MODIS NDVI Research Articles

Spatial data modelling of atmospheric water availability and stress in Jharkhand, India

Due to climate and human activity, water supply in Jharkhand, India, fluctuates. This study measured AWS and AWA in Jharkhand, India. MODIS NDVI and Terra Climate data processed by Google Earth Engine (GEE) are used in the analysis. Based on mean annual values for NDWI, run-off, precipitation, and ET, AWS and AWA are categorised. Results show significant regional differences in ET, precipitation, runoff, AWS, and AWA in Jharkhand. Purbi Singhbhum has far higher ET rates than Garhwa, Palamu, and Chatra. High AWS levels in Sahibganj, Godda, Pakur, Garhwa, and Kodarma indicate serious water scarcity. Therefore, these locations need focused water management approaches. Different districts including Giridih, Chatra, Jamtara, Latehar, Simdega, and Hazaribagh have different levels of AWS, highlighting the need for individualised treatments. The AWA index helps resource managers make educated decisions by detecting water-scarce places. Climate variability affects temperatures and precipitation, worsening AWS in high-AWS areas. Water scarcity mitigation requires local infrastructure development and water management measures. To verify remote sensing data accuracy, future study should improve data resolution and include in-situ measurements. These findings can help policymakers and stakeholders in Jharkhand create efficient water resource management strategies to reduce water stress and assure water supply.

Unveiling fractional vegetation cover dynamics: A spatiotemporal analysis using MODIS NDVI and machine learning

Understanding the dynamics of Fractional Vegetation Cover (FVC) is crucial for effective environmental monitoring and management, especially in regions like Pakistan that are sensitive to climate change. This study employs an innovative approach using MODIS NDVI data and the Pixel Dichotomy Model (PDM) to analyze the spatiotemporal dynamics of FVC across Pakistan from 2003 to 2020. Our findings reveal an overall increasing trend in FVC, with the highest value recorded in 2017 (0.37) and the lowest in 2004 (0.26). The Hurst exponent analysis (R/S ratio = 0.718) indicates a degree of long-term memory in the FVC time series. Rainfall was found to positively correlate with FVC (r = 0.6), while Land Surface Temperature (LST) and the Compounded Night Light Index (CNLI) exhibited negative correlations (r = −0.59 and r = −0.43, respectively). The Random Forest regression model highlighted CNLI as the most influential predictor (importance = 62.4%), emphasizing the need to consider human-induced factors in environmental management. These results provide critical insights for sustainable land management and contribute to understanding vegetation-climate interactions in arid and semi-arid environments."

Spatial downscaling of GRACE-derived groundwater storage changes across diverse climates and human interventions with Random Forests

The Gravity Recovery and Climate Experiment (GRACE) satellites launched in 2002, which were followed by the GRACE Follow-On mission in 2018, represented a significant advancement in global groundwater storage monitoring. However, the inherent limitation of a relatively low spatial resolution confines its applicability primarily to large river basins and aquifers. This study introduces a novel statistical downscaling approach using Random Forests (RF) to enhance the spatial resolution from 3° × 3° to 0.25° × 0.25° for GRACE-derived groundwater storage (GWS) changes. Focusing on sub-Saharan Africa, the Middle East, and South Asia across diverse climates and human interventions, the analysis spans from February 2003 to December 2022. Predictors encompass hydrological fluxes and their accumulation, including IMERG precipitation, GLEAM evapotranspiration, and GLDAS runoff, alongside GLDAS soil moisture and MODIS NDVI. RF models show robust performance across 114 aquifers, with a Pearson’s correlation coefficient (CC) exceeding 0.92 for training sets and 0.85 for validation sets. Validation against in-situ groundwater level observations from wells in India reveals satisfactory performance, with positive Spearman’s CC values between in-situ groundwater levels and downscaled terrestrial water storage anomaly (TWSA) for a majority of dug and bore wells. This study concludes with the generation of monthly downscaled GWS changes (0.25° × 0.25°) spanning from February 2003 to December 2022. These datasets serve as a foundation for facilitating finer-scale hydrological studies and formulating local groundwater management strategies.

