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Transformation and risk assessment of the East Kolkata Wetlands (India)using fuzzy MCDM method and geospatial technology

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Geography and Sustainability 2022年第3期3卷 191-203页

作者： Biraj Kanti Mondal Suchitra Kumari Arijit Ghosh Prabuddh Kumar Mishra Department of Geography Netaji Subhas Open UniversityKolkata 700064India Department of Commerce St.Xavier’s College(Autonomous)Kolkata 700016India Department of Mathematics St.Xavier’s College(Autonomous)Kolkata 700016India Department of Geography Shivaji CollegeUniversity of DelhiNew Delhi 110027India

The East Kolkata Wetlands (EKW) is located on the eastern periphery of the city of Kolkata and extends up to theBidyadhari-Matla River confluence. It is a Ramsar Site and acts as an absorber basin for a large number of con-taminants drained from Kolkata. Agricultural lands, sewage-fed fisheries, garbage dumping fields, horticulture,and built-up areas are included in this protected area, that covers approximately 125 km2. It reveals that climatechange reduces the variety of wetland ecosystem services and increases socio-economic vulnerability and eco-nomic stress. The human encroachment, reclamation of land for agriculture, aquaculture, and urban expansion inand around Kolkata has recently adversely threatened the EKW. The remotely sensed data, socio-economic data,and responses of inhabitants have been used to analyse the EKW’s risk and vulnerability. We employed geospatialanalysis by using the Multi-Criteria Decision Making (MCDM) method using nine risk factors. An in-depth analysisof the EKW using geospatial techniques and the Fuzzy Analytic Hierarchy Process (FAHP) helped to understandthe EKW transformations through vulnerability and risk analysis. The results show that the transformation of thewetland to aquaculture, eutrophication and pollution, road proximity, waste dumping, population density, andgrowth are the main factors for the deteriorating health, quality, and environment of the EKW. It also reveals thatquantitative and qualitative evaluations of ecosystem services, wetland degradation, transformation, and cause-effect rapport should be periodically assessed using scientific methods like FAHP, RS, GIS to formulate resilient,integrated plans and strategy for the sustainable management of the EKW.

关键词： East Kolkata Wetlands(EKW) Fuzzy Analytic Hierarchy Process(FAHP) Multi-Criteria Decision Making(MCDM) geospatial technology Vulnerability Management

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Leveraging geospatial technology for Smallholder Farmer Credit Scoring

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Journal of Geographic Information System 2023年第5期15卷 524-539页

作者： Susan A. Okeyo Galcano C. Mulaku Collins M. Mwange Department of geospatial and Space technology University of Nairobi Nairobi Kenya

According to the Food and Agriculture Organization of the United Nations (FAO), there are about 500 million smallholder farmers in the world, and in developing countries, such farmers produce about 80% of the food consumed there;their farming activities are therefore critical to the economies of their countries and to the global food security. However, these farmers face the challenges of limited access to credit, often due to the fact that many of them farm on unregistered land that cannot be offered as collateral to lending institutions;but even when they are on registered land, the fear of losing such land that they should default on loan payments often prevents them from applying for farm credit;and even if they apply, they still get disadvantaged by low credit scores (a measure of creditworthiness). The result is that they are often unable to use optimal farm inputs such as fertilizer and good seeds among others. This depresses their yields, and in turn, has negative implications for the food security in their communities, and in the world, hence making it difficult for the UN to achieve its sustainable goal no.2 (no hunger). This study aimed to demonstrate how geospatial technology can be used to leverage farm credit scoring for the benefit of smallholder farmers. A survey was conducted within the study area to identify the smallholder farms and farmers. A sample of surveyed farmers was then subjected to credit scoring by machine learning. In the first instance, the traditional financial data approach was used and the results showed that over 40% of the farmers could not qualify for credit. When non-financial geospatial data, i.e. Normalized Difference Vegetation Index (NDVI) was introduced into the scoring model, the number of farmers not qualifying for credit reduced significantly to 24%. It is concluded that the introduction of the NDVI variable into the traditional scoring model could improve significantly the smallholder farmers’ chances of accessing cred

关键词： Credit Scoring Machine Learning geospatial technology Migori

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Soil erosion susceptibility mapping of Hangu Region,Kohat Plateau of Pakistan using GIS and RS-based models

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Journal of Mountain Science 2024年第8期21卷 2547-2561页

