Changes In Vegetation Cover Research Articles

Spatial and Temporal Characteristics of NDVI in the Weihe River Basin and Its Correlation with Terrestrial Water Storage

The Weihe River Basin (WRB) of China is located in an arid and water-scarce semi-arid region with a fragile ecological environment, and it is meaningful to study the spatial and temporal changes in vegetation and terrestrial water storage changes in a small-scale inland basin. This study uses normalized difference vegetation index (NDVI) data and Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-On (GRACE-FO) time-variable gravity field models to derive changes in vegetation cover and water storage in the WRB from 2002 to 2020. Firstly, taking NDVI as the breakthrough point, the temporal and spatial characteristics of vegetation were analyzed by trend analysis method and F-test. Then, GRACE and GRACE-FO were used to derive water storage variations. Finally, the correlation between NDVI and water storage variations is discussed using the Pearson correlation analysis. The results show that the overall trend of NDVI is increasing, and the increasing trend is more evident before 2014, and after that, there is a significant fluctuation. The spatial distribution shows a large spatial variability, but the growing area still accounts for the majority, and the change varies by vegetation type, among which the cultivated vegetation is more influential. The overall change in terrestrial water storage showed a decreasing trend of −0.09 cm/yr, and also reflected a solid intra-annual regular cycle, i.e., reaching a trough from October to November and a peak from May to June each year. The correlation is 0.6 on the time scale, and there was a 3-month lag between NDVI and TWS. On the spatial scale, the percentage of areas with a negative correlation was about 95.4%, probably due to increased water consumption and evapotranspiration. The study’s results can help to understand the relationship between vegetation and water storage in the WRB and provide scientific support for local environmental management.

Identification method of vegetation cover changes derived from mosaic Landsat-8 Data: Case Study Sumatera, Kalimantan, and Java Island

Vegetation cover changes information is essential in forest and plantation management. Low-resolution remote sensing in mosaic data format has been widely used for vegetation cover change mapping on a global scale by implementing the threshold method. However, the problem with this method in Landsat-8 - which has a higher resolution - is that it is challenging to obtain the appropriate value due to the diversity of Indonesia’s land ecosystems. This research aimed to determine the threshold value for detecting devegetation and revegetation to identify vegetation cover changes rapidly. This research used Oldeman Climate Classification with zone A and B as the references. The threshold values to classify vegetation cover changes were derived from the delta Normalized Burn Ratio (dNBR) of Analysis Ready Data (ARD) Landsat-8 - taken in Sumatera as the pilot area. Moreover, the proposed method was also applied to Kalimantan and Java Islands, with have identical climates. The results indicated that dNBR threshold for devegetation was more than 0.376, while revegetation was less than -0.269. These results were evaluated using high-resolution satellite imagery and ground-truth information. It concluded that Kalimantan Island obtained good results in mapping vegetation cover change with the threshold; however, Java Island required further adjustment for the threshold.

Soil temperature mitigation due to vegetation biophysical feedbacks

Understanding the interactions between temperature regimes and vegetation cover is one of the key scientific topics in global environmental change and land surface process research. Recently, the prominent global greening trend has inspired considerable interest in examining climate feedbacks to vegetation changes. However, the impacts of recent widespread vegetation cover changes on soil temperature have been less documented and have received insufficient attention. Here, a high-resolution regional climate model was used to examine the potential impact of vegetation cover changes over the past 37 years, obtained as satellite observational data, on regional air and soil temperatures across the Heilong-Amur River Basin. Our sensitivity experiments indicated that the 1.85% increase in the regional fractional vegetation cover (FVC) from 1982 to 2018 cooled the air temperature by 0.045 °C and the soil temperature by 0.19 °C and that this cooling effect would continue in the near future, from 2016 to 2053. We found that soil temperatures were more sensitive to vegetation cover changes than air temperatures, particularly in mid-high latitude forests; this finding helps to explain the nonsignificant decrease in soil temperatures in forests over the past 40 years detected in the ERA5-Land reanalysis. Our results also suggest that vegetation restoration in forest ecosystems could mitigate the effects of climate change on permafrost environments by increasing the number of soil freezing days.

