The resurgence of locally acquired malaria cases in the USA and the persistent global challenge of malaria transmission highlight the urgent need for research to prevent this disease. Despite significant eradication e...The resurgence of locally acquired malaria cases in the USA and the persistent global challenge of malaria transmission highlight the urgent need for research to prevent this disease. Despite significant eradication efforts, malaria remains a serious threat, particularly in regions like Africa. This study explores how integrating Gregor’s Type IV theory with Geographic Information Systems (GIS) improves our understanding of disease dynamics, especially Malaria transmission patterns in Uganda. By combining data-driven algorithms, artificial intelligence, and geospatial analysis, the research aims to determine the most reliable predictors of Malaria incident rates and assess the impact of different factors on transmission. Using diverse predictive modeling techniques including Linear Regression, K-Nearest Neighbor, Neural Network, and Random Forest, the study found that;Random Forest model outperformed the others, demonstrating superior predictive accuracy with an R<sup>2</sup> of approximately 0.88 and a Mean Squared Error (MSE) of 0.0534, Antimalarial treatment was identified as the most influential factor, with mosquito net access associated with a significant reduction in incident rates, while higher temperatures correlated with increased rates. Our study concluded that the Random Forest model was effective in predicting malaria incident rates in Uganda and highlighted the significance of climate factors and preventive measures such as mosquito nets and antimalarial drugs. We recommended that districts with malaria hotspots lacking Indoor Residual Spraying (IRS) coverage prioritize its implementation to mitigate incident rates, while those with high malaria rates in 2020 require immediate attention. By advocating for the use of appropriate predictive models, our research emphasized the importance of evidence-based decision-making in malaria control strategies, aiming to reduce transmission rates and save lives.展开更多
Check dams are widely used on the Loess Plateau in China to control soil and water losses,develop agricultural land,and improve watershed ecology.Detailed information on the number and spatial distribution of check da...Check dams are widely used on the Loess Plateau in China to control soil and water losses,develop agricultural land,and improve watershed ecology.Detailed information on the number and spatial distribution of check dams is critical for quantitatively evaluating hydrological and ecological effects and planning the construction of new dams.Thus,this study developed a check dam detection framework for broad areas from high-resolution remote sensing images using an ensemble approach of deep learning and geospatial analysis.First,we made a sample dataset of check dams using GaoFen-2(GF-2)and Google Earth images.Next,we evaluated five popular deep-learning-based object detectors,including Faster R-CNN,You Only Look Once(version 3)(YOLOv3),Cascade R-CNN,YOLOX,and VarifocalNet(VFNet),to identify the best one for check dam detection.Finally,we analyzed the location characteristics of the check dams and used geographical constraints to optimize the detection results.Precision,recall,average precision at intersection over union(IoU)threshold of 0.50(AP_(50)),IoU threshold of 0.75(AP_(75)),and average value for 10 IoU thresholds ranging from 0.50-0.95 with a 0.05 step(AP_(50-95)),and inference time were used to evaluate model performance.All the five deep learning networks could identify check dams quickly and accurately,with AP_(50-95),AP_(50),and AP_(75)values higher than 60.0%,90.0%,and 70.0%,respectively,except for YOLOv3.The VFNet had the best performance,followed by YOLOX.The proposed framework was tested in the Yanhe River Basin and yielded promising results,with a recall rate of 87.0%for 521 check dams.Furthermore,the geographic analysis deleted about 50%of the false detection boxes,increasing the identification accuracy of check dams from 78.6%to 87.6%.Simultaneously,this framework recognized 568 recently constructed check dams and small check dams not recorded in the known check dam survey datasets.The extraction results will support efficient watershed management and guide future studies on soil erosion in the Loess Plateau.展开更多
