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Black and odorous water seriously affects the ecological balance of rivers and the health of people living nearby. Satellite remote sensing technology with its advantages of a large range, long-time series, low cost, and high efficiency, has provided a new area for water quality detection. Much archived remote sensing satellite data can be further processed and used as a data source for black and odorous water detection. In this paper, Gaofen-2 remote sensing data with a spatial resolution of 1 m is leveraged as the data source. To enrich the data source in the northern coastal zone of China, we have built a high-quality remote sensing dataset, called the remote sensing images for black and odorous water detection (RSBD) dataset, which is collected from the Gaofen-2 satellite in Yantai, China. In addition, we propose a network with an encoder-decoder discriminant structure for black and odorous water detection. In the network, an augmented attention module is designed to capture a more comprehensive semantic feature representation. Further, the median balancing loss function is adopted to solve the imbalance issues. Experimental results demonstrate that the network is superior to other state-of-the-art semantic segmentation methods on our dataset. [ABSTRACT FROM AUTHOR]

To assess the status and change trend of forest in China, an indicator framework was developed using SDG sub-indicators. In this paper, we propose an improved methodology and a set of workflows for calculating SDG indicators. The main modification include the use of moderate and high spatial resolution satellite data, as well as state-of-the-art machine learning techniques for forest cover classification and estimation of forest above-ground biomass (AGB). This research employs GF-1 and GF-2 data with enhanced texture information to map forest cover, while time series Landsat data is used to estimate forest AGB across the whole territory of China. The study calculate two SDG sub-indicators: SDG15.1.1 for forest area and SDG15.2.1 for sustainable forest management. The evaluation results showed that the total forest area in China was approximately 219 million hectares at the end of 2021, accounting for about 23.51% of the land area. The average annual forest AGB from 2015 to 2021 was estimated to be 105.01Mg/ha, and the overall trend of forest AGB change in China was positive, albeit with some spatial differences. [ABSTRACT FROM AUTHOR]

Advanced Very High Resolution Radiometer (AVHRR) onboard National Oceanic and Atmospheric Administration (NOAA) satellites can provide over 40 years of global remote sensing observations, which can be used to retrieve long-term aerosol optical depth (AOD). This is of great significance to the study of global climate change. In this paper, we proposed an algorithm to jointly calculate AOD and land surface properties from AVHRR observations. With assumptions that AOD doesn't vary in adjacent space and earth surface property doesn't vary in two days, the algorithm considered non-Lambertian surface reflection based on the shape of bidirectional reflectance distribution function (BRDF shape) and obtained AOD and surface property by optimal estimation (OE) method. The algorithm has been applied to NOAA-7, 9, 11, 14, 16, 18, and 19 satellites and AVHRR-retrieved AOD with 5 × 10 km over China (15°–60° N, 70°–140°E) has been obtained from 1982 to 2016. Comparisons of AVHRR-retrieved AOD against AErosol RObotic NETwork (AERONET) (in and around China) and China Aerosol Remote Sensing Network (CARSNET) AOD show good consistency with 62.62% points within the uncertainty of Δτ = ± (0.05 + 0.25τ) and root-mean-square error (RMSE) of 0.26. Further comparison of the monthly mean AOD of multiple AOD datasets in the 'Beijing', 'Dalanzadgad', 'NCU_Taiwan' and 'Kanpur' stations shows that the results of the algorithm are stable. The yearly averaged AOD data also has similar agreements with MERRA-2 (The Modern-Era Retrospective analysis for Research and Applications, Version 2) and AVHRRDB data (AVHRR 'Deep Blue' aerosol data set). The multi-year mean correlation coefficient is 0.70 and 0.61 and the percentages within the uncertainty are 80.01% and 67.29% compared with MERRA-2 AOD and AVHRRDB AOD respectively. [ABSTRACT FROM AUTHOR]

The ecological environment in the Tarim River Basin is extremely fragile. The ecological problems resulting from local economic development in this region have been a hot topic for the ecological scientists. There is a new trend to use information technologies to monitor the ecological environment dynamically by means of the modem information technologies. To meet the constant requirements of long-term monitoring of a region ecological environment, this paper focuses on the vegetation coverage and land desertification on the basis of the information technologies of remote sensing (RS), geographic positioning systems (GPS), geographic information systems (GIS), and data warehouse, applying the theory of the integrative spatial and attribute data, seamless integration of multi-source data. The ecological environment for the lower reaches of the Tarim River is analysed qualitatively and quantitatively. The investigated database is established and managed through multisource and multi-type massive data, which are collected from three periods of the remote sensing images and background data in 1999, 2002 and 2004, respectively. The methods of system classification, remote sensing interpretation, vegetation coverage, conversion models construction, and massive data built database are applied to analyse the data concerned during the dynamically monitoring the ecological environment in the Tarim River Basin. The results show that the vegetation coverage has increased gradually each year after the emergent water transportation project was started, whereas the desertification area has decreased, some sand areas have obviously retreated. The speed of improvement in the east part of the river has been higher than in the west. The ecological environment in the lower reaches of the Tarim River has tended to improve after the emergent water transportation project in the lower reaches of the Tarim River Basin. In conclusion, the emergent water delivery plays a decisive and significant role in promoting the ecological environment in this region. [ABSTRACT FROM AUTHOR]

Monitoring and estimating potential snow disasters in pastoral areas of northern Xinjiang Province are important for decision-making in hazard reduction and prevention. In this paper, four scenes of NOAA/AVHHR (Advanced Very high Resolution Radiometer) images were combined with ground observation data in the north of Xinjiang Province to establish a model for monitoring snow depth. Using a linear spectral decomposition method, the pixel-based snow coverage and snow classification were studied. The spatial characteristics of snow, grassland, animal and climate factors were used to develop two new quantified indices for estimating the potential snow hazard grade and for integrated evaluation of snow disasters to grassland animal husbandry. The criteria for snow hazard grade and snow disaster evaluation were established. Results indicated that: (i) a pixel-based index K1, based on grassland yield, animal capacity, utilisable grassland area coefficient and seasonal grazing utilisation scenarios, can be quantitatively integrated to reflect the grassland capability of resisting snow disasters; (ii) the snow hazard index (K) systematically expresses the spatial and temporal changes of grassland and snow cover, and analyses, and predicts and evaluates the snow hazard grade under conditions where climatic and animal husbandry information may be unavailable during snow disasters. This index plays an important role in studies on early warning of snow hazard in pastoral areas; (iii) the integrated snow disaster evaluation index (E) and related classification criteria reflect the details of snow disaster magnitude in temporal and spatial scales, which provide the basic information for dynamic monitoring and integrated evaluation on snow disasters in pastoral areas. [ABSTRACT FROM AUTHOR]