Estimation of vegetation cover changes using normalized difference vegetation index (NDVI) in Mount Halimun Salak National Park, Indonesia

The forestry and Other Land Use (FOLU) sector in Indonesia is expected to contribute 60% of the greenhouse gas emission reduction. The priority location for enhancing carbon stock as the mitigation action is natural forests, such as Mount Halimun Salak National Park (MHSNP). The objectives of this research were to estimate vegetation cover changes in 2016, 2019, 2022, and to analyze the forestry programs affecting forest cover in MHSNP. This research used Landsat 8 satellite imagery. NDVI was categorized into five classes, specifically class 1 (the clouds/non-vegetation), class 2 (very low dense vegetation), class 3 (low dense vegetation), class 4 (moderately dense vegetation), and class 5 (highly dense vegetation). MHSNP vegetation cover consecutive in 2016, 2019 and 2022 is dominated by class 1 (35,94% or 31.508,45 ha), class 2 (30,86% or 27.053,73 ha), and class 5 (58,76% or 51.543,18%), respectively. In general, the large vegetation cover from 2016 to 2022 is increasing from 87.662,06 ha to 87.716,88 ha and is significantly denser. It might be caused by the success of the forestry program in MHSNP, such as increasing the rehabilitation area, tree adoptions, and restoration in collaboration with communities and companies.

Effects of Vegetation Change on Soil Erosion by Water in Major Basins, Central Asia

The uncertainties in soil erosion (SE) are further intensified by various factors, such as global warming, regional warming and humidification, and vegetation cover changes. Moreover, quantitative evaluations of SE in major basins of Central Asia (CA) under changing environments have rarely been conducted. This study conducted quantitative evaluation of SE in four major basins (Syr Darya Basin (SDB), Amu Darya Basin (ADB), Ili River Basin (IRB) and Tarim River Basin (TRB) using the Revised Universal Soil Loss Equation (RUSLE) and analyzed the main driving factors. SE quantities in the basins presented relatively consistent upward fluctuating trends from 1982 to 2017. Vegetation cover variation fluctuated significantly from 1982 to 2017. Specifically, vegetation cover decreased continuously in SDB, ADB, and IRB, but increased gradually in TRB. Pixels with positive spatial variation of vegetation mainly occurred around lakes and oases near rivers. The Normalized Difference Vegetation Index (NDVI) showed higher correlation with precipitation (80.5%) than with temperature (48.3%). During the study period, the area of arable land (AL) exhibited the largest change among all land use types in CA. Under long-term human activities, the proportion of NDVI of other land types converting to AL was the highest. In the structural equation model (SEM), precipitation, temperature, Shannon Diversity Index (SHDI), and NDVI strongly influenced SE. Overall, the major basins in CA were jointly affected by climate, human activities, and vegetation. Specifically, climatic factors exerted the strongest influence, followed by SHDI (human activities). SE was found to be relatively serious in ADB, SDB, and IRB, with SE in SDB even approaching that in the Loess Plateau. Under the background of global changes, appropriate water and land resource management and optimization configurations should be implemented in CA with reference to TRB in order to relieve local SE problems.

Wavelet-based spatiotemporal analyses of climate and vegetation for the Athabasca river basin in Canada

Monitoring spatiotemporal changes in climate and vegetation coverage are crucial for various purposes, including water, hazard, and agricultural management. Climate has an impact on vegetation, however, studying their relationship is challenging. We implemented the Least-Squares Wavelet (LSWAVE) software for investigating trend, coherency, and time lag estimation between climate and vegetation time series. We utilized Normalized Difference Vegetation Index (NDVI) time series provided by the Terra satellite and hybrid climate time series. We found that the seasonal cycles of climate and NDVI are coherent with time delay. For the entire Athabasca River Basin (ARB), the most coherent component was the annual cycle with 84% annual coherency between vegetation and temperature and 46% between vegetation and precipitation. The annual cycles of temperature and precipitation led the ones in vegetation by about two and three weeks, respectively. Relatively lower coherency was observed in the mountainous region (upper ARB) and higher coherency in the middle ARB. From the cross-spectrograms, a clear time delay pattern was observed between the annual cycles of climate and vegetation since 2000 but not for other high-frequency seasonal cycles. The results also highlighted the advantages of LSWAVE algorithms over traditional algorithms, such as linear regression and correlation. Furthermore, we analyzed the annual land use and land cover data provided by the Terra and Aqua satellites and discussed their linkage with the climate and NDVI results.