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.

Soil moisture estimation based on MODIS NDVI and LST productions in areas with no data

Soil moisture estimation based on MODIS NDVI and LST productions in areas with no data

Ecosystem functional types of the circumpolar Arctic tundra based on the seasonal dynamics of vegetation productivity

Biodiversity, when viewed through the combined lenses of compositional, structural, and functional attributes, provides for a holistic understanding of the complexities found within community assemblages and ecosystems. However, advancement in our understanding of how ecosystem functional diversity interacts with structural and compositional diversity metrics is lacking, in part because universally applied methodologies to derive ecosystem functional classifications are still under development and vary widely across scales, extents and biomes. This study presents a methodology to construct ecosystem functional types (EFTs), or areas of the land surface that function similarly, using the MODIS NDVI record, for the terrestrial circumpolar Arctic. EFTs were derived from the seasonal dynamics of NDVI, over the Arctic tundra at 250 m resolution and compared to bioclimate subzones and to structurally and compositionally defined vegetation units of the Circumpolar Arctic Vegetation Map (CAVM). Correspondence analyses of CAVM EFTs to previously delineated CAVM bioclimatic subzones, physiognomic (vegetation) units and floristic provinces revealed a general congruence, indicating convergence across composition, structure, and function; yet also demonstrated substantial functional variability even within bioclimate subzones and vegetation units. Strong latitudinal gradients in ecosystem function are present, with EFT richness ranging from low (34) in northernmost regions to high (45) in southernmost regions. Locally, the mountainous regions of northern Alaska, and eastern and western Siberia had high spatial variability in ecosystem functioning. Aside from these generalities, we found that EFTs varied widely within individual mapped vegetation units, successfully capturing the functional dimension of biodiversity across the circumpolar Arctic tundra.

Block-scale Oil Palm Yield Prediction Using Machine Learning Approaches Based on Landsat and MODIS Satellite Data

Due to environmental threats and weather uncertainty concerns, oil palm yield prediction is crucial for sustaining crop production. This can be achieved through machine learning and utilising remotely sensed data to predict crop yield. However, the comparative studies on remotely sensed data in adopting the machine learning models are still limited due to the data accessibility. Therefore, we compare and evaluate the prediction accuracy between different satellites, namely MODIS and Landsat-7, using machine learning algorithms and the topology of deep neural networks. Random forest and stacking outperformed linear regression, ridge regression, and lasso regression for both Landsat-7 NDVI (R2= 0.78–0.80; RMSE=1.00- 1.26 tonnes per hectare; MAE=0.77- 0.79 tonnes per hectares; MAPE=0.03-0.04 tonnes per hectare) and MODIS NDVI (R2= 0.60–0.65 tonnes per hectares; RMSE= 2.72–2.81 tonne per hectares; MAE= 1.42-1.55, MAPE= 1.01- 1.02 tonnes per hectares). The Landsat-7 NDVI revealed that neural networks with a deeper network topology (R2= 0.85; RMSE= 1.42 tonnes per hectare; MAE=0.57 tonnes per hectares; MAPE=0.06 tonnes per hectare) outperformed neural networks with a baseline and broader network topologies in terms of performance. In contrast, MODIS-NDVI revealed that the neural network with a wider network topology had the highest overall prediction accuracy and the lowest prediction error (R2= 0.75; RMSE= 2.81 tonnes per hectare; MAE=2.27 tonnes per tonnes; MAPE= 0.13). Because of its higher spatial resolution in comparison to MODIS, landsat-7 NDVI used in neural networks with a deep network topology provided the best model performance. Although the use of NDVI as a single input factor may cause uncertainty in some extents, it is an efficient and reliable method for improving yield estimation with the use of medium-resolution satellites, which has important implications for early warning towards the reduction in yield production.

