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Surface coal mining causes vegetation disturbance while providing an energy source. Thus, much attention is given to monitoring the vegetation of surface coal mining regions. Multitemporal satellite imagery, such as Landsat time-series imagery, is an operational environment monitoring service widely used to access vegetation traits and ensure vegetation surveillance across large areas. However, most of the previous studies have been conducted with change detection models or threshold-based methods that require multiple parameter settings or sample training. In this paper, we tried to analyze the change traits of vegetation in surface coal mining regions using shape-based clustering based on Normalized Difference Vegetation Index (NDVI) time series without multiple parameter settings and sample training. The shape-based clustering used in this paper applied shape-based distance (SBD) to obtain the distance between time series and used Dynamic Time Warping Barycenter Averaging (DBA) to generate cluster centroids. We applied the method to a stack of 19 NDVI images from 2000 to 2018 for a surface coal mining region located in North China. The results showed that the shape-based clustering used in this paper was appropriate for monitoring vegetation change in the region and achieved 79.0% overall accuracy in detecting disturbance-recovery trajectory types. more...

Understanding the direct and indirect effects of niche and neutral processes in structuring species diversity is particularly challenging because environmental factors are often geographically structured. Here, we used Structural Equation Modeling to quantify direct and indirect effects of geographic distance, the Amazon River's opposite margins, and environmental differences in temperature, precipitation, and vegetation density (Normalized Difference Vegetation Index-NDVI) on ant beta diversity (Jaccard's dissimilarity) across Amazon basin. We used a comprehensive survey of ground-dwelling ant species from 126 plots distributed across eight sampling sites along a broad environmental gradient. We found that geographic distance and NDVI differences were the major direct predictors of ant composition dissimilarity. The major indirect effect was that of temperature through NDVI, whereas precipitation neither had direct or indirect detectable effects on beta diversity. Thus, ant compositional dissimilarity seems to be mainly driven by a combination of isolation by distance (through dispersal limitation) and selection imposed by vegetation density, and indirectly, by temperature. Our results suggest that neutral and niche processes have been similarly crucial in driving the current beta diversity patterns of Amazonian ground-dwelling ants. more...

Quantifying forest systems is of importance for ecological services and economic benefits in ecosystem models. This study aims to map the percent tree cover (PTC) of various forest stands in the Buyuk Menderes Basin, located in the western part of Turkey with different characteristics in the Mediterranean and Terrestrial transition regions Sentinel-2 data with 10-m spatial resolution. In recent years, some researches have been carried out in different fields to show the capabilities and potential of Sentinel-2 satellite sensors. However, the limited number of PTC researches conducted with Sentinel-2 images reveals the importance of this study. This study aimed to demonstrate reliable PTC data in landscape planning or ecosystem modeling by introducing an advanced approach with high spatial, spectral, and temporal resolution and more cost-effective. In this study, a regression tree algorithm, one of the popular machine learning techniques for ecological modeling, was used to estimate the tree cover's dependent variable based on high-resolution monthly metrics' spectral signatures. Six frames of TripleSat images were used as training data in the regression tree. Monthly Sentinel-2 bands and produced metrics including NDVI, LAI, fCOVER, MSAVI2, and MCARI were almost the first time used as predictor variables. Stepwise linear regression (SLR) was applied to select these predictor bands in the regression tree and a correlation coefficient of 0.83 was obtained. Result PTC maps were produced and the results were evaluated based on coniferous and broadleaf. The results were tested using high spatial resolution TripleSat images and higher model accuracy was determined in both forest types. The high correlation is due to the Sentinel 2 satellite's band characteristics and the metrics are directly related to the tree cover. As a result, the high-accuracy availability of the Sentinel2 satellite is seen to map the PTC on a regional scale, including complex forest types between the Mediterranean and terrestrial transition climates. more...

In recent decades, most of the natural and semi-natural ecosystems around the world have experienced excessive environmental loads due to human unsustainable activities that threaten their health and sustainability. Accordingly, the idea of ecosystem health has been proposed to monitor simultaneously the structure, function as well as the services that an ecosystem provides. In this research, moderate resolution imaging spectroradiometer 8-day land surface temperature product was used to measure temperature and land cover changes of Lake Urmia Basin for three months of June, July and August in a time period of 2000–2018. Time series data analysis was carried out using linear regression, Fourier series and Mann–Kendall test. Moreover, Moran index was used to analyze changes of the spatial pattern of indicators. To show the influence of Lake Urmia ecosystem services on its overall health, Total Ecosystem Health model was established by introducing normalized difference vegetation index, lake temperature, Moran index and temperature of the basin as indicators of Vigor, Structure, Resilience and Ecosystem Services, respectively. Based on output of the model, 54, 41 and 4% of the regions have obtained weak, moderate and strong ecosystem health, respectively. The results showed that despite the moderating role of Lake Urmia as a temperature regulator for its surrounding areas especially within 10 km, the decline of ecosystem health status in 95% of regions has occurred during the years 2000–2018. Therefore, to benefit from the ecosystem regulating services, the sustainability of other components involved in the total ecosystem health should be also ensured. more...

The risk of forest fires is one of the main environmental, social, and economic problems facing the countries of the Mediterranean basin, in particular Algeria, Morocco, Portugal, Spain, and France. For several decades, thousands of hectares in northern Algeria have been highly vulnerable and vulnerable to this scourge. In northwestern Algeria, the decline in rainfall is particularly pronounced in the mid-1970s. This rainfall decreases by around 50%. This long and regular dryness has destroyed more than 37,000 ha/year of the vulnerable forest. The aim of this study is to assess the fire risk of forest by a combination of a classical climatic analysis approach of drought using SPI (Standardized Precipitation Index) and remote sensing data (NDVI: normalized difference vegetation index). The NDVI is evaluated through Google Earth Engine Web GIS based software that mainly uses Modis images that cover the time span from 2006 to 2019. Both of these data confirm, the spatial variation of drought along the period of study, and a substantial drought effect in the region; this coincides with the outbreak of forest fires. more...

As an important parameter of the Earth’s energy budget, the planetary albedo of Earth varies with the dynamics of atmospheric and surface variables. In this study, we investigated the spatiotemporal characteristics and driving factors of the global planetary albedo using the Clouds and the Earth’s Radiant Energy System (CERES) Energy Balanced and Filled (EBAF) dataset and the Geodetector method. The results revealed that the planetary albedo can be decomposed into atmospheric and surface contributions, and the planetary albedo in the middle and low latitudes was predominantly affected by the atmospheric contribution. The global planetary albedo and the atmospheric and surface contributions exhibited decreasing trends of − 0.0020, − 0.0015, and − 0.0004/decade from 2001 to 2018, respectively, which were closely related to the variations of atmospheric and surface variables. The cloud fraction was the driving factor of the atmospheric contribution in the middle and low latitudes, and its influence was further enhanced by the aerosol optical thickness (AOT), ice water path (IWP), and liquid water path (LWP). The snow/ice coverage and normalized difference vegetation index (NDVI) were the driving factors of the surface contribution in the snow/ice-covered and vegetated areas, respectively. The interaction relationships between the surface variables were mainly bi-enhanced and nonlinearly enhanced. These results provide useful information about the driving factors of the planetary albedo and are benefit for improving the parametrization of the planetary albedo. more...

Sleep quality leads to many healthy problems, which has caused global concern. Most studies have focused on the sleep quality in some large cities, ignoring the elderly in rural areas who may have more serious sleep problems. Therefore, this study aimed to understand the sleep status of the elderly in rural areas of Anhui Province and the influence of environmental factors on it. The data of carbon dioxide (CO2) concentrations for this study were obtained from the Dalhousie University atmospheric composition analysis group. The data of normalized differential vegetation index (NDVI) in 2014 was produced and processed by remote sensing inversion on the basis of medium resolution satellite images. The 2686 individuals were selected from rural areas of Anhui Province by multi-stage stratified cluster sampling. The Pittsburgh Sleep Quality Index (PSQI) was used to assess sleep quality. The independent, interactive, and mediating effects of CO2 and green space on sleep quality were evaluated by generalized linear model, interaction model, and mediating effect model, respectively. In this study, we found that the prevalence of sleep disturbance in the elderly was 58.40%. In the univariate model, we found that the risk of sleep disturbance increased by 1.6% for each 1 μg/m3 increase in concentrations of CO2, while decreased by 1.5% for every 0.1 increase in NDVI. In the interaction model, the negative correlation between green space and sleep quality decreased with concentrations of CO2 increased. In addition, green space was a mediating factor between CO2 and sleep quality in the mediating effect model. more...

