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The purpose of this study is rangeland biomass estimation and its spatial–temporal dynamics. Remote sensing has been a significant method for estimating biomass in recent years. The connection between vegetation index and field biomass will be used to assign probabilities, but in some cases, it does not provide acceptable results because of soil background and geographical and temporal variability.

In this study, the normalized difference red-edge (NDRE) index was used to calculate the rangeland biomass in comparison to five vegetation indices. Field measurements of biomass of natural rangeland in the West of Iran were taken in 2015, 2018 and 2021, and SENTINEL-2 data were used for analysis.

The results indicated that the overall advantage of NDRE stems from the fact that it adjusts for changes in leaf water content while overcoming the detrimental effects of soil substrate heterogeneity, both of these factors have a significant impact on pasture biomass. These results suggest that an NDRE-based biomass estimation model might be useful for estimating and monitoring biomass in large rangelands with significant geographical and temporal variability.

Identifying the best vegetation index to establish a vegetation-based biomass regression model for rangelands in large areas with different climatic conditions, plant compositions and soil types is the overall aim of this study.

Drought monitoring is an important process for national agricultural and environmental planning. Droughts are normal recurring climatic phenomena that affect people and landscapes. They occur at different scales (locally, regionally, and nationally), and for periods of time ranging from weeks to decades. In Zimbabwe drought is increasingly becoming an annual phenomenon, with varying parts of the country being affected. The purpose of this paper is to analyse the spatial variations in the seasonal occurrences of drought in Zimbabwe over a period of five years.

The Vegetation Condition Index (VCI), which shows how close the Normalized Difference Vegetation Index of the current time is to the minimum Normalized Difference Vegetation Index calculated from the long-term record for that given time, was used to monitor drought occurrence in Zimbabwe. A time series of dekadal Normalized Difference Vegetation Index, calculated from SPOT images, was used to compute seasonal VCI maps from 2005 to 2010. The VCI maps were then classified into three drought severity classes (severe, moderate, and mild) based on the relative changes in the vegetation condition from extremely bad to optimal.

The results showed that droughts occur annually in Zimbabwe though, on average, the droughts are mostly mild. The occurrence and the spatial distribution of drought in Zimbabwe was also found to be random affecting different places from season to season thus the authors conclude that most parts of the country are drought prone.

Remote sensing technologies utilising such indices as the VCI can be used for drought monitoring in Zimbabwe.

In the past three decades, remote sensing-based models for estimating crop yield have addressed critical problems of general food security, as the unavailability of grains such as rice creates serious worldwide food insecurity problems. The main purpose of this study was to compare the potential of time-series Landsat-8 and Sentinel-2 data to predict rice yield several weeks before harvest on a regional scale.

To this end, the sum of normalized difference vegetation index (NDVI)-based models created the best agreement with actual yield data at the golden time window of six weeks before harvest when rice grains were in milky and mature growth stages. The application of nine other vegetation indicators was also investigated in the golden time window in comparison to NDVI.

The findings of this study demonstrate the viability of identifying locations with poor and superior performance in terms of production management approaches through a rapid and economical solution for early rice grain yield assessment. Results indicated that while some of those, such as enhanced vegetation index (EVI) and optimized soil adjusted vegetation index, were able to estimate rice yield with high accuracy, NDVI is still the best indicator to predict rice yield before harvest. However, experiments can be conducted in different regions in future studies to evaluate the generalizability of the approach.

To achieve this objective, the authors considered the following purposes: using Sentinel-2 time-series data, determining the appropriate growth stage for estimating rice yield and evaluating different vegetation indices for estimating rice yield.

In this paper a desertification risk index (DRI) based on the integration of climatic data and the normalised difference vegetation index (NDVI), obtained from National Oceanic Atmospheric Administration advance very high‐resolution radiometer (NOAA‐AVHRR) images, is discussed at the light of the aridity index and some eco‐physiological parameters. The good correlation between DRI, the aridity index and the eco‐physiological parameters suggests that DRI could be useful to measure the desertification risk. One advantage of DRI is that, with the help of a geographic information system (GIS), DRI maps can be easily obtained in short time and at relatively low costs.

