Fractional Vegetation Cover Research Articles

Monitoring Biophysical Variables (FVC, LAI, LCab, and CWC) and Cropland Dynamics at Field Scale Using Sentinel-2 Time Series

Biophysical variables play a crucial role in understanding phenological stages and crop dynamics, optimizing ultimate agricultural practices, and achieving sustainable crop yields. This study examined the effectiveness of the Sentinel-2 Biophysical Processor (S2BP) in accurately estimating crop dynamics descriptors, including fractional vegetation cover (FVC), leaf area index (LAI), leaf chlorophyll a and b (LCab), and canopy water content (CWC). The evaluation was conducted using estimation quality indicators (EQIs) and comprehensive ground throughout the entire growing season at the field scale. To identify soil and vegetation pixels, the spectral unmixing technique was employed. According to the EQIs, the best retrievals were obtained for FVC in around 99.9% of the 23,976 pixels that were analyzed during the growth season. For LAI, LCab, and CWC, over 60% of the examined pixels had inputs that were out-of-range. Furthermore, in over 35% of the pixels, the output values for LCab and CWC were out-of-range. The FVC, LAI, and LCab estimates agreed well with ground measurements (R2 = 0.62–0.85), whereas a discrepancy was observed for CWC estimates when compared with ground measurements (R2 = 0.51). Furthermore, the uncertainties of FVC, LAI, LCab, and CWC estimates were 0.09, 0.81 m2/m2, 60.85 µg/cm2, and 0.02 g/cm2 through comparisons to ground FVC, LAI, Cab, and CWC measurements, respectively. Considering EQIs and uncertainty metrics, the order of the estimation accuracy of the four variables was FVC > LAI > LCab > CWC. Our analysis revealed that temporal variations of FVC, LAI, and LCab were primarily driven by field-scale events like sowing date, growing period, and harvesting time, highlighting their sensitivity to agricultural practices. The robustness of S2BP results could be enhanced by implementing a pixel identification algorithm, like embedding spectral unmixing. Overall, this study provides detailed, pixel-by-pixel insights into the performance of S2BP in estimating FVC, LAI, LCab, and CWC, which are crucial for monitoring crop dynamics in precision agriculture.

Analysis of Spatial and Temporal Changes in FVC and Their Driving Forces in the Inner Mongolia Section of the Yellow River Basin

To investigate the spatial and temporal changes in fractional vegetation coverage (FVC) and their driving forces in different regions of the Inner Mongolia section of the Yellow River Basin, this paper observed the spatial trends and stability of FVC in these regions based on the MOD13Q1 information regarding the 2000–2020 period as a data source. It used the dimidiate pixel model to invert FVC, and based on the centre of gravity migration model, the coefficient of variation and the Mann–Kendall and Sen’s slope estimator test, it studied the spatial variation trend and stability of FVC in the four relevant areas of the Inner Mongolia section; an attribution analysis using a geodetector was also conducted. The following results were found: (1) in terms of temporal FVC change in the relevant areas, from 2000 to 2020, the overall FVC showed an increasing trend, indicating an obvious hierarchy of change as per different seasonal scales (summer > growing season > fall > spring). There is a mutation point in FVC in different areas, and the FVC sequence is random. (2) Regarding spatial change, the overall FVC showed a trend of being high in the eastern regions and low in the western regions and low–high–low from the north to the south; the stability of the Hetao Irrigation District–Wuliangsuhai Area changed more significantly with the successive seasons, and the degraded areas of FVC were mainly distributed in the city centre of the Kundulun River–Daheihe River Area and in the Hetao Irrigation District in the summer. (3) In terms of driving factors, soil type had a relatively higher explanatory power regarding the Hetao Irrigation District–Wuliangsuhai Area, rainfall had a relatively higher explanatory power regarding the Morin River–Wuding River Area and the Kundulun River–Daheihe River Area, and land use had a relatively higher explanatory power regarding the Ten Kongtui–Heidaigou Area.