Shoreline identification is a complicated and time-consuming process for larger and inaccessible areas such as the coastal region of West Bengal (India) using traditional and repetitive ground surveying techniques. Though the recent advancement of geospatial technologies has overcome many of these issues, low spatial resolution, mixed pixels, on-screen shoreline delineation accuracy are not entirely reliable. Moreover, coastal land changes along the shoreline, especially islands, are the major concern mainly related to mangroves destruction. normalised difference vegetation index (NDVI)-Tasseled-Cap transformation technique has been used to automatically generate shoreline using seven Landsat image pairs (1989–2018). The shorelines have been quantified and analysed by end point rate (EPR) and linear regression rate (LRR) algorithms in the digital shoreline analysis system (DSAS) add-in tool within the ArcGIS platform. The rate of shoreline change has been analysed for delineating zones of accretion and erosion. The result reveals that the coastal region of West Bengal has undergoing erosion at the rate of − 3.36 m/year and − 4.70 m/year as per EPR and LRR methods, respectively. Moreover, the study also shows the zone along the islands is more susceptible to erosion than the other zones. The island-wise shoreline study reveals except Lothian Island, the rest of the selected islands viz. Bulchery, Dalhousie, Bhangaduni, Sagar and Baghmara Reserve Forest are erosion-prone. The results would be valuable for coastal and forest conservation planners to identify the most vulnerable zones and propose appropriate policy measures. more...

Susceptibility assessment of landslides over a large area depends on the basic spatial unit of mapping, usually by using grid cell or slope unit. Both units are used in this study for the assessment of small shallow and clustered landslides in vegetated slopes in Malipo, southwest China. Information value (IV) model was used to generate landslide susceptibility assessment map, while improved information value (IIV) model was used to determine whether the mapping unit is at risk of landslide. Seven factors, including slope angle, slope aspect, elevation, normalized difference vegetation Index (NDVI), Soil Moisture Content (SMC), distance to river and road were used as landslide influence factors. The Area under curve (AUC) values of the slope unit IIV, IV and grid cell were 0.814, 0.802 and 0.702 respectively for success rate. For prediction rate, the AUC values of the slope unit and grid cell were 0.803(IIV), 0.790(IV) and 0.699 respectively. Our results showed slope unit is more suitable than grid cell for assessing susceptibility of Small, Shallow and Cluster Landslide. Improved information value model increases the accuracy of susceptibility assessment model for this characteristic landslide. more...

Silicon (Si) deficiency is one of the most important issues, especially in rice crop. Foliar Si application in rice production is an alternative way to quickly improve not only grain yield but also aroma quality (2-acetyl-1-pyrroline; 2-AP), especially in Thai jasmine rice. The objective of this study was to assess the potential of exogenous application Si as foliar spray in regulating 2-acetyl-1-pyrroline (2-AP) content in KDML105 and RD6 rice grain and in improving yield traits. Overall, growth characters were retained in KDML105, whereas shoot height (14.9 % over the control) and leaf length (23.4 % over the control) of RD6 treated with 2 % Si were significantly reduced. Leaf greenness (SPAD), normalized difference vegetation index (NDVI) and net photosynthetic rate (Pn) of KDML105 were greater than those in RD6, in relation to EVI (enhanced vegetation index), GNDVI (green normalized difference vegetation index), NDVI and OSAVI (optimized soil adjusted vegetation index) detection by UAV sensors. Vegetation indices, GNDVI, OSAVI and EVI exhibited close relation with SPAD (R2 = 0.896), shoot height (R2 = 0.4406) and aboveground biomass (R2 = 0.7791), respectively. Si concentration in flag leaf tissues of KDML105 peaked (1.14 mg g−1 DW) when treated with 2 % Si, whereas it exhibited 1.6 folds increase in rice husk (0.75 mg g−1 DW; R2 = 0.5266) over the control. The content of 2-AP in KDML105 was higher compared with RD6 and was enhanced by 2 % Si treatment (1.39 folds over the control). Yield traits in KDML105 including panicle weight, grain fertility percentage, number of panicles per m2, total grain weight and harvest index (HI) were significantly increased by 2 % Si foliar application over the control. Based on the results, Si foliar application is effective in improving grain yield and in regulating 2-AP aromatic content, which further needs to be tested for temporal variations across locations. more...

An effective crop yield prediction is critical for making precise nitrogen (N) management decisions. A multi-site-year study was conducted across eastern Canada with the objectives to (1) construct machine-learning random forest regression (RFR) models for canola yield prediction; and (2) develop site-specific N recommendations using the RFR modelling approach. Soil characteristics, weather, plant growth and spectral index data collected from 2013 to 2015 were used to train the RFR models and the 2016 data for model validation in terms of making N decisions. Results showed that canola yields responded positively to N application rates in 16 out of the 18 site-year environments, with sometimes higher yields and an average of 8% higher N use efficiency for the split-N strategy than for the same amount of N applied only at preplant. Heat stress and precipitation distribution were identified as of critical importance in total yield variation. The RFR model by combining weather, soil and plant growth features with the spectral indices displayed highly improved prediction reliability, up to 85%, with 53–57% lower root mean square errors, compared with the model based only on leaf chlorophyll or normalized difference vegetation index. Based on the RFR algorithm, an average economic optimum N rate of 150 kg N ha−1 was recommended for most canola production scenarios in the test year. This study demonstrated that the machine learning-based RFR modelling approach can be used to implement optimal nutrient management strategies for sustainable crop production, which is sensitive and better adapted to environment-induced abiotic stresses. more...

The variations in total water storage anomaly and groundwater balance were examined during 2002–2016. Based on the empirical orthogonal function, it was found that the water resource underwent high variability. The linear trend of hydro-climatic variables, total water storage anomaly, water budget, and its components have been investigated. For the area average, all the variables increased except for the potential evapotranspiration, which had decreased during the period of study. For the spatial distribution of trends in total water storage (TWS) in the northern Sub-Saharan Africa (NSSA), 44.76% underwent significant negative changes, whereas the proportion of areas that underwent significant negative changes was 24.84%. The results indicated that the precipitation and soil moisture were determinant factors for positive changes in TWS, whereas the potential evapotranspiration and temperature contributed to the reduction in TWS, respectively, implying a harmful effect of rising in temperature on water resources over the region. Moreover, a non-significant positive interdependence between the area-averaged TWS and the water budget (WB) was acquired. Positive correlations between normalized difference vegetation index (NDVI) and TWS (WB) occupied 48.93% (37.86%), but negative correlations occupied 34.59% (28.49%) of the total area. Results of the relationships between TWS and NDVI were higher than that of NDVI and water budget over the period 2002–2015. The TWS and WB (water resources) played essential roles in the positive changes in the ecosystem. These findings are valuable for the management of agriculture, water resources, environment, and ecosystem, profiting from the implementation of policies at regional and local scales. more...

Management zones (MZs) are efficient for applying site-specific management in agricultural fields. This study proposes an approach for generating MZs using time-series clustering (TSC) to also enable time-specific management. TSC was applied to daily remote sensing retrievals in a California vineyard during four growing seasons (2015–2018) using three datasets: evapotranspiration (ET), leaf area index (LAI), and normalized difference vegetation index (NDVI). Distinct MZs were delineated based on similarities in pixel-level temporal dynamics for each dataset, using dissimilarity index to determine the optimal number of clusters and compare TSC results. The differences between the cluster centers were calculated, along with the ratio between the centers’ differences and the range of each dataset, denoting the degree of difference between MZs centers. Similarity between MZs from each factor was quantified using Cramer’s V and Frechet distances. Finally, an aggregated (multi-factor) MZ map was generated using multivariate clustering. The resulting MZs were compared to a 2016 yield map to determine the significance of differences between means and distribution among MZs. The findings show that LAI TSC achieved the best cluster separation. The NDVI and LAI MZs maps were nearly identical (Cramer’s V of 0.97), while ET showed weaker similarities to NDVI and LAI (0.61 and 0.62, respectively). Similar findings were observed for the Frechet distances. The yield values were found to be significantly different among MZs for all TSC maps. TSC may be further utilized for defining within-field spatial variability and temporal dynamics for precision irrigation practices that account for spatial and temporal variability. more...