DETECTING VEGETATION LOSS AND ITS ECOLOGICAL IMPACTS USING GEOSPATIAL TECHNIQUES IN POTISKUM LOCAL GOVERNMENT AREA, YOBE STATE, NIGERIA

Ensuring good management and sustainability of natural resources is paramount for future generations to thrive and live in cleaner and safer environment. This research examines the land use/land cover changes over three decades (1990 to 2019) in Potiskum using geospatial techniques. The Land use and land cover changes of the years 2010 and 2019 showed a continuous trend of vegetation being converted to cultivated areas while cultivated areas are converted to built-up areas. The findings show significant changes in vegetation cover across the study area between 1990 and 2019. Specifically, the years 2010 and 2019 witnessed a significant reduction in vegetation cover of 38% compared to 1990 and 2000 which reveals a decrease of 14.5%. From 2000 to 2010, a decrease of 8% in the vegetation cover was found. Cumulatively, the vegetation loss in the study area was found to be 60.5% between 1990 and 2019. This is highly significant and almost irreversible due to the fragile nature of the environment. The several causative factors responsible for the vegetation loss include intensification of agricultural activities, deforestation, rapid urbanization and exponential population growth. These causative factors have led to many ecological impacts such as drought, desert encroachment, sand storm, flooding, rise in temperatures, soil erosion and rainfall variations. The research recommends afforestation and reforestation measures, legislation on sustainable utilization of natural resources, tree planting campaign and subsidies on alternative sources of energy.

Spatiotemporal patterns and drivers of soil erosion in Yunnan, Southwest China: RULSE assessments for recent 30 years and future predictions based on CMIP6

Soil erosion is a critical driving force of regional-scale ecosystem dynamics. Quantifying soil erosion and its responses to climate and land cover changes comprise an important part of regional ecological security assessment. Taking account of rainfall and vegetation cover changes in Yunnan Province, Southwest China, from 1986 to 2015, this study explored the spatiotemporal patterns of soil erosion intensity in Yunnan over this 30-year period, using the Revised Universal Soil Loss Equation. Sensitivity analyses were performed to evaluate the roles of rainfall (R) and vegetation cover (C) changes in soil erosion. Based on the climate model CMIP6, the carbon emission socio-economic sharing scenarios SSP126 and SSP585 were compared, using a random forest model, to predict soil erosion risk in Yunnan in different future periods. The results indicated the following: (1) During 1986–2015, soil erosion was detected in 33.55% of the land area of Yunnan, among which the areas of slight, moderate, severe, very severe, and extremely severe erosion accounted for 19.55%, 7.64%, 3.23%, 2.08%, and 1.04%, respectively; the 30-year mean annual soil erosion modulus over Yunnan was 1289.72 t km−2 a−1. (2) The area percentage change of five soil erosion intensity levels consistently showed a generally declining trend of soil erosion during the 30-year period. Comparing 1995–2006 vs. 1986–1995 and 2006–2015 vs. 1996–2005, area percentages were 1.56% and 2.4% for intensively reduced, respectively, and correspondingly 16.65% and 22.12% for reduced, 68.11% and 71.15% for no significant change, 12.79% and 4.02% for enhanced, and 0.08% and 0.03% for intensively enhanced. (3) Factor C was a more sensitive driver than R for soil erosion in this period. (4) Under the SSP126 and SSP585 scenarios, soil erosion risk was predicted to rise in the 21st century; and the provincial average soil erosion risk was larger for the SSP585 than the SSP126 scenario. Compared with the average of the 30-year period, predicted soil erosion in the SSP585 and SSP126 scenarios in 2100 would increase in western and southern Yunnan, while the SSP585 scenario predicted more severe soil erosion in southern and southwestern regions than SSP126. Our historical assessments and future forecasts suggest vegetation protection in karst areas and restoration in southern Yunnan will be critical for future soil erosion control and regional ecological security in Yunnan.