An Angle Effect Correction Method for High-Resolution Satellite Side-View Imaging Data to Improve Crop Monitoring Accuracy

In recent years, the advancement of CubeSat technology has led to the emergence of high-resolution, flexible imaging satellites as a pivotal source of information for the efficient and precise monitoring of crops. However, the dynamic geometry inherent in flexible side-view imaging poses challenges in acquiring the high-precision reflectance data necessary to accurately retrieve crop parameters. This study aimed to develop an angular correction method designed to generate nadir reflectance from high-resolution satellite side-swing imaging data. The method utilized the Anisotropic Flat Index (AFX) in conjunction with a fixed set of Bidirectional Reflectance Distribution Function (BRDF) parameters to compute the nadir reflectance for the Jilin-1 GP01/02 multispectral imager (PMS). Crop parameter retrieval was executed using regression models based on vegetation indices, the leaf area index (LAI), fractional vegetation cover (FVC), and chlorophyll (T850 nm/T720 nm) values estimated based on angle corrected reflectance compared with field measurements taken in the Inner Mongolia Autonomous Region. The findings demonstrate that the proposed angular correction method significantly enhances the retrieval accuracy of the LAI, FVC, and chlorophyll from Jilin-1 GP01/02 PMS data. Notably, the retrieval accuracy for the LAI and FVC improved by over 25%. We expect that this approach will exhibit considerable potential to improve crop monitoring accuracy from high-resolution satellite side-view imaging data.

Spatiotemporal analysis of urban expansion in Srinagar city, Kashmir

Urbanization is a multifaceted process and a global phenomenon that encompasses various environmental and land cover changes. The expansion of Srinagar city, Kashmir, has changed its Land Use and Land Cover (LULC) due to an increase in population, horizontal expansion of the city, migration, and an increase in the built-up area. This has resulted in a change in Land Surface Temperature (LST). This study evaluates the dynamics of LULC in Srinagar city and its effect on LST using remote sensing, geographic information systems (GIS), statistical analysis techniques, and Landsat 4–5 TM and Landsat-8 OLI datasets. We analysed the LULC in Srinagar city from 2000 to 2020. The study calculated the Normalized Difference Vegetation Index (NDVI), LST, Fractional Vegetation Cover (FVC), and Emissivity from satellite remote sensing data of Srinagar city and analysed the relationship between them. The study shows that in terms of percentage change in LULC in Srinagar city from the year 2000 to 2020, built-up area showed the maximum change (75.58), followed by dense forest (− 62.74), vegetation (− 40.2), water bodies (− 25.41%), crop (23.16%), swamp (− 8.22), sparse forest (− 7.89), and open spaces (− 6.02). The findings of this study hold significance for researchers, urban planners, and policymakers to understand urbanization trends and their implications in the city.

An Investigation on Potential Dispersal of Airborne Pollen Over China and Their Impact on Climate as Ice Nuclei Using RegCM‐Pollen