The present study was conducted to analyze cropping intensity of four blocks (Mogra-Chinsurah, Polba-Dadpur, Singur and Haripal) of the Gangetic alluvial zone of India using multi-dated Sentinel-2 data in 2018–19 cropping year. It was observed that during peak growing stage all crops ascribed higher Normalized Difference Vegetation Index NDVI values (0.4 to 0.73) and NDVI became as low as 0.06 when the fields were vacant. Sentinel-2 data acquired in the peak crop growing period during each cropping season were carefully selected, and NDVI was computed over the whole study area. Rule-based classification was applied for cropping sequence and cropping intensity classification based on the occurrence and non-occurrence of crops using NDVI threshold (0.4). Sentinel-2 images acquired on 22/10/2018, 6/12/2018, 30/1/2019 and 30/4/2019 were used for masking of trees and non-agricultural area. October 22, January 30 and April 30 imageries demonstrated peak crop growing period during kharif, rabi and pre-kharif seasons whereas December 6 image represented occurrence of no or little crop in the study area. Crop acreage was the highest in Polba-Dadpur block during all the three seasons. The crop–fallow—crop sequence occupied the highest areas (43%) followed by crop–crop–crop sequence (39%). 50% and 39% of the total cultivated land was under 200% and 300% cropping intensities. Overall, accuracies of cropping system and cropping intensity classification were 88.54% and 87.85%, respectively. Sentinel-2 data can be successfully used for cropping system analysis which helps in crop planning and management. more...

Poor irrigation management is a common issue of irrigated agriculture. Assessment of irrigation system performance is essential to improve the irrigation system, which requires real-time data. Satellite data has been found the best alternate. This manuscript presents a methodology to assess irrigation system performance using combination of satellite and ground data. The developed methodology was applied on a canal system of Punjab Pakistan. High resolution satellite images were processed to images of normalized difference vegetation index (NDVI), evaporative fraction (EF) and evapotranspiration (ET). These images in combination with ground data provided information on pixel to pixel vegetated area, water availability/ requirement consequently the irrigation system performance indicators. The results revealed that potential water requirement of the area was 401.66 million cubic meters (MCM) and available canal water supply was 247.14 MCM, thus indicating shortage of 38%. Adequacy of the system was found to be 74% while its reliability varied from 35–73%. Average coefficient of variance (Cv) was found 0.43 showing poor reliability of the system. Performance in equity terms revealed that head areas of irrigation channels were receiving more water supplies than the tail areas. Strong correlation was found between crop yields and water supplies i.e., crop yield was strongly dependent on water supplied (R2 = 0.80). more...

The typical steppe in Inner Mongolia is an important component of the Eurasian steppes. It plays a dominant role in preventing desertification and against sandstorms, but highly sensitive and vulnerable to climate change. Based on long-term observed precipitation data and remotely sensed Normalized Difference Vegetation Index (NDVI) images, the non-stationary behavior of summer precipitation and its linkage with vegetation change were investigated, by means of incorporating time-varying and physical based explanatory covariates in non-stationary modeling. Results indicated that time-dependent models exhibited good performance to reproduce the temporal variations of eco-hydrological variables. The non-stationarity of summer precipitation was prominently visible for the majority of sites during the period from 1957 to 2017, with the mean behavior described as a linear or nonlinear time-varying pattern. In general, the steppe has experienced a decreasing trend in summer precipitation, but whether the decline tends to maintain or weaken or strengthen depends on the spatial location of the site studied. Differences appeared in the changes of vegetation in summer from 1998 to 2017 in different sub-regions. Evidences for the presence of stationary evolution was found in most sub-regions in the middle part, together with a linear increase in the westernmost sub-regions while a non-linear decrease in the easternmost sub-regions. Covariate analyses further highlighted the role of precipitation variabilities in the modeling of the NDVI-related vegetation dynamics over the steppe. The potential relations of summer precipitation to vegetation growth were characterized as both linear and non-linear positive forms. In particular, precipitation extremes could be responsible for the occurrences of exceptional cases in vegetation condition. The fluctuations in summer precipitation have crucial significance for future predictions of vegetation succession. Findings from this study would lead to additional insights to understanding the effect of climate change on grassland ecosystem processes. more...

Surface temperature and precipitation are factors effecting vegetation growth. Vegetation coverage change is one of the important factors influencing global and regional climate change. Dynamic monitoring of vegetation change can reflect the trend of climate change to a certain extent. Three-River Headwaters are located in the hinterland of the Qinghai-Tibet Plateau. It has the characteristics of “high, cold, and dry” (higher altitude, cold and dry weather) and its ecosystem is fragile. In recent years, with the global climate change, a series of eco-environmental problems such as river flow cutoff, permafrost degradation, and vegetation destruction has occurred in the headwaters area, which are closely related to climate and vegetation changes. At the same time, in order to solve the problem of ecological environment degradation in the region, various ecological restoration policies have implemented. Several uncertainties in the relationship between vegetation and climate change in the Three-River Headwaters region. This study aims to find out the uncertainties. In this study, the spatial distribution of vegetation coverage was calculated by using NDVI (normalized difference vegetation index) from the first-level product of MODIS (moderate resolution imaging spectroradiometer) remote sensing data. Combining policy factors, the relationship between rainfall, surface temperature, and vegetation growth status were analyzed. The results show that during the study period (1948–2019), the temperature rose significantly and the rainfall increased especially after the implementation of ecological restoration policy (after 2000). Vegetation coverage increased year-by-year (2000–2015). The rainfall effect on surface temperature and vegetation growth, when the summer rainfall increased, the temperature decreased, leads to vegetation coverage decreased (for example, 2001, 2003, 2008 and 2011); the dependence of vegetation on rainfall has obvious lag in Three-River Headwaters in summer. In the years with suitable rainfall and higher temperature in summer, the vegetation grows better and the vegetation coverage increases. This is mainly because the Three-River Headwaters is located in the alpine zone, and vegetation growth is more dependent on temperature. The implementation of ecological restoration policy promotes vegetation coverage. Studying the impact of climate and policy factors on vegetation cover is of great scientific significance and practical value for understanding the ecological restoration mechanism in high cold and arid regions. more...

The identification of archaeological features via geophysical techniques is widely used for the prospection of new archaeological proxies. However, there are very few integration studies with remote sensing techniques have been reported, especially in the Malaysian context. This study aims to integrate remote sensing analysis throughout surface features through the expediency of Vegetation Indices (VIs) with the ground investigation technique of Electrical Resistivity Tomography (ERT) data to identify archaeological traces in Sungai Batu, Bujang Valley, Kedah. Normalized Difference Vegetation Index (NDVI) and Digital Elevation Model (DEM) were generated to provide visual interpretation and elevation profile to extract the archaeological features. Farther, statistical analyses were conducted to show a strong correlation between two variables data: eight VIs with Resistivity of Buried Properties (RBP) and Resistivity of Ancient River (RAR). The results obtained show that almost all indices attained a high correlation, especially DVI and RDVI which RAR (r = 0.94) and RBP (r = 0.95). In contrast, MSAVI shows a high negative correlation with RAR (r = −0.93) and RBP (r = −0.95). However, NDVI indicates the low correlation in RAR (r = 0.66) compared to RBP (r = 0.95) because we assume this algorithm is less appropriate in detecting underground water rather than extracting the vegetation spectral characteristics. It is therefore recommended that the application of the appropriate algorithm is a Normalized Difference Water Index (NDWI) compared to NDVI. Finally, it is anticipated that the outcome of this study could be used to map the potential area of archaeological interest and in the planning for further fieldwork investigation. more...