Tracking states and transitions in semiarid rangelands: A spatiotemporal archetypal analysis of productivity dynamics using wavelets

Climate change poses challenges in classifying ecosystem dynamics, as they are influenced by shifting dynamics resulting from changes in climate forces and meteorological variables, including temperature and water availability. To address this, our study presents a novel approach using Continuous Wavelet Transform (CWT) and power spectrum analysis to classify vegetation dynamics, considering the time-dependent variability of ecosystem frequencies. We applied our method to centred and standardized MODIS NDVI time series for the period 2000–2021, using an experimental field station in northern Patagonia as a case study. By performing a continuous wavelet transform on the data for each pixel, we obtained instantaneous power spectra, capturing variability across different dates and pixels. These spectrums were then consolidated into a comprehensive database, and subsequently classified using archetypal analysis. We identified a convex combination of archetypal spectrums that best represented the entire power spectrum database. Mapping the resulting archetypes and their weights in both space and time allowed us to explore pixels' variations in archetype weights in relation to factors such as time, topography, and climate. In addition, to examine the potential relationship between the NDVI time series and climate drivers, we computed the Average Cross-Wavelet Power Spectrum (ACWPS) to different climatic indices. Three archetypes were sufficient to explain the majority of power spectrum variability in the study area. These archetypes exhibited distinctive characteristics: 1) medium-frequency variability (2–4 years), 2) low-frequency variability (>4 years), and 3) an annual (i.e. seasonal) cycle with low-frequency variability. Spatially, the first two archetypes were predominantly observed in highland steppes, while the third archetype prevailed in lowland areas associated with meadows. At the beginning of the studied period, Archetypes 1 and 3 dominated, but after the Puyehue-Cordón Caulle Volcanic Complex eruption in 2011 their prominence diminished, and Archetype 2 became more prevalent in the whole study area. Finally, all three NDVI series representative of archetypes showed a relative peak at approximately four years, which could be linked to the Indian Ocean Dipole variability. These results highlight an abrupt shift in the system's behaviour, primarily related to changes in variability distribution rather than mean values. This disturbance-induced transition aligns with the theory of state and transitions in ecological system dynamics. We propose that the states in this model are not fixed but represent alternative dynamic behaviours, akin to different types of limit cycles. Consequently, employing a wavelet analysis-based classification method provides a robust means of studying and understanding such variability and transitions, thereby offering clarity and comprehension of ecosystem states. Notably, this methodology proves particularly effective for large databases of detailed time series.

Comparison between Satellite Derived Solar-Induced Chlorophyll Fluorescence, NDVI and kNDVI in Detecting Water Stress for Dense Vegetation across Southern China

In the context of global climate change and the increase in drought frequency, monitoring and accurately assessing the impact of hydrological process limitations on vegetation growth is of paramount importance. Our study undertakes a comprehensive evaluation of the efficacy of satellite remote sensing vegetation indices—Normalized Difference Vegetation Index (MODIS NDVI product), kernel NDVI (kNDVI), and Solar-Induced chlorophyll Fluorescence (GOSIF product) in this regard. Initially, we applied the LightGBM-Shapley additive explanation framework to assess the influencing factors on the three vegetation indices. We found that Vapor Pressure Deficit (VPD) is the primary factor affecting vegetation in southern China (18°–30°N). Subsequently, using Gross Primary Productivity (GPP) estimates from flux tower sites as a performance benchmark, we evaluated the ability of these vegetation indices to accurately reflect vegetation GPP changes during drought conditions. Our findings indicate that SIF serves as the most effective surrogate for GPP, capturing the variability of GPP during drought periods with minimal time lag. Additionally, our study reveals that the performance of kNDVI significantly varies depending on the estimation of different kernel parameters. The application of a time-heuristic estimation method could potentially enhance kNDVI’s capacity to capture GPP dynamics more effectively during drought periods. Overall, this study demonstrates that satellite-based SIF data are more adept at monitoring vegetation responses to water stress and accurately tracking GPP anomalies caused by droughts. These findings not only provide critical insights into the selection and optimization of remote sensing vegetation product but also offer a valuable framework for future research aimed at improving our monitoring and understanding of vegetation growth status under climatic changes.

