Extent Of Water Bodies Research Articles

Characterization of Water Bodies through Hydro-Physical Indices and Anthropogenic Effects in the Eastern Northeast of Brazil

Brazil, despite possessing the largest renewable freshwater reserves in the world (8.65 trillion m3 annually), faces growing challenges in water management due to increasing demand. Agriculture, responsible for 68.4% of water consumption, is one of the main drivers of this demand, especially in the São Francisco River Basin, where irrigation accounts for 81% of total water withdrawals. Water bodies play a crucial role in sustaining ecosystems and supporting life, particularly along the East-West axis of Alagoas, a water-rich region in the ENEB. This study aimed to map and quantify the spatiotemporal variations of water bodies in the ENEB region and assess the impacts of human activities using MODIS satellite data, applying hydrological indices such as NDWI, MNDWI, and AWEI. Between 2003 and 2022, significant variations in the extent of water bodies were observed, with reductions of up to 100 km2 during dry periods and expansions of up to 300 km2 during wet seasons compared to dry periods. AWEI and MNDWI proved to be the most effective indices for detecting water bodies with MODIS data, providing accurate insights into water dynamics. Additionally, the MapBiomas Rios dataset, despite being resampled from a 30 m to a 500 m resolution, offered the most accurate representation of water bodies due to its methodology for data acquisition. Changes in albedo and surface temperature were also detected, highlighting the influence of climate change on the region’s water resources. These findings are crucial for guiding the sustainable management of water resources, not only in Alagoas but also in other regions of Brazil and similar semi-arid areas around the world. The study demonstrates the hydrological variability in the state of Alagoas, indicating the need for adaptive strategies to mitigate the impacts of climate change and anthropogenic pressures, supporting the need for informed decision-making in water resource management at both local and national levels.

Evaluating coastal lagoon sustainability through the driver-pressure-state-impact-response approach: a study of Khenifiss Lagoon, southern Morocco

Coastal lagoons are valuable ecosystems, providing socioeconomic benefits and supporting human populations and biodiversity. However, these systems face several challenges, making them vulnerable to both natural and human factors. In this study, we apply the Driver-Pressure-State-Impact-Response (DPSIR) Approach to conduct a comprehensive socioeconomic and environmental assessment of the Khenifiss Lagoon to promote sustainable development and support decision-makers. Located on the southern Atlantic coast of Morocco, the lagoon was designated a natural reserve in 1962, a biological reserve in 1983, and a protected wetland under the Ramsar Convention since 1980. This study represents the initial endeavor to conduct a comprehensive global and multidisciplinary environmental assessment of the lagoon by using a wide range of data sources, including relevant publications and reports, satellite images and remote sensing data, field observations, and interviews, all analyzed under the DPSIR framework. Our findings show that both natural and human factors have an impact on the ecosystem. Natural Factors associated with the geomorphological features of the region likely contribute to the silting of the lagoon, possibly intensified by a large shipwreck stuck at its inlet. Meanwhile, human factors encompass population growth (at a rate of 2% per year), tourism, shellfish farming, fishing, shellfish harvesting, and salt extraction. Our results reveal significant changes in the lagoon’s condition in recent years, including a reduction in water body extent, a probable decrease in depth, and an increase in the accumulation of solid waste, plastics, and wastewater in three sectors spanning a total surface area of 464 ha (equivalent to 7% of the lagoon), a substantial expansion of the salt mining area encompassing 368 ha, and a remarkable loss of biodiversity, manifested in declining fish stocks and seabird populations. This study showed that the lagoon is positioned as a potential site for economic growth and serves to alert stakeholders and the local population to the ecosystem’s environmental issues. Based on the findings of this study, we highly recommend regulating human activities within the lagoon, the removal of the wreck at the entrance, proper waste management, community awareness programs, and strict monitoring and enforcement of regulations to protect the environment.