The Effect of Urban Expansion on Peripheral Agricultural Lands in Makurdi City

This study investigated the effects of urban sprawl on peripheral agricultural lands in Makurdi City over the period 1999-2012. The study conducted a land use and vegetation cover change (LULCC) analysis using remote sensing (RS) and geographic information system (GIS) data to analyze two epochs of Land Sat imagery, 1999 and 2012, acquired to achieve the objective. It was found that between 1999 and 2012, urban sprawl had a negative impact on peripheral agricultural land in Makurdi town, as it significantly reduced peripheral agricultural land. The study also revealed that despite the adverse impact of urban sprawl on peripheral agricultural lands in Makurdi town, its socio-economic impact has led to the establishment of infrastructural facilities such as roads, electricity, markets, and parks. Therefore, the study recommended that the agricultural landscape plan be incorporated by the government into urban land use planning systems so that urbanization does not interfere with agricultural activities and thus maximize their collective benefits.

Effects of drought-induced holm oak dieback on BVOCs emissions in a Mediterranean forest

Climate change is impairing tree physiology and growth, causing an increase in tree dieback in many Mediterranean forests. These desiccation phenomena are leading to changes in land cover and plant community composition. Mediterranean plants are capable to emit large amount of Biogenic Volatile Organic Compounds (BVOCs), whose emission and biosynthesis is strongly affected by environmental conditions. This study evaluates the seasonal changes in understory species composition in two forest stands in Southern Tuscany characterized by different levels of Quercus ilex L. crown defoliation (low and high defoliation, LD and HD) and the relationship with BVOCs emissions over three years. We found significant changes in the understory plant community following Q. ilex crown defoliation and mortality, observing an increment in the number of shrubs both in HD and LD stands. The environmental sampling of BVOCs fully reflected the changes in vegetation cover and composition, with a reduction in the amount of monoterpene emissions due to the increasing rates of defoliation and mortality of Q. ilex trees. Our results suggest that terpene emissions from Mediterranean forests would be modified by an increase of Q. ilex dieback, with important consequences for functioning of this forest ecosystem and its atmospheric chemistry.

A photographic transect method for field measurement of vegetation cover along an ecotone gradient in a desert environment

Vegetation cover is essential to the ecologic and biogeochemical functioning of drylands. These systems are marked by spatiotemporal variation in vegetation cover and biomass. Regular monitoring of vegetation cover in drylands is critical for their conservation and management. This study examines the effectiveness of photo-based grid point intercept field survey for quantifying vegetation characteristics in drylands. The field data provides ground truth data for space borne remote sensing to analyze changes in vegetation cover. The study integrates rapid field photo sampling, GPS, and GIS to maximize raw data collection in the field and allow for subsequent sampling in the laboratory setting. Paired T-tests and simple linear regression were employed to assess sample size. Quantification of vegetation cover using minimum distance (MD), spectral angle mapper (SAM), and support vector machine (SVM) automated classification were assessed using visual sampling as a reference. Fifty sampling points are sufficient for quantifying vegetation cover of steppe, but are less adequate for desert steppe. The assessment of automated classifications of photo plots revealed that SAM outperformed both MD and SVM. Photo-based Grid-point Intercept provides a cost- and time-effective technique for data collection to support satellite and air platform remote sensing analysis of vegetation dynamics.

Remote sensing for monitoring the impacts of agroforestry practices and precipitation changes in particle size export trends

Recent land use changes, the absence of soil protection between crop periods, and extreme precipitation events have been highlighted as major influential factors in the fluctuations of sediment export in the last decades at the catchment scale worldwide. In this regard, soil erosion and fine-particle export are two of the major concerns of soil nutrient loss and water-quality decrease (e.g., increasing turbidity and vector of chemicals). However, while rainfall effects have been well-monitored, recent land use changes and management need additional approaches to evaluate their effect. In Mediterranean mountainous environments, in addition to forest management, agricultural practices during different cropland stages likely increase sediment and particle-bound chemicals in the drainage system. Moreover, most catchments lack instrumentalization. Thus, there is a gap in the knowledge on the processes influencing the sediment exported in ungauged catchments. To evaluate the processes involved, remote sensing and seasonal sampling of suspended sediments were examined for 5 years in a representative agroforestry system in three sub-catchments (SBCs) with different proportions of land uses. Temporal trends of NDVI, EVI, MSAVI, SAVI, and NDWI indices were analyzed for monitoring the vegetation status. With this information, we attempt to evaluate the soil response in terms of particle size export to land use change, vegetation status, and precipitation distribution in fine-grained sediment-reaching streams. Our findings not only highlight the significant effect of heavy precipitation events and vegetation cover on the grain-size fraction of the exported sediment but also reveal the existence of more complex factors influencing the export dynamics. A silt-increasing trend due to the increase of individual heavy precipitations from 2017 onward despite the total precipitation amount not increasing was detected. It is shown that indices such as NDVI and NDMI help detect small changes in vegetation cover, while EVI, SAVI, and MSAVI are more robust for detecting general patterns in large vegetated areas and preventing the appearance of artefacts in the data. Results from this study suggest that land use changes combined with short-scale changing trends of rainfall likely explain most of the possible effects observed in terms of sediment export changes.

