To determine the suitable indices for vegetation cover and production assessment based on the remote sensing data, simultaneous digital data with field data belonging to the spring rangeland of the Semirom-Isfahan province were analyzed. During two years of monitoring the annual, grass, forb, and shrub vegetation cover and the total production data from 86 were collected. The Global Positioning System (GPS) was used to measure the coordinates of plots and transects. Geometric correction and histogram equalization were applied in image processing, and image digital numbers were converted to reflectance numbers. In the next stage, all vegetation indices were calculated from the Advanced Wide Field Sensor (AWiFS) image data and compared with the vegetation cover estimates, at monitoring points, made during field assessments. A linear regression model was used to select suitable vegetation indices. The results showed that there were significant relationships between the satellite data and the vegetative characteristics. Among the indices, the Normalized Difference Vegetation Index (NDVI) consistently showed significant relationships with the vegetation cover. The estimation of the vegetation cover with the NDVI vegetation index was more accurately predicted within rangeland systems. Using the produced model from the NDVI index vegetation crown cover, percentage maps were produced in three class percentages for each image. Generally introduced indices provided accurate quantitative estimation of the parameters. Therefore, it was possible to estimate cover and production as important factors for range monitoring using the AWiFS data. The Remote sensing data and the Geographic Information System are the most effective tools in natural resource management.

An increasing body of evidence has linked greenspace and various health outcomes in children and adolescents, but the conclusions were inconsistent. For this review, we comprehensively summarized the measurement methods of greenspace, resultant health outcomes, and potential mechanisms from epidemiological studies in children and adolescents (aged ≤19 years). We searched for studies published and indexed in MEDLINE and EMBASE (via Ovid) up to April 11, 2022. There were a total of 9,291 studies identified with 140 articles from 28 countries finally assessed and included in this systematic review. Over 70% of the studies were conducted in highly urbanised countries/regions, but very limited research has been done in low-and middle-income countries and none in Africa. Measures of greenspace varied. Various health outcomes were reported, including protective effects of greenspace exposure on aspects of obesity/overweight, myopia, lung health, circulatory health, cognitive function, and general health in children and adolescents. The associations between greenspace exposure and other health outcomes were inconsistent, especially for respiratory health studies. We pooled odds ratios (OR) using random-effects meta-analysis for health outcomes of asthma (OR = 0.94, 95%CI: 0.84 to 1.06), allergic rhinitis (OR = 0.95; 95% CI: 0.73 to 1.25), and obesity/overweight (OR = 0.91, 95%CI: 0.84 to 0.98) with per 0.1 unit increase in normalized difference in vegetation index (NDVI). These associations have important implications for the assessment and management of urban environment and health in children and adolescents.

Crops are prone to various types of stress, such as caused by heavy metals, drought and pest/disease, during their life cycle. Heavy metal stress in crops poses a serious threat to crop quality and human health. However, differentiating between heavy metal and non-heavy metal stress presents a great challenge, since responses to environmental stress in crops are complex and uncertain, with different stressors possibly triggering similar canopy reflectance responses. This study aims to infer the occurrence probability of heavy metal stress (i.e., Cd stress) on a regional scale by integrating satellite-derived vegetation index and spatio-temporal characteristics of different stressors with a Bayesian method. The study area is located in the Hunan Province, China. Seven scenes of Sentinel-2 satellite images from 2016 to 2017 were collected, as well as Cd concentrations in the soil. First, the probability of rice being stressed was screened using the normalized difference red-edge index (NDRE) at all the growth stages of rice. Further, the stressed rice was used as input, along with the coefficients of spatio-temporal variation (CSTV) derived from NDRE, for a Bayesian method to infer rice exposed to Cd pollution. The results demonstrated that NDRE was a sensitive indicator for assessing stress levels in rice crops. The CSTV with a threshold of 2.7 successfully detected rice under Cd as well as abrupt stress on a regional scale. A high map accuracy for Cd induced stress in rice was achieved with an accuracy of 81.57%. This study suggests that vegetation index obtained from satellite images can assist in capturing crop stress, and that the used Bayesian method can be very useful for distinguishing a specific stressor in crops by incorporating temporal-spatial characteristic of different stressors in crops into satellite-derived vegetation index.