Investigation of changes in land use/land cover using principal component analysis and supervised classification from operational land imager satellite data: a case study of under developed regions, Pakistan

Monitoring and understanding Land Use/Land Cover (LU/LC) is critical for sustainable development, as it can impact various environmental, social, and economic systems. For example, deforestation and land degradation can lead to soil erosion, loss of biodiversity, and greenhouse gas emissions, affecting the quality of soil, air, and water resources. The present research examined changes in (LU/LC) within the underdeveloped regions of Balochistan and Sindh provinces, which are situated in Pakistan. In order to monitor temporal variations of LU/LC, we employed Geographic Information System (GIS) technique, to conduct an analysis of satellite imagery obtained from the Landsat 8 Operational Land Imager (OLI) during the time period spanning from 2013 to 2023. In order to obtain an accurate LU/LC classification, we used principal component analysis (PCA) and a supervised classification approach using the maximum likelihood algorithm (MLC). According to the results of our study, there was a decrease in the extent of water bodies (− 593.24 km2) and vegetation (− 68.50 km2) by − 3.43% and − 0.40% respectively. In contrast, the area occupied by settlements in the investigated region had a 2.23% rise, reaching a total of 385.66 square kilometers. Similarly, the extent of barren land also expanded by 1.60%, encompassing a total area of 276.04 square kilometers, during the course of the last decade. The overall accuracy (94.25% and 95.75%) and K value (91.75% and 93.50%) were achieved during the year 2013 and 2023 respectively. The enhancement of agricultural output in Pakistan is of utmost importance in order to improve the income of farmers, mitigate food scarcity, stimulate economic growth, and facilitate the expansion of exports. To enhance agricultural productivity, it is recommended that the government undertake targeted initiatives that aimed at enhancing water infrastructure and optimizing land use to foster a sustainable ecological framework. Integrating the sustainable ecological framework provides a foundation for informed decision-making and effective resource management. By identifying areas of urban expansion, agricultural intensification, or alterations in natural LU/LC, stakeholders can design targeted conservation strategies, mitigating potential environmental degradation and promoting biodiversity conservation. In conclusion, the integration of GIS and Remote Sensing (RS) may effectively facilitate the monitoring of land use patterns over a period of time. This combined approach offers valuable insights and recommendations for the judicious and optimal management of land resources, as well as informing policy decisions.

Performance of water indices for large-scale water resources monitoring using Sentinel-2 data in Ethiopia

Evaluating the performance of water indices and water-related ecosystems is crucial for Ethiopia. This is due to limited information on the availability and distribution of water resources at the country scale, despite its critical role in sustainable water management, biodiversity conservation, and ecosystem resilience. The objective of this study is to evaluate the performance of seven water indices and select the best-performing indices for detecting surface water at country scale. Sentinel-2 data from December 1, 2021, to November 30, 2022, were used for the evaluation and processed using the Google Earth Engine. The indices were evaluated using qualitative visual inspection and quantitative accuracy indicators of overall accuracy, producer’s accuracy, and user’s accuracy. Results showed that the water index (WI) and automatic water extraction index with shadow (AWEIsh) were the most accurate ones to extract surface water. For the latter, WI and AWEIsh obtained an overall accuracy of 96% and 95%, respectively. Both indices had approximately the same spatial coverage of surface water with 82,650 km2 (WI) and 86,530 km2 (AWEIsh) for the whole of Ethiopia. The results provide a valuable insight into the extent of surface water bodies, which is essential for water resource planners and decision-makers. Such data can also play a role in monitoring the country’s reservoirs, which are important for the country’s energy and economic development. These results suggest that by applying the best-performing indices, better monitoring and management of water resources would be possible to achieve the Sustainable Development Goal 6 at the regional level.