AbstractPollen can serve as an effective ice‐nuclei (IN), altering cloud microphysical and radiative properties, thus precipitation and cloud life cycles. Here, a nationwide pollen emission inventory with a horizontal resolution of 5 km was established based on a parameterization scheme of mass balance of pollen grain fluxes surrounding the plant crowns, and using satellite observational data sets (including leaf area index and fractional vegetation cover) as well as pollen emission rates. The potential emission is then implemented in RegCM‐pollen model which treated pollen as aerosol tracers. Besides, pollen‐IN parameterization schemes were incorporated in RegCM‐pollen to simulate the interactions between pollen and ice clouds. Investigations show that the mean annual pollen emission in China is 2.65 × 107 grains m−2 yr−1, mainly distributed in the south and northeast of China. The IN magnitude is mainly determined by a combination of temperature and pollen concentration. Notably, an increasing number concentration of pollen grains produces opposite effects in Southern China (SC) and Northern China (NC). The weakened upward motion and vertical transport of water vapor in NC made ice clouds hardly form, resulting in cloud forcing (CF) of +0.86 W/m2. In contrast, it generates a CF of −0.84 W/m2 in SC mainly owing to expanded cloud cover. The changes in shortwave radiative forcing is more significant compared to longwave radiative forcing in the two regions. At the surface, the net radiative forcing in NC is +0.74 W/m2, while it is a −0.51 W/m2 in SC. Among them, downward shortwave radiative forcing is approximately twice that of upward longwave radiative forcing in SC and 1.4 times in NC. Surface temperature shows rising over NC, ranging from 0.05 to 0.25 K. In SC, it is primarily decreasing by −0.12 to −0.03 K. The pollen‐IN effect also causes a decline of precipitation in NC (−0.17 mm/day) and a rise in SC (0.09 mm/day). Our results suggest that the pollen effect on ice clouds is complex, yet significant in understanding its impact on radiation and climate of the atmosphere.

Water Soil Availability Prediction in Coffee Land Plantations with Remote Sensing

Abstract The availability of soil water is the basis of plant growth, so its availability is important key for coffee plants growing in South Malang. Climate change makes the availability of water for these plants a complex problem because changing temperature and humidity. Remote sensing is a fast and cheap way to predict water availability in coffee plantations with large land areas and rapid climate change. The aims of this research to estimate water soil availability in coffee land plantations to meet plant water needs. The water content in coffee plantations can be estimated by combining the vegetation index values with a combination of Normalized Difference Vegetation Index (NDVI), Fractional Vegetation Cover (FVC), Land Surface Temperature (LST), and Land Surface Emissivity (LSE). The method used in this research is to make tentative maps obtained from overlays of all combining the vegetation index values. The selection of research locations is based on the area of the coffee plantation using the smallest pixel, with an area of 3.000.000 m square meters. Each location of the research location was carried out four times for sampling the soil moisture content. The results showed that the correlation value between water content and the estimation results had a correlation of 0.814, with a regression value of 0.663 and a probability value of 0.9226. This shows that it is possible to estimate the water content of coffee plantations using remote sensing made it easier for coffee farmers to take action based on actual conditions on the land.

Analyzing Spatiotemporal Variations and Driving Factors of Grassland in the Arid Region of Northwest China Surrounding the Tianshan Mountains

The Tianshan Mountains, the largest arid mountain range in Central Asia, feature diverse terrains and significant landscape heterogeneity. The grasslands within the Xinjiang Tianshan region are particularly sensitive to climate change and human activities. However, until recently, the patterns and mechanisms underlying grassland changes in this region have been unclear. In this study, we analyzed spatial and temporal changes in grassland fractional vegetation cover (FVC) from 2001 to 2020, analyzed spatial and temporal changes in grassland, and predicted future trends using Global Land Surface Satellite (GLASS) FVC data, trend analysis, and the Hurst index method. We also explored the driving mechanisms behind these changes through the structural equation model (SEM). The results showed that from 2001 to 2020, the grassland FVC in the Tianshan region of Xinjiang was higher in the central and western regions and lower in the northern and southern regions, showing an overall fluctuating growth trend, with a change in the growth rate of 0. 0017/a (p < 0.05), and that this change was spatially heterogeneous, with the sum of significant improvement (20.6%) and slight improvement (29.9%) being much larger than the sum of significant degradation (0.6%) and slight degradation (9.5%). However, the Hurst index (H = 0.47) suggests that this trend may not continue, and there is a risk of degradation. Our study uncovers the complex interactions between the Tianshan barrier effect and grassland ecosystems, highlighting regional differences in driving mechanisms. Although the impacts of climatic conditions in grasslands vary over time in different regions, the topography and its resulting hydrothermal conditions are still dominant, and the extent of the impact is susceptible to fluctuations of varying degrees due to extreme climatic events. Additionally, the number of livestock changes significantly affects the grasslands on the southern slopes of the Tianshan Mountains, while the effects of nighttime light are minimal. By focusing on the topographical barrier effect, this study enhances our understanding of grassland vegetation dynamics in the Tianshan Mountains of Xinjiang, contributing to improved ecosystem management strategies under climate change.