Accurate measurements of ground-level PM₂.₅ (particulate matter with aerodynamic diameters equal to or less than 2.5 μm) concentrations are critically important to human and environmental health studies. In this regard, satellite-derived gridded PM₂.₅ datasets, particularly those datasets derived from chemical transport models (CTM), have demonstrated unique attractiveness in terms of their geographic and temporal coverage. The CTM-based approaches, however, often yield results with a coarse spatial resolution (typically at 0.1° of spatial resolution) and tend to ignore or simplify the impact of geographic and socioeconomic factors on PM₂.₅ concentrations. In this study, with a focus on the long-term PM₂.₅ distribution in the contiguous United States, we adopt a random forests-based geostatistical (regression kriging) approach to improve one of the most commonly used satellite-derived, gridded PM₂.₅ datasets with a refined spatial resolution (0.01°) and enhanced accuracy. By combining the random forests machine learning method and the kriging family of methods, the geostatistical approach effectively integrates ground-based PM₂.₅ measurements and related geographic variables while accounting for the non-linear interactions and the complex spatial dependence. The accuracy and advantages of the proposed approach are demonstrated by comparing the results with existing PM₂.₅ datasets. This manuscript also highlights the effectiveness of the geographical variables in long-term PM₂.₅ mapping, including brightness of nighttime lights, normalized difference vegetation index and elevation, and discusses the contribution of each of these variables to the spatial distribution of PM₂.₅ concentrations.

Conversion of mangroves to shrimp ponds creates fragmentation and eutrophication. Detection of the spatial variation of foliar nitrogen is essential for understanding the effect of eutrophication on mangroves. We aim (i) to estimate nitrogen variability across mangrove landscapes of the Mahakam delta using airborne hyperspectral remote sensing (HyMap) and (ii) to investigate links between the variation of foliar nitrogen mapped and local environmental variables. In this study, multivariate prediction models achieved a higher level of accuracy than narrow-band vegetation indices, making multivariate modeling the best choice for mapping. The variation of foliar nitrogen concentration in mangroves was significantly influenced by the local environment: (1) position of mangroves (seaward/landward), (2) distance to the shrimp ponds, and (3) predominant mangrove species. The findings suggest that anthropogenic disturbances, in this case shrimp ponds, influence nitrogen variation in mangroves. Mangroves closer to the shrimp ponds had higher foliar nitrogen concentrations.

The tropical cyclones impact both the eastern and western coasts of India, causing severe socio-environmental problems. This study analyzed shoreline changes and vegetation degradation caused by cyclone Nisarga and monsoon events in Maharashtra coastal zone and Mumbai region, India. In this study, the shoreline change was studied using the Net Shoreline Movement (NSM) statistical technique embedded in the digital shoreline analysis system (DSAS) tool. The effects of the cyclone on the vegetation were mapped using the Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), and the rainfall distribution from Global Precipitation Measurement (GPM) data. The correlation between rainfall data and vegetation loss was analyzed using geographically weighted regression. The results also show that 90% of the events were concentrated in the 80–300 mm classes, being classified as sudden increases. This cyclone caused erosion in 56.32% of the shoreline; the highest erosion level was observed along the coastal zone of Maharashtra (near Mumbai city). Cyclone Nisarga has also impacted the vegetation loss most prominently in the region, with mean EVI in pre-cyclone equal to 0.4 and post-cyclone equal to 0.2. These eco-physical studies using geospatial technology are needed to understand the behavior of changes in shoreline and vegetation and can also help coastal managers plan for resilient coastal systems after the passage of tropical cyclones.