Assessing regressive erosion effects: Unveiling riverside land use land cover changes post hydroelectric project construction

In Ecuador, the Coca Codo Sinclair Hydroelectric Project (CCSHP) provides 34 % of the country's energy demand. However, since its inauguration in 2016, regressive erosion in the Coca River has accelerated, causing the collapse of the San Rafael waterfall in 2020 and damage to oil and electricity infrastructure. This study aimed to assess the effects of regressive erosion along the Coca River through changes in land use and land cover (LULC) to identify the impact of CCSHP operations on migration, deposition, and river channel erosion during 2017–2021. The methodology includes: i) Creation of cloud-free mosaics with Sentinel-2 satellite imagery; ii) LULC classification LULC classification post-operation of the CCSHP (2017), before (2019) and after (2021) the San Rafael waterfall collapse, using the Random Forest algorithm; iii) Estimation of variations in the fluvial dynamics of the Coca River. The results showed that between 2017 and 2019, the extent of water bodies and bare soil increased by 258.58 ha (25 %) and 5195.45 ha (71 %), respectively. Meanwhile, between 2019 and 2021, the extent of sand deposits increased by 387.71 ha (42 %). These changes reflect an acceleration of regressive erosion since the construction and implementation of the CCSHP, information that can be used as an initial tool to analyze the impact of hydropower in the upper Amazon and strengthen environmental protection and risk management policies.

Freshwater megafauna shape ecosystems and facilitate restoration.

Freshwater megafauna, such as sturgeons, giant catfishes, river dolphins, hippopotami, crocodylians, large turtles, and giant salamanders, have experienced severe population declines and range contractions worldwide. Although there is an increasing number of studies investigating the causes of megafauna losses in fresh waters, little attention has been paid to synthesising the impacts of megafauna on the abiotic environment and other organisms in freshwater ecosystems, and hence the consequences of losing these species. This limited understanding may impede the development of policies and actions for their conservation and restoration. In this review, we synthesise how megafauna shape ecological processes in freshwater ecosystems and discuss their potential for enhancing ecosystem restoration. Through activities such as movement, burrowing, and dam and nest building, megafauna have a profound influence on the extent of water bodies, flow dynamics, and the physical structure of shorelines and substrata, increasing habitat heterogeneity. They enhance nutrient cycling within fresh waters, and cross-ecosystem flows of material, through foraging and reproduction activities. Freshwater megafauna are highly connected to other freshwater organisms via direct consumption of species at different trophic levels, indirect trophic cascades, and through their influence on habitat structure. The literature documenting the ecological impacts of freshwater megafauna is not evenly distributed among species, regions, and types of ecological impacts, with a lack of quantitative evidence for large fish, crocodylians, and turtles in the Global South and their impacts on nutrient flows and food-web structure. In addition, population decline, range contraction, and the loss of large individuals have reduced the extent and magnitude of megafaunal impacts in freshwater ecosystems, rendering a posteriori evaluation more difficult. We propose that reinstating freshwater megafauna populations holds the potential for restoring key ecological processes such as disturbances, trophic cascades, and species dispersal, which will, in turn, promote overall biodiversity and enhance nature's contributions to people. Challenges for restoration actions include the shifting baseline syndrome, potential human-megafauna competition for habitats and resources, damage to property, and risk to human life. The current lack of historical baselines for natural distributions and population sizes of freshwater megafauna, their life history, trophic interactions with other freshwater species, and interactions with humans necessitates further investigation. Addressing these knowledge gaps will improve our understanding of the ecological roles of freshwater megafauna and support their full potential for facilitating the development of effective conservation and restoration strategies to achieve the coexistence of humans and megafauna.

Long-Term Monitoring of Surface Water Dynamics and Analysis of Its Driving Mechanism: A Case Study of the Yangtze River Basin