Monitoring and Mapping Vegetation Cover Changes in Arid and Semi-Arid Areas Using Remote Sensing Technology: A Review

Vegetation cover change is one of the key indicators used for monitoring environmental quality. It can accurately reflect changes in hydrology, climate, and human activities, especially in arid and semi-arid regions. The main goal of this paper is to review the remote sensing satellite sensors and the methods used for monitoring and mapping vegetation cover changes in arid and semi-arid. Arid and semi-arid lands are eco-sensitive environments with limited water resources and vegetation cover. Monitoring vegetation changes are especially important in arid and semi-arid regions due to the scarce and sensitive nature of the plant cover. Due to expected changes in vegetation cover, land productivity and biodiversity might be affected. Thus, early detection of vegetation cover changes and the assessment of their extent and severity at the local and regional scales become very important in preventing future biodiversity loss. Remote sensing data are useful for monitoring and mapping vegetation cover changes and have been used extensively for identifying, assessing, and mapping such changes in different regions. Remote sensing data, such as satellite images, can be obtained from satellite-based and aircraft-based sensors to monitor and detect vegetation cover changes. By combining remotely sensed images, e.g., from satellites and aircraft, with ground truth data, it is possible to improve the accuracy of monitoring and mapping techniques. Additionally, satellite imagery data combined with ancillary data such as slope, elevation, aspect, water bodies, and soil characteristics can detect vegetation cover changes at the species level. Using analytical methods, the data can then be used to derive vegetation indices for mapping and monitoring vegetation.

Automated mapping of Portulacaria afra canopies for restoration monitoring with convolutional neural networks and heterogeneous unmanned aerial vehicle imagery.

Ecosystem restoration and reforestation often operate at large scales, whereas monitoring practices are usually limited to spatially restricted field measurements that are (i) time- and labour-intensive, and (ii) unable to accurately quantify restoration success over hundreds to thousands of hectares. Recent advances in remote sensing technologies paired with deep learning algorithms provide an unprecedented opportunity for monitoring changes in vegetation cover at spatial and temporal scales. Such data can feed directly into adaptive management practices and provide insights into restoration and regeneration dynamics. Here, we demonstrate that convolutional neural network (CNN) segmentation algorithms can accurately classify the canopy cover of Portulacaria afra Jacq. in imagery acquired using different models of unoccupied aerial vehicles (UAVs) and under variable light intensities. Portulacaria afra is the target species for the restoration of Albany Subtropical Thicket vegetation, endemic to South Africa, where canopy cover is challenging to measure due to the dense, tangled structure of this vegetation. The automated classification strategy presented here is widely transferable to restoration monitoring as its application does not require any knowledge of the CNN model or specialist training, and can be applied to imagery generated by a range of UAV models. This will reduce the sampling effort required to track restoration trajectories in space and time, contributing to more effective management of restoration sites, and promoting collaboration between scientists, practitioners and landowners.

