Overlay Analysis Method Research Articles

Application Of Remote Sensing Technology And Geographic Information Systems For Tsunami Vulnerability Analysis In Mamuju District

This research aims to analyze the vulnerability to tsunami disasters in Mamuju District, West Sulawesi, using remote sensing technology and Geographic Information Systems (GIS). Mamuju District is at high risk of tsunamis due to its coastal location and its position between active tectonic plates. The analysis utilizes five main parameters: elevation, slope gradient, distance from rivers, distance from the coastline, and land cover. The GIS overlay analysis method is applied to map tsunami hazard zones, combining weighting and scoring for each parameter. The results indicate that 3% of the area has very high vulnerability, 7% of the area has high vulnerability, while 82% of the area has low to very low vulnerability. Areas with very high vulnerability include the coastal zone directly adjacent to the shoreline and residential areas. This study is expected to serve as a reference for local governments and disaster management agencies in efforts to mitigate and plan safer spatial arrangements against tsunami threats. Mitigation strategy recommendations include the construction of evacuation routes, safe shelters, and the regular enhancement of disaster education and simulations for the community. Furthermore, this research encourages the use of mountainous areas as primary evacuation zones and collaboration with research institutions for the development of more innovative mitigation technologies.

AHP and geospatial technologies for identifying groundwater exploration target regions in Dindigul, southern India

Recent advances in space technology are being widely utilized in resource mapping and management as they provide a high level of accuracy over a short duration and at a lower cost, when compared to traditional methods. Remote sensing and the geographical information system (GIS) play a vital role in collecting and analyzing information used to map resources and earth surface features. For example, anthropogenic activities and climate change affect the natural interaction between the surface and ground water making the monitoring and management of groundwater resources mandatory for future sustainable development. In this study, we attempt to identify the groundwater potential zones in the Dindigul district of Tamil Nadu, India using these emerging technologies. To map regions with groundwater potential, the analytical hierarchical process (AHP) was used to assign weights to all classes in each thematic layer