Increasing human desire for urbanization and, as a result, urban growth and development has caused the replacement of natural objects such as vegetation and mountains with building structures and roads. This exacerbates the phenomenon of urban heat islands (UHI), one of the main threats to the quality of human life. In this research, a post-classification change detection algorithm is proposed based on UHI classification maps are generated using multi-temporal Landsat images from a rapidly developing urban area, in Iran in 17-years’ time interval. The main research objective is to investigate the effects of constructing new built-up areas on urban heat islands considering the land surface temperature, vegetation coverage, and surface object types. The production of UHI classification maps is based on the probabilistic reasoning rules for integrating the LST, Albedo, and NDVI as lines of evidence considering land use/land cover (LU/LC) object types. The analysis of the UHI change detection results in the rapidly developing urban area in this research indicates that the area percentage of built-up object class has increased by 12.1% in 17 years. This amount of construction led to a 23.7% increase in the high UHI class, which is equivalent to 2931 hectares. Moreover, the regions with medium and low UHI have decreased by − 16.09 and − 9.12%, respectively, which indicates an increase in the intensities of urban heat islands during this period. more...

Green and blue spaces are important landscape elements in a city and there is strong literary evidence available regarding the ecological, social, cultural and recreational benefits of these spaces to people and urban sustainability. Comprehensive quantitative assessment of these spaces is gaining scientific recognition for their utility in sustainable urban planning and drafting greening policies. This study analyzed the spatiotemporal change in urban green and blue spaces in Chandannagar city, India through quantitative assessment for the years 1991, 2001, 2011, and 2020 using Landsat data of 30 m spatial resolution. Normalized Difference Vegetation Index and Normalised Difference Water Index was applied to extract information on urban green and blue spaces. The classified maps were validated through field observations and Google earth images. Spatio-temporal analysis was carried out at ward level to analyze the distribution of urban green spaces (UGS) at the micro level. The result indicated that there was a reduction in green and blue spaces in the central and eastern parts of the city while there was a good amount of UGS in the western part of the city. The findings of this study shed light on important policy implications for the UGS planning in the city. more...

Annual crop yield fluctuation due to natural and anthropogenic factors is a major concern of the Ethiopian Government. For an immediate response to drastically changing crop yields and resulting harvest failures and to enhance the country’s food security in general, extensive area crop growth monitoring and early prediction of production are needed. In this study, we developed an early maize (Zea mays) yield forecasting model using Sentinel-2 MSI (Multispectral Instrument); the study was carried out in the Abaya district of the Oromia Regional State in Ethiopia. The model consists of the following components: (1) Sentinel-2 image-based cropland identification for different crop development stages, (2) extraction of time series Sentinel-2 vegetation indices for crop growth monitoring, and (3) a simple linear stepwise forward regression approach for yield prediction. We tested different spectral indices regarding their performance in describing the crop development and eventually predicting the expected yield. The result showed that (1) the linear Red-Edge Enhanced Vegetation Index (Red-Edge EVI), (2) the combination of the Enhanced Vegetation Index (EVI) and the Green Vegetation Index (GVI), (3) the combination of the Red-Edge EVI and Soil Adjusted Vegetation Index (SAVI), and (4) the combination of the Normalized Difference Vegetation Index (NDVI), Red-Edge EVI and SAVI offer the best predictive model about 2 months before harvesting with the highest coefficients of determination (R2) of 0.73, 0.80, 0.84, and 0.88, respectively. The correlation for the GVI was generally lowest compared to established models, and no evidence of a peak correlation for NDVI was observed. Our approach showed a high accuracy of detecting maize fields, detecting crop phenology, and early predicting of grain yield for the study year 2018. Our simple model may generate early warning information, which may support in-time decision-making regarding food supply when critical yield fluctuations are to be expected. more...

Urban nature—such as greenness and parks—can alleviate distress and provide space for safe recreation during the COVID-19 pandemic. However, nature is often less available in low-income populations and communities of colour—the same communities hardest hit by COVID-19. In analyses of two datasets, we quantified inequity in greenness and park proximity across all urbanized areas in the United States and linked greenness and park access to COVID-19 case rates for ZIP codes in 17 states. Areas with majority persons of colour had both higher case rates and less greenness. Furthermore, when controlling for sociodemographic variables, an increase of 0.1 in the Normalized Difference Vegetation Index was associated with a 4.1% decrease in COVID-19 incidence rates (95% confidence interval: 0.9–6.8%). Across the United States, block groups with lower income and majority persons of colour are less green and have fewer parks. Our results demonstrate that the communities most impacted by COVID-19 also have the least nature nearby. Given that urban nature is associated with both human health and biodiversity, these results have far-reaching implications both during and beyond the pandemic. Access to green space has been a critical, and contentious, issue for neighbourhood inequality and health outcomes. This Analysis looks at how the COVID-19 pandemic interacts with availability of nature for urban residents. more...

Floods are the most frequent and most distractive natural disaster around the globe. Pakistan is facing frequent flooding since 1929 and floods in the Indus river basin cost more than 7000 lives and caused mighty changes in land use and land covers (LULC) since 1947. District Layyah hit by flood on August 1, 2010. Landsat ETM+ with 30 m spatial resolution was utilized to investigate the LULC changes in district Layyah for the 2010 flood. It was revealed water area increased 8.05% from July 3 (379.13 km2) to August 20 (656.02 km2) in district Layyah. Vegetation cover increased from 1149.62 km2 on July 3 to 1842.23 km2 on August 20 in district Layyah and showed a 20.13% increment. Barren/built-up area showed a decrement of 28.18% from 1911.72 km2 in pre-flood analysis to 941.90 km2 in the post-flood analysis. Total 15 union councils (UC) of district Layyah were affected by flood from which 10 lies in tehsil Layyah and 5 belongs to tehsil Karor Lal Esan. Flood affects 177 settlements in district Layyah from which 156 belong to tehsil Layyah and 21 were from tehsil Karor Lal Esan. These results suggest that the impacts of the flood on LULC need more attention to cope with the challenge of frequent flooding and impacts in Pakistan. more...

The permafrost of Mohe County and its suburbs in the Daxing’an Mountains has been influenced by the urbanization. Remote sensing, GIS technology and numerical simulation was used to study the temperature variations of permafrost with the changes in surface vegetation that cover Mohe County and suburban areas, and the law of permafrost degradation on the study area was analyzed. The research results show that the urban area of the study area increased 114.42% from 2000 to 2016, and the urbanization process is continuing to accelerate. The Normalized Difference Vegetation Index map of 2017 in Mohe County and its suburbs was studied and the maximum proportion of vegetation coverage was different in the four seasons. The numerical calculation model results show that the permafrost temperature change in the study area cyclically fluctuates in a cosine form. The annual variation curve of permafrost temperature gradually decreased and its accompanying phase lag increased with depth. The annual temperature change value with the different depths of the town was greater than the natural ground. The maximum permafrost thawing depths of the town and natural ground were 4.2 m and 2.82 m in 50 a, and the degradation rates of the two permafrost are, respectively, 0.88 cm/a and 0.46 cm/a. These results show that urbanization has accelerated the degradation of permafrost. more...

The present study was conducted to establish prediction models for grain yield and nitrogen (N) uptake using normalized difference vegetation index (NDVI) measurements with the GreenSeeker optical sensor for different cultivar groups of basmati rice (Oryza sativa L.) and to define the optimum sensing timing. Sensor readings were collected at 21, 28, 35, 42, and 49 days after transplanting (DAT) from multi-cultivar and multi-rate N fertilization experiments conducted in 2016 and 2017. Prediction model established by regressing NDVI day−1 as the determinant of plant biomass with grain yield and N uptake at maturity following exponential functions revealed that sensing the crop before or after 35 DAT (panicle initiation stage) was not accurate and did not predict satisfactorily the yield or N uptake potential. Regression analysis generated two potential and viable yield or N uptake prediction models: one for the basmati rice cultivar CSR30 (tall cultivar), and the other for a PB-PUSA (group of semi-dwarf cultivars). Validation of the prediction models using an independent experiment conducted in 2018 revealed that sensing the crop at the panicle initiation stage provide grain yield and N uptake predictions close to the observed grain yield (R2 = 0.86, RMSE = 6.1%) and N uptake (R2 = 0.75, RMSE = 8.5%). This study showed that yield and N uptake potential in basmati rice can be predicted using in-season NDVI data measured with the GreenSeeker optical sensor. more...