Flooding is becoming a yearly reoccurring event in many communities and cities in Nigeria, leading to the destruction of properties and deaths;hence, we must take measures to either prepare for the impact or curb the ...Flooding is becoming a yearly reoccurring event in many communities and cities in Nigeria, leading to the destruction of properties and deaths;hence, we must take measures to either prepare for the impact or curb the occurrence. The study identified flood vulnerability levels of communities in Isoko North LGA based on physical environmental domains such as land use, elevation, and proximity to river channel (drainage) using geospatial techniques. Also, attributes that could contribute to the resilience capacity building of the communities were assessed. From the study, 73.93% of the entire area is moderately and highly vulnerable to flood, while among the communities, seventeen (17) are categorized as moderately vulnerable, and four (4) are lowly vulnerable. The overall resilience capacity of the communities indicated can build a substantial capacity towards community resilience (3.02, 0.06). However, there is a need to encourage collaboration with stakeholders to improve mitigation action and enhance various shortcomings toward resilience capacity building.展开更多
Inverse distance weighting (IDW) interpolation and viewshed are two popular algorithms for geospatial analysis.IDW interpolation assigns geographical values to unknown spatial points using values from a usually scatte...Inverse distance weighting (IDW) interpolation and viewshed are two popular algorithms for geospatial analysis.IDW interpolation assigns geographical values to unknown spatial points using values from a usually scattered set of known points,and viewshed identifies the cells in a spatial raster that can be seen by observers.Although the implementations of both algorithms are available for different scales of input data,the computation for a large-scale domain requires a mass amount of cycles,which limits their usage.Due to the growing popularity of the graphics processing unit (GPU) for general purpose applications,we aim to accelerate geospatial analysis via a GPU based parallel computing approach.In this paper,we propose a generic methodological framework for geospatial analysis based on GPU and its programming model Compute Unified Device Architecture (CUDA),and explore how to map the inherent parallelism degrees of IDW interpolation and viewshed to the framework,which gives rise to a high computational throughput.The CUDA-based implementations of IDW interpolation and viewshed indicate that the architecture of GPU is suitable for parallelizing the algorithms of geospatial analysis.Experimental results show that the CUDA-based implementations running on GPU can lead to dataset dependent speedups in the range of 13-33-fold for IDW interpolation and 28-925-fold for viewshed analysis.Their computation time can be reduced by an order of magnitude compared to classical sequential versions,without losing the accuracy of interpolation and visibility judgment.展开更多
The Indo-Gangetic Plain(IGP)is one of the most seismically vulnerable areas due to its proximity to the Himalayas.Geographic information system(GIS)-based seismic characterization of the IGP was performed based on the...The Indo-Gangetic Plain(IGP)is one of the most seismically vulnerable areas due to its proximity to the Himalayas.Geographic information system(GIS)-based seismic characterization of the IGP was performed based on the degree of deformation and fractal dimension.The zone between the Main Boundary Thrust(MBT)and the Main Central Thrust(MCT)in the Himalayan Mountain Range(HMR)experienced large variations in earthquake magnitude,which were identified by Number-Size(NS)fractal modeling.The central IGP zone experienced only moderate to low mainshock levels.Fractal analysis of earthquake epicenters reveals a large scattering of earthquake epicenters in the HMR and central IGP zones.Similarly,the fault fractal analysis identifies the HMR,central IGP,and south-western IGP zones as having more faults.Overall,the seismicity of the study region is strong in the central IGP,south-western IGP,and HMR zones,moderate in the western and southern IGP,and low in the northern,eastern,and south-eastern IGP zones.展开更多
Mangroves are woody plant communities that appear in tropical and subtropical regions,mainly in intertidal zones along the coastlines.Despite their considerable benefits to humans and the surrounding environment,their...Mangroves are woody plant communities that appear in tropical and subtropical regions,mainly in intertidal zones along the coastlines.Despite their considerable benefits to humans and the surrounding environment,their existence is threatened by anthropogenic activities and natural drivers.Accordingly,it is vital to conduct efficient efforts to increase mangrove plantations by identifying suitable locations.These efforts are required to support conservation and plantation practices and lower the mortality rate of seedlings.Therefore,identifying ecologically potential areas for plantation practices is mandatory to ensure a higher success rate.This study aimed to identify suitable locations for mangrove plantations along the southern coastal frontiers of Hormozgan,Iran.To this end,we applied a hybrid Fuzzy-DEMATEL-ANP(FDANP)model as a Multi-Criteria Decision Making(MCDM)approach to determine the relative importance of different criteria,combined with geospatial and remote sensing data.In this regard,ten relevant sources of environmental criteria,including meteorological,topographical,and geomorphological,were used in the modeling.The statistical evaluation demonstrated the high potential of the developed approach for suitable location identification.Based on the final results,6.10%and 20.80%of the study area were classified as very-high suitable and very-low suitable areas.The obtained values can elucidate the path for decision-makers and managers for better conservation and plantation planning.Moreover,the utility of charge-free remote sensing data allows cost-effective implementation of such an approach for other regions by interested researchers and governing organizations.展开更多
