the vegetation ecological quality (VEQ) of the TNS using a single vegetation indicator. However, vegetation ecosystems are complex and need to be evaluated through various indicators. We constructed the vegetation ecological quality index (VEQI) by downscaling net primary productivity, leaf area index, fractional vegetation cover, land surface temperature, vegetation moisture, and water use efficiency of vegetation. The spatiotemporal characteristics and main contributing factors of VEQ in the TNS from 2000 to 2020 were investigated using SEN+Mann−Kendall, Hurst exponent, geographical detector, and residual trend analysis testing. The results suggest that VEQ in the TNS showed an improving trend over the 21-year study period. The areas with significant improvements were concentrated in the central and eastern parts of the TNS. Significant deterioration occurred only sporadically in various urban areas. Characteristics of future unsustainable VEQ trends could be detected across the TNS. Precipitation, vegetation type, soil type, elevation, and solar radiation exhibited the greatest impact on VEQ throughout the TNS. Human activities (e.g., afforestation and government investments) were the dominant factors and had a relative contribution of 65.24% to vegetation area change. Our results provide clues for assessing environmental recovery and sustainable development in other regions.

Quantifying the influences of climate change and human activities on the grassland in the Southwest Transboundary Basin, China.

The Southwest Transboundary Basin (STB), which contains a part of the Tibetan Plateau and Southwest China, covers a large area of grassland that has been experiencing significant climate change (Cc) and human activities (Ha). However, variabilities of the grasslands and respective contributions of the drivers have not been fully understood. In this study, we quantitatively investigated the influences of Cc and Ha on the grasslands from 1982 to 2015 across the STB, using residual analysis, trend analysis, and partial correlation analysis. Particularly, vapor pressure deficit (VPD), which is increasingly paid attention to in current studies, was included in our analysis as a critical driver of alpine grasslands. Additionally, a new method, pure climate-induced grassland pixel, which combined the coefficient of variation, was proposed to significantly improve the pixel purity with higher coefficient correlation and lower error and bias. Results show that changes in the climate play an important role in the grassland coverage of STB during 1982-1999 and 2000-2015, while Ha only had a relatively weak negative impact on grassland during 2000-2015, mainly in the Lancang River Basin. Specifically, the grasslands showed an increasing trend in 1982-1999, dominated by climate change with a contribution rate of 80.87%. Temperature had the greatest contribution, followed by VPD and precipitation in this period. On the contrary, the grasslands showed a decreasing trend in 2000-2015, dominated by both Cc and Ha with the contributions of 53.16% and 46.84%, respectively. The findings of this study provide a valuable reference for future ecological restoration in other similar alpine regions.

Impacts of climate change and human activities on different degraded grassland based on NDVI

Grassland degradation has emerged as a serious socio-economic and ecological problem, endangering both long-term usage and the regional biogeochemical cycle. Climate change and human activities are the two leading factors leading to grassland degradation. However, it is unclear what the degradation level caused by these two factors is. Using the normalized difference vegetation index (NDVI) and coefficient of variation of NDVI (CVNDVI), the spatial distribution features of grassland degradation or restoration were analyzed in Qilian County in the northeast of the Qinghai–Tibet Plateau. The dominant climate variables affecting NDVI variation were selected through the combination of random forest model and stepwise regression method to improve the residual trend analysis, and on this basis, twelve possible scenarios were established to evaluate the driving factors of different degraded grasslands. Finally, used the Hurst index to forecast the trend of grassland degradation or restoration. The results showed that approximately 55.0% of the grassland had been degraded between 2000 and 2019, and the area of slight degradation (NDVIslope > 0; CVNDVI (slope) > 0; NDVIvalue > 0.2) accounted for 48.6%. These regions were centered in the northwest of Qilian County. Climate and human activities had a joint impact on grassland restoration or degradation. Human activities played a leading role in grassland restoration, while climate change was primarily a driver of grassland degradation. The regions with slight degradation or re-growing (NDVIslope > 0; CVNDVI (slope) > 0), moderate degradation (NDVIslope < 0; CVNDVI (slope) > 0), and severe degradation or desertification (NDVIslope < 0; CVNDVI (slope) < 0) were dominated by the joint effects of climate and anthropogenic activity accounted for 34.3%, 3.3%, and 1.3%, respectively, of the total grassland area. Grasslands in most areas of Qilian County are forecasted to continue to degrade, including the previously degraded areas, with continuous degradation areas accounting for 54.78%. Accurately identifying the driving factors of different degraded grassland and predicting the dynamic change trend of grassland in the future is the key to understand the mechanism of grassland degradation and prevent grassland degradation. The findings offer a reference for accurately identifying the driving forces in grassland degradation, as well as providing a scientific basis for the policy-making of grassland ecological management.

Impact of climatic changes and anthropogenic activities on ecosystem net primary productivity in India during 2001–2019

Climate change and anthropogenic activities have altered the terrestrial ecosystem dynamics around the globe. Due to the complex ecosystem-atmosphere interactions at different scales, these impacts are difficult to quantify and are poorly understood, especially in developing countries with limited ground-based observations. This study analyzed the impact of climatic changes and anthropogenic activities on ecosystem net primary productivity (NPP) in India using remote sensing-based observations, correlation analysis, and Residual Trend analysis (RESTREND). Using different climate variables such as precipitation, temperature, and solar radiation, along with Land Use and Land Cover (LULC) and NPP maps, we first classified the ecosystems (ES) into two categories: natural ecosystems – influenced only by climate change (ESc), covering about 19.7% of the area, and human-influenced ecosystems – influenced by both climate change and anthropogenic activities (ESc+a), covering about 80.3% of the area. RESTREND analysis was performed on both ESc and ESc+a to analyze the relative contributions of climate change and human activities to changes in NPP. The correlation analysis between NPP and climate variables suggested that precipitation was the dominant control of NPP in about 72% area, whereas temperature and solar radiation controlled NPP in Himalayan and forest-dominated regions, respectively. The human-influenced ecosystems (ESc+a) experienced an increasing trend in NPP, whereas natural ecosystems (ESc) experienced a decreasing trend, particularly in forest-dominated regions. Overall, NPP increased in the country during the study duration. The contributions of climatic changes and anthropogenic activities varied spatially and temporally. In general, climatic factors enhanced the NPP, whereas human activities contributed to a slight decline in NPP. These findings improve our understanding of how ecosystems in India are influenced by climate change and anthropogenic activities in recent decades. The results from this study will aid in identifying ecological hotspots and key drivers for better ecosystem management strategies.