In-depth insights into the profound impacts of climate change and human activities on water resources are garnered through the dynamic changes in surface water, a crucial aspect of effective water resource management and the preservation of aquatic ecosystems. This paper introduces an innovative approach employing the random forest algorithm for the systematic extraction and monitoring of surface water at large regional or national scales. This method integrates spectral bands, spectral indices, and digital elevation model data, offering a novel perspective on this critical task. A data-filling model is proposed to mitigate the impact of missing data due to cloud cover. Leveraging the capabilities of the Google Earth Engine (GEE), detailed information on surface water dynamics during the rainy and dry seasons in the Yangtze River Basin (YRB) from 1991 to 2021 is extracted using Landsat time series imagery. The analysis encompasses spatial-temporal variation characteristics and trends, with a specific focus on the intricate interplay between the areal extent of surface water and hydro-meteorological factors in each sub-basin of the YRB. Importantly, this includes considerations of potential groundwater contributions to surface water. Key findings from our research include: (1) Achieving a remarkable overall classification accuracy of 0.96 ± 0.03 in obtaining reliable surface water datasets with the support of GEE. (2) Identifying significant trends, such as a noteworthy increase in rainy season surface water bodies (+248.0 km2·yr−1) and a concerning decrease in surface ice/snow cover during both rainy and dry seasons, with change rates of −39.7 km2·yr−1 and −651.3 km2·yr−1, respectively. (3) Uncovering the driving mechanisms behind these changes, revealing positive correlations between the areal extent of rainy season surface water bodies and precipitation, as well as negative correlations between surface ice/snow cover area and average surface skin temperature. It is crucial to note that these driving factors exhibit variation among secondary river systems, underscoring the complexity of surface water dynamics. Furthermore, comparative analyses with existing surface water products are conducted, contributing to a deeper understanding of the advantages and uncertainties inherent in our proposed extraction method. The proposed method for large-scale surface water extraction not only enhances the monitoring of spatio-temporal surface water dynamics in the YRB but also provides valuable insights for the sustainable utilization and protection of water resources, considering the potential role of groundwater in supplementing surface water.

Combining topography and reflectance indices for better surface water detection

Since the Normalized Difference Water Index (NDWI) was proposed, water indices have served as useful tools for surface water detection. However, existing water indices are highly influenced by atmospheric and other environmental conditions and suffer from limited performance, especially in urban areas. At the core of the limitation is the sole dependency on the spectral distribution of reflectance signals. To overcome this, we propose to utilize topographic data as additional information for better water detection. Accordingly, the new index, namely Combined Water Index (CWI), is developed as the product of the topographic index and the reflectance-based index. These two indices excellently compensate each other: the former is free from noise issues but invariant over time while the latter can capture temporal dynamics of waterbody extents. The CWI is applied to four study areas of different development levels (natural, medium-sized cities, and megalopolis) in the Han River basin, South Korea. The water detection results of the CWI is promising, particularly in the heavily developed urban setting, demonstrated through visual images as well as various statistical measures.

Mapping inundated bathymetry for estimating lake water storage changes from SRTM DEM: A global investigation

Lake water budgets are sensitive to climate change and human activities and have been undergoing rapid changes worldwide over the past few decades. Satellite altimeters greatly promote the large-scale monitoring of lake level and storage variations. However, limited spatiotemporal coverage of the existing individual altimeters prevents long-term and spatially seamless observations on global lakes. These limitations are partially surmounted by the gridded digital elevation model (DEM) data, which can facilitate spatially contiguous measurements of lake bathymetry exposed at the low-level period. The high-resolution SRTM DEM has been widely used for estimating lake level variations due to its early collection time and reliable data quality, while how many lakes worldwide and to what extent of the newly inundated areas can be monitored based on SRTM DEM remains unexplored. Here, we conduct a quasi-global investigation of SRTM DEM regarding its capability of mapping inundated-area bathymetry and estimating lake storage variations. After generating a global-scale lake inventory (122,444 lakes below 60°N, >1 km2) representing the maximum water inundation extents between 1984 and 2020, the feasibility of mapping lake inundated-area bathymetry was evaluated by comparing the maximum inundated area with the waterbody extent derived from SRTM DEM and analyzing the characteristics of interannual and seasonal variability of each lake. Results show that the maximum exposed lake bathymetries from the SRTM DEM data cover an area of 372,986 km2, accounting for 15.95% of the maximum water area of our investigated lakes. Climate-driven lake expansions or human-induced reservoir impoundments after 2000 (the SRTM mission period) are the main contributors to the higher availability of SRTM DEM-based bathymetry mapping on the Tibetan Plateau, India, eastern China, and Amazon Plains. As the newly launched Surface Water and Ocean Topography (SWOT) mission is anticipated to monitor lake water budget variation at an unprecedented resolution, this study provides a base map for supplementing inundated bathymetry information. The joint use of SRTM and SWOT-based observations will extend the time and space extents of lake-level measurements, thereby enhancing our understanding of lake dynamics at a longer timescale.