Annual winter crop distribution from MODIS NDVI timeseries to improve yield forecasts for Europe

Crop yield forecasts allow policy makers to anticipate market behaviour and regulate prices. Annual updates on which crops are grown where can improve crop yield forecast accuracy. Existing efforts to map crops across the European Union resulted in late-season map availability or short time series that do not meet forecasting requirements. We propose a new approach to retrieve annual winter crop maps and improve forecasting efforts by identifying pixels with dominant winter crop signals using moderate resolution imagery. These pixels are distinguished from summer crop signals based on their senescence date. When this date precedes the theoretical maturity date of a winter crop, expressed in GDD, the pixel is labelled as having a dominant winter crop signal. Our 2018 map accurately identified 77% and 83% of dominantly winter-crop area, when compared to farmers’ declaration data and a high-resolution crop map for Europe, respectively. While the resulting annual winter crop maps underestimated winter crop area, derived region-specific annual NDVI profiles better described winter crop phenology as compared to the use of static maps. Regression analysis between these regional NDVI profiles and statistical wheat yield data indicates that our annual maps help explain more yield variability than static maps, with an RMSE reduction of 3% for the EU27 as whole. The proposed approach is applicable to long historical timeseries and provides maps before the end of the agricultural season. Those maps positively impact crop yield description, notably in eastern, northern, and northeastern European regions.

ASSESSMENT OF THE RELATIONSHIP BETWEEN LAND SURFACE TEMPERATURE AND VEGETATION USING MODIS NDVI AND LST TIMESERIES DATA IN KADUNA METROPOLIS, NIGERIA

This study assessed the relationship between land surface temperature (LST) and vegetation using MODIS NDVI and LST timeseries data in Kaduna Metropolis. MOD13Q1 and MOD11A2 datasets were accessed using Google Earth Engine. Mann-Kendall trend test was used to analyse the trends in LST and NDVI. Pearson Moment Correlation Coefficient and Linear Regression were used to examine the relationship between LST and NDVI. Mann-Kendall trend test revealed monotonic downward trend in NDVI with a Z-statistics of -1.2758, but upward trend in daytime and nighttime LST, with a Z-statistics of 0.567 and 2.107 respectively. For the relationship, vegetation showed strong negative relationship with daytime LST with -0.704. Vegetation also showed weak positive relationship with nighttime LST. The linear regression analysis revealed that vegetation was able to predict 49.5% of LST in Kaduna Metropolis, with R2 value of 0.495 and a standard error of estimate is 2.459. The study concluded that loss of vegetation is responsible for the increase in land surface temperature. The study therefore recommended regulatory agencies should ensure that trees are planted whenever they are removed due to infrastructural development in order to prevent UHI phenomenon and planting of trees should be encouraged in order to regulate the urban climate.

Analysis of the Dynamics of Vegetation in the Sudan-Sahelian Zone: Case of the Moutourwa Agricultural Production Basin, Far-North Cameroon

The Moutourwa area in the Far North region of Cameroon is facing a deterioration of its climatic conditions. In this part of the Sudano-Sahelian zone, the soils are fragile and agriculture is not very productive due to various droughts. The statistical analysis study of climatic variability based on satellitic data in the area aims to analyze the dynamics of vegetation. This involves studying the spatio-temporal dynamics of vegetation using vegetation indices. The approach adopted to carry out this study is based on the descriptive statistics of the MODIS NDVI data series from the period 2010 to 2020. The study also analyzes the VCI drought indices in this study area. It appears from these analyzes that the year 2012 has the high average and the year 2015, the lowest average of our study period with 0.51 and 0.274 respectively. The years 2010, 2011, 2013, 2014, 2017, 2018 and 2019 remained around an average of 0.3. The observation of drought indices such as the VCI examines the fluctuation of their values characteristic of the heterogeneity of the vegetation state.