作者： Fakhrul ISLAM Liaqat Ali WASEEM Tehmina BIBI Waqar AHMAD Muhammad SADIQ Matee ULLAH Walid SOUFAN Aqil TARIQ Department of Geology Khushal Khan Khattak UniversityKarak 27200Pakistan Department of Geography Government College University FaisalabadPunjab 38000Pakistan Institute of Geology The University of Azad Jammu&KashmirMuzaffarabad 13100Pakistan Department of Earth Sciences Changan UniversityXi’an 710003China NCEG University of PeshawarPeshawar 25120Pakistan Faculty of Earth Sciences Geography and AstronomyUniversity of ViennaVienna 1090Austria Plant Production Department College of Food and Agriculture SciencesKing Saud UniversityRiyadh 11451Saudi Arabia Department of Wildlife Fisheries and AquacultureCollege of Forest ResourcesMississippi State UniversityMS 39762USA

Soil erosion is a crucial geo-environmental hazard worldwide that affects water quality and agriculture,decreases reservoir storage capacity due to sedimentation,and increases the danger of flooding and ***,this study uses geospatial modeling to produce soil erosion susceptibility maps(SESM)for the Hangu region,Khyber Pakhtunkhwa(KPK),*** Hangu region,located in the Kohat Plateau of KPK,Pakistan,is particularly susceptible to soil erosion due to its unique geomorphological and climatic ***,the Hangu region is characterized by a combination of steep slopes,variable rainfall patterns,diverse land use,and distinct soil types,all of which contribute to the complexity and severity of soil erosion *** factors necessitate a detailed and region-specific study to develop effective soil conservation *** this research,we detected and mapped 1013 soil erosion points and prepared 12 predisposing factors(elevation,aspect,slope,Normalized Differentiate Vegetation Index(NDVI),drainage network,curvature,Land Use Land Cover(LULC),rainfall,lithology,contour,soil texture,and road network)of soil erosion using GIS ***,GIS-based statistical models like the weight of evidence(WOE)and frequency ratio(FR)were applied to produce the SESM for the study *** SESM was reclassified into four classes,i.e.,low,medium,high,and very high *** results of WOE for SESM show that 16.39%,33.02%,29.27%,and 21.30%of areas are covered by low,medium,high,and very high zones,*** contrast,the FR results revealed that 16.50%,24.33%,35.55%,and 23.59%of the areas are occupied by low,medium,high,and very high ***,the reliability of applied models was evaluated using the Area Under Curve(AUC)*** validation results utilizing the area under curve showed that the success rate curve(SRC)and predicted rate curve(PRC)for WOE are 82%and 86%,respectively,while SRC and PRC for FR are 85%and 96%,res

关键词： Soil erosion geospatial technology Statistical models Hangu Pakistan

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Relationships between land use types and urban heat island intensity in Hulu Langat district, Selangor, Malaysia

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Ecological Processes 2023年第3期12卷 26-39页

作者： Muhammad Rendana Wan Mohd Razi Idris Sahibin Abdul Rahim Hazem Ghassan Abdo Hussein Almohamad Ahmed Abdullah Al Dughairi Motrih Al-Mutiry Department of Chemical Engineering Faculty of Engineering Universitas Sriwijaya Department of Earth Sciences and Environment Faculty of Science and Technology Universiti Kebangsaan Malaysia Department of Environmental Science Faculty of Science and Natural Resources Universiti Malaysia Sabah Geography Department Faculty of Arts and Humanities Tartous University Department of Geography College of Arabic Language and Social Studies Qassim University Department of Geography College of Arts Princess Nourah Bint Abdulrahman University

Background Urban heat island(UHI) is an urban climate phenomenon that primarily responds to urban conditions and land use change. The extent of hard surfaces significantly influences the thermal properties of the land. To address this issue, a novel approach quantifying the association between land use and UHI is developed. This study offers a new technique for effectively estimating the effect of land use on the UHI intensity using the combination of urban heat intensity index(UHII) and land contribution index(LCI) derived from Landsat 8 OLI images. The timeseries thermal effect of land use on the UHI intensity can be determined according to the ratio in mean temperature between specific land use and the whole study site. The study was conducted in the Hulu Langat district, Malaysia during 2014–*** The UHI intensity rose from 0.19 in 2014 to 0.70 in 2021. The negative value of LCI for vegetation areas and water bodies obtained its negative contribution to the urban heat island, while the positive value of LCI for bare areas and built-up areas showed its positive effect on the urban heat island. The LCI value for urban areas showed a significant increase in the 7 years such as 0.51, 0.66, 0.69, and 0.75 for periods 2014, 2016, 2018, and 2021, respectively. The change in LCI from 2014 to 2021 for the transformation of bare area and forest was recorded to be 0.23 and-0.02, respectively. Thus, the conversion of forests into urban areas had a negative effect on the increment of UHI intensity. Conclusions Overall, these findings are useful for policy-making agency in developing an effective policy for reducing high UHI intensity and planning long-term land use management.

