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Studying the spatiotemporal evolutionary characteristics of vegetation and the effect of precipitation changes is necessary for understanding the regional ecological environment. We used trend analysis, partial correlation analysis, significance tests, and residual trend analysis to analyze the spatiotemporal evolution characteristics and driving factors of fractional vegetation cover (FVC) in the Jinghe River Basin (JRB) from 1998 to 2019. The results showed that vegetation coverage in the JRB significantly improved from 1998 to 2019. FVC showed an increasing trend in 90.64% of the areas in the JRB, and the overall annual change in FVC was extremely significant (p ≤ 0.01). However, precipitation in the JRB showed an insignificant increasing trend; the annual distribution of precipitation developed in a uniform direction and the precipitation centroid tended to move backward. The area with a significant correlation between the FVC change and the precipitation concentration index accounted for the largest proportion (18.47%). Precipitation changes in the JRB generally favored vegetation recovery; however, the effect was limited and non-precipitation factors dominated the changes in FVC. Our study contributes to a more comprehensive understanding of the effects of changes in precipitation patterns on vegetation cover to facilitate regional ecological protection. • FVC in the JRB has improved significantly and is likely to continue to improve. • Precipitation in the JRB is increasing and developing in a uniform direction. • The effect of changes in the precipitation structure on FVC must be considered. • Non-precipitation factors dominated the FVC changes in the JRB. [ABSTRACT FROM AUTHOR]

Studying the changes of land use and its impacts on ecological condition in coastal areas is of great significance for understanding the evolution of the regional ecological conditions and even global change. In this study, the study area encompassed 10 provincial administrative units of China's coastal areas, covering a total of 56 cities. Based on the land use and land cover data in 1980, 1990, 1995, 2000, 2005, 2010 and 2015 and the corresponding elevation data, we assessed ecological conditions and its temporal dynamic evolution and spatial differentiation characteristics with the ecological grade index method. The effects of the elevation differentiation and land-sea gradient on the ecological condition in China's coastal areas were analyzed. The results showed that the ecological conditions of China's coastal areas were basically stable and deteriorated on the whole although partially improved from 1980 to 2015. With Hangzhou Bay as a boundary-belt, ecological conditions in southern parts were better than that in the northern parts. The ecological grade index differed significantly with the variation of elevation. The areas with elevation below 10 m were in relatively poor ecological condition, and the regions below 30 m had the most obvious changes of ecological conditions. Moreover, the ecological conditions increased with elevation, with a gradual turn-around trend of improvement at above 400 m. There was a gradient characteristic of the ecological grade index in China's coastal areas, showing a high-low-high pattern from land to sea. Furthermore, the maximum value of ecological condition changes appeared at a distance of 10 km to the coastline, and the values decreased with the increasing of distance to the coastline. [ABSTRACT FROM AUTHOR]

Desertification causes not only a reduction of vegetation cover and surface moisture but also the expansion of sandy land. Considering that the modified soil adjusted vegetation index (MSAVI) is linearly related to canopy cover, albedo plays a significant role in the surface energy balance, and the sandy feature index (SFI) can identify sandy land. This paper proposed a desertification monitoring index (DMI) based on MSAVI, albedo and SFI to accurately monitor desertification. In addition, three classification methods (quantile grading, the susceptibility index segmentation method, and Jenks Natural Breaks) were used to divide the DMI into non-desertification, mild desertification, moderate desertification, and severe desertification. The relative exponential entropy model was adopted to select the optimal method for desertification classification. Spatiotemporal distributions of desertification in Duolun County were finally observed from 2005 to 2020. The results show that (1) although MSAVI, albedo and SFI have their own advantages in monitoring desertification, a single indicator is not suitable for various land uses. The DMI can combine the advantages of MSAVI, albedo and SFI in desertification monitoring and is more consistent with the real desertification levels. (2) Compared with quantile grading and susceptibility index segmentation methods, Jenks Natural Breaks has the smallest relative exponential entropy (2.124), with an overall classification accuracy of 88.21%, indicating that Jenks Natural Breaks is an optimal method for desertification monitoring. (3) Although approximately 1708.60 km2 of desertification areas have improved from 2005 to 2020, accounting for 44.23% of the whole area, the remaining areas are still under fluctuating or degraded conditions. This shows that the local government has made some progress in desertification protection, but the areas under fluctuating or degraded conditions still require protection or management. • Constructing the desertification monitoring index (DMI) by integrating multidimensional spatial information. • Using relative exponential entropy model to evaluate desertification classification methods. • Desertification protection categories were divided into four regions, and management strategies were suggested. [ABSTRACT FROM AUTHOR]