Detection of inland water bodies under dense biomass by CYGNSS

The reliable detection of inland water bodies under dense biomass is not possible with traditional spaceborne remote sensing systems. In this investigation, we demonstrate the capability of the National Aeronautics and Space Administration (NASA) Cyclone Global Navigation Satellite System (CYGNSS) mission to detect and quantify the extent of small water bodies under dense biomass up to 400 ton/ha in complex heterogenous scenes such as tropical rainforests. A novel inland water body product based on L1 CYGNSS data is developed using a combination of peak surface reflectivity Γp and its spread with respect to time delay and Doppler shift, as characterized by the power ratio Pratio. Only L1 CYGNSS mission data has the potential to study Earth's surface water dynamics because raw Intermediate Frequency (IF) data availability is limited. Several coherence indices are used to verify that the L-band Global Positioning System (GPS) signals are being coherently reflected by surface water bodies and attenuated by the intervening vegetation canopy. These indices include full and computationally fast versions of the entropy associated with Delay Doppler Maps (DDMs), Efull and Efast, as well as the time derivative of the phase φpeak of the reflected GPS signal. The capability to accurately resolve surface water extent under dense vegetation has been shown to improve the estimation by current state-of-art surface water extension datasets used by global methane CH4 emission models in wetlands.

Operation mercury: Impacts of national‐level armed forces intervention and anticorruption strategy on artisanal gold mining and water quality in the Peruvian Amazon

AbstractArtisanal and small‐scale gold mining (ASGM), a wealth‐generating industry in many regions, is nonetheless a global challenge for governance and a threat to biodiversity, public health, and ecosystem integrity. In 2019, the Peruvian government mobilized a targeted, large‐scale armed intervention against illegal ASGM, which has caused deforestation and water resource degradation in this Tropical Biodiversity Hotspot. Before the intervention, the extent of waterbodies created by mining (mining ponds) was increasing by 33%–90%/year; after, they decreased by 4%–5%/year in targeted zones. Mining activity indicators showed 70%–90% abandonment. New mining activity accelerated in nearby areas outside the targeted area (pond area increases: 42%–83%; deforestation increases +3–5 km2/year). Far from intervention zones, mining remained stable during the study period. Our analysis demonstrates that targeted, large‐scale government intervention can have positive effects on conservation by stopping illegal mining activity and shifting it to permitted areas, thereby setting the stage for governance. Continued conservation efforts must further address the impacts of informal mining while (1) limiting environmental degradation by legal mining; (2) remediating former mining areas to reduce erosion and enable reforestation or alternative uses of the landscape; and (3) sustaining such efforts, as some miners began to return to intervention areas when enforcement relaxed in 2022.

Performance analysis of change detection techniques for land use land cover

<p><span lang="EN-US">Remotely sensed satellite images have become essential to observe the spatial and temporal changes occurring due to either natural phenomenon or man-induced changes on the earth’s surface. Real time monitoring of this data provides useful information related to changes in extent of urbanization, environmental changes, water bodies, and forest. Through the use of remote sensing technology and geographic information system tools, it has become easier to monitor changes from past to present. In the present scenario, choosing a suitable change detection method plays a pivotal role in any remote sensing project. Previously, digital change detection was a tedious task. With the advent of machine learning techniques, it has become comparatively easier to detect changes in the digital images. The study gives a brief account of the main techniques of change detection related to land use land cover information. An effort is made to compare widely used change detection methods used to identify changes and discuss the need for development of enhanced change detection methods.</span></p>

Understanding the linkages between spatio-temporal urban land system changes and land surface temperature in Srinagar City, India, using image archives from Google Earth Engine.