Spatial-Temporal Evolution Characteristics and Driving Force Analysis of NDVI in Hubei Province, China, from 2000 to 2022

Exploring the characteristics of vegetation dynamics and quantitatively analyzing the potential drivers and the strength of their interactions are of great significance to regional ecological environmental protection and sustainable development. Therefore, based on the 2000–2022 MODIS NDVI dataset, supplemented by climatic, topographic, surface cover, and anthropogenic data for the same period, the Sen+Mann–Kendall trend analysis, coefficient of variation, and Hurst exponent were employed to examine the spatial and temporal characteristics and trends of NDVI in Hubei Province, and a partial correlation analysis and geographical detector were used to explore the strength of the influence of driving factors on the spatial differentiation of NDVI in vegetation and the underlying mechanisms of interaction. The results showed that (1) the mean NDVI value of vegetation in Hubei Province was 0.762 over 23 years, with an overall increasing trend and fluctuating upward at a rate of 0.01/10a (p < 0.005); geospatially, there is a pattern of “low east and high west”; the spatial change in NDVI shows a trend of “large-scale improvement in the surrounding hills and mountains and small-scale degradation in the middle plains”; it also presents the spatial fluctuation characteristics of “uniform distribution in general, an obvious difference between urban and rural areas, and a high fluctuation of rivers and reservoirs”, (2) the future trend of NDVI in 70.76% of the region in Hubei Province is likely to maintain the same trend as that of the 2000–2022 period, with 70.78% of the future development being benign and dominated by sustained improvement, and (3) a combination of partial correlation analysis and geographical detector analysis of the drivers of vegetation NDVI change shows that land cover type and soil type are the main drivers; the interactions affecting the distribution and change characteristics of NDVI vegetation all showed two-factor enhancement or nonlinear enhancement relationships. This study contributes to a better understanding of the change mechanisms in vegetation NDVI in Hubei Province, providing support for differentiated ecological protection and project implementation.

Extraction and spatio-temporal analysis of phenological dates of winter wheat in north Henan Province of China from 2003 to 2018 based on MODIS NDVI time series.

Monitoring of spatio-temporal changes in crop phenology is an important part of the remote sensing of agricultural ecosystems. In this study, the segment turning point method was utilised to determine several phenological dates of winter wheat in northern Henan from 2003 to 2018. The spatio-temporal variation characteristics of these main phenological dates were analyzed, and the effects of temperature and precipitation on phenological changes were investigated. The results showed that: (1) The segment turning point method had strong space-time adaptability, and the RMSE of extracted phenoloical dates of multi-stations in a single year or single station in multi-years was less than 10d. (2) Roughly bounded by 114°E, the trefoil stage, tillering stage, overwintering stage and rising stage of winter wheat in the west were earlier than those in the east of northern Henan in 2018. (3) From 2003 to 2018, the interannual change rates of the trefoil date, tillering date, overwintering date, rising date, booting date, and milky date of winter wheat were 6.92 d/10a, 4.36 d/10a, 0.74 d/10a, -0.1 d/10a, -3.97 d/10a and -2.91 d/10a, indicating the trend of delaying pre-winter phenology and advancing post-winter phenology. (4) The delay of pre-winter phenology and the advance of post-winter phenology of winter wheat were significantly related to the increase in growing season temperature. The results of the study should provide a basis for further understanding of the effects of climate change on winter wheat phenology and to provide a reference for remote sensing monitoring of winter wheat phenology.

Comprehensive Assessment of NDVI Products Derived from Fengyun Satellites across China