Vegetation Coverage in the Desert Area of the Junggar Basin of Xinjiang, China, Based on Unmanned Aerial Vehicle Technology and Multisource Data

Vegetation coverage information is an important indicator of desert ecological environments. Accurately grasping vegetation coverage changes in desert areas can help in assessing the quality of ecosystems and maintaining their functions. Improving remote sensing methods to detect the vegetation coverage in areas of low vegetation coverage is an important challenge for the remote sensing of vegetation in deserts. In this study, based on the fusion of MOD09GA and MOD09GQ data, 2019–2021 low-altitude unmanned aerial vehicle (UAV) remote sensing data, and other factors (such as geographical, topographic, and meteorological factors), three types of inversion models for vegetation coverage were constructed: a multivariate parametric regression model, a support vector machine (SVM) regression model, and a back-propagation neural network (BPNN) regression model. The optimal model was then used to map the spatial distribution of vegetation coverage and its dynamic change in the Junggar Basin of Xinjiang, China, over 22 years (from 2000 to 2021). The results show that: (1) The correlation between enhanced vegetation index (EVI) obtained from image fusion and vegetation coverage in desert areas is the highest (r = 0.72). (2) Among the geographical and topographic factors, only longitude and latitude were significantly correlated with vegetation coverage (p < 0.05). The average monthly temperature and precipitation from the previous six months were correlated with the vegetation coverage (p < 0.05), but the vegetation coverage of the current month had the highest correlation with the average temperature (r = −0.27) and precipitation (r = 0.33) of the previous month. (3) Among the multivariate parametric models established by selecting the five aforementioned factors, the multiple linear regression model performed the best (R2 = 0.64). (4) The SVM regression model was superior to the other regression models (R2 = 0.80, mean squared error = 8.35%). (5) The average vegetation coverage in the desert area of the Junggar Basin was 7.36%, and from 2000–2021, the vegetation coverage in 54.59% of the desert area increased.

IDENTIFICATION OF INTERESTED PARTIES AND MAPPING OF ECOSYSTEM SERVICES IN THE TERRITORIES OF UGAM CHOTKAL STATE NATIONAL NATURE PARK

Plants, which are the main components of ecosystems, are known to be affected by natural and anthropogenic factors. Studying the dynamic change of vegetation cover and its driving factors is essential for understanding the interactions between plants and ecosystems. This is of great importance in the social, economic and political life of the stakeholders. Based on the household survey and secondary data in the stakeholder analysis in this thesis, the main stakeholders with a stake in the territories of the Ugam-Chotkal State National Nature Park were identified. The images of May, June, July with up to 20% cloudiness taken by Lansad 4-5 and 7-8 in 1980 and 2020 were downloaded and maps showing the greenness of the Ugam Chotkal State National Nature Park were created.

Responses of NDVI to Climate Change and LUCC along Large-Scale Transportation Projects in Fragile Karst Areas, SW China

The fragile karst habitat is extremely sensitive to human activities such as large-scale engineering construction. To explore the influence of the construction and operation of the GH (Guiyang-Huangguoshu) highway on the vegetation within a certain range and the response of NDVI to climate factors, Landsat data were used to synthesize annual NDVI maps using the maximum value compositing method. Trend, correlation, and coefficient of variation analyses were performed. The results demonstrate that: (1) During the construction and operation periods, NDVI showed an overall upward trend, and the NDVI value and growth rate in the contrast area were greater than those in the core area; (2) the correlation between temperature and vegetation cover along the GH highway was stronger than that between precipitation and vegetation; (3) construction of the GH highway has had a significant impact on the surrounding vegetation, with the impact on vegetation ecology along the road mainly concentrated within the 2 km range. The increase of artificial surfaces along the road has had a great impact on the NDVI, and the vegetation cover change in the core area is more significant than that in the contrast area; and (4) the overall disturbance of the GH highway project to the surrounding ecology was mainly observed in the form of low and medium fluctuations. This study aims to provide a reference for environmental assessment and management in karst areas.

Tropical surface temperature response to vegetation cover changes and the role of drylands.

