Analysis Of Spatio-temporal Changes Research Articles

Remote Sensing Monitoring and Analysis of Spatiotemporal Changes in China’s Anthropogenic Carbon Emissions Based on XCO2 Data

The monitoring and analysis of the spatiotemporal distribution of anthropogenic carbon emissions is an important part of realizing China’s regional “dual carbon” goals; that is, the aim is for carbon emissions to peak in 2030 an to achieve carbon neutrality by 2060, as well as achieving sustainable development of the ecological environment. The column-averaged CO2 dry air mole fraction (XCO2) of greenhouse gas remote sensing satellites has been widely used to monitor anthropogenic carbon emissions. However, selecting a reasonable background region to eliminate the influence of uncertainty factors is still an important challenge to monitor anthropogenic carbon emissions by using XCO2. Aiming at the problems of the imprecise selection of background regions, this study proposes to enhance the anthropogenic carbon emission signal in the XCO2 by using the regional comparison method based on the idea of zoning. First, this study determines the background region based on the Open-Data Inventory for Anthropogenic Carbon dioxide (ODIAC) dataset and potential temperature data. Second, the average value of the XCO2 in the background area was extracted and taken as the XCO2 background. On this basis, the XCO2 anomaly (XCO2ano) was obtained by regional comparison method. Finally, the spatiotemporal variation characteristics and trends of XCO2ano were analyzed, and the correlations between the number of residential areas and fossil fuel emissions were calculated. The results of the satellite observation data experiments over China from 2010 to 2020 show that the XCO2ano and anthropogenic carbon emissions have similar spatial distribution patterns. The XCO2ano in China changed significantly and was in a positive growth trend as a whole. The XCO2ano values have a certain positive correlation with the number of residential areas and observations of fossil fuel emissions. The purpose of this research is to enhance the anthropogenic carbon emission signals in satellite observation XCO2 data by combining ODIAC data and potential temperature data, achieve the remote sensing monitoring and analysis of spatiotemporal changes in anthropogenic carbon emissions over China, and provide technical support for the policies and paths of regional carbon emission reductions and ecological environmental protection.

Analysis of spatiotemporal changes and driving factors of blue-green space in six metropolitan areas of the Yangtze River Delta based on the PLUS model

This study aims to examine the evolution of blue-green space and urban development in the Yangtze River Delta region using Arcgis spatial analysis, landscape pattern index, and PLUS model to inform urban planning and environmental protection efforts. The results indicate that:1) Blue space in the region's six metropolitan areas initially increased, then decreased significantly from 2000-2020, while green space continued to decline. However, recent environmental policies have mitigated this trend. 2) Improved living standards have led to increased green space within cities, but construction land occupation has reduced green space outside of cities. 3) Blue-green space is becoming more fragmented, scattered, and homogenized in shape, and that economic development influences green space, while different regional development patterns impact blue space. 4) Socioeconomic and natural factors both drive blue-green space to change. There is a "dual desirability" between blue-green space protection and urban development. In conclusion, the scale and landscape pattern of blue-green space have changed significantly in the last 20 years, which are greatly influenced by urban development, and the blue-green space differences among metropolitan areas are obvious. Therefore, it is necessary to protect and optimize the blue-green space pattern of the metropolitan areas.

Analysis of spatiotemporal changes in cultural heritage protected cities and their influencing factors: Evidence from China

In the Anthropocene, it is essential to analyze the spatiotemporal characteristics and causes of land use and landscape pattern changes in cultural heritage protected cities (CHPCs) to promote the sustainable development of CHPCs. Here we use PLUS model to explore and forecast the spatiotemporal changes from 2000 to 2060 in Pingyao, Gucheng, She and Langzhong, where the Four Ancient Cities of China are located. First, we found the spatiotemporal changes and the driving factors with land use transfer matrix, landscape metrics and a random forest analysis strategy. Furthermore, we simulated and predicted the spatiotemporal patterns in 2030 and 2060 by applying a CA model and landscape indexes, testing the carbon peak and carbon neutralization target achieved. The results demonstrate that urban expansion leads to an increase tendency in land use diversity and landscape heterogeneity, as well as adversely affecting the landscape pattern of ecological land. Altitude, socioeconomic conditions, and policies regarding the development and utilization of land resources all significantly impact the four cities’ spatiotemporal changes. Both the land use pattern and the landscape pattern will become more complex significantly in the future. This study offers a new insight to compare and analyze the spatiotemporal changes of different CHPCs.