Study on the Annual Runoff Change and Its Relationship with Fractional Vegetation Cover and Climate Change in the Chinese Yellow River Basin

In the context of global climate change and ecological restoration projects, significant changes have been observed in the fractional vegetation cover (FVC) in the Yellow River basin. The increased vegetation growth accelerates water consumption, exacerbating drought and water scarcity issues, thereby heightening regional water resource shortage risks. This study targets the Yellow River basin in China, employing a pixel-based model to convert NDVI into FVC datasets. We establish a pixel-wise mathematical model for annual runoff and environmental factors based on residual analysis and methods like multiple linear regression. Using climate model data from CMIP6 as independent variables, in conjunction with the statistical model, we elucidate the spatiotemporal characteristics of annual runoff in the Yellow River basin under future climate scenarios. Our results indicate that, under four different climate scenarios, the average annual runoff in the Yellow River basin is projected to increase. The increases are quantified as 0.008 mm/a, 0.065 mm/a, 0.25 mm/a, and 0.24 mm/a for SSP126, SSP245, SSP370, and SSP585 scenarios, respectively. From 2022 to 2040, the spatial distribution of the runoff change rates under the SSP245 and SSP370 scenarios show an increasing trend in upstream areas such as the Qinhe and Longmen regions, with rates ranging from 6.00 to 8.61 mm/a. During the period from 2041 to 2060, all four climate scenarios indicate minimal changes in the runoff depth in the northern part of the Yellow River basin. From 2061 to 2080, under the SSP126 and SSP245 scenarios, the spatial distribution of the runoff shows significant increases in the river source area and a decreasing trend in the middle reaches, with rates ranging from 4.52 to 11.39 mm/a. For the period from 2081 to 2100, the runoff change rates vary significantly under the four climate scenarios. These findings provide a detailed understanding of how future climate scenarios could impact water resource distribution in the Yellow River basin, offering critical insights for regional water management and policy making to mitigate potential water scarcity challenges.

Effect of shade on simultaneous estimation of non-photosynthetic and photosynthetic vegetation cover using the NDVI-NSSI normalized difference triangular space

ABSTRACT The non-photosynthetic vegetation – soil separation index (NSSI) and normalized difference vegetation index (NDVI) can be used to simultaneously estimate the fractional cover of non-photosynthetic vegetation (fNPV), photosynthetic vegetation (fPV), and bare soil (fBS) in vegetation ecosystems. However, these estimates suffer from problems due to shading by dense vegetation or topography. Based on field-measured data and on hyperspectral data acquired by unmanned aerial vehicles, we analyse how shading affects the morphology of NDVI-NSSI and enhanced vegetation index – NSSI (EVI-NSSI). We also investigate the simultaneous estimation of fNPV, fPV, and fBS. The results show that the NDVI-NSSI normalized difference feature space mitigates the impact of shade. Shade causes the NDVI-NSSI and EVI-NSSI to shift rightward parallel to the BS axis and leftward parallel to the PV axis, respectively. Although such shifts may hinder the determination of NPV, PV, and BS endmembers, they do not affect the spectral separation of NPV. Overall, with the NDVI-NSSI and EVI-NSSI methods, the estimation error for fNPV under shaded conditions is approximately 5% greater than that under illuminated conditions. Shaded green vegetation more strongly affects the estimation error of fPV and fBS in EVI-NSSI than in NDVI-NSSI, whereas fBS and fPV depend more strongly on shade than fNPV.