Vegetation cover creates competing effects on land surface temperature: it typically cools through enhancing energy dissipation and warms via decreasing surface albedo. Global vegetation has been previously found to overall net cool land surfaces with cooling contributions from temperate and tropical vegetation and warming contributions from boreal vegetation. Recent studies suggest that dryland vegetation across the tropics strongly contributes to this global net cooling feedback. However, observation-based vegetation-temperature interaction studies have been limited in the tropics, especially in their widespread drylands. Theoretical considerations also call into question the ability of dryland vegetation to strongly cool the surface under low water availability. Here, we use satellite observations to investigate how tropical vegetation cover influences the surface energy balance. We find that while increased vegetation cover would impart net cooling feedbacks across the tropics, net vegetal cooling effects are subdued in drylands. Using observations, we determine that dryland plants have less ability to cool the surface due to their cooling pathways being reduced by aridity, overall less efficient dissipation of turbulent energy, and their tendency to strongly increase solar radiation absorption. As a result, while proportional greening across the tropics would create an overall biophysical cooling feedback, dryland tropical vegetation reduces the overall tropical surface cooling magnitude by at least 14%, instead of enhancing cooling as suggested by previous global studies.

Lake Chad vegetation cover and surface water variations in response to rainfall fluctuations under recent climate conditions (2000−2020)

Monitoring the evolution of the Sahelian environment is a major challenge because the great Sahelian droughts, marked by significant environmental consequences and social impacts, contributed, for example, to the drying up of Lake Chad. We combined remote sensing images with a water level database from the Hydroweb project to determine the response of Lake Chad vegetation cover and surface water variations to rainfall fluctuations in the Lake Chad watershed under recent climate conditions. The variance in lake surface water levels was determined by computing the monthly anomaly time series of surface water height and area from the Hydroweb datasets. The spatiotemporal variability of watershed rainfall and vegetation cover of Lake Chad was highlighted through multivariate statistical analysis. The spatial distribution of correlations between watershed rainfall and Lake Chad vegetation cover was investigated. The results show an increase in watershed rainfall, vegetation cover, and surface water area and height, as their slopes were all positive i.e., 5.1 10−4 (mm/day); 4.26 10−6 (ndvi unit/day); 1.2 10−3 (km2/day) and 6 10−5 (m/day), respectively. The rainfall variations in the watershed drive those of Lake Chad vegetation cover and surface water, as the rainfall trend was strongly and positively correlated with those of vegetation cover (0.79), surface water height (0.57), and area (0.53). The time lag between the watershed rainfall fluctuations and lake surface water variations corresponded to approximately ∼112 days. Between rainfall variations and vegetation cover changes, the spatial distribution of the time lag showed a response time of <16 days in the western shores of the lake and on both sides of the great barrier, about 16 days in the bare soils of the northern basin and the eastern part of the south basin, and >64 days in the marshlands of the southern basin. For the analysis of lakes around the world, this research provides a robust method that computes the spatiotemporal variances of their trends and seasonality and correlates these with the spatiotemporal variances of climate changes. The correlations obtained have strong potential for predicting future changes in lake surface water worldwide.

Solar photovoltaic program helps turn deserts green in China: Evidence from satellite monitoring

Solar energy is considered one of the key solutions to the growing demand for energy and to reducing greenhouse gas emissions. Thanks to the relatively low cost of land use for solar energy and high power generation potential, a large number of photovoltaic (PV) power stations have been established in desert areas around the world. Despite the contribution to easing the energy crisis and combating climate change, large-scale construction and operation of PV power stations can change the land cover and affect the environment. However, few studies have focused on these special land cover changes, especially vegetation cover changes, which hinders understanding the effects of the extensive development of solar energy. Here, we used Continuous Change Detection and Classification - Spectral Mixture Analysis (CCDC-SMA) based on Landsat images to monitor changes in vegetation abundance before and after the PV power stations deployment. To reduce the interference of PV shading on vegetation abundance estimation, we improved the vegetation (VG) fraction from SMA and developed the Photovoltaics-Adjusted Vegetation (PAVG) fraction for vegetation abundance measurements in PV power stations. Results show that PV power stations in China's 12 biggest deserts expanded from 0 to 102.56 km2 from 2011 to 2018, mainly distributed in the central part of north China. The desert vegetation in the deployment area of PV power stations presented a significant greening trend. Compared to 2010, the greening area reached 30.80 km2, accounting for 30% of the total area of PV power stations. Overall, the large-scale deployment of PV power stations has promoted desert greening, primarily due to government-led Photovoltaic Desert Control Projects and favorable climatic change. This study shows the great benefits of PV power stations in combating desertification and improving people's welfare, which bring sustainable economic, ecological and social prosperity in sandy ecosystems.