Spatiotemporal Landscape Pattern Analyses Enhanced by an Integrated Index: A Study of the Changbai Mountain National Nature Reserve

The analysis of spatiotemporal changes of landscape patterns is of great significance for forest protection. However, the selection of landscape metrics is often subjective, and existing composite landscape metrics rarely consider the effects of spatial correlation. A more objective approach to formulating composite landscape metrics involves proper weighting that incorporates spatial structure information into integrating individual conventional metrics selected for building a composite metric. This paper proposes an integrated spatial landscape index (ISLI) based on variogram modeling and entropy weighting. It was tested through a case study, which sought to analyze spatiotemporal changes in the landscape pattern in the Changbai Mountains over 30 years based on six global land-cover products with a fine classification system at 30 m resolution (GLC_FCS30). The test results confirm: (1) spatial structure information is useful for weighting conventional landscape pattern metrics when constructing ISLI as validated by correlation analysis between the incorporated conventional metrics and their variogram ranges. In terms of the range parameters of different land cover types, broadleaf forest and needleleaf forest have much larger range values than those of other land cover types; (2) DIVISION and PLAND, two of the conventional landscape metrics considered for constructing ISLI, were assigned the greatest weights in computing ISLI for this study; and (3) ISLI values can be used to determine the dominant landscape types. For the study area, ISLI values of broadleaf forests remained the largest until 2020, indicating that forest landscape characteristics were the most prominent during that period. After 2020, the dominance of needleleaf forest gradually increased, with its ISLI value reaching a maximum of 0.91 in 2025. Therefore, the proposed ISLI not only functions as an extension and complement to conventional landscape metrics but also provides more comprehensive information concerning landscape pattern dynamics.

Spatiotemporal changes in land use and landscape fragmentation in coastal plain areas—A case study of Yancheng City, China

Coastal regions in China have the highest levels of economic development due to their prominent geographical locations and corresponding supporting policies. Global research concerns now include the spatiotemporal analysis of urban land use and landscape fragmentation in coastal plains. Research on coastal land use in China and related topics can provide a scientific basis for urban land use and sustainable development in other countries. In this study, a quantitative analysis of spatiotemporal changes in land use and landscape fragmentation was carried out based on land use data from Yancheng City, China. The dynamic degree of land use, Markov transition matrix and landscape indices were all subject to analysis. The results demonstrated that land use patterns in Yancheng City underwent substantial changes from 1990 to 2020. Cropland was found to be the dominant landscape in Yancheng City. Water rapidly increased whereas Grassland and Woodland dramatically decreased during the study period. Frequent transformations in and out of urban land were observed. In the late study period, the Tidal flat was gradually transformed into Grassland and Water. Variations in the landscape indices indicated significant changes in the landscape patterns. The landscape patterns in Yancheng City over a 30-year study period were found to be complicated. The special characteristics of Tidal flat land areas further intensified the landscape heterogeneity of the spatial range. Based on the above analyses, it was concluded that human activities (urban construction, beach reclamation, land development, and utilization activities) increased landscape fragmentation in Yancheng City throughout the three stages and gradually increased the degree of disturbance. These findings indicated that human intervention, the social economy and urban planning development could influence landscape fragmentation in coastal cities. This study used a spatially explicit approach to understand the relevant drivers of urban land-use change in a coastal plain. Moreover, it reduced the knowledge gap between land-use change and landscape fragmentation research from a temporal and spatial perspective.