Land-use and land-cover (LULC) is an important component for sustainable natural resource management, and there are considerable impacts of the rapid anthropogenic LULC changes on environment, ecosystem services, and land surface processes. One of the significant adverse implications of the rapidly changing urban LULC is the increase in the Land Surface Temperature (LST) resulting in the urban heat island effect. In this study, we used a time series of Landsat satellite images from 1992 to 2020 in the Srinagar city of the Kashmir valley, North-western Himalaya, India to understand the linkages between LULC dynamics and LST, derived from the archived images using the Google Earth Engine (GEE). Furthermore, the relationship between LST, urban heat island (UHI), and biophysical indices, i.e., Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI), was also analysed. LULC change detection analysis from 1992 to 2020 revealed that the built-up area has increased significantly from 12% in 1992 to 40% in 2020, while the extent of water bodies has decreased from 6% in 1992 to 4% in 2020. The area under plantations has decreased from 26% in 1992 to 17% in 2020, and forests have decreased from 4 to 2% during the same period. Urban sprawl of Srinagar city has resulted in the depletion of natural land covers, modification of natural drainage, and loss of green and blue spaces over the past four decades. The study revealed that the maximum LST in the city has increased by 11°C between 1992 and 2020. During the same period of time, the minimum LST in the city has increased by 5°C, indicating the impact of urbanization on the city environment, which is reflected by the observed changes in various environmental indices. UHI impact in the city is quite evident with the maximum LST at the city centre having increased from 13.03°C in 1992 to 22.01°C in 2020. The findings shall serve as a vital source of knowledge for urban planners and decision-makers in developing sustainable urban environmental management strategies for Srinagar city.

Unsupervised Machine Learning for GNSS Reflectometry Inland Water Body Detection

Inland water bodies, wetlands and their dynamics have a key role in a variety of scientific, economic, and social applications. They are significant in identifying climate change, water resource management, agricultural productivity, and the modeling of land–atmosphere exchange. Changes in the extent and position of water bodies are crucial to the ecosystems. Mapping water bodies at a global scale is a challenging task due to the global variety of terrains and water surface. However, the sensitivity of spaceborne Global Navigation Satellite System Reflectometry (GNSS-R) to different land surface properties offers the potential to detect and monitor inland water bodies. The extensive dataset available in the Cyclone Global Navigation Satellite System (CYGNSS), launched in December 2016, is used in our investigation. Although the main mission of CYGNSS was to measure the ocean wind speed in hurricanes and tropical cyclones, we show its capability of detecting and mapping inland water bodies. Both bistatic radar cross section (BRCS) and signal-to-noise ratio (SNR) can be used to detect, identify, and map the changes in the position and extent of inland waterbodies. We exploit the potential of unsupervised machine learning algorithms, more specifically the clustering methods, K-Means, Agglomerative, and Density-based Spatial Clustering of Applications with Noise (DBSCAN), for the detection of inland waterbodies. The results are evaluated based on the Copernicus land cover classification gridded maps, at 300 m spatial resolution. The outcomes demonstrate that CYGNSS data can identify and monitor inland waterbodies and their tributaries at high temporal resolution. K-Means has the highest Accuracy (93.5%) compared to the DBSCAN (90.3%) and Agglomerative (91.6%) methods. However, the DBSCAN method has the highest Recall (83.1%) as compared to Agglomerative (82.7%) and K-Means (79.2%). The current study offers valuable insights and analysis for further investigations in the field of GNSS-R and machine learning.