Understanding trends of land use land cover (LULC) changes is important for biodiversity monitoring and conservation planning, and identifying the areas affected by change and designing sustainable solutions to reduce...Understanding trends of land use land cover (LULC) changes is important for biodiversity monitoring and conservation planning, and identifying the areas affected by change and designing sustainable solutions to reduce the changes. The study aims to evaluate and quantify the historical changes in land use and land cover in Mukumbura (Ward 2), Mt Darwin, Zimbabwe, from 2002 to 2022. The objective of the study was to analyse the LULC changes in Ward 2 (Mukumbura), Mt Darwin, Northern Zimbabwe, for a period of 20 years using geospatial techniques. Landsat satellite images were processed using Google Earth Engine (GEE) and the supervised classification with maximum likelihood algorithm was employed to generate LULC maps between 2002 and 2022 with a five (5) year interval, investigating the following variables, forest cover, barren land, water cover and the fields. Findings revealed a substantial reduction in forest cover by 38.8%, water bodies (wetlands, ponds, and rivers) declined by 55.6%, whilst fields (crop/agricultural fields) increased by 93.3% and the barren land cover increased by 26.3% from 2002 to 2022. These findings point to substantial changes in LULC over the observed years. LULC changes have resulted in habitat fragmentation, reduced biodiversity, and the disruption of ecosystem functions. The study concludes that if these deforestation trends, cultivation, and settlement land expansion continue, the ward will have limited indigenous fruit trees. Therefore, the causes for LULC changes must be controlled, sustainable forest resources use practiced, hence the need to domesticate the indigenous fruit trees in arborloo toilets.展开更多
This study presents an AI-driven Spatial Decision Support System (SDSS) aimed at transforming groundwater suitability assessments for domestic and irrigation uses in Visakhapatnam District, Andhra Pradesh, India. By e...This study presents an AI-driven Spatial Decision Support System (SDSS) aimed at transforming groundwater suitability assessments for domestic and irrigation uses in Visakhapatnam District, Andhra Pradesh, India. By employing advanced remote sensing, GIS, and machine learning techniques, groundwater quality data from 50 monitoring wells, sourced from the Central Ground Water Board (CGWB), was meticulously analysed. Key parameters, including pH, electrical conductivity, total dissolved solids, and major ion concentrations, were evaluated against World Health Organization (WHO) standards to determine domestic suitability. For irrigation, advanced metrics such as Sodium Adsorption Ratio (SAR), Kelly’s Ratio, Residual Sodium Carbonate (RSC), and percentage sodium (% Na) were utilized to assess water quality. The integration of GIS for spatial mapping and AI models for predictive analytics allows for a comprehensive visualization of groundwater quality distribution across the district. Additionally, the irrigation water quality was evaluated using the USA Salinity Laboratory diagram, providing essential insights for effective agricultural water management. This innovative SDSS framework promises to significantly enhance groundwater resource management, fostering sustainable practices for both domestic use and agriculture in the region.展开更多
Monitoring of the earth’s surface has been significantly improved thanks to optical remote sensing by satellites such as SPOT,Landsat and Sentinel-2,which produce vast datasets.The processing of this data,often refer...Monitoring of the earth’s surface has been significantly improved thanks to optical remote sensing by satellites such as SPOT,Landsat and Sentinel-2,which produce vast datasets.The processing of this data,often referred to as Big Data,is essential for decision-making,requiring the application of advanced algorithms to analyze changes in land cover.In the age of artificial intelligence,supervised machine learning algorithms are widely used,although their application in urban contexts remains complex.Researchers have to evaluate and tune various algorithms according to assumptions and experiments,which requires time and resources.This paper presents a meta-modeling approach for urban satellite image classification,using model-driven engineering techniques.The aim is to provide urban planners with standardized solutions for geospatial processing,promoting reusability and interoperability.Formalization includes the creation of a knowledge base and the modeling of processing chains to analyze land use.展开更多