Machine learning-based prediction and assessment of recent dynamics of forest net primary productivity in Romania

While the analysis of spatio-temporal changes in the net primary productivity (NPP) of forests can provide critical information on carbon cycle and climate change, these ecological trends have remained unclear in many countries worldwide, including Romania. By using complex (satellite, forest and climate) data, many sophisticated (machine learning) algorithms and some widely applied (the Mann-Kendall test and Sen's slope estimator) statistical procedures, this study investigates, for the first time, recent forest NPP trends (1987–2018) that occurred in Romania, in relation to climate change that affected the country over the past decades. Following the modelling, mapping and assessment of NPP dynamics, results showed almost exclusively positive trends for this ecological parameter, which accounts for ∼99% of all forest NPP changes that occurred throughout the country, after 1987. Interestingly, almost three quarters (∼73%) of all NPP increasing trends are statistically significant, which indicates that Romania's forests have recently experienced a large-scale improvement in carbon fluxes and stocks. Investigations of eco-climatic relationships suggest that climate change has partially contributed to these surprising NPP dynamics observed in recent decades. All these findings can provide valuable information for forest management and for many stakeholders and policymakers who operate in the forestry and climate fields in Romania.

An improved framework for assessing the impact of different urban development strategies on land cover and ecological quality changes -A case study from Nanjing Jiangbei New Area, China

Land cover (LC) change has been highly valued by policy makers for driving the evolution of regional ecological and environmental quality. The core area of Nanjing Jiangbei New Area (NJNA) has undergone the development process from traditional urbanization to new-type urbanization (NTU), however the impacts of different urban development strategies or pathways on the change pace and direction of land cover and ecological quality are not adequately understood in a quantitative manner which is not conducive for similar regions to learn lessons from past development processes. Based on RapidEye, GF-1 and SPOT-6 images acquired in 2009, 2015 and 2021, a Modified Mask-RCNN framework was devised and implemented to create a high accuracy LC dataset first in this analysis. Then, the applicability or efficacy of the Remote Sensing Ecological Index (RSEI) model and the Regional Optimized Remote Sensing Ecological Index (RO-RSEI) model, developed from the Landsat observations was compared in characterizing the ecological quality. Finally, the attribution analysis of spatio-temporal changes in LC and their induced ecological quality evolutions was carried out in conjunction with socio-economic factors. The results showed that the overall accuracies of the LC classifications were estimated at 91.50 % (2009), 91.25 % (2015), and 92.75 % (2021), respectively. The mean values of RO-RSEI analyses were at 0.652, 0.649, and 0.656, respectively, indicating that in the past 12 years, the ecological quality first got degraded then got ameliorated along with the urbanization. The urban green spaces got restored and increased in the NTU process, and protecting the forest and cropland was also reinforced thanks to the strict enforcement of related policies. These positive changes are attributed to the popularization of ecological civilization philosophy and the escalation of social awareness of environmental protection and low carbon development. The proposed framework can provide technical reference for ecological quality monitoring under the progress of NTU.

Analysis of Spatiotemporal Changes of Ecological Environment Quality and Its Coupling Coordination with Urbanization in the Yangtze River Delta Urban Agglomeration, China.

It is inevitable that urban agglomeration will have a coercive impact on the regional Ecological Environment Quality (EEQ) as a consequence of high-speed urbanization. Balancing the EEQ and urbanization development has become a problem worthy of attention. In order to objectively evaluate the EEQ of the Yangtze River Delta Urban Agglomeration (YRDUA) and explore the impact of the urbanization process on it, this paper is based on the Modified Remote Sensing Ecological Index (MRSEI) and the Comprehensive Night Light Index (CNLI), respectively, and conducts a quantitative assessment of the YRDUA in China from 2000 to 2020. The results show that: (1) From 2000 to 2020, the MRSEI of the YRDUA first decreased and then increased, and the ecological environment quality degraded first and then improved; however, there were significant differences between regions. The ecological environment quality in the south is obviously better than that in the north, and the ecological environment quality in the north changes more drastically, and the low value area of MRSEI will gradually move downstream as time changes; (2) During the study period, the YRDUA formed a hierarchical and progressive urbanization pattern. The inland urbanization process expanded from east to west along the Yangtze River, and the urbanization process of coastal cities expanded from Shanghai as the center to the north and south with high-intensity urbanization cities concentrated in Shanghai and its surrounding cities and low-intensity urbanization cities distributed in the western part of the urban agglomeration; (3) The Coupling Coordination Degree (CCD) between urbanization and EEQ in the YRDUA has continuously improved with an increase of 28.57% in the past 21 years, and the number of cities with high level coupling continues to rise, while the number of medium level coupling cities and low level coupling cities has declined. As a large-scale and long-term analysis of changes in the EEQ and the urbanization process, this study can provide theoretical support for policymakers to formulate mesoscale development planning, EEQ monitoring, and environmental protection policies.