Urban greenness is an element of vital importance for the population quality of life, and forest inventory is considered the most appropriate method for its assessment. Remote sensing has become an attractive alternative for the accomplishment of forest inventory, facilitating urban flora mapping. The present study aimed to identify the main species of trees in Teresina, Piaui, and evaluate the botanical identification accuracy by using high-resolution satellite images (Worldview-2) as compared to on-site inventory. We used the e-Cognition 8.7 software for the mapping, segmentation, and classification of the vegetal species and ERDAS Imagine 9.2 for accuracy verification. The NDVI (Normalized Difference Vegetation Index) was used to analyze the natural vegetation condition. The outskirts of the city presented higher values of NDVI. An amount of 1,392 individuals from 53 species and 28 families, were identified. Among these, the families Anacardiaceae (20.7%), Fabaceae (19.8%), Meliaceae (9.4%), Myrtaceae (6.9%), Arecaceae (6.1%), and Combretaceae (5.5%) were the most prevalent. Amongst the 53 species identified, the 16 most abundant were chosen for the analysis. The classification had a satisfactory result for the 16 vegetal species with a general classification accuracy of 69.43% and a kappa agreement index of 0,68. The species that obtained the highest accuracy were Ficus benjamin (87,5%), Terminalia cattapa (83,3%), Syzygium malaccense (82,4%), Mangifera indica (76,8%), Caesalpinia ferrea (75,9%), Pachira aquatica (73,9%), and Tabebuia sp (75,9%). The results showed that it is feasible, although challenging, to classify biodiverse vegetation in an urban environment using high-resolution satellite images. Our findings support the use of geotechnologies for inventorying urban forest in tropical cities. more...

The study of land suitability (LS) for agriculture is one of the most competent mechanisms that show concern for cultivable land and forecasts the supply of sustainable production in semi-arid areas. In view of this, the purpose of the present study is to suggest a conceptual framework for the analysis of LS that would greatly enhance green cover to tackle environmental challenges caused by groundwater recharge (water resources). In semi-arid areas, physiographic components play a fundamental role in agriculture. LULC, NDVI, TGSI, soil, geology, geomorphology, slope, DD, LD, and availability of nutrients affect agricultural production. Analysis of LS may lead to formulating strategies for improving agricultural productivity. A multi-criterion decision-making methodology focused on GIS using the Landsat 8 (OLI) dataset was used to evaluate LS maps in semi-arid regions for agriculture. For LS analysis, the structure and recommendations of the Food and Agriculture Organization were followed and it was found that 23.79% of the land is “highly suitable”, 20.17% of the land is “moderately suitable”, while approximately 28.28% of the total area is calculated to be “marginally suitable” for forestation, approximately 23.20% is “not suitable” for forestation, and water resources or water bodies are 4.53%. The study's methods, techniques, and conclusions can be useful in determining the LS for agriculture in semi-arid regions. The multi-criterion decision-making tool AHP incorporated with GIS introduces a new approach and the findings of the study may be useful for the identification of suitable agricultural land in any part of the world. more...

The Multan district is mainly prone to riverine floods but has remained understudied. Chenab flood-2014 was the worst flood that this district experienced in recorded history. This study applies remote sensing (RS) techniques to estimate the extent, calculate duration, assess the major causes and resulting impacts of the flood-2014, using Landsat-8 OLI images. These images were obtained for pre-flood, during-flood and post-flood instances. Secondary data of flood causing factors were obtained for comprehensive analysis. Spatially trained and validated datasets were obtained through Google Earth platform and Global positioning system. The supervised classification with maximum likelihood algorithm was used to classify land use and land cover of the study area. The Modified Normalized Difference Water Index was utilized to detect flood inundation extent and duration, and Normalized Difference Vegetation Index was utilized to monitor vegetation coverage and changes. The analysis allowed us to assess flood causes, and calculate the extent of the flooded areas with duration and recession, as well as damages to standing crops and built-up areas. The results revealed that the flood-2014 occurred due to heavy rains in early September in upper Chenab catchment. The flood inundation continued for around two months, which heavily affected agriculture and built-up areas. The present study introduces practical use of RS techniques to provide basis for effective flood inundation mapping and impact assessment, as an application for early flood response and recovery in the world. more...

In the current context of rapid development and urbanization, land use and land cover (LULC) types have undergone unprecedented changes, globally and nationally, leading to significant effects on the surrounding ecological environment quality (EEQ). The urban agglomeration in North Slope of Tianshan (UANST) is in the core area of the Silk Road Economic Belt of China. This area has experienced rapid development and urbanization with equally rapid LULC changes which affect the EEQ. Hence, this study quantified and assessed the spatial-temporal changes of LULC on the UANST from 2001 to 2018 based on remote sensing analysis. Combining five remote sensing ecological factors (WET, NDVI, IBI, TVDI, LST) that met the pressure-state-response(PSR) framework, the spatial-temporal distribution characteristics of EEQ were evaluated by synthesizing a new Remote Sensing Ecological Index (RSEI), with the interaction between land use change and EEQ subsequently analyzed. The results showed that LULC change dominated EEQ change on the UANST: (1) From 2001 to 2018, the temporal and spatial pattern of the landscape on the UANST has undergone tremendous changes. The main types of LULC in the UANST are Barren land and Grassland. (2) During the study period, RSEI values in the study area were all lower than 0.5 and were at the [good] levels, reaching 0.31, 0.213, 0.362, and 0346, respectively. In terms of time and space, the overall EEQ on the UANST experienced three stages of decline–rise–decrease. (3) The estimated changes in RSEI were highly related to the changes of LULC. During the period 2001 to 2018, the RSEI value of cropland showed a trend of gradual increase. However, the rest of the LULC type’s RSEI values behave differently at different times. As the UANST is the core area of Xinjiang’s urbanization and economic development, understanding and balancing the relationship between LULC and EEQ in the context of urbanization is of practical application in the planning and realization of sustainable ecological, environmental, urban, and social development in the UANST. more...

Residential greenness exposure has been linked to a number of physical and mental disorders. Nevertheless, evidence on the association between greenness and geriatric depression was limited and focused on developed countries. This study was aimed to investigate whether the relationship between residential greenness exposure and geriatric depression exists among the elderly with long-term care insurance (LTCI) in Shanghai, China. In 2018, a total of 1066 LTCI elderly from a cross-sectional survey completed a questionnaire in Shanghai. Residential greenness indicators, including normalized difference vegetation index (NDVI) and soil-adjusted vegetation index (SAVI), were calculated from the Landsat 8 imagery data in different buffers (100-m, 300-m, and 500-m). Mediation analysis by perceived social support was conducted to explore potential mechanisms underlying the associations. In the fully adjusted model, one IQR increase of NDVI and SAVI in the 300-m buffer size was associated with an 11.9% (PR: 0.881, 95% CI: 0.795, 0.977) and 14.7% (PR: 0.853, 95% CI: 0.766, 0.949) lower prevalence of geriatric depression, respectively. Stronger association was observed in the elderly with lower education level, living in non-central area, and lower family monthly income. Perceived social support significantly mediated 40.4% of the total effect for NDVI 300-m buffer and 40.3% for SAVI 300-m buffer to the greenness-depression association, respectively. Our results indicate the importance of residential greenness exposure to geriatric depression, especially for the elderly with lower education level, living in non-central area, and lower family monthly income. Perceived social support might mediate the association. Well-designed longitudinal studies are warranted to confirm our findings and investigate the underlying mechanisms. more...

Unmanned aerial vehicle (UAV) imaging has been increasingly applied for environmental monitoring due to its high spatial resolution. However, the digital number representations of UAV images cannot precisely represent the true radiance of ground objects due to the complexity of solar radiation transmission through the atmosphere. Nonetheless, previous studies have not considered the radiometric information of UAV images in terms of the atmospheric effects obtained under different meteorological conditions. This is addressed in the present work by proposing an atmospheric correction algorithm for UAV imagery based on Gordon’s standard near-infrared (NIR) atmospheric correction applied to water color remote sensing. First, the atmospheric path radiance (Lpath) and the diffuse transmittance (t) of water bodies in each spectral band of UAV images are obtained by measurements of the radiance of clear water bodies. Then, the obtained values of Lpath and t are employed in the atmospheric correction processing of UAV imagery. As such, the proposed method is generally applicable to areas with clear water bodies. The atmospheric correction performance of the proposed method is compared with that of dark pixel atmospheric correction. The vegetation radiance corrected by the proposed correction (Lv'(λi)) was more closer to the measurement than that corrected by dark pixel correction (Lv''(λi)). Three vegetation indices (ExG, NGRDI, and NDVI) were used to further compare the performance of the two atmospheric correction. The average percentile differences of EXG (12.41%, 19.15% and 10.18%) obtained from the UAV images corrected using the proposed algorithm (AC_W) were less than those (48.18%, 18.75%, and 58.9%) obtained from the UAV images corrected using the dark pixel method (AC_D). So did those of NGRDI and NDVI. The proposed method is demonstrated to perform distinctly better. Despite some error (5–7%), the proposed method provides an alternative for applying an atmospheric correction to UAV imagery under different meteorological conditions. The proposed method is also demonstrated to be simpler and more operable than the atmospheric aerosol optical thickness method. more...