Panulirus argus virus 1 (PaV1) is considered a major threat to spiny lobsters Panulirus argus. In this study Geospatial analysis was used to analyze PaV1 distribution in an artisanal fishery of spiny lobster Panulirus...Panulirus argus virus 1 (PaV1) is considered a major threat to spiny lobsters Panulirus argus. In this study Geospatial analysis was used to analyze PaV1 distribution in an artisanal fishery of spiny lobster Panulirus argus population from the north coast of the Yucatan Peninsula. Adult and sub-adult P. argus and seabed coverage data were collected from thirty artisanal fishing sites. Five seabed coverage types were identified: seagrass;sand/seagrass mixture;sand only;coral/sand mixture;and seaweed. No juveniles were examined. Of the 358 collected lobsters, PaV1 was identified in four organisms (three sub-adults and one adult) from two fishing sites (termed A & B), both found in a seagrass coverage area. Overall prevalence was of 1.12%. Prevalence was of 20% (2/10) at one site and of 12.6% (2/16) at the other.展开更多
Management of forest lands considering multi-functional approaches is the basis to sustain or enhance the provi-sion of specific benefits,while minimizing negative impacts to the environment.Defining a desired managem...Management of forest lands considering multi-functional approaches is the basis to sustain or enhance the provi-sion of specific benefits,while minimizing negative impacts to the environment.Defining a desired management itinerary to a forest depends on a variety of factors,including the forest type,its ecological characteristics,and the social and economic needs of local communities.A strategic assessment of the forest use suitability(FUS)(namely productive,protective,conservation-oriented,social and multi-functional)at regional level,based on the provision of forest ecosystem services and trade-offs between FUS alternatives,can be used to develop management strategies that are tailored to the specific needs and conditions of the forest.The present study assesses the provision of multiple forest ecosystem services and employs a decision model to identify the FUS that sup-ports the most present and productive ecosystem services in each stand in Catalonia.For this purpose,we apply the latest version of the Ecosystem Management Decision Support(EMDS)system,a spatially oriented decision support system that provides accurate results for multi-criteria management.We evaluate 32 metrics and 12 as-sociated ecosystem services indicators to represent the spatial reality of the region.According to the results,the dominant primary use suitability is social,followed by protective and productive.Nevertheless,final assignment of uses is not straightforward and requires an exhaustive analysis of trade-offs between all alternative options,in many cases identifying flexible outcomes,and increasing the representativeness of multi-functional use.The assignment of forest use suitability aims to significantly improve the definition of the most adequate management strategy to be applied.展开更多
Potato is one of the key crops for ensuring food security and can be a potential substitute for cereal crops due to its high yielding nature and nutritional value.Crop nutrient management practices within potato field...Potato is one of the key crops for ensuring food security and can be a potential substitute for cereal crops due to its high yielding nature and nutritional value.Crop nutrient management practices within potato fields are implemented uniformly without considering crop requirements and soil variability,causing uneven and low yield.However,yield can be increased by identifying growth and yield-limiting factors.Geospatial tools are robust and effective in identifying the spatial variations within the field.Proximal sensing allows quick analysis of soil and plant characteristics,decreases the need for laborious and expensive soil and plant sampling,and strengthens precision agriculture techniques.The aim of the study was to quantify the soil spatial variability and identify potato crop growth and yield limiting factors for the optimization of inputs.Two fields were selected in the subtropical region of Pakistan(Koont,Rawalpindi),and each field was cultivated with two different potato varieties.A grid sampling approach was developed to collect soil samples and tuber yields.The soil was tested for nitrogen(N),phosphorus(P),potassium(K),pH,electrical conductivity(E.C),temperature,and moisture content(M.C)by using a soil proximal sensor.Normalized difference vegetation index(NDVI)was recorded using a handheld GreenSeeker,and chlorophyll was estimated using a chlorophyll meter.Descriptive statistics and correlation analysis for soil and crop parameters were performed in Minitab 21,while geostatistical analysis was