Satellite-based weather index insurance has recently been considered in order to reduce the high basis risk of station-based weather index insurance. However, the use of satellite data with a relatively low spatial resolution has not yet made it possible to determine the satellite indices free of disturbing landscape elements such as mountains, forests and lakes.

In this context, the Normalized Difference Vegetation Index (NDVI) was used based on both Moderate Resolution Imaging Spectroradiometer (MODIS) (250 × 250 m) and high-resolution Landsat 5/8 (30 × 30 m) images to investigate the effect of a higher spatial resolution of satellite-based weather index contracts for hedging winter wheat yields. For three farms in north-east Germany, insurance contracts both at field and farm level were designed.

The results indicate that with an increasing spatial resolution of satellite data, the basis risk of satellite-based weather index insurance contracts can be reduced. However, the results also show that the design of NDVI-based insurance contracts at farm level also reduces the basis risk compared to field level. The study shows that higher-resolution satellite data are advantageous, whereas satellite indices at field level do not reduce the basis risk.

To the best of the author’s knowledge, the effect of increasing spatial resolution of satellite images for satellite-based weather index insurance is investigated for the first time at the field level compared to the farm level.

To investigate whether the desertification risk index (DRI) which was originally developed for the coastal area of Turkey in a previous work, could be used as an effective desertification indicator in other Mediterranean areas such as the Lebanon.

The calculation of the DRI is based on the use of climatic factors and the normalized difference vegetation index (NDVI). As a result, climatic data were obtained and spatial interpolation techniques were applied to derive temperature and precipitation maps within a GIS environment while the NDVI was derived from satellite imagery. Spatial models were employed in order to produce the DRI map of Lebanon. Geographical analysis and standard statistical techniques were employed to investigate the relationships between: desertification risk and two topographic factors, namely, elevation and distance from the sea and desertification risk and the type of land cover. The accuracy of the index was assessed by comparison with recently published official maps and documents.

The paper demonstrates the efficiency of a desertification index to identify areas at risk. The DRI map proved to be accurate when compared to the map of desertification prone areas recently produced by the Lebanese Ministry of Agriculture. The areas with the highest degree of desertification risk are located in the North‐Eastern part of the country, in the area of the Bekaa Valley. This is in agreement with the reports of the United Nations Convention for combating desertification. A strong correlation was found between desertification risk and distance from the sea (the larger the distance the higher the risk) while shrubland appears to be the land cover type with the highest risk of desertification.

This research work demonstrates how satellite imagery and modern spatial analysis techniques could provide an essential alternative to traditional methods.

This paper aims to study the temporal and spatial variation of vegetation and the influence of climate change on vegetation coverage in the Yellow River basin, China. The current study aimed to evaluate the role of a series of government-led environmental control projects in restoring the ecological environment of the Yellow River basin.

This paper uses unary linear regression, Mann–Kendall and wavelet analyses to study the spatial–temporal variations of vegetation and the response to climate changes in the Yellow River, China.

The results showed that for the past 17 years, not only the mean annual increase rate of the Normalized Difference Vegetation Index (NDVI) was 0.0059/a, but the spatial heterogeneity also yields significant results. The vegetation growth in the southeastern region was significantly better than that in the northwestern region. The variation period of the NDVI in the study area significantly shortened, and the most obvious oscillation period was half a year, with two peaks in one year. In addition, there are positive and negative effects of human activities on the change of vegetation cover of the Loess Plateau. The project of transforming cultivated land to forest and grassland promotes the increase of vegetation cover of the Loess plateau. Unfortunately, the regional urbanization and industrialization proliferated, and the overloading of grazing, deforestation, over-reclamation, and the exploitation and development of the energy area in the grassland region led to the reduction of the NDVI. Fortunately, the positive effects outweigh the negative ones.

This paper provides a comprehensive insight to analysis of the vegetation change and the responses of vegetation to climate change, with special reference to make the planning policy of ecological restoration. This paper argues that ecological restoration should be strengthened in areas with annual precipitation less than 450 mm.