Studying the spatiotemporal changes in coastlines and the underlying mechanisms is important for the rational development and utilization of coastal resources and sustainable economic development. For the first time, this study analyzes twenty years of spatiotemporal changes in the coastline of Jiaozhou Bay (JB), Qingdao, by integrating high-resolution remote sensing images from Google Maps and Landsat time series data at five-year intervals from 2000 to 2020. Five coastlines were extracted using the normalized difference water index and Otsu threshold segmentation method. The index of coastline type diversity (ICTD), index of coastline utilization degree (ICUD), and end point rate were applied to the systematic study of the evolution of the coastline of JB for the first time. The results show that the total length of coastline decreased, with the natural coastline accounting for only approximately 10% of the sharp decrease in the first five years and slow increase in the last 15 years. The ICTD was >0.7, and the ICUD increased from 231.7 to 261.5, indicating an increase in the influence of humans. The coastline also moved seaward with an average accretion rate of 47.5 km/year, resulting in a 24.5 km2 reduction in the area of the bay over the 20 years. This was caused by human activities such as port construction. In addition, the degree of correlation between coastline changes and socioeconomic factors was analyzed using grey correlation analysis. Based on this, the forces driving the changes to the coastline were explored, and suggestions for coastal zone management were proposed. [ABSTRACT FROM AUTHOR]

The republic of Tatarstan is one of the most growing state in Russia in terms of industrialization and modernization with various natural disasters and intense human activities which brought dramatic changes in the ecological process and then led to serious ecological vulnerability. Therefore this research work proposed an analytical framework based on remote sensing (RS), geographical information system (GIS), and analytical hierarchy process (AHP) for spatiotemporal ecological vulnerability analysis at pixel level from 2010 to 2020 and developed a driver-pressure-state-impact-response (DPSIR) framework based on 23 indicators by the AHP weight method to compute ecological vulnerability index (EVI). Further, EVI was classified into five levels based on natural breaks in ArcGIS software as potential, slight, light, moderate, and heavy levels. All 23 indicators were generated from different remote sensing and socio-economic data, processed through digital image processing techniques in terms of removing errors, projection, standardization, and results were saved in GIS format. Results indicate that from 2010 to 2020, EVI was continuously increased from 0.419 to 0.429, and its changes associated with regional vulnerability events and their impact in the region. The moderate level EVI was covering the highest area in all three years with very few changes and continuously increasing. Results also indicate that higher human-socio-economic activities and pressure on natural resources increased ecological vulnerability. This research work is useful to identify main causes and responsible indicators for ecological vulnerability as well as suitable for real-time EVI mapping, monitoring at any scale and region. • Develop EVI based on satellite, socio-economic and demographic data. • Used remote sensing, GIS, GPS and DIP techniques for EVI analysis. • Analysis all indicators under DPSIR framework with AHP weight method. • Identify spatiotemporal changes in ecological vulnerability and their cause. • Suggestion for sustainable development and protection of eco-environment. [ABSTRACT FROM AUTHOR]

Abstract: The recent decades witness the rapid urbanization around the world. Phoenix, as one of the fastest growing city in United States, experienced fast urbanization during the global urbanization tide. This study analyzes spatiotemporal characteristics during the urban expansion in Phoenix metropolitan area by the support of GIS. In this study, we divided the land use of Phoenix into four classes: urban, agricultural land, desert and recreation land, and chose 4 class-level metrics: PLAND, PD, LSI, and LPI and 3 landscape-level metrics: SHDI, AWMPFD, and CONTAG, to qualify the urbanization dynamics of Phoenix. From 1912 to 2000, desert occupied the largest area of land in Phoenix, but its area kept decreasing; the urban area and its shape complexity kept increasing, and its fragmentation increased first and then declined dramatically; the agricultural area increased at first but then large proportion of its area converted to urban use; recreation land also showed a similar pattern to urban area. [ABSTRACT FROM AUTHOR]

Although the aquaculture industry is a major supplier of aquatic products used as protein sources for humans, the rapid expansion of aquaculture in recent decades has led to numerous environmental problems in coastal and lakeside areas. Nonetheless, the spatiotemporal understanding of aquaculture ponds is limited, despite playing a crucial role in the management, planning, and development within the aquaculture industry. Remote sensing images can be used to acquire information pertaining to aquaculture; however, the automatic identification of aquaculture ponds remains an important challenge due to their complex spectral and spatial characteristics. This study utilized a decision-tree classifier using Landsat data derived from the Google Earth Engine (GEE) cloud platform to automatically identify large-scale aquaculture ponds in Jiangsu Province through seven time slices between 1988 and 2018. The results showed that the area allocated to aquaculture ponds in Jiangsu Provinces has continually increased from 660.29 km2 in 1988 to 4097.95 km2 in 2018, and three regions with dense aquaculture pond distributions have developed in the coastal zone, as well as in the south and center of Jiangsu Province. The area devoted to aquaculture ponds grew exponentially from 1988 to 2003 and later experienced a slower growth rate. Moreover, inland aquaculture pond areas began to decrease since 2013; however, a rapid expansion endures in the coastal area. Socio-economic development and industry policies are the main drivers of aquaculture growth; however, the strengthening of national environmental protection policies in the 21st century has contributed to a slowdown in these trends. Image 1 • A decision tree classifier was proposed to automatically extract aquaculture ponds. • Key aquaculture regions formed in the coastal, south, and center inland zones. • The area of aquaculture ponds expanded about 6.2 times over 1988–2018. • The center of gravity showed a generally northward trajectory after 1993. • Environmental protection slowing the uncontrolled expansion of aquaculture ponds. [ABSTRACT FROM AUTHOR]