Spatio-temporal variability of terrestrial water storage in the Yangtze River Basin: Response to climate changes

The Yangtze River Basin (YRB) is an important region for China's economic development. However, it has a complex terrain layout, most of which is affected by monsoon weather, and the geographical and temporal distribution of water resources is severely unbalanced. Therefore, the detailed analysis of spatio-temporal water mass changes is helpful to the development and rational utilization of water resources in the YRB. In this study, the variation of terrestrial water storage (TWS) is monitored by Gravity Recovery and Climate Experiment (GRACE) satellite gravity. We find that the University of Texas Center for Space Research (CSR) solution shows a notable difference with the Jet Propulsion Laboratory (JPL) in space, but the general trend is consistent in time series. Then the GRACE inferred water mass variation reveals that the YRB has experienced several drought and flood events over the past two decades. Global Land Data Assimilation System (GLDAS) results are similar to GRACE. Furthermore, the overall precipitation trend tends to be stable in space, but it is greatly influenced by the strong El Niño-Southern Oscillation (ENSO), which is the response to global climate change. The upper YRB is less affected by ENSO and shows a more stable water storage signal with respect to the lower YRB.

Analysis of spatio-temporal changes and driving forces of cultivated land in China from 1996 to 2019

Cultivated land is an important prerequisite and guarantee for food production and security, and the change of cultivated land resources in China has always been concerned. National land survey is an effective way to accurately grasp the area and distribution of cultivated land resources. However, due to the differences in technical means and statistical standards at different stages, there are obvious breakpoints among the cultivated land area data of the three land surveys in China, which hinders the in-depth study of the spatio-temporal distribution of cultivated land resources in long-time series. The Autoregressive Integrated Moving Average model is used to reconstruct and mine the cultivated land area data from 1996 to 2019 based on the data of the third land survey in China. The spatio-temporal variation characteristics of cultivated land area are explored by using Geographic Information System spatial analysis, and the driving factors of cultivated land change are analyzed based on Geographical Detector (GeoDetector) from the perspective of social, economic, agricultural and natural. The results show that the area of cultivated land in China decreased continuously from 1996 to 2019, with a sharp decrease from 1996 to 2004 and a slow decrease from 2005 to 2019. From 1996 to 2019, there were obvious spatial differences in the change of cultivated land area in 31 provincial units. From 1996 to 2008, the cultivated land area in 29 provinces showed a downward trend, especially in the central and northern regions such as Shaanxi, Sichuan and Inner Mongolia. From 2008 to 2019, the cultivated land area in the underdeveloped areas of Heilongjiang, Jilin, Liaoning, Xinjiang, Gansu and Tibet increased significantly, while the rest showed a downward trend. Factor detection found that the q values of population, regional gross domestic product grain output, the proportion of the added value of the primary industry and average slope were all more than 0.5, which had an important impact on the change of cultivated land area. The explanatory power of the interaction between factors on the change of cultivated land area is enhanced in different degrees compared with the single factor effect, which is manifested in the enhancement of bilinear or nonlinear enhancement, and the interaction of different factors promotes the change of cultivated land area. The change of cultivated land area is the result of complex interaction between factors, and is closely related to the land policy in the same period.

