Accurate methods to estimate the aboveground biomass (AGB) of mangroves are required to monitor the subtle changes over time and assess their carbon sequestration. The AGB of forests is a function of canopy-related information (canopy density, vegetation status), structures, and tree heights. However, few studies have attended to integrating these factors to build models of the AGB of mangrove plantations. The objective of this study was to develop an accurate and robust biomass estimation of mangrove plantations using Chinese satellite optical, SAR, and Unmanned Aerial Vehicle (UAV) data based digital surface models (DSM). This paper chose Qi’ao Island, which forms the largest contiguous area of mangrove plantation in China, as the study area. Several field visits collected 127 AGB samples. The models for AGB estimation were developed using the random forest algorithm and integrating images from multiple sources: optical images from Gaofen-2 (GF-2), synthetic aperture radar (SAR) images from Gaofen-3 (GF-3), and UAV-based digital surface model (DSM) data. The performance of the models was assessed using the root-mean-square error (RMSE) and relative RMSE (RMSEr), based on five-fold cross-validation and stratified random sampling approach. The results showed that images from the GF-2 optical (RMSE = 33.49 t/ha, RMSEr = 21.55%) or GF-3 SAR (RMSE = 35.32 t/ha, RMSEr = 22.72%) can be used appropriately to monitor the AGB of the mangrove plantation. The AGB models derived from a combination of the GF-2 and GF-3 datasets yielded a higher accuracy (RMSE = 29.89 t/ha, RMSEr = 19.23%) than models that used only one of them. The model that used both datasets showed a reduction of 2.32% and 3.49% in RMSEr over the GF-2 and GF-3 models, respectively. On the DSM dataset, the proposed model yielded the highest accuracy of AGB (RMSE = 25.69 t/ha, RMSEr = 16.53%). The DSM data were identified as the most important variable, due to mitigating the saturation effect observed in the optical and SAR images for a dense AGB estimation of the mangroves. The resulting map, derived from the most accurate model, was consistent with the results of field investigations and the mangrove plantation sequences. Our results indicated that the AGB can be accurately measured by integrating images from the optical, SAR, and DSM datasets to adequately represent canopy-related information, forest structures, and tree heights.

Air pollution kills approximately 4.2 million people every year. As air pollution varies significantly in different urban areas, the assessment of urban emissions is key to taking appropriate actions and formulating policies for sustainable built environments and to promote the wellbeing of people. The overarching goal of this study was to generate fine resolution aerosol optical depth (AOD) using Landsat imagery and examine both the socioeconomic inequalities of air pollution exposure and the air quality variation related to different land-use categories. This study has focused on a period of unusual population growth, 2000-2010, in the Phoenix Metropolitan Area. It was found that socioeconomic factors, vegetation indexes, and land use land cover types are all strong predictors of AOD, and the negative coefficients of socioeconomic values reveal insight into the social inequality of air pollution exposure. Results suggest that the government regulation on air pollution during the study period helped to improve air quality. Meanwhile, planting vegetation to mitigate air pollution should be carefully examined in order to find the right vegetation species and spatial configuration of vegetation cover in urban settings.

Canada's abundant and high-quality water resources support a growing human population, as well as thriving industrial and agricultural economies. However, recent intense drought conditions have raised concerns for current water resource availability. Patterns of long-term ground and surface water (GSW) changes, and their response to environmental conditions, land-use dynamics, and socioeconomic changes are not well-understood across this large and diverse country. To address this crucial gap, we identified regions of ground and surface water (GSW) changes in all the Provinces of Canada between 2002 and 2016 from the Gravity Recovery and Climate Experiment (GRACE) and Global Land Data Assimilation System (GLDAS) datasets. We explored the relationships between GSW changes and environmental, socioeconomic, and land-use dynamics over time. We found that all the Provinces of Canada gained a net 4.46 mm Liquid Water Equivalent (LWE) per year, equivalent to a total increase of 66.9 mm LWE. GSW increases were significantly associated with the normalized difference vegetation index and evapotranspiration rates. In contrast, GSW declines were significantly related to deforestation rate, urban expansion, and economic development (median household income). Despite apparent widespread post-drought recovery detected from 2002 to 2016, the rapid GSW declines were also observed in almost all of Western Canada and part of Ontario, amounting to a net loss of 66.13 mm. This indicates that a pronounced drought had emerged. It is anticipated that Canada will be experiencing more frequent and severe droughts under ongoing climate change and increasing demand for water resources.

Among the most biodiverse environments in the world, the Mekong River Basin has experienced extensive socio-economic development in the past few decades resulting in land use change and associated ecological and environmental stresses. To assess possible impacts on sensitive ecosystems and their services in the Lower Mekong River Basin (LMB), we examine spatial and temporal changes in forest community extent, as well as tree density and canopy coverage, in relation to carbon uptake and climate change. We employed the Mann–Kendall test to identify regions of likely significant ecological and environmental change within forest ecosystems. We developed a new approach to eliminate the classification of false positives and false negatives. We quantified the annual rate of ecological changes using Sen’s slope. Ordinary Least Squares (OLS) regression analysis was performed to analyze the impact of tree and leaf density changes on the regional environment, particularly with regard to net primary productivity (NPP) and surface temperature trends. Across the LMB, a total of 2.81% of forest extent has been lost. Cambodia experienced the most prominent forest loss among all countries, while Thailand and Myanmar had net forest gain. NPP trends were starkly divergent from the north to the south and declined across the majority of northern Laos. The LMB region experienced increasing trends for both daytime and nighttime temperatures except for small portions of Thailand and Vietnam. An annual 0.0876°C increase in surface temperature can be expected for every 1% decrease in tree density in forest degraded regions, while a substantial annual carbon productivity increase can be expected in reforested regions. In addition, the effectiveness of policy enforcement at the government level is critical for reforestation in LMB.

Cities in warmer climate zones have experienced extreme heat and unbearable drought conditions due to climate change. Developing a practical and integrated framework to tackle the trade‐off between land surface temperature (LST) reduction and water conservation is essential for heat mitigation and resilience planning. We developed a multi‐objective framework of spatial optimization for priority areas that considers environmental justice. We identified the priority areas (i.e., residential districts, socio‐economically disadvantaged neighborhoods, hotspot regions, and opportunity areas) using ECOSTRESS‐based LST, actual evapotranspiration (ETa, as a proxy to outdoor water use [OWU]), Landsat‐based LST and ETa changes (2000–2020), and the evaporative stress index. The configuration of vegetation was spatially optimized to achieve the highest co‐benefits by equally balancing both objectives. We found that LST decreased and OWU increased as the vegetation percentage rose from 25% to 45%. The trade‐off between heat reduction and water conservation indicates that LST is not significantly reduced, as OWU reaches above the mid‐level if we focus mainly on LST reduction. The associated green space configurations suggest that grass coverage gradually increases as tree coverage decreases slightly in unmanaged soil. Overall, hotspot regions have a higher potential to lower LST by consuming less water than other areas with the same percentage of new vegetation. Based on this perspective, we suggest that urban planners and city managers prioritize hotspot regions by considering environmental justice and co‐benefits to ensure highly efficient LST amelioration and OWU conservation.