performed in Arc Map 10.8 to show spatial variability and to generate kriged maps of different soil properties.The coefficient of variation of soil properties and plant parameters showed moderate to high variability within the field,except for pH and temperature.The correlation matrix suggested that N,P,K,E.C.,chlorophyll,NDVI,plant height,and leaf area had a significant relationship with potato yield.Most of the soil and plant parameters had a medium to high range of influence(20 to 90 m)and varied greatly within the field.Kriged maps of plant and soil parameters also showed spatial variations and were aligned with descriptive statistics and correlations.Quantification of soil spatial variability within potato fields can assist in measuring yield-limiting soil characteristics to establish management zones for variable rate fertilization for optimum tuber yield and low environmental impact.展开更多
文摘The resurgence of locally acquired malaria cases in the USA and the persistent global challenge of malaria transmission highlight the urgent need for research to prevent this disease. Despite significant eradication efforts, malaria remains a serious threat, particularly in regions like Africa. This study explores how integrating Gregor’s Type IV theory with Geographic Information Systems (GIS) improves our understanding of disease dynamics, especially Malaria transmission patterns in Uganda. By combining data-driven algorithms, artificial intelligence, and geospatial analysis, the research aims to determine the most reliable predictors of Malaria incident rates and assess the impact of different factors on transmission. Using diverse predictive modeling techniques including Linear Regression, K-Nearest Neighbor, Neural Network, and Random Forest, the study found that;Random Forest model outperformed the others, demonstrating superior predictive accuracy with an R<sup>2</sup> of approximately 0.88 and a Mean Squared Error (MSE) of 0.0534, Antimalarial treatment was identified as the most influential factor, with mosquito net access associated with a significant reduction in incident rates, while higher temperatures correlated with increased rates. Our study concluded that the Random Forest model was effective in predicting malaria incident rates in Uganda and highlighted the significance of climate factors and preventive measures such as mosquito nets and antimalarial drugs. We recommended that districts with malaria hotspots lacking Indoor Residual Spraying (IRS) coverage prioritize its implementation to mitigate incident rates, while those with high malaria rates in 2020 require immediate attention. By advocating for the use of appropriate predictive models, our research emphasized the importance of evidence-based decision-making in malaria control strategies, aiming to reduce transmission rates and save lives.
基金This research was supported by the National Natural Science Foundation of China(41977064)the National Key R&D Program of China(2021YFD1900700).
文摘Check dams are widely used on the Loess Plateau in China to control soil and water losses,develop agricultural land,and improve watershed ecology.Detailed information on the number and spatial distribution of check dams is critical for quantitatively evaluating hydrological and ecological effects and planning the construction of new dams.Thus,this study developed a check dam detection framework for broad areas from high-resolution remote sensing images using an ensemble approach of deep learning and geospatial analysis.First,we made a sample dataset of check dams using GaoFen-2(GF-2)and Google Earth images.Next,we evaluated five popular deep-learning-based object detectors,including Faster R-CNN,You Only Look Once(version 3)(YOLOv3),Cascade R-CNN,YOLOX,and VarifocalNet(VFNet),to identify the best one for check dam detection.Finally,we analyzed the location characteristics of the check dams and used geographical constraints to optimize the detection results.Precision,recall,average precision at intersection over union(IoU)threshold of 0.50(AP_(50)),IoU threshold of 0.75(AP_(75)),and average value for 10 IoU thresholds ranging from 0.50-0.95 with a 0.05 step(AP_(50-95)),and inference time were used to evaluate model performance.All the five deep learning networks could identify check dams quickly and accurately,with AP_(50-95),AP_(50),and AP_(75)values higher than 60.0%,90.0%,and 70.0%,respectively,except for YOLOv3.The VFNet had the best performance,followed by YOLOX.The proposed framework was tested in the Yanhe River Basin and yielded promising results,with a recall rate of 87.0%for 521 check dams.Furthermore,the geographic analysis deleted about 50%of the false detection boxes,increasing the identification accuracy of check dams from 78.6%to 87.6%.Simultaneously,this framework recognized 568 recently constructed check dams and small check dams not recorded in the known check dam survey datasets.The extraction results will support efficient watershed management and guide future studies on soil erosion in the Loess Plateau.