Remotely sensed spectral vegetation indices (VIs) are widely used and have benefited numerous disciplines interested in the analysis of biomass, vegetation, plant stress, plant health, crop production, and change detection. This study’s objective is to propose a vegetation index based on short wave infrared (SWIR) for detecting the changes in geographical features using multi-temporal and multi-spectral satellite imagery. The proposed a vegetation index SWIR based normalized difference vegetation index (SNDVI) can play a significant role in monitoring vegetation plant’s health and growth and also increase the accuracy of classification and change detection. In this study, Landsat satellite imagery of different periods (1996, 2003, 2010 and 2017) has been used to develop the vegetation index. The proposed vegetation index is useful for increasing the accuracy of classification and change detection of geographical features. Using the Normalized Difference Vegetation Index (NDVI) method, the accuracy of classification of orchards, vegetation and rangelands has been obtained at 96.90%, 94.68% and 92.29% in 2017. While using the SNDVI method, higher accuracy of 98.09%, 95.50% and 92.02% has been obtained in the classification of orchards, vegetation and rangelands. The proposed SNDVI method can be significant in monitoring the health and growth of vegetation plants and detecting the changes in orchards biomass and other vegetation plants. more...

The modern cities of the world are characterized by continuous warming status. Land surface temperature (LST) is a very important part of the environmental status of mixed urban land and it is intimately associated with normalized difference vegetation index (NDVI). The current study makes an attempt on the seasonal fluctuation of LST-NDVI relation on diversified land surface material in Raipur City of India by using a large Landsat dataset for different seasons from 1991–92 to 2018–19. The results present the considerable rising trend of mean LST (1.6 °C, 5.3 °C, 4.8 °C, and 1.1 °C in the pre-monsoon, monsoon, post-monsoon, and winter, respectively) during the study period. The LST and NDVI produce a strong negative correlation (− 0.74, − 0.54, − 0.63, and − 0.49 in these four seasons) on plants; a moderate negative correlation (− 0.42, − 0.34, − 0.42, and − 0.21 in the four aforesaid seasons) on the barren land and urban settlement; and an insignificant correlation (0.27, 0.08, 0.05, and 0.19 in the four abovementioned seasons) on water bodies. This study is helpful in future planning for the ecological development of a city in tropical environment. more...

There was an inconsistent positive correlation between tree ring width and NDVI in the Tianshan Mountains, and this correlation gradually weakened from west to east and decreased with increasing elevation. Tree ring width and the normalized difference vegetation index (NDVI) are two effective indicators used to monitor changes in forest dynamics. Most studies focus on the relationship between tree ring width and regional NDVI in the mountainous areas of arid regions, failing to reflect the relationship between forest radial growth and forest canopy activities. In this paper, four study areas were selected ranging from west to east on the northern slope of the Tianshan Mountains in China. Tree cores of Picea schrenkiana were collected at three elevations in each study area, and NDVI values were extracted for the forest cover at each location. Emphasis was placed on the relationship between the tree ring width and NDVI values of montane evergreen coniferous forests in an arid region in the period 2000–2012. There was an inconsistent positive correlation between tree ring width and NDVI in the Tianshan Mountains, and the relationship became increasingly weak from west to east and decreased with increasing elevation. The varied forest growth and its response to climatic factors as a result of the different environmental conditions present in arid mountain regions may be the underlying reason for the inconsistency between the tree ring width and NDVI of forests. Drought stress may be the major driving factor of the positive correlation between the tree ring width and NDVI of montane evergreen coniferous forests in arid regions. This result may be useful for integrating tree ring width and NDVI to study the ecology of montane evergreen coniferous forests in arid regions. more...

Rapid urbanization has caused severe deterioration of air quality globally, leading to increased hospitalization and premature deaths. Therefore, accurate prediction of air quality is crucial for mitigation planning to support urban sustainability and resilience. Although some studies have predicted air pollutants such as particulate matter (PM) using machine learning algorithms (MLAs), there is a paucity of studies on spatial hazard assessment with respect to the air quality index (AQI). Incorporating PM in AQI studies is crucial because of its easily inhalable micro-size which has adverse impacts on ecology, environment, and human health. Accurate and timely prediction of the air quality index can ensure adequate intervention to aid air quality management. Therefore, this study undertakes a spatial hazard assessment of the air quality index using particulate matter with a diameter of 10 μm or lesser (PM10) in Selangor, Malaysia, by developing four machine learning models: eXtreme Gradient Boosting (XGBoost), random forest (RF), K-nearest neighbour (KNN), and Naive Bayes (NB). Spatially processed data such as NDVI, SAVI, BU, LST, Ws, slope, elevation, and road density was used for the modelling. The model was trained with 70% of the dataset, while 30% was used for cross-validation. Results showed that XGBoost has the highest overall accuracy and precision of 0.989 and 0.995, followed by random forest (0.989, 0.993), K-nearest neighbour (0.987, 0.984), and Naive Bayes (0.917, 0.922), respectively. The spatial air quality maps were generated by integrating the geographical information system (GIS) with the four MLAs, which correlated with Malaysia's air pollution index. The maps indicate that air quality in Selangor is satisfactory and posed no threats to health. Nevertheless, the two algorithms with the best performance (XGBoost and RF) indicate that a high percentage of the air quality is moderate. The study concludes that successful air pollution management policies such as green infrastructure practice, improvement of energy efficiency, and restrictions on heavy-duty vehicles can be adopted in Selangor and other Southeast Asian cities to prevent deterioration of air quality in the future. more...

Sand-dust events (SDE) are an increasing concern in many arid and semi-arid regions of the world, which have severely damaged air quality and human health in recent years. This study was conducted to monitor the SDE in western Iran using the dust storm index anomaly (DSIA) during 2000–2018. The spatiot-emporal change detection and statistical analysis were used to understand the impacts of normalized difference vegetation cover anomaly (NDVIA) and land surface temperature anomaly (LSTA) on the SDE activities. The area has suffered from the highest dust pollution in 2004, 2009, and 2012 (DSIA > + 40) while it experienced the lowest dust pollution in 2002 and 2017 (DSIALSTA−DSIA = + 0.52; R NDVIA−DSIA= -0.41); P more...

The terrestrial biosphere plays an active role in governing the climate system by regulating carbon exchange between the land and the atmosphere. Analysis of vegetation biophysical parameters and gross primary production (GPP) makes it convenient to monitor vegetation's health. A light use efficiency (LUE) model was employed to estimate daily GPP from satellite-driven data and environmental factors. The LUE model is driven by four major variables, namely normalized difference vegetation index (NDVI), photosynthetically active radiation (PAR), air temperature, and moisture for which both satellite-based and ERA5-Land data were applied. In this study, the vegetation health of Dibru Saikhowa National Park (DSNP) in Assam has been analyzed through vegetation biophysical and biochemical parameters (i.e., NDVI, EVI, LAI, and chlorophyll content) using Sentinel-2 data. Leaf area index (LAI) varied between 1 and 5.2, with healthy forests depicted LAI more than 2.5. Daily GPP was estimated for January (winter) and August (monsoon) 2019 for tropical evergreen and deciduous forest types. A comparative analysis of GPP for two seasons has been performed. In January, GPP was found to be 3.6 gC m−2 day−1, while in August, GPP was 5 gC m−2 day−1. The outcome of this study may be constructive to forest planners to manage the National Park so that net carbon sink may be attained in DSNP. more...