Aerosols have severe effects on climate, human health and ecosystems. The impact of aerosols on climate, ecosystems and human health can be better understood if the variability of optical properties of aerosols is accurately known. In this paper, we have used Moderate Resolution Imaging Spectroradiometer (MODIS) datasets of aerosol optical depth (AOD) at 550 nm, Angstrom exponent (440/870) (AE) and enhanced vegetation index (EVI) over Pakistan. We have also analyzed the relationship between meteorological variables (e.g., temperature, relative humidity (RH) and wind speed (WS)) and aerosol optical properties to acquire a deep knowledge of aerosol variability. The coefficients of determination (R²) between Aqua-AOD and AERONET-AOD are found to be 0.6724 over Lahore and 0.7678 over Karachi. Aqua-AOD has also been validated with AOD data from Terra, MISR and SeaWiFS. High AOD (0.8–1) and low AE (0.4–0.8) have been observed over south and southwestern Pakistan indicating the presence of dust aerosols. In northeastern Pakistan, EVI is negatively correlated with AOD. The northeastern Pakistan is characterized by high values of AOD (~1) during all seasons. AOD showed a significant interannual variation with the lowest values (0.22) in January and the highest (0.58) in July. AE is observed to be lower in spring and summer than in winter and autumn seasons in south and south-western Pakistan. A positive R (≥0.6) is observed between AOD and temperature over the southwestern Pakistan while a negative R (−0.4 ≤) is observed between AOD and RH over the southwestern Pakistan.

The “Golden Triangle” is located on the border between Myanmar, Laos, and Thailand, and slash-and-burn cultivation is an ancient and typical land type in this region. With the development of the “The Belt and Road” strategy of China and the climate change, the vegetation information is bound to change intensively under the combined influence of alternative plantation projects and economic policies. Here we used MOD13Q1-normalized differential vegetation index (NDVI) and meteorological data to analyze the variation of vegetation coverage and its correlation with climatic factors (temperature and precipitation) during the period of 2000–2018 by using trend analysis, stability analysis, and partial correlation analysis. The results showed that the overall vegetation coverage of this region exerted the trend of improvement and became more stable over time. Spatially, the agglomeration degree became weaker as time goes during 2000–2018. The precipitation was more closely correlated with NDVI than temperature, indicating that precipitation could be the main limiting factor influencing vegetation change in this area. The correlation between NDVI and climatic factors exhibited differences among different seasons, with NDVI being less correlated with temperature and precipitation in spring and summer and more correlated with them in autumn and winter. Investigating the long-term vegetation coverage of this region and analyzing the trend of climate change is beneficial to understand the development trend of the ecological environment and resource potential in this region. Simultaneously, it can provide a favorable ecological evaluation for The Belt and Road strategy and provide important scientific suggestions and guidance for the sustainable development of ecosystems and human society under the drastic environmental changes.

Spatial planning is crucial for sponge city (SC) construction; however, prioritizing SC sites at the watershed scale has not been fully explored. In this study, a multi-criteria decision model, considering demand and suitability of SC construction, was established by monitoring, model simulation, and index calculation. This new model was then tested in a rapidly urbanizing watershed, Beijing, China, and the priority of SC construction at both grid scale (1km×1km) and subwatershed scale was ranked. The results showed that the highest priority was found in emerging regions where urbanization is ongoing and followed by urban core areas. In addition, six indexes were identified by clustering heatmaps as key factors affecting the priority of SC planning, including topographic index, water pollution index, pollution rate based on the state standard of surface water environment quality, urbanization planning, urban levels, and vegetation index, which could guide SC planning in data-lacking regions. The approach and findings in this study cannot only provide helpful references for watershed managers and urban planners but also can be easily used in other regions.

The Pantanal is an extensive flooded plain, rich in biodiversity and considered a Biosphere Reserve and World Heritage Site. It has great complexity and can be divided into regions due to its each distinct characteristic. Nhecolândia is a very peculiar region because it is made up of thousands of freshwater and brackish ponds. The study objective was to evaluate the physical-chemical parameters of the Nhecolândia ponds and to analyze the vegetation indexes generated from UAV aerial photographs in order to identify what best distinguishes freshwater and brackish ponds and to differentiate study area features. The in-field and image data collection were performed on June 20, 2015. The aerial photographs were processed to obtain mosaic which served as a vegetation index basis. The indexes and wavelengths in the visible region analyses were performed for each of the area’s ponds. It was observed that bays and salines have a differentiated spectral behavior. The excess green and normalized excess green vegetation indexes presented results enough to separate freshwater from brackish ponds, plus to differentiate many study area features.