文摘Flooding is becoming a yearly reoccurring event in many communities and cities in Nigeria, leading to the destruction of properties and deaths;hence, we must take measures to either prepare for the impact or curb the occurrence. The study identified flood vulnerability levels of communities in Isoko North LGA based on physical environmental domains such as land use, elevation, and proximity to river channel (drainage) using geospatial techniques. Also, attributes that could contribute to the resilience capacity building of the communities were assessed. From the study, 73.93% of the entire area is moderately and highly vulnerable to flood, while among the communities, seventeen (17) are categorized as moderately vulnerable, and four (4) are lowly vulnerable. The overall resilience capacity of the communities indicated can build a substantial capacity towards community resilience (3.02, 0.06). However, there is a need to encourage collaboration with stakeholders to improve mitigation action and enhance various shortcomings toward resilience capacity building.
基金Project supported by the National Natural Science Foundation of China (No. 61002009)the Science and Technology Planning Project of Zhejiang Province (No. 2010C31018)the Scientific Research Fund of Hangzhou Normal University (No. HSKQ0042),China
文摘Inverse distance weighting (IDW) interpolation and viewshed are two popular algorithms for geospatial analysis.IDW interpolation assigns geographical values to unknown spatial points using values from a usually scattered set of known points,and viewshed identifies the cells in a spatial raster that can be seen by observers.Although the implementations of both algorithms are available for different scales of input data,the computation for a large-scale domain requires a mass amount of cycles,which limits their usage.Due to the growing popularity of the graphics processing unit (GPU) for general purpose applications,we aim to accelerate geospatial analysis via a GPU based parallel computing approach.In this paper,we propose a generic methodological framework for geospatial analysis based on GPU and its programming model Compute Unified Device Architecture (CUDA),and explore how to map the inherent parallelism degrees of IDW interpolation and viewshed to the framework,which gives rise to a high computational throughput.The CUDA-based implementations of IDW interpolation and viewshed indicate that the architecture of GPU is suitable for parallelizing the algorithms of geospatial analysis.Experimental results show that the CUDA-based implementations running on GPU can lead to dataset dependent speedups in the range of 13-33-fold for IDW interpolation and 28-925-fold for viewshed analysis.Their computation time can be reduced by an order of magnitude compared to classical sequential versions,without losing the accuracy of interpolation and visibility judgment.
文摘The Indo-Gangetic Plain(IGP)is one of the most seismically vulnerable areas due to its proximity to the Himalayas.Geographic information system(GIS)-based seismic characterization of the IGP was performed based on the degree of deformation and fractal dimension.The zone between the Main Boundary Thrust(MBT)and the Main Central Thrust(MCT)in the Himalayan Mountain Range(HMR)experienced large variations in earthquake magnitude,which were identified by Number-Size(NS)fractal modeling.The central IGP zone experienced only moderate to low mainshock levels.Fractal analysis of earthquake epicenters reveals a large scattering of earthquake epicenters in the HMR and central IGP zones.Similarly,the fault fractal analysis identifies the HMR,central IGP,and south-western IGP zones as having more faults.Overall,the seismicity of the study region is strong in the central IGP,south-western IGP,and HMR zones,moderate in the western and southern IGP,and low in the northern,eastern,and south-eastern IGP zones.
基金funded by Erasmus+ICM programme for a 3-month and 5-month stay at Lund University,Lund,Sweden,and thank the European Union.
文摘Mangroves are woody plant communities that appear in tropical and subtropical regions,mainly in intertidal zones along the coastlines.Despite their considerable benefits to humans and the surrounding environment,their existence is threatened by anthropogenic activities and natural drivers.Accordingly,it is vital to conduct efficient efforts to increase mangrove plantations by identifying suitable locations.These efforts are required to support conservation and plantation practices and lower the mortality rate of seedlings.Therefore,identifying ecologically potential areas for plantation practices is mandatory to ensure a higher success rate.This study aimed to identify suitable locations for mangrove plantations along the southern coastal frontiers of Hormozgan,Iran.To this end,we applied a hybrid Fuzzy-DEMATEL-ANP(FDANP)model as a Multi-Criteria Decision Making(MCDM)approach to determine the relative importance of different criteria,combined with geospatial and remote sensing data.In this regard,ten relevant sources of environmental criteria,including meteorological,topographical,and geomorphological,were used in the modeling.The statistical evaluation demonstrated the high potential of the developed approach for suitable location identification.Based on the final results,6.10%and 20.80%of the study area were classified as very-high suitable and very-low suitable areas.The obtained values can elucidate the path for decision-makers and managers for better conservation and plantation planning.Moreover,the utility of charge-free remote sensing data allows cost-effective implementation of such an approach for other regions by interested researchers and governing organizations.