The impacts of climate change on vegetation cover in different regions in China are not entirely clear because of the interference of non-climatic factors, such as human activity. This study aims to analyze the spatiotemporal differences in climate impacts qualitatively and quantitatively by applying trend, correlation, and multiple linear regression (MLR) analyses to the data of Normalized Difference Vegetation Index (NDVI) and two climatic factors (air temperature and precipitation) during 1982–2015 in China. The MLR equation linking two climatic variables with NDVI was used to identify the NDVI trend caused by climate change. We demonstrated that the central and eastern regions of China, dominated by deciduous and evergreen broadleaf forests, experienced a rapid increase in NDVI from 1982 to 2015. The response of NDVI to variations in temperature and precipitation exhibited large spatiotemporal differences across China, which was closely related to climatic conditions and vegetation types. Overall, warming, particularly the sharp rise in spring, was the main climatic driving force behind China’s NDVI increase, and precipitation also influenced the NDVI increase in temperate grassland and desert regions due to the relatively arid climate, particularly in summer. The contributions of climate change to the total NDVI trend (CC) showed a large spatiotemporal heterogeneity across China. Overall, only 45% of the pixels (with a resolution of 8 km) in the study area showed that the MLR equations between NDVI and two climatic factors were significant at the 0.05 significance level during the growing season (April–October), and the average CC of these pixels was 38%. Among the eight vegetation sub-regions of China, the temperate desert and Qinghai-Tibet Plateau alpine meadow regions generally exhibited relatively larger CCs than other vegetation sub-regions in different seasons. At a national scale, the regional average CC reached 64% during the growing season. These results at multiple scales can help to deeply understand the mechanisms of regional environmental variation and sustainability. more...

Wheat and rapeseed are significant crops in Czech agriculture and remote sensing has huge potential for their management, given Sentinel-1 can overcome issues of cloudiness and monitor vegetation development via radar backscatter. This study compares radar and optical data characterizing the development of wheat and rapeseed in an agricultural cooperative in the Czech Republic. Radar Vegetation Index (RVI) and Normalized Difference Vegetation Index (NDVI) time-series of the main vegetation seasons between 2015 and 2018 are processed, analysed, and compared with each other. In 2018, the comparison of data with ground measurement by camera was also used. The temporal development of RVI is affected by noise, which is caused by the composition of imagery from different Relative orbits. The separation of imagery according to the Relative orbit used seemed to provide results more comparable to the phenological curve. Simple linear regression between NDVI and RVI illustrated that considering Relative orbit can slightly increase the Coefficient of determination. By selecting a suitable Relative orbit, the coefficient of determination between NDVI and RVI increased from 0.281 to 0.387 in the case of wheat and from 0.233 to 0.316 in the case of rape monitoring. The RVI for rapeseed and the height of canopy correlation was 0.392. The results for RVI presented in this article demonstrated that monitoring wheat and rapeseed development by Sentinel-1 has potential, however more research needs to be conducted in the areas of spatial and temporal noise removal. more...

Landsat-8 spectral values have been used to map the earth’s surface information for decades. However, forest types and other land-use/land-cover (LULC) in the mountain terrains exist on different altitudes and climatic conditions. Hence, spectral information alone cannot be sufficient to accurately classify the forest types and other LULC, especially in high mountain complex. In this study, the suitability of Landsat-8 spectral bands and ancillary variables to discriminate forest types, and other LULC, using random forest (RF) classification algorithm for the Hindu Kush mountain ranges of northern Pakistan, was discussed. After prior-examination (multicollinearity) of spectral bands and ancillary variables, three out of six spectral bands and five out of eight ancillary variables were selected with threshold correlation coefficients r2 more...

The present study assesses the temporal dynamics of above ground biomass (AGB) in Kaimoor Wildlife Sanctuary (KWS), Uttar Pradesh, India using series of Landsat data for the years 1989, 2000, 2010 and 2018. The satellite images were preprocessed for surface reflectance, and subsequently we computed Normalized Difference Vegetation Index (NDVI), Soil Adjusted Vegetation Index (SAVI), and Enhanced Vegetation Index (EVI). A forest density map was generated by slicing the computed NDVI and this was used as a basis for sampling strategy. A total of 30 sampling locations were randomly identified. Plot size of 30 m × 30 m was established. Tree species were enumerated at each plot and the girth at breast height and tree height measurements were recorded for the year 2018. Tree and plot level AGB (i.e. AGBF, t ha−1) was computed by multiplying the tree volume and specific gravity of wood. AGB (i.e. AGBP) prediction models were developed as linear regression equations for the year 2018 by assessing the vegetation indices and the AGBF. The significant AGBP models (R2 = 0.94; P = 0.0001) were applied for all study years after the data correction among the Landsat sensor series. The AGBP was over estimated (22.67 t ha−1; 7.75%) compared to AGBF (t ha−1). Moreover, EVI (R2 = 0.90) was found to be a better predictor for AGB compared to NDVI (R2 = 0.69) or SAVI (R2 = 0.77). The AGBP of KWS ranged between 289 ± 36 (in 1989) and 292 ± 40 (in 2018) with an average decadal positive change of 1.06%. more...

This study of vegetation dynamics in the coastal region of Tanzania provides a fundamental basis to better understand the nature of the factors that underlie observed changes. The Tanzanian coast, rich in biodiversity, is economically and environmentally important although the understanding of the nature and causes of vegetation change is very limited. This paper presents an investigation of the relationship between vegetation dynamics in response to climate variations and human activities using Moderate Resolution Imaging Spectroradiometer (MODIS), Normalized Difference Vegetation Index (NDVI), meteorological, and Globeland30 Landsat data sets. Spatio-temporal trends and the relationship of NDVI to selected meteorological variables were statistically analyzed for the period 2000–2018 using the Mann-Kendall test and Pearson correlation respectively. The results reveal a significant positive trend in temperature (β > 0, Z = 2.87) and a non-significant trend in precipitation (|Z| < 1.96). A positive relationship between NDVI and precipitation is observed. Coastal Tanzania has therefore experienced increased temperatures and variable moisture conditions which threaten natural vegetation and ecosystems at large. Classified land cover maps obtained from GlobeLand30 were analyzed to identify the nature and scale of human impact on the land. The analysis of land use and land cover in the region reveals an increase in cultivated land, shrubland, grassland, built-up land and bare land, while forests, wetland and water all decreased between 2000 and 2020. The decrease in forest vegetation is attributable to the fact that most livelihoods in the region are dependent on agriculture and harvesting of forest products (firewood, timber, charcoal). The findings of this study highlight the need for appropriate land-use planning and sustainable utilization of forest resources. more...

As an important ecotone, the alpine timberline is the boundary between closed-canopy montane forest and alpine vegetation, and is highly sensitive to global and regional climate changes. We provided a way to identify and extract the alpine timberline in Yarlung Zangpo Grand Canyon Nature Reserve by using remote sensing data and spatial analysis based on land use/land cover classification and NDVI distribution characteristics. Combining DEM data, the influence of slope and aspect on the distribution of alpine timberline was explored. The results showed that the alpine timberline in Yarlung Zangpo Grand Canyon is transitional timberline, with the upper boundary approximately distributed at the elevation of 3422–4373 m, the lower boundary at approximately 3270–4164 m, with a width of about 110–280 m. Alpine timberline was mainly distributed on steep and very steep slopes ranging from 25° to 45°. The maximum elevation of both the upper and lower boundaries occurred on steep slopes. The distribution of alpine timberline varies with aspects, with sunny slopes having a higher boundary than shady slopes. more...

Water is an important factor that affects local ecological environments, especially in drylands. The hydrological cycle and vegetation dynamics in Central Asia (CA) have been severely affected by climate change. In this study, we employed data from Gravity Recovery and Climate Experiment (GRACE), Global Land Data Assimilation System (GLDAS), Global Land Evaporation Amsterdam Model, and Climate Research Unit to analyze the spatiotemporal changes in hydrological factors (terrestrial water storage (TWS), evapotranspiration, precipitation, and groundwater) in CA from 2003 to 2015. Additionally, the spatiotemporal changes in vegetation dynamics and the influence of hydrological variables on vegetation were analyzed. The results showed that the declining rates of precipitation, evapotranspiration, GRACE-TWS change, GLDAS-TWS change and GW change were 0.40 mm/year, 0.11 mm/year, 50.46 mm/year (p p p p more...