Integrating Satellite Imagery and Ground-Based Measurements with a Machine Learning Model for Monitoring Lake Dynamics over a Semi-Arid Region

The long-term variability of lacustrine dynamics is influenced by hydro-climatological factors that affect the depth and spatial extent of water bodies. The primary objective of this study is to delineate lake area extent, utilizing a machine learning approach, and to examine the impact of these hydro-climatological factors on lake dynamics. In situ and remote sensing observations were employed to identify the predominant explanatory pathways for assessing the fluctuations in lake area. The Great Salt Lake (GSL) and Lake Chad (LC) were chosen as study sites due to their semi-arid regional settings, enabling the testing of the proposed approach. The random forest (RF) supervised classification algorithm was applied to estimate the lake area extent using Landsat imagery that was acquired between 1999 and 2021. The long-term lake dynamics were evaluated using remotely sensed evapotranspiration data that were derived from MODIS, precipitation data that were sourced from CHIRPS, and in situ water level measurements. The findings revealed a marked decline in the GSL area extent, exceeding 50% between 1999 and 2021, whereas LC exhibited greater fluctuations with a comparatively lower decrease in its area extent, which was approximately 30% during the same period. The framework that is presented in this study demonstrates the reliability of remote sensing data and machine learning methodologies for monitoring lacustrine dynamics. Furthermore, it provides valuable insights for decision makers and water resource managers in assessing the temporal variability of lake dynamics.

STUDY AND ANALYSIS OF LAND USE/LAND COVER CHANGES OF JODHPUR CITY AND ITS IMPACTS ON ECONOMY AND ENVIRONMENT (1990-2022)

Urban land use change is a major issue globally. Land cover is a cover that covers any surface and the changes described include urban area land, water body, residential, forest land wasteland, agriculture, and recreation etc. The development of the built-up area separates the city limits.' (PA Khadke & RU Kharat 2017) Land use change plays a major role in the developmental activity of a developing country. Due to the rapid development of urbanization and the dramatically increasing population, fertile agricultural land has been converted into built-up area in relation to the need for housing and the need for infrastructure. The extent of open space and surface water bodies have also been encroached upon. Land use is derived from the human use of land and can be mainly classified into residential, agricultural, commercial use, industrial, etc. Land use and land cover changes are caused by socio-economic and natural factors. The increase in population as well as migration of people from rural to urban is the most important reason for land use change. (P. Sangardas, S. Ishwari, 2019). In the presented research paper, the study and analysis of land use change of Jodhpur city and its impact on economy and environment is to be done.

Evaluation of the Occurrence of Staphylococcaceae with Reduced Susceptibility to Cefoxitin in Wild Ungulates in Brandenburg, Germany, Based on Land Use-Related Factors.

ABSTRACTInteractions between natural and human-used environments have a significant influence on the spread of antimicrobial resistance in wild ecosystems. Despite current knowledge, fundamental questions about the degree of impact of land use-related factors on the spread of antimicrobial-resistant staphylococci in European wild game animal populations have not yet been answered with certainty. In this study, we evaluated the occurrence of Staphylococcaceae showing reduced susceptibility to cefoxitin in nasal swabs of fallow deer (Dama dama), red deer (Cervus elaphus), roe deer (Capreolus capreolus), and wild boar (Sus scrofa) hunted in Brandenburg, Germany. Evaluations were focused on the use of open-source data regarding the extent as well as the degree of land use, especially for settlement or animal husbandry. Results showed that the detection rate of Staphylococcaceae showing a non-wild-type phenotype for cefoxitin differed between animal species of the studied hunting districts. Statistical analyses of results combined with data on land use features revealed that a high density of cattle or poultry in a county may be associated with an increased detection rate in roe deer or wild boar, respectively. Furthermore, positive correlations were determined between the prevalence of non-wild-type Staphylococcaceae in roe deer or fallow deer and the proportional extent of surface water bodies in the corresponding area. The presented approach establishes a general basis for a risk-oriented assessment of the effects of human activities on the epidemiology of transmissible microorganisms in the human-animal-environment interface, including antimicrobial-resistant bacteria.IMPORTANCE Intensive research regarding the impact of land use-related factors on the prevalence and distribution of antimicrobial-resistant Staphylococcaceae in game ungulate populations is necessary for adequately determining risks related to interactions between wild animals, domestic animals, and humans in common geographic locations. This systematic approach for the analysis of the observations in specific hunting districts of Brandenburg, Germany, adds an innovative value to the research strategy of antimicrobial resistance in wild game animals, which is in accordance with current recommendations worldwide. Thus, results and information obtained in this study build a relevant foundation for future risk assessment regarding the safety of game products. Furthermore, the data generated represent an important basis for improving existing guidelines in land use practices and hunting practices. The use of existing open source data collections provided by official governmental and nongovernmental entities increases not only the impact but also the applicability and comparability of information beyond the regional level.