文摘Understanding trends of land use land cover (LULC) changes is important for biodiversity monitoring and conservation planning, and identifying the areas affected by change and designing sustainable solutions to reduce the changes. The study aims to evaluate and quantify the historical changes in land use and land cover in Mukumbura (Ward 2), Mt Darwin, Zimbabwe, from 2002 to 2022. The objective of the study was to analyse the LULC changes in Ward 2 (Mukumbura), Mt Darwin, Northern Zimbabwe, for a period of 20 years using geospatial techniques. Landsat satellite images were processed using Google Earth Engine (GEE) and the supervised classification with maximum likelihood algorithm was employed to generate LULC maps between 2002 and 2022 with a five (5) year interval, investigating the following variables, forest cover, barren land, water cover and the fields. Findings revealed a substantial reduction in forest cover by 38.8%, water bodies (wetlands, ponds, and rivers) declined by 55.6%, whilst fields (crop/agricultural fields) increased by 93.3% and the barren land cover increased by 26.3% from 2002 to 2022. These findings point to substantial changes in LULC over the observed years. LULC changes have resulted in habitat fragmentation, reduced biodiversity, and the disruption of ecosystem functions. The study concludes that if these deforestation trends, cultivation, and settlement land expansion continue, the ward will have limited indigenous fruit trees. Therefore, the causes for LULC changes must be controlled, sustainable forest resources use practiced, hence the need to domesticate the indigenous fruit trees in arborloo toilets.
文摘This study presents an AI-driven Spatial Decision Support System (SDSS) aimed at transforming groundwater suitability assessments for domestic and irrigation uses in Visakhapatnam District, Andhra Pradesh, India. By employing advanced remote sensing, GIS, and machine learning techniques, groundwater quality data from 50 monitoring wells, sourced from the Central Ground Water Board (CGWB), was meticulously analysed. Key parameters, including pH, electrical conductivity, total dissolved solids, and major ion concentrations, were evaluated against World Health Organization (WHO) standards to determine domestic suitability. For irrigation, advanced metrics such as Sodium Adsorption Ratio (SAR), Kelly’s Ratio, Residual Sodium Carbonate (RSC), and percentage sodium (% Na) were utilized to assess water quality. The integration of GIS for spatial mapping and AI models for predictive analytics allows for a comprehensive visualization of groundwater quality distribution across the district. Additionally, the irrigation water quality was evaluated using the USA Salinity Laboratory diagram, providing essential insights for effective agricultural water management. This innovative SDSS framework promises to significantly enhance groundwater resource management, fostering sustainable practices for both domestic use and agriculture in the region.
文摘Monitoring of the earth’s surface has been significantly improved thanks to optical remote sensing by satellites such as SPOT,Landsat and Sentinel-2,which produce vast datasets.The processing of this data,often referred to as Big Data,is essential for decision-making,requiring the application of advanced algorithms to analyze changes in land cover.In the age of artificial intelligence,supervised machine learning algorithms are widely used,although their application in urban contexts remains complex.Researchers have to evaluate and tune various algorithms according to assumptions and experiments,which requires time and resources.This paper presents a meta-modeling approach for urban satellite image classification,using model-driven engineering techniques.The aim is to provide urban planners with standardized solutions for geospatial processing,promoting reusability and interoperability.Formalization includes the creation of a knowledge base and the modeling of processing chains to analyze land use.