The purpose of the study was to investigate how the regulatory factors (i.e., temperature, moisture, vegetation indexes, and leaf traits) of soil respiration differed in the different croplands. A field study including 2019 soybean, maize, and sweet potato and 2020 peanut, pea, and sesame growing seasons was performed. The soil respiration, soil temperature, moisture, hyperspectral vegetation indexes, leaf area index (LAI), and chlorophyll content (SPAD value) were measured. The seasonal cumulative soil respiration was 0.523 ± 0.040, 0.383 ± 0.029, 0.358 ± 0.018, 0.561 ± 0.010, 0.570 ± 0.017, and 0.586 ± 0.028 kg C m−2 in the soybean, maize, sweet potato, peanut, pea, and sesame croplands, respectively. Soil respiration was significantly (P 0.05) in 2020. The main regulatory factors of soil respiration were temperature, normalized difference vegetation index (NDVI), difference vegetation index (DVI), and SPAD value in the soybean and maize croplands, while they were temperature, NDVI, photochemical reflectance index (PRI), and LAI in the sweet potato, peanut, pea, and sesame croplands. A model based on soil temperature, hyperspectral vegetation indexes, and LAI (or SPAD value) explained 77.2‒89.1% of the variation in soil respiration in the soybean, maize, peanut, pea, and sesame croplands. This study demonstrated the potential applicability of vegetation indexes, LAI, and SPAD value in estimating soil respiration in different croplands at a large scale. more...

The Xinjiang region of China is among the most sensitive regions to global warming. Based on the meteorological and hydrological observation data, the regional wet-to-dry climate regime shifts in Xinjiang were analyzed and the impacts of climatic shift on the eco-hydrological environment of Xinjiang were assessed in this study. The results showed that temperature and precipitation in Xinjiang have increased since the mid-1980s, showing a warming-wetting trend. However, drought frequency and severity significantly increased after 1997. The climate of Xinjiang experienced an obvious shift from a warm-wet to a warm-dry regime in 1997. Since the beginning of the 21st century, extreme temperatures and the number of high temperature days have significantly increased, the start date of high temperature has advanced, and the end date of high temperature has delayed in Xinjiang. In addition, the intensity and frequency of extreme precipitation have significantly increased. Consequently, regional ecology and water resources have been impacted by climatic shift and extreme climate in Xinjiang. In response, satellite-based normalized difference vegetation index showed that, since the 1980s, most regions of Xinjiang experienced a greening trend and vegetation browning after 1997. The soil moisture in Xinjiang has significantly decreased since the late 1990s, resulting in adverse ecological effects. Moreover, the response of river runoff to climatic shift is complex and controlled by the proportion of snowmelt to the runoff. Runoff originating from the Tianshan Mountains showed a positive response to the regional wet-to-dry shift, whereas that originating from the Kunlun Mountains showed no obvious response. Both climatic shift and increased climate extremes in Xinjiang have led to intensification of drought and aggravation of instability of water circulation systems and ecosystem. This study provides a scientific basis to meet the challenges of water resource utilization and ecological risk management in the Xinjiang region of China. more...

Floodplain wetlands have considerable ecological and economic significance. The study area, consisting of the Bhagirathi–Jalangi Floodplain (BJF), is endowed with numerous wetlands. Based on the tentative time of origin and wetland index map, the investigation selects eight major riparian wetlands and their surroundings from the meander belt of the rivers Bhagirathi and Jalangi. The study primarily emphasizes on periodic mapping for areal change detection and identification of emerging issues between 1955 and 2018. The data used for the present research includes Topographical maps, Landsat imagery, Google maps, Sentinel 2B imagery of different time periods and some ancient maps which depict the old courses of river Bhagirathi and Jalangi. By using geospatial techniques and field investigation, the study reveals that from 1955 to 2018, wetlands have been shrinking rapidly. It is found from the investigation that total wetland area has reduced by about 39.60% due to land reclamation and wetland transformation. The land use change detection depicts that agricultural expansion, which has increased up to 35%, is the dominant control factor for wetland decay. More than 87% of the selected wetlands have experienced area loss. Among the selected wetlands, 25% have lost more than 90% of their areal extent during the stipulated study period. Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI) are the two indices used for measuring the concentration of aquatic plants. Geospatial analysis suggests that the NDVI value is higher in pre-monsoon season in comparison with post-monsoon. High NDVI value which is ≥ 0.51 covers more than 81% of the wetland surface area. In contrast, low NDWI value (≤ 0.20), covering more than 60% of the wetland surface area, is found in 75% of the selected wetlands. The study finally concludes that due to accelerated anthropogenic intervention floodplain wetlands in the BJF require special attention to ensure future sustainability. more...

The most expedient means of analysing global climate change is to analyse the precipitation along with major components of the global atmospheric circulation. Like other Coastal areas, Coastal Andhra is also vulnerable to extreme weather events. So, in this study, the trend and pattern of the precipitation of the Coastal Andhra have been analyzed using daily and monthly rainfall data of 36 years (1983–2018). The standardized precipitation index, rainfall anomaly index and Mann–Kendall tests have been used to analyse the trend and pattern of precipitation. The result showed that the average annual rainfall was 161 cm in 1983 which first declined to 147 cm in 1991 but increased to 181 cm in 2001 and again declined rapidly to 91 cm in 2018. Apart from this normalized difference vegetation index (NDVI) has been used for the years 1983, 1991, 2001, 2011 and 2018 for the validation of dryness and wetness. The statistical analysis shows that rainfall in the study area shows a declining trend at the rate of − 1.27 cm per year as per the result obtained by the Sen, slope. Further, the association between mean annual rainfall and NDVI is found to be very strong with a higher positive value of the coefficient of determination. more...

Widespread changes in forest structure and distribution have been documented in northern Patagonia over the past century. We employed LPJ-GUESS, a dynamic global vegetation model (DGVM) to investigate the role of climate, atmospheric carbon dioxide (CO2), and fire on simulated forest cover during the twentieth century. Our objective was to assess the drivers responsible for forest change to temperature, precipitation, radiation, fire and atmospheric CO2 Simulations using observed changes in climate and CO2 from 1930 to 2010, showed an increase in forest cover under changing climate and CO2, because of higher carbon assimilation and net primary production. The model results were compared with a remote-sensing-derived biomass map and ‘greening’ indices from the normalized difference vegetation index. Model simulations and satellite data both show increased greening at high and low elevations. In contrast, simulations using pre-industrial climate and CO2 conditions resulted in a decrease in fire frequency and lower simulated biomass than is reflected by present-day vegetation. Our simulations shows that climate is the primary driver and CO2 fertilization is the secondary driver of forest expansion in northern Patagonia. We suggest that rising CO2 mitigates climate-induced drought stress due to increases in water-use efficiency. more...

Spatiotemporal changes in land use/land cover (LU/LC) have been relevant factors in causing the changes in Urban Heat Intensity (UHI) pattern across rural and urban areas of cities all over the world. Studies conducted have shown that this relation does not depend on extent meaning UHI that can be an important factor not only for a country but for environment of a city also. Over the years, it is reflected in local environment of cities. In this study, such relations are shown over Lucknow city of India for 4 decades from 1985 to 2015 using freely available 100 m resolution Decadal Land use map for India for year 1985, 1995 & 2005 and CGLS-100 product for year 2015. Land Surface Temperature (LST) is calculated from Landsat series data using NDVI-based emissivity method. LST is correlated with 5 LU classes Built-up, Agricultural, Vegetation, Water & Barren. LC indices Normalised Difference Built-up Index (NDBI), Normalised Difference Vegetation Index (NDVI), Normalised Difference Water Index (NDWI) & Normalised Difference Barren Index (NDBaI) are correlated with Normalised LST (NLST), respectively, and it is found that NLST is positively related with all indices except for NDVI with strong negative correlation and R2 of 0.27. Results show reduction of 22 km2 in area affected from UHI for Lucknow from 1985 to 2015 and expansion of UHI region for Lucknow towards its outer boundary. The study helps in understanding the locality of UHI effect in a city of a developing country with the help of open source data. more...