Ecological construction status of photovoltaic power plants in China’s deserts

Solar photovoltaic (PV) is one of the most environmental-friendly and promising resources for achieving carbon peak and neutrality targets. Despite their ecological fragility, China’s vast desert regions have become the most promising areas for PV plant development due to their extensive land area and relatively low utilization value. Artificial ecological measures in the PV plants can reduce the environmental damage caused by the construction activity and promote the ecological condition of fragile desert ecosystems, therefore yield both ecological and economic benefits. However, the understanding of the current status and ecological benefits of this approach in existing desert PV plants is limited. Here we surveyed 40 PV plants in northern China’s deserts to identify the ecological construction modes and their influencing factors. We quantified the ecosystem service value (ESV) provided by these PV plants using remote sensing data and estimated the potential for ESV enhancement. Our results show that PV plant construction in desert regions can significantly improve the ecosystem, even with natural restoration measures (M1) alone, resulting in a 74% increase in average fractional vegetation cover (FVC) during the growing season, although the maximum average FVC of only about 10%. The integrated mode M4, which combined artificial vegetation planting M2 and sand control measures M3, further enhance the average growing season FVC to 14.53%. Currently, 22.5% of plants lack ecological measures, 40% employ only a single measure, but 92% of new plants since 2017 have adopted at least one ecological construction mode. The main influencing factors include surface type, policy support, water resources, ecological construction costs, and scientific management guidance. If artificial ecological construction were incorporated, a significant ESV could be achieved in these PV plants, reaching $8.9 million (a 7.7-fold increase) if assuming a targeted 50% vegetation coverage. This study provides evidence for evaluating the ecological benefit and planning of large-scale PV farms in deserts.

Spatial Characteristics of Aeolian Sand Transport Affected by Surface Vegetation along the Oshang Railway

Blown sand transport plays a pivotal role in determining the optimal placement of sand protection facilities along railways in sandy areas. Surface vegetation cover significantly influences blowing sand activities along the Oshang Railway (from Otog Front Banner to Shanghai-Temple Town). In this study, the spatial characteristics of aeolian sand transport along the railway were derived from field observations conducted at five different locations, each with varying fractional vegetation cover (FVC). The results indicate that sand-transport intensity does not fully correlate with the wind energy environment, primarily due to differences in surface vegetation cover among the observation sites. We utilize the dimensionless ratio Q·g·f/(0.136 × DP·ρa) to represent the sand transport rate (Q), the sand-moving wind frequency (f) and drift potential (DP), exhibiting a negative exponential trend with FVC. Sand transport is effectively restrained when FVC is greater than or equal to 20%. Conversely, when FVC is less than 20%, sand transport intensity exponentially increases with decreasing fractional vegetation cover. After careful analysis, we propose a simple empirical expression that incorporates the influence of both the wind field and fractional vegetation cover to assess sand transport on a flat surface. The study offers valuable insights for designing wind-blown sand protection measures along railways and evaluating wind-blown sand movement on a flat surface affected by vegetation.

Urban Morphology Influencing the Urban Heat Island in the High-Density City of Xi’an Based on the Local Climate Zone

Urban form plays a critical role in enhancing urban climate resilience amidst the challenges of escalating global climate change and recurrent high-temperature heatwaves. Therefore, it is crucial to study the correlation between urban spatial form factors and land surface temperature (LST). This study utilized Landsat 8 remote sensing data to estimate LST. Random forest nonlinear analysis was employed to investigate the interaction between the urban heat island (UHI) and six urban morphological factors: building density (BD), floor area ratio (FAR), building height (BH), fractional vegetation coverage (FVC), sky view factor (SVF), and impervious surface fraction (ISF), within the framework of local climate zones (LCZs). Key findings revealed that Xi’an exhibited a significant urban heat island effect, with over 10% of the study area experiencing temperatures exceeding 40 °C. Notably, the average LST of building-class LCZs (1-6) was 3.5 °C higher than that of land cover-class LCZs (A-C). Specifically, compact LCZs (1-3) had an average LST 3.02 °C higher than open LCZs (4-6). FVC contributed the most to the variation in LST, while FAR contributed the least. ISF and BD were found to have a positive impact on LST, while FVC and BH had a negative influence. Moreover, SVF was observed to positively influence LST in the compact classes (LCZ2-3) and open low-rise class (LCZ6). In the open mid-rise class (LCZ5), SVF and LST showed a U-shaped relationship. There is an inverted U-shaped relationship between FAR and LST, with the inflection point occurring at 1.5. The results of nonlinear analysis were beneficial in illustrating the complex relationships between LST and its driving factors. The study’s results highlight the effectiveness of utilizing LCZ as a detailed approach to explore the relationship between urban morphology and urban heat islands. Recommendations for enhancing urban climate resilience include strategies such as increasing vegetation coverage, regulating building heights, organizing buildings in compact LCZs in an “L” or “I” shape, and adopting an “O” or “C” configuration for buildings in open LCZs to aid planners in developing sustainable urban environments.