文摘Panulirus argus virus 1 (PaV1) is considered a major threat to spiny lobsters Panulirus argus. In this study Geospatial analysis was used to analyze PaV1 distribution in an artisanal fishery of spiny lobster Panulirus argus population from the north coast of the Yucatan Peninsula. Adult and sub-adult P. argus and seabed coverage data were collected from thirty artisanal fishing sites. Five seabed coverage types were identified: seagrass;sand/seagrass mixture;sand only;coral/sand mixture;and seaweed. No juveniles were examined. Of the 358 collected lobsters, PaV1 was identified in four organisms (three sub-adults and one adult) from two fishing sites (termed A & B), both found in a seagrass coverage area. Overall prevalence was of 1.12%. Prevalence was of 20% (2/10) at one site and of 12.6% (2/16) at the other.
基金the Catalan Government Predoctoral Schol-arship(AGAUR-FSE 2020 FI_B200147)SuFoRun Marie Sklodowska-Curie Research and Innovation Staff Exchange(RISE)Program(Grant No.691149)the Spanish Ministry of Science and Innovation(PID2020-120355RB-IOO).
文摘Management of forest lands considering multi-functional approaches is the basis to sustain or enhance the provi-sion of specific benefits,while minimizing negative impacts to the environment.Defining a desired management itinerary to a forest depends on a variety of factors,including the forest type,its ecological characteristics,and the social and economic needs of local communities.A strategic assessment of the forest use suitability(FUS)(namely productive,protective,conservation-oriented,social and multi-functional)at regional level,based on the provision of forest ecosystem services and trade-offs between FUS alternatives,can be used to develop management strategies that are tailored to the specific needs and conditions of the forest.The present study assesses the provision of multiple forest ecosystem services and employs a decision model to identify the FUS that sup-ports the most present and productive ecosystem services in each stand in Catalonia.For this purpose,we apply the latest version of the Ecosystem Management Decision Support(EMDS)system,a spatially oriented decision support system that provides accurate results for multi-criteria management.We evaluate 32 metrics and 12 as-sociated ecosystem services indicators to represent the spatial reality of the region.According to the results,the dominant primary use suitability is social,followed by protective and productive.Nevertheless,final assignment of uses is not straightforward and requires an exhaustive analysis of trade-offs between all alternative options,in many cases identifying flexible outcomes,and increasing the representativeness of multi-functional use.The assignment of forest use suitability aims to significantly improve the definition of the most adequate management strategy to be applied.
文摘Potato is one of the key crops for ensuring food security and can be a potential substitute for cereal crops due to its high yielding nature and nutritional value.Crop nutrient management practices within potato fields are implemented uniformly without considering crop requirements and soil variability,causing uneven and low yield.However,yield can be increased by identifying growth and yield-limiting factors.Geospatial tools are robust and effective in identifying the spatial variations within the field.Proximal sensing allows quick analysis of soil and plant characteristics,decreases the need for laborious and expensive soil and plant sampling,and strengthens precision agriculture techniques.The aim of the study was to quantify the soil spatial variability and identify potato crop growth and yield limiting factors for the optimization of inputs.Two fields were selected in the subtropical region of Pakistan(Koont,Rawalpindi),and each field was cultivated with two different potato varieties.A grid sampling approach was developed to collect soil samples and tuber yields.The soil was tested for nitrogen(N),phosphorus(P),potassium(K),pH,electrical conductivity(E.C),temperature,and moisture content(M.C)by using a soil proximal sensor.Normalized difference vegetation index(NDVI)was recorded using a handheld GreenSeeker,and chlorophyll was estimated using a chlorophyll meter.Descriptive statistics and correlation analysis for soil and crop parameters were performed in Minitab 21,while geostatistical analysis was performed in Arc Map 10.8 to show spatial variability and to generate kriged maps of different soil properties.The coefficient of variation of soil properties and plant parameters showed moderate to high variability within the field,except for pH and temperature.The correlation matrix suggested that N,P,K,E.C.,chlorophyll,NDVI,plant height,and leaf area had a significant relationship with potato yield.Most of the soil and plant parameters had a medium to high range of influence(20 to 90 m)and varied greatly within the field.Kriged maps of plant and soil parameters also showed spatial variations and were aligned with descriptive statistics and correlations.Quantification of soil spatial variability within potato fields can assist in measuring yield-limiting soil characteristics to establish management zones for variable rate fertilization for optimum tuber yield and low environmental impact.