PARTICULAR FEATURES IN CHANGES OF CLIMATE COMFORT FOR HUMAN IN BELARUS FOR MONTHS AND SEASONS OF THE YEAR

In this article the study of comfort level of climate has been performed on the basis of analysis of spatiotemporal changes of bioclimatic indexes: effective temperature, equivalent-effective temperature and bioclimatic air heat content index for the period from 1966 to 2020. The increase in average temperatures, the decrease in wind speed and insignificant changes in air humidity led to the improvement in the comfort level of climate and weather and decrease in frequency of days with very cold weather. Usually, the increase in climate comfort level was less evident in mid-seasons than in winter and summer. In total, since late 1970s the most significant increase in values of climate comfort indices was observed in July–August and November–December; significant increase in values of effective temperature and equivalenteffective temperatures was also observed in February–April. This pattern of changes corresponds to climate changes during distinct phases of warming period, characterized by more significant increase in the average temperature in certain
 seasons. Thus, due to the spatiotemporal heterogeneity of climate warming, the nature of changes, bioclimatic comfort
 indices is also heterogeneous. Nevertheless, it is possible to state that values of comfort indexes steadily increase since 1980s.

Optimal ranges of social-environmental drivers and their impacts on vegetation dynamics in Kazakhstan

Kazakhstan is part of the Eurasian Steppes, the world's largest contiguous grassland system. Kazakh grassland systems are largely understudied despite being historically important for agropastoral practices. These grasslands are considered vulnerable to anthropogenic activities and climatic variability. Few studies have examined vegetation dynamics in Central Asia owing to the complex impacts of moisture, climatic and anthropogenic forcings. A comprehensive analysis of spatiotemporal changes of vegetation and its driving factors will help elucidate the causes of grassland degradation. Here, we investigated the individual and pairwise interactive influences of various social-environmental system (SES) drivers on greenness dynamics in Kazakhstan. We sought to examine whether there is a relationship between peak season greenness and its drivers – spring drought, preceding winter freeze-thaw cycles, percent snow cover and snow depth – for Kazakhstan during 2000–2016. As hypothesized, snow depth and spring drought accounted for 60 % and 52 % of the variance in the satellite-derived normalized difference vegetation index (NDVI) in Kazakhstan. The freeze-thaw process accounted for 50 % of NDVI variance across the country. In addition, continuous thawing during the winter increased vegetation greenness. We also found that moisture and topographic factors impacted NDVI more significantly than socioeconomic factors. However, the impacts of socioeconomic drivers on vegetation growth were amplified when they interacted with environmental drivers. Terrain slope and soil moisture had the highest q-values or power of determinant, accounting for ~70 % of the variance in NDVI across the country. Socioeconomic drivers, such as crop production (59 %), population density (48 %), and livestock density (26 %), had significant impacts on vegetation dynamics in Kazakhstan. We found that most of the pairwise interactive influences of the drivers exhibited bi-factor enhancement, and the interaction between soil moisture and elevation was the largest (q = 0.92). Our study revealed the optimal ranges and tipping points of SES drivers and quantified the impacts of various driving factors on NDVI. These findings can help us identify the factors causing grassland degradation and provide a scientific basis for ecological protection in semiarid regions.

Spatio-temporal assessment of land use dynamics and urbanization: linking with environmental aspects and DPSIR framework approach.

Urbanization is the main force of the global environmental as well as land use land cover changes (LULC). Urbanization is caused by prompt increase in population growth, migration, and urge for employment. In this study, Geographic Information System (GIS) was applied for the analysis and representation of spatio-temporal changes in LULC in Peshawar district and these results were linked with environmental aspects and Driver-Pressure-State-Impact-Response (DPSIR) framework approaches. For LULC classification, the Landsat freely available satellite imageries were used. The analysis revealed that the vegetation cover has increased from 37.8% of the total area to 71.3% during 1990-2020 and this change in vegetation is attributed to the government initiatives of Billion Tree Tsunami afforestation project after 2014 which has substantially decreased the barren land (from 66% in 1990 to 19% in 2020) in southeastern part of Peshawar district. Although, there was reduction in the vegetation cover in the past but due to extensive plantation between 2014 and 2020 resulted rapid increase in vegetation cover in the study area. The results of the present study detected a remarkable increase in built-up area which has increased almost 224.6% from 1990 to 2020. The study area population has increased from 2.12 million during 1998 to 4.26 million in 2017. The DPSIR results revealed that drivers and pressure have adverse effects on the carrying capacity of natural resources which have resulted deterioration of ecosystem. The resulted reduced capacity leading towards land degradation, loss of agricultural land, decline the groundwater level and resulted in pluvial flooding in Peshawar district. Government and environmental protection agency should implement the land use bylaws to reduce the rapid and unplanned urban growth and its negative impacts on natural environment.

