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.

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.

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.

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.

Yunnan Province in southwest China is characterized by a vast area, diverse climate types, rich ecosystem types, and unique biodiversity resources. With consideration of global climate change, there is an urgent need to evaluate the response of vegetation to drought in Yunnan. This study utilized the MOD13A3, MOD17A2, and Tropical Rainfall Measuring Mission (TRMM) 3B43 remote sensing products. The TRMM 3B43 downscaled monthly precipitation data were used to calculate the tropical rainfall condition index (TRCI) for Yunnan. The TRCI was used as a drought index, and the temporal and spatial changes in TRCI, gross primary productivity (GPP), and vegetation condition index (VCI) from 2009 to 2018 were explored. The response of vegetation to drought was evaluated under different time scales and varying land-use types. The results showed that during 2009–2018, (1) at an annual scale, the drought in Yunnan showed a weakening trend, and at a spatial scale, the drought showed a weakening trend from northwest to southeast. This weakening trend was more noticeable for cultivated land than for forest, grassland, and other land-use types. (2) GPP and VCI showed overall increasing trends at an annual scale, indicating gradual improvements in the GPP of vegetation and vegetation status, whereas the summer vegetation index showed a decreasing trend. (3) Although both the GPP and the growth state of vegetation were affected by drought, the responses of GPP and VCI to drought differed under different temporal scales and different land-use types. The responses of GPP and VCI to drought during spring were greater than those over other seasons, and the response of VCI to drought was more sensitive than that of GPP. Drought had a high impact on the GPP and vegetation growth of cultivated land and grassland with shallow root systems, whereas the impact of drought on forest was relatively stable.

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.