关键词： geospatial technology Urban heat island Land contribution index Land surface temperature Environment

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Soil quality estimation using environmental covariates and predictive models:an example from tropical soils of Nigeria

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Ecological Processes 2022年第1期11卷 946-967页

作者： Isong Abraham Isong Kingsley John Paul Bassey Okon Peter Ikor Ogban Sunday Marcus Afu Department of Soil Science Faculty of AgricultureUniversity of CalabarCalabar PMB 1115Nigeria Department of Soil Science and Soil Protection Faculty of AgrobiologyFoodand Natural ResourcesCzech University of Life SciencesKamýcká12916500 PragueCzech Republic

Background:Information addressing soil quality in developing countries often depends on results from small experimental plots,which are later extrapolated to vast areas of agricultural *** approach often results in misin-formation to end-users of land for sustainable soil nutrient *** objective of this study was to estimate the spatial variability of soil quality index(SQI)at regional scale with predictive models using soil–environmental ***:A total of 110 composite soil samples(0–30 cm depth)were collected by stratified random sampling schemes at 2–5 km intervals across the Cross River State,Nigeria,and selected soil physical and chemical properties were *** employed environmental covariates derived from a digital elevation model(DEM)and Senti-nel-2 imageries for our modelling *** measured soil quality using two approaches[total data set(TDS)and minimum data set(MDS)].Two scoring functions were also applied,linear(L)and non-linear(NL),yielding four indices(MDS_L,MDS_NL,TDS_L,and TDS_NL).Eleven soil quality indicators were used as TDS and were further screened for MDS using principal component analysis(PCA).Random forest(RF),support vector regression(SVR),regression kriging(RK),Cubist regression,and geographically weighted regression(GWR)were applied to predict SQI in unsampled ***:The computed SQI via MDS_L was classified into five classes:≤0.38,0.38–0.48,0.48–0.58,0.58–0.68,and≥0.68,representing very low(classⅤ),low(classⅣ),moderate(classⅢ),high(classⅡ)and very high(classⅠ)soil quality,*** model was robust in predicting soil quality(R^(2)=0.21,CCC=0.39,RMSE=0.15),while RF was a model with inferior performance(R^(2)=0.02,CCC=0.32,RMSE=0.15).Soil quality was high in the southern region and low in the northern *** soil quality class(>49%)and moderate soil quality class(>14%)dominate the study area in all predicted models ***:Structural stability index,sand content,soil oga

关键词： Decision support system geospatial technology Predictive model Remote sensing Soil quality

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geospatial technologies for Chang’e-3 and Chang’e-4 lunar rover missions

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Geo-Spatial Information Science 2020年第1期23卷 87-97页

作者： Kaichang Di Zhaoqin Liu Wenhui Wan Man Peng Bin Liu Yexin Wang Sheng Gou Zongyu Yue State Key Laboratory of Remote Sensing Science Institute of Remote Sensing and Digital EarthChinese Academy of SciencesBeijingChina

This paper presents a brief overview of the geospatial technologies developed and applied in Chang’e-3 and Chang’e-4 lunar rover *** mapping techniques were used to produce topographic products of the landing site with meter level resolution using orbital images before landing,and to produce centimeter-resolution topographic products in near real-time after *** positioning techniques were used to determine the locations of the two landers using descent images and orbital basemaps immediately after *** surface operations,visual-positioning-based rover localization was performed routinely at each waypoint using Navcam *** topographic analysis and rover localization results directly supported waypoint-to-waypoint path planning,science target selection and scientific investigations.A GIS-based digital cartography system was also developed to support rover teleoperation.

关键词： geospatial technology lunar rover landing site mapping topographic analysis rover localization and navigation

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