NDVI data are crucial for agricultural and environmental research. The Fengyun-3 (FY-3) series satellites are recognized as primary sources for retrieving NDVI products on a global scale. To apply FY-3 NDVI data for long-term studies, such as climate change, this study conducted a thorough evaluation to detect the potentials of the FY-3B and FY-3D satellites for generating a long time series NDVI dataset. For this purpose, the spatiotemporal consistency between the FY-3B and FY-3D satellites was evaluated, and their performances were compared. Then, a grey relational analysis (GRA) method was applied to detect the factors influencing the consistency among the different satellites, and a gradient boosting regression (GBR) model was constructed to create a long-term FY-3 NDVI product. The results indicate an overall high consistency between the FY-3B and FY-3D NDVIs, suggesting that they could be used as complementary datasets for generating a long-term NDVI dataset. The correlations between the FY-3D NDVI and the MODIS NDVI, as well as the leaf area index (LAI) measurements, were both higher than those of FY-3B, which indicates a better performance of FY-3D in retrieving NDVI data. The grey correlation degrees between the NDVI differences and four parameters, which were land cover (LC), DEM, latitude (LAT) and longitude (LON), were calculated, revealing that the LC was the most related to the NDVI differences. Finally, a GBR model with FY-3B NDVI, LC, DEM, LAT and LON as the input variables and FY-3D NDVI as the target variable was established and achieved a robust performance. The R values between the GBR-estimated NDVI and FY-3D NDVI reached 0.947, 0.867 and 0.829 in the training, testing and validation datasets, respectively, indicating the feasibility of the established model for generating long time series NDVI data by combining data from the FY-3B and FY-3D satellites.

Growth unveiled: decoding the start of grassland seasons in Austria

ABSTRACT The start of the growing season (SOS) in grasslands is a critical factor that significantly affects grassland dynamics, production and quality. In the context of grassland fodder production, the exact detection of the start of the growing season allows farmers and land managers to precisely plan harvest schedules for hay production or silage making. However, the NDVI time-series based detection of the SOS for grassland poses challenges due to the gradual onset of greenness and the wide variety of environmental conditions. These challenges emphasize the need for novel approaches to improve satellite-based SOS detection accuracy. This study establishes a new methodology for identifying the SOS specifically tailored to grassland regions in Austria. To accomplish this, a synergistic approach combining remote sensing (MODIS NDVI at 250 m) and weather data analysis was adopted. Results indicate that the integrated approach outperforms alternative methods in estimating SOS. The study demonstrated a more robust and accurate estimates of SOS that were generally consistent with the phenological observations of a reference species (Forsythia suspensa) and in agreement with the observed SOS dates based on visual interpretation of 10 m resolution Sentinel-2 images. For future studies, the integration of additional validation techniques is crucial.

Trend analysis of MODIS NDVI time series and its relationship to temperature and precipitation in Northeastern of Iran.

Vegetation plays a crucial role in providing organic matter and regulating energy exchange on the Earth's surface. This study investigates the changes in vegetation cover, temperature, and precipitation in northeastern Iran during 2001-2020. MODIS-NDVI time series data and climatic data from 11 synoptic stations were utilized. The nonparametric Mann-Kendall method was employed to detect trends in vegetation cover and climatic variables. Additionally, the correlation between climatic parameters and vegetation was examined. Trend analysis revealed significant increases/decreases in vegetation cover in 32%/26% of the region, respectively. The increasing trend in vegetation cover was predominantly observed in highlands, suggesting that a warmer climate has enhanced the living conditions for plants in these regions. The vegetation trend map indicates an expansion of vegetation cover in the northern and central parts of Iran during the past 20years, whereas the southern and eastern portions experienced declines. The relationship between vegetation and elevation revealed that vegetation increased above 1,850m and decreased below 850m. Trend analysis showed no significant trend in precipitation data since the beginning of the twenty-first century, but an increasing trend in temperature was observed in 82% of the region's area, excluding the western strip. Correlation coefficients between temperature, precipitation, and vegetation indicated that declining temperatures are the limiting factors for vegetation in the highlands, while in lowland areas, the decrease in precipitation significantly diminishes vegetation growth.

Potential impact of future climate change on grassland cover in Burkina Faso.

The objective of this study was to analyse the potential impact of future climate change on grassland cover in Burkina Faso. MODIS NDVI 250m time series were used to monitor changes in grassland over 2000-2022. The random forest regression (RFR) model was fit by regressing reference data of grassland cover against current climatic and other environmental predictors to predict the current grassland cover map (2022). Projected climate model data of CMIP6 used under SSP 126 and SSP 585 scenarios were integrated into the fit RFR model to predict future change. The results revealed that grassland areas were largely dominated by non-significant productivity change (55%) during 2000-2022. In this period, grassland area knew more increased productivity (35%) than decrease (10%). Burkina Faso is predicted to face more decreased areas of grassland cover than increase by 2061-2080 under SSP 126 and SSP 585 scenarios. The findings of this study can help to set up appropriate adaptation measures to combat climate change in Burkina Faso.