The purpose of this paper is to better understand landscape dynamics in arid and semi‐arid environments. Land degradation has recently become an important issue for land management in western China. The oasis ecosystem is especially sensitive to environmental disturbances, such as abnormal/extreme precipitation events, variations in the water supply from the upper watersheds, fluctuations in temperature, etc. Satellite remote sensing of terrestrial ecosystems can provide us with the temporal dynamics and spatial distributions of green cover over large areas of landscape. Seasonal green cover data are especially important in assessing landscape health (e.g. desertification, rate of urban sprawl, natural disturbances) in arid and semi‐arid regions. In this study, green cover data are derived from vegetation indices retrieved from moderate resolution imaging spectroradiometer (MODIS) sensors onboard the satellite Terra.

Satellite images recorded during the period from April 2000 to December 2005 are analyzed and the spatial distribution and temporal changes of the Ejin Oasis quantified.

This study shows that it is possible to derive important parameters linked to landscape sensitivity from MODIS and the derived imagery, such as normalized difference vegetation index (NDVI) time‐series data. Such a MODIS‐based time‐series monitoring system is particularly useful in arid and semi‐arid environments. The results of landscape sensitivity analysis prove the effectiveness of the method in assessing landscape sensitivity from the years 2001‐2005.

The novel strategy used in this investigation is based on the T‐S fuzzy model, which is in turn based on fuzzy theory and fuzzy operations.

Simulation results based on fuzzy models will help to improve the monitoring techniques used to evaluate land degradation and to estimate the newest tendency in landscape green cover dynamics in the Ejin Oasis.

The growing rate of desertification in Northwestern China and Mongolia that is occurring as a result of the conflict between economic development and natural conservation has been demonstrated in many studies. There have, for example, been some large studies using variations in bi‐weekly normalized difference vegetation index (NDVI) satellite images as a parameter for evaluating the vegetation dynamics in these areas. The purpose of this paper is to identify multi‐temporal variation in vegetated and non‐vegetated areas in remotely sensed satellite images to assess the status of desertification in East Asia.

Spatial data derived from these satellite images are applied to evaluate vegetation dynamics on a regional level, to identify the areas most vulnerable to desertification.

Analytical results indicate that the desert areas in East Asia are primarily distributed over Southern Mongolia, Central and Western Inner Mongolia, and Western China (the Taklimakan Desert). These desert areas expanded from 2000 to 2002, shrunk in 2003, then expanded again from 2003 to 2005. The areas most at risk for desertification are principally distributed in Southeastern Mongolia, and Eastern Inner Mongolia.

Simulation results based on data for deserts distributed throughout Northwestern China and Mongolia indicate that the proposed fuzzy model‐based method would be helpful for assessing and monitoring desertification. These analytical results will help administrators refine planning processes, define the boundaries of protected areas, and facilitate decisions for prioritizing areas for desertification protection.

Year‐to‐year fluctuations of rainfall in the northern Negev desert provide an opportunity to characterize and assess the temporal dynamics of desertification, phenology, and drought processes. Such information was retrieved and analyzed by combined use of satellite imageries in the reflectivity and thermal spectral bands. Data covering four years of coarse spatial resolution and images from a high revisit time satellite, namely the NOAA‐14, were used. The images were processed to produce the normalized difference vegetation index (NDVI) and the land surface temperature (LST). These measures were applied to the sand field in the northwestern Negev (Israel), which is almost totally covered by biological soil crusts, and to an adjacent region in Sinai (Egypt), consisting mainly of bare dune sands. Various manipulations of the data were applied. Time series presentation of the NDVI and LST reveals that the NDVI values correspond to the reaction of the vegetation to rainfall and that LST values represent seasonal climatic fluctuation. Scatterplot analysis of LST vs NDVI demonstrates the following: the two different biomes (Sinai and the Negev) exhibit different yearly variation of the phenological patterns (two seasons in Sinai moving along the LST axis, and three seasons in the Negev, where the NDVI axis represents the growing season); the Sinai has an ecosystem similar to that found in the Sahara, while the Negev, only a few kilometers away, has an ecosystem similar to the one found in the Sahel; and drought indicators were derived by using several geometrical expressions based on the two extreme points of the LST‐NDVI scatterplot. The later analysis led to a discrimination function that aims to distinguish between the drought years and the wet years in both biomes. Results from the current study show that a great deal of information on dryland ecosystems can be derived from four, out of five, NOAA/AVHRR spectral bands. The NDVI is derived from the red and the near‐infrared bands and the LST from the two thermal bands. Combined use of these two products provides more information than any product alone.