Fractal analysis of spatio-temporal changes of forest cover in Istanbul, Turkey

In this study, the spatio-temporal changes in forest cover in Istanbul, one of the provinces with the most changes in forest areas in Turkey due to the pressure of urbanization and industrialization, were investigated using fractal analysis. The areal changes and changes in spatial patterns were determined to assess the spatio-temporal changes in the period 2000–2017. Fragmentation/compactness and heterogeneity/homogeneity of forest cover were determined by fractal dimension and lacunarity index, respectively. The results show that the forest areas have significantly decreased and become more fragmented and heterogeneous. In conclusion, this study reveals that fractal analysis can provide considerable information in the examination and interpretation of spatial changes in forest areas.

Analysis of spatiotemporal changes and driving factors of desertification in the Africa Sahel

The transitional characteristics of desert grasslands in the Sahel determine the ecosystem’s fragility, which is extremely susceptible to the expansion and reversal of land desertification under the influence of climate change and anthropogenic activities. Accordingly, monitoring desertification dynamics is essential to combat this process. Based on Moderate Resolution Imaging Spectroradiometer (MODIS) data, this study analysed the applicability of different feature space models to monitor desertification levels in the Sahel from 2000 to 2020, revealing the optimal monitoring model, analysing the spatiotemporal changes and primary driving factors. The results were as follows: In the Sahel, the albedo-modified soil adjusted vegetation index (MSAVI) based on the point-to-point model is the best for desertification monitoring, with an overall accuracy of 86.78%. Generally, the level of desertification was reduced from 2000 to 2020, the area of extremely severe desertification decreased by retreating northward; and the areas of light, moderate, and severe desertification increased slowly by expanding northward. Light, moderate, and severe desertification lands were more sensitive to climate change and anthropogenic activities, undergoing greater change intensity. Precipitation was the most influential factor determining the spatial distribution of desertification in the Sahel, with anthropogenic activities also having a significant effect on the desertification level. This study comprehensively analysed desertification patterns in the Sahel and identified the primary driving factors, which are essential to inform Sahelian desertification control mechanisms in the future.

Spatiotemporal Variations of Aerosols in China during the COVID-19 Pandemic Lockdown

The widespread nature of the coronavirus disease 2019 (COVID-19) pandemic is gradually changing people’s lives and impacting economic development worldwide. Owing to the curtailment of daily activities during the lockdown period, anthropogenic emissions of air pollutants have greatly reduced, and this influence is expected to continue in the foreseeable future. Spatiotemporal variations in aerosol optical depth (AOD) can be used to analyze this influence. In this study, we comprehensively analyzed AOD and NO2 data obtained from satellite remote sensing data inversion. First, data were corrected using Eidetic three-dimensional-long short-term memory to eliminate errors related to sensors and algorithms. Second, taking Hubei Province in China as the experimental area, spatiotemporal variations in AOD and NO2 concentration during the pandemic were analyzed. Finally, based on the results obtained, the impact of the COVID-19 pandemic on human life has been summarized. This work will be of great significance to the formulation of regional epidemic prevention and control policies and the analysis of spatiotemporal changes in aerosols.

Spatiotemporal Change Analysis of Soil Moisture Based on Downscaling Technology in Africa

