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Spatial And Seasonal Change Detection In Vegetation Cover

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Spatial Analysis of Land Use and Cover Changes: Implications of the Green Legacy Initiative on Climate Action in the Awash Basin, Ethiopia. Analyzing land cover changes with Landsat imagery from 1993, 2003, and 2023, this study evaluates the Green Legacy program’s impact on land surface temperature (LST). Therefore, this study attempts to combine the machine learning model XGBoost-SHAP, the spatial statistical model Geodetector, and traditional regression model residual analysis to form a multidimensional, comprehensive framework for exploring the driving mechanisms of vegetation cover spatial changes. Anthropogenic activities and natural climate changes are the central driving forces of global ecosystems and agriculture changes. Climate changes, such as rainfall and temperature changes, have had the greatest impact on different types of plant production around the world. In the present study, we investigated the spatiotemporal variation of major crops

Crop water needs vary from season to season, these tools can be used to identify farmland changes and project changes in different seasons. These data are ideal for tracking the overall change of vegetation over a long time period and with great spatial precision.

SPOT-based vegetation cover change map (1998–2010) calculated based on ...

ABSTRACT Vegetation indices (VIs) were used to detect when and where vegetation changes occurred. However, different VIs have different

Detecting trend and seasonal changes in satellite image time series

Amilcare Porporato and his team studied how vegetation cover in arid regions responds to changes in soil moisture, as well as the adaptation In this paper, we investigate the features of spatiotemporal change in fractional vegetation cover (FVC) throughout the Yellow River Basin between 2000 and 2022 and identify the driving factors behind the change using the MODIS normalized difference vegetation index (NDVI) as a data source.

In this paper we analyze the temporal and spatial variations in the fractional vegetation cover in the Hulun Lake region from 1986 to 2017 and its response to changes in climate parameters and human activities; additionally, the impact of changes in climate parameters and human activities on FVC are discussed.

The Grain for Green Program (GFGP) plays a critical role in enhancing watershed vegetation cover. Analyzing changes in vegetation cover Remote sensing is a valuable tool for surveying submerged aquatic vegetation (SAV) distribution patterns at extensive spatial and temporal scales. Only regular mapping over successive time periods (e.g., months, years) allows for a quantitative assessment of SAV loss or recolonization extent. Still, there are only a limited number of studies assessing temporal

The Yan River Basin of the Loess Plateau is a key region for ensuring the environmental protection and sustainable development of the Yellow River Basin. Therefore, it is essential to identify how vegetation cover has changed and determine the factors that have driven these changes. In this study, we applied a three-dimensional vegetation cover model to Depending on where and when it occurs, vegetation cover change can affect local climate by altering the surface energy balance. Based on satellite data, this study provides the first data-driven

The vegetation canopy maps show the spatial heterogeneity of canopy cover in Marjan rangeland and its capability of estimating and monitoring the canopy cover of rangeland vegetation at different Classification and change detection are the most used methods to study desertification from remote sensing data. Additionally, land cover/land use change and vegetation and its attributes (e.g., Normalized Difference Vegetation Index – NDVI) are the most used variables to study desertification using remote sensing techniques. In this study, we proposed a novel framework based on a change detection algorithm and a trend analysis method that could integrate both short-term disturbance detection and long-term trends to comprehensively assess vegetation change.

The Tarim River Basin (TRB) in Northwest China has an extremely fragile ecological environment that is highly sensitive to climate change. Understanding the long-term change dynamics of vegetation coverage in this arid zone is critically important for predicting future trends as well as for improving regional ecological protection and soil and water

(2) The OPGD model detection showed the optimal spatial scale of vegetation cover in this study region was 2 km. Optimal discrete parameter combinations for slope, elevation, temperature and GDP are quantile breaks with 9 intervals, which contribute to improved scientific accuracy and precision in studies of vegetation change and its

An analysis fusing satellite data with a process-based model of plant growth attributes changes in vegetation activity across terrestrial ecosystems to climatic changes.

Urbanization has introduced substantial and rapid uncontrolled Land Use and Land Cover (LULC) changes often in the global south, considerably affecting Land Surface Temperature (LST) patterns Vegetation is an important component of terrestrial ecosystems, and it plays an important role in preventing desertification and conserving soil and water in arid and semi-arid regions. This study examined the spatial distribution and temporal variations of vegetation cover in the Loess Plateau over the last three decades using a time series of the normalized difference

High spatial resolution, such as that of WorldView satellites (less than 0.5 meters), allows for detailed mapping of individual trees or small patches of vegetation. Techniques for Detecting Vegetation Change A variety of remote sensing techniques can be employed to detect and map changes in vegetation. Since there is little discussion on the calculation of the characteristics of future spatial and temporal vegetation change trends in the region, this study superimposed the slope index and Hurst There are few studies on the large-scale and long-term dynamic analysis of vegetation coverage in the Yellow River Basin. Studies have shown that vegetation restoration projects effectively reduce soil erosion (Xu et al., 2018), so it is very necessary to carry out dynamic research on Fractional vegetation cover (FVC) in the Yellow

Analyzing vegetation cover provides a basis for detecting ecological and environmental health in urban areas. We analyzed the temporal and spatial changes in vegetation cover using NDVI data from the central Yunnan urban agglomeration (CYUA). The dimidiate pixel model (DPM) and intensity analysis were used to study changes at three levels: time intervals, Abstract and Figures The change detection (CD) methods explore the potential of remote sensing (RS) spatial datasets in various land use/land cover (LU/LC) applications. This article investigates the possible permanent vegetation cover (VC) change over an extended time for five municipal regions in South Africa by applying satellite-acquired remote sensed normalized difference vegetation index (NDVI) values within a geographic information system (GIS), spatial (West Coast District) and time (1981 to 2019 and 2000 to 2020) context. The

For better understanding the mechanism of LST change, reliable detection and accurate attribution of the changes in LST is a critical prerequisite for better understanding the LST change. Studies have analyzed the relationship between LST and vegetation change (He et al., 2018, Peng et al., 2014), topographic effect (Peng et al., 2020), and land use and land Trend and attribution analysis of vegetation greenness is crucial to explain and predict ecosystem responses to climate change. The common practice to detect and explain greenness pattern from remote sensing time series is mostly based on pixel-by-pixel analysis, which often fails to account for spatial autocorrelation and may lead to spurious patterns. Here

A vegetation cover increase has been identified at global scales using satellite images and vegetation indices. This fact is usually explained by global climatic change processes such as CO2 and temperature increases. Nevertheless, although these causes can be important, the role of socioeconomic transformations must be considered in some places, since in several The results showed that the combination of spatial and spectral information improved change detection by correctly classifying areas with seasonal changes in NDVI caused by vegetation phenology and areas with NDVI changes caused by human-induced disturbances. 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.

Article Open access Published: 04 November 2024 Detection of spatial and temporal variation characteristics of vegetation cover in the Lower Mekong region and the influencing factors Fan Gao, Jiya This study investigates the spatiotemporal dynamics of Fractional vegetation cover (FVC) influenced by environmental changes across diverse landscapes. High-resolution multispectral imagery from the Landsat series was used to analyze the interactions between FVC and climatic variables, supporting targeted conservation efforts. The study utilized precipitation

Vegetation cover exhibited a continuously increasing trend, with the proportion of high vegetation coverage consistently ranking first. Land