WaterMaskAnalyzer (WMA)—A User-Friendly Tool to Analyze and Visualize Temporal Dynamics of Inland Water Body Extents

Freely available satellite imagery from the EU Copernicus program can record water surfaces precisely and at high temporal resolution. This paper provides the development status of the open-source demo software “WaterMaskAnalyzer” (WMA) for the determination of water body extents. The application allows simple to use on-demand monitoring of inland water dynamics by the Otsu-thresholding algorithm that automatically classifies water bodies. The tool can answer various hydrological issues related to disaster and water management, nature conservation, or water body monitoring. The first results from investigations of the Sentinel-1 time series in VH polarization show high accuracies with R2 = 0.824 compared to in situ measurements for the Quitzdorf reservoir in Saxony, Germany. Small or indented-shaped water bodies, as well as those with forested riparian zones, such as the Cranzahl (VH: R2 = 0.102 and VV: R2 = 0.251) and Klingenberg reservoirs (VH: R2 = 0.091 and VV: R2 = 0.146), only achieve a low R2 for VV and VH polarization but receive equally low RMSEs of 0.045 km2 (Cranzahl) and 0.077 km2 (Klingenberg). By separating out outliers and using correction factors, fast improvements in the accuracies can be expected. For future improvements, alternate classification methods and diverse new ground-truth data lead us to expect the next big step in development.

Changes in extent of open-surface water bodies in China's Yellow River Basin (2000–2020) using Google Earth Engine cloud platform

Dynamics of open surface water is of great significance to understand the impacts of global changes and human activities on water resources. However, long-term changes of open-surface water bodies in the Yellow River Basin have remained poorly characterized. In the study, 26,681 high-quality Landsat images were processed using the Google Earth Engine (GEE) cloud platform to study the long-term changes of the open water bodies in the Yellow River Basin from 2000 to 2020. A percentile-based image synthesis method was used to collect training samples. The multi-index water body extraction rule (MIWER) was proposed to rapidly extract surface water bodies. The results show that (1) The MIWER is suitable for long-term and large-scale surface water mapping based on Landsat images; (2) The average area of permanent and seasonal water in the last 20 years is 9062.59 km 2 and 6918.83 km 2 , respectively, accounting for 56.7% and 43.3% of the total surface water in the Yellow River Basin, respectively; (3) From 2000–2020, both permanent and seasonal water in the whole Yellow River Basin and sub-basins have increased. In general, the total open surface water shows an increasing trend with obvious spatial heterogeneity; (4) The changes of surface water in the Yellow River Basin are related to climate change and intensive human activities, but the influencing factors vary in different regions. The findings of this study demonstrate a novel robust, low-cost water extraction method that satisfies the requirements of terrestrial water inundation mapping and management across the Yellow River Basin. The methodology of this study is expected to be extended to global surface waters, providing valuable information for global surface water resource planning and management.

Spatio-temporal dynamics and drivers of land use land cover change in Farta district South Gonder, Ethiopia

ABSTRACT This study aims to investigate the trend of LULCC and driving forces over the last 30 years (1989 to 2019) in Farta District, Ethiopia. The study revealed that the extent of forest, shrubland, water body, and settlement increased by 5508.09ha, 3375.9ha, 770.58ha, and 666.81ha respectively. However, grassland and agricultural land decreased by 5228.55ha and 5148.63ha respectively. Expansion of settlement, eucalyptus plantation, and agriculture are the major identified proximate drivers for LULC change, while population growth and technological factors are the major underlying factors for LULC changes in the study area.