ФИЗИЧЕСКИ ОБОСНОВАННАЯ СХЕМА ПАРАМЕТРИЗАЦИИ ПРОЕКТИВНОГО ПОКРЫТИЯ РАСТИТЕЛЬНОСТИ

The leaf area index (LAI) and fractional vegetation cover (FVC) are very important parameters in land-atmosphere interactions. In this study, a very simple but robust and mechanism-based method was developed to derive FVC data based on the relationships between the canopy gap fraction, LAI, and direct solar extinction coefficient. For validation, the LAI data and NDVI-based and mechanism-based FVC data were assimilated into the integrated urban land model (IUM). Using the mechanism-based FVC data as the input, the simulation of the annual average land surface temperatures (LSTs) in the Beijing area were improved compared with those using the NDVI-based FVC data as the input.

A new computationally efficient algorithm to generate global fractional vegetation cover from Sentinel-2 imagery at 10 m resolution

ABSTRACT The multi-angle method in the pixel dichotomy model (PDM) is used to produce fine spatial resolution (FSR) products of fractional vegetation cover (FVC), which involves numerous iterative computations. This phenomenon causes a low computational efficiency for global FVC. Here, we established the multi-angle equations, and simplified the two-stream approximation method to obtain multi-angle reflectance, which calculated the KDM parameters possible. Moreover, to accelerate the calculation of parameters in the multi-angle equations, land cover data were utilized as priority knowledge, which aims to decrease iterative computations to speed up the computational speed. Finally, we proposed an accelerated pixel dichotomy coupled linear kernel-driven model (PDKDM-A). By validating PDKDM-A using field measurements and FVC products, we found that the global FVC computed for various land covers exhibited strong agreement with the field measurements. The root mean square errors (RMSEs) were consistently below 0.150, except for grasslands, croplands, and cropland/natural vegetation mosaics. When comparing FVC products on the same scale, we discovered that the RMSE was less than 0.103, except for deciduous broadleaf forests. Our study demonstrates that PDKDM-A is an efficient algorithm, with the potential to quickly generate accurate global FVC products with fine spatial resolution.

Dynamic Change and Attribution Regarding Fractional Vegetation Coverage in Mengdong River Basin