Research on the sustainability of "greening" process in the Mu Us Sandy Land based on the spatiotemporal stability of ecological land.

In environmentally sensitive areas, especially the arid and semi-arid regions, the greening stability process and its influencing factors can directly affect the sustainable development of the ecological environment. In this study, multi-source remote sensing data such as land use/cover data, MODIS NDVI, and soil moisture, methods such as stability index, vegetation quantitative remote sensing, and Geodetector were employed to analyze the sustainability of the greening process in the Mu Us Sandy in 2000-2020, which were viewed from three aspects: changes in stability of land use types and function, soil moisture change and influencing factors on greening stability. The results showed that, (1) From the stability of land use types, continuous stable ecological land accounted for more than 50%, showing that decreased from northwest toward southeast. (2) From the functional stability, NDVI showed a fluctuated growth (0.035/a), with an increasing distribution pattern from northwest to southeast. Additionally, Vegetation changes were unstable and concentrated in the western part of the study area (OtogBanner and Otog Front Banner), while the eastern part was stable, in which vegetation improvement took the main position. Moreover, mobile dunes almost disappeared, and semi-fixed dunes decreased and gradually shrank to the west of the sandy area, while fixed dunes soared and were concentrated in the middle of the sandy land. (3) From the soil moisture change, soil moisture at different underground depths showed an overall increasing trend, but the deep soil moisture was higher than the shallow, and spatial distribution varied greatly. (4) From the influencing factors, natural factors significantly influence greening stability, among which precipitation had a particularly profound impact, and interactions with other natural and social factors were higher explanatory. The paper aims to explore whether the ecological environment is developing in a good and orderly direction in the Mu Us Sandy Land, and the potential factors that cause its changes, to provide a theoretical basis for scientific governance in the Mu Us Sandy Land and other arid and semi-arid areas in the future.

Disentangling the response of vegetation dynamics to natural and anthropogenic drivers over the Qinghai-Tibet Plateau using dimensionality reduction and structural equation model

The Qinghai-Tibet Plateau (QTP), also referred to as the “roof of the world”, is a distinct alpine ecosystem located at an average elevation of more than 4000 m. Climate variation and anthropogenic activity have affected vegetation development on the QTP during the past few decades, but it is still debatable how significant a role any of these causes has played. According to land surface air temperature data around the world, during the past 50 years, temperatures on the QTP have risen twice as rapidly as the average global level. Previous studies have employed the residual method to examine the impacts of anthropogenic activities on vegetation development, with a lack of actual data validation and less attention to explaining the complex interactions and pathways between vegetation growth, natural factors (climate change and topography), and anthropogenic drivers. Furthermore, although the relationships between vegetation dynamics and terrain have been addressed, the interactions in which meteorological factors influence vegetation growth in accordance with topographical variations remain inadequately elucidated. The raster datasets employed in this study include MODIS NDVI, meteorology, topography, as well as anthropogenic activity from 2000 to 2018. A suite of methods including partial correlation coefficient (PCC), principal component analysis (PCA), and partial least squares structural equation model (PLS-SEM) were applied to explore the interrelationships between various influencing factors and regional vegetation development over the past two decades. The results suggested that: (1) an elevation-specific temperature threshold existed, indicating that temperature could promote or inhibit vegetation growth depending on whether it is below or above this threshold, respectively, which was controlled by a trendline equation; (2) the PLS-SEM and PCA results showed that vegetation development was dominantly promoted by temperature and was most sensitive to precipitation; (3) topography, especially elevation, affected vegetation growth indirectly by influencing climatic change, while anthropogenic drivers had positively contributed to vegetation dynamics on the QTP under the influence of implementing ecological management such as the nature reserves establishment.