Evaluating the long-term spatiotemporal variability in soil moisture (SM) over Africa is crucial for understanding how crop production is affected by drought or flooding. However, the lack of continuous and stable long-term series and high-resolution soil moisture records impedes such research. To overcome the inconsistency of different microwave sensors (Advanced Microwave Scanning Radiometer-EOS, AMSR-E; Soil Moisture and Ocean Salinity, SMOS; and Advanced Microwave Scanning Radiometer 2, AMSR2) in measuring soil moisture over time and depth, we built a time series reconstruction model to correct SM, and then used a Spatially Weighted Downscaling Model to downscale the SM data from three different sensors to a 1 km spatial resolution. The verification of the reconstructed data shows that the product has high accuracy, and can be used for application and analysis. The spatiotemporal trends of SM in Africa were examined for 2003–2017. The analysis indicated that soil moisture is declining in Africa as a whole, and it is notably higher in central Africa than in other subregions. The most significant decrease in SM was observed in the savanna zone (slope < −0.08 m3 m−3 and P < 0.001), followed by South Africa and Namibia (slope < −0.07 m3 m−3 and P < 0.01). Seasonally, the most significant downward trends in SM were observed during the spring, mainly over eastern and central Africa (slope < −0.07 m3 m−3, R < −0.58 and P < 0.001). The analysis of spatiotemporal changes in soil moisture can help improve the understanding of hydrological cycles, and provide benchmark information for drought management in Africa.

Спутниковый мониторинг процессов эвтрофирования участка трансграничной р. Аргунь (Хайлар) с использованием спектрального индекса цветения поверхностных водорослей (SABI), по данным дистанционного зондирования Landsat

The article is devoted to the monitoring of eutrophication processes in the transboundary section of the Argun River according to Landsat 8 OLI remote sensing data. To assess the impact of intense algal blooms in the water of the Argun River, a 956 km long section from the city of Khailar (China) to the village of Argunsk (Russia) was selected for the time period from May to October 2020, corresponding to the growing season. Based on the analysis of spatio-temporal changes in the surface algal bloom index (SABI), the main areas of eutrophication and time intervals of the greatest deterioration in the quality of the waters of the Argun River were identified. The maximum values of the SABI index indicate the development of algae near the water surface, and low or negative values are an indicator of the development of algae in the deeper layers of the water column, or their absence. It is noted that the SABI index is best suited for assessing the intensity of blooming of river water, which does not have a significant depth, in cases of predominantly superficial concentration of algae. In order to separate the mask of the water surface of the river to ensure the extraction of the SABI index only from the pixels related to the water body, the spectral water index MNDWI was used, the positive values of which indicate belonging to the water surface. To assess the possible sources of algae biomass entry into river waters, a comparative analysis of SABI changes in the system of floodplain lakes along the Argun River – Khara-Nur, Burgat-Nur, Khuretuy, Bara-Nur, Pandza-Nur, Khorubo-Nur, Hulun Lake located in China, was carried out

Analysis of spatio-temporal changes in Arctic Ocean ecosystem using machine learning and its impact on marine transportation system

Arctic Ocean is large and important ecosystem. Growth of human impact in last decades of XXI century leads to significant changes in different environmental variables. Main goal of this research was analysis of spatio-temporal structure of Arctic Ocean ecosystem to find main causes of its changing and impact on Arctic transportation system. For analysis of Arctic Ocean ecosystem structure, we used Bayesian network, while for spatio-temporal analysis – k-means clustering. This analysis was produced for 3 timestamps – 2007, 2012 and 2017. Main drivers of this ecosystem structure changes generally occur from human activity in this region – shipping and other marine operations. In last years, level of anthropogenic impact significantly increased, large number of territories was turned into agricultural lands, this leads to increase in number of biogenic substances falls into Arctic waters. Spatio-temporal analysis shows very significant changes in last 10 years. Despite the fact, that spatially areas of different zones in Arctic Ocean doesn’t change, qualitative changes here look major. The same as ecosystem structure, the main cause of spatio-temporal changes mainly caused by marine shipping impact.

The fate of urban wetlands in Kumasi: An analysis of customary governance and spatio-temporal changes

This study uses the city of Kumasi to explore two burning issues in urban Africa: a. implications of public and customary land governance for wetland sustainability; and b. depletion of urban wetlands. Officials of state and customary institutions were interviewed for in-depth information on the politics around wetlands management; while changes in land use and land cover (LULC) were tracked using GIS techniques. The study reveals 39% loss of wetlands between 1986 and 2019 to residential, commercial and industrial uses. This is explained by the dual positions of customary and state institutions in the management of wetlands; and the various conflicting outcomes their interplay produce. The study proposes functional collaboration between customary and state institutions in the management of urban wetlands.