The situation of rocky desertification in the southwestern part of China is very serious and has been included as one of three major ecological problems. In this study, using Landsat images as the data sources, we estimated the fractional vegetation coverage (FVC) in the Mengdong River Basin in the Hunan Province over the past 40 years, analyzed its spatio-temporal variation characteristics, and explored the driving mechanism (climate and anthropogenic) using the Mann–Kendall, Hurst index, and partial correlation methods. Specifically, the impact of ecological engineering on the recovery of vegetation cover in rocky desertification areas was analyzed and discussed in this study. The results demonstrate the following: (1) The changes in FVC in the study area before and after the rocky desertification management ecological project differed significantly, with a very small change in the rate of change in the mean value of FVC between 1987 and 1999 (<0.1%), while the FVC had a significant linear growth trend between 2000 and 2022 (>0.9%). (2) The Hurst index of FVC ranged from −0.233 to 2.476, with an average value of 0.864. The area with an H value greater than 0.75 accounted for 80.12%, indicating that the future trend in the vast majority of regions will develop in accordance with the current change trend. (3) The average partial correlation coefficients between FVC and precipitation and between FVC and temperature were −0.02 and 0.27, respectively, showing that FVC is more sensitive to temperature than precipitation. The combination of climate change and human activities is the main cause of FVC change. The contributions of climate change (precipitation and temperature) and human activities to FVC variation are about 30% and 70%, respectively. Ecological restoration projects have a significant positive effect on the recovery of vegetation in rocky desertification areas. The results of this study are intended to provide a scientific basis for analyzing the characteristics of vegetation restoration in existing rocky desertification areas and ecological management in future rocky desertification areas.

Elucidate the complex drivers of significant greening on the Loess Plateau from 2000 to 2020

Greening and its drivers in the Loess Plateau of China have been extensively studied, but attributional explorations regarding vegetation restoration in different climatic zones remain unclear. This study quantitatively evaluated greening on the Loess Plateau based on Fraction Vegetation Cover (FVC) calculated by MOD13Q1 dataset, compared FVC in different climatic zones, and explored the relationship between greening and precipitation, evaporation, soil water content (SWC), surface water bodies, and human activities. The study found that FVC on the Loess Plateau increased significantly at a rate of 0.538% yr−1 from 2000 to 2020, resulting in 94% greening. The greening rate was higher in the middle temperate zones (0.557% yr−1) than in the southern temperate zones (0.532% yr−1), but its average FVC was lower. This suggests that the greening rate (0.551% yr−1) is higher within 5 km of water bodies. Cropland (15.26%), grassland (13.66%) and trees (12.57%) had higher FVC increment than other types, while the six-fold increase in urban area is a significant impediment to greening. Additionally, there is a significant positive correlation with FVC when the urban economy exceeds 100 billion. The interaction of natural factors and human activities made the greatest contribution to greening, as calculated by the GeoDetector. It is hypothesized that there is significant potential for greening in the middle temperate zones. However, large-scale tree planting may not have a catalytic effect and could even be counterproductive. To achieve sustainable greening of the Loess Plateau in the future, shrubs and artificial water storage facilities may be key.

Validation of Remotely Sensed Land Surface Temperature at Lake Baikal’s Surroundings Using In Situ Observations

The accuracy of Land Surface Temperature (LST) products retrieved from satellite data in mountainous-coastal areas is not well understood. This study presents an analysis of the spatial and temporal variability of the differences between the LST and in situ observed air and surface temperatures (ISTs) for the southeastern slope of Lake Baikal’s surroundings. The IST was measured at 12 ground observation sites located on the southeastern macro-slope of the Primorskiy Ridge (Baikal, Russia) within an elevation range of 460–1656 m above sea level from 2009 to 2021. LST was estimated using 617 Landsat (7 and 8) images from 2009–2021, taking into account brightness temperature, surface emissivity and vegetation cover fraction. The comparison of the LST from satellite data and the IST from ground observation showed relatively high differences, which varied depending on the season and site type. A neural network was suggested and calibrated to improve the LST data. The corrected remote-sensed temperature was found to reproduce the IST very well, with mean differences of about 0.03 °C and linear correlation coefficients of 0.98 and 0.95 for the air and surface IST.

Enhanced post-wildfire vegetation recovery in prescribed-burnt Mediterranean shrubland: A regional assessment

The primary objective of prescribed burning (PB) treatment is to promote increased ecosystem resistance to high fire severity and ecosystem adaptive resilience after unplanned wildfires under worsening climates. Yet, empirical evidence involving regional-scale assessments with enough spatial representativeness on how vegetation will respond to PB-wildfire interactions in Mediterranean shrublands is absent. This is particularly relevant to provide comprehensive insights on the effectiveness of PB policies and programs in southern European countries, where shrublands are the main PB target. Here, we investigated the PB effect on vegetation responses in the short-term (1–3 years) after subsequent wildfire encounters in Mediterranean shrublands at the regional scale across mainland Portugal. We leveraged geospatial cloud-computing platforms and remote sensing data (optical and synthetic aperture radar -SAR-) and techniques with physical basis to procure reliable proxies for vegetation fractional cover (FCOVER), vigor (normalized difference vegetation index; NDVI) and structural complexity (cross-polarization ratio of C-band SAR backscatter data; SAR-CR) at the landscape scale throughout the time series. The role of site productivity and topographic configuration variables as putative drivers of vegetation responses was also analyzed. We found that the mitigating effect of PB on wildfire severity translated into stronger vegetation responses in treated areas than in untreated counterparts after the first PB-wildfire encounter. Although FCOVER, NDVI and SAR-CR recovered gradually in both areas, the recovery rate was markedly higher in PB-treated areas 3-years after the PB-wildfire encounter than at earlier times in the time series. The effect of PB treatment was stronger for the FCOVER recovery response than for NDVI and SAR-CR. The effect of the PB treatment on FCOVER and NDVI responses was modulated by site environmental characteristics, such that their differences between PB-treated and untreated counterparts were maximal for both higher altitudes and warmer, drier locations in the landscape.

Spatial-temporal Evolution and Driving Factors of Desertification Based on Computer Interpretation in Tarim River Basin

The Tarim River Basin is an important life hinge in Xinjiang, which plays a vital role in maintaining ecological stability. In recent years, it is faced with challenges such as the expansion of sandy land, the decrease of precipitation and sand-dust storm, which lead to the aggravation of desertification and the contradiction between man and land. This study used remote sensing images from 2000 to 2020 to analyze desertification trends through automatic interpretation by computer, combined with NDVI(Normalized Difference Vegetation Index) and FVC(Fractional Vegetation Cover) indicators. The results showed that the total area of desertification land decreased, but the area of serious desertification land increased. Precipitation, Sunshine, wind speed, air temperature, cultivated land area change, urbanization and other factors affect desertification, of which cultivated land area change, temperature change and precipitation change are the most important factors. Policies focused on preventing wind erosion have been inadequate as the economy has developed and conflicts between people and land have intensified.

Impacts of land use change on surface infiltration capacity and urban flood risk in a representative karst mountain city over the last two decades

Urban areas are undergoing rapid expansion, resulting in diminished vegetation cover and an escalating urban flood problem, and posing substantial threats to aquatic ecosystems, human safety, and socioeconomic structures. However, research on urban flood risk using methods like the Analytic Hierarchy Process (AHP), further exploration of spatiotemporal variations is needed, especially within the context of karst mountainous urban clusters. This study focuses on Guiyang city within a karst mountainous urban cluster, investigating fractional vegetation cover (FVC), infiltration capacity, and urban flood risk. Methodologies encompass slope trend analysis, the utilization of the Google Earth Engine, and geographic detectors to assess the dynamics of FVC and infiltration capacity. AHP is employed to assign weights to infiltration capacity, land use and land cover (LULC), FVC, and elevation, culminating in the generation of flood risk maps through linear weighted overlay. The results showed that: (1) The average FVC in Guiyang City was 0.7440 in 21 years, with relatively low levels in the central and southern regions. The FVC increased by 0.21% per year from 2000 to 2020, driven mainly by land use change and human activities. (2) The infiltration capacity of the city of Guiyang has increased as a whole, mainly due to reforestation efforts in specific areas, while the infiltration capacity of impervious areas tends to decrease. (3) The central urban areas exhibit greater vulnerability, with an estimated ten-year average growth rate of 73.36% in very high flood risk areas by 2030, affecting 7.38% of Guiyang's total area. These urban flood risk maps serve as valuable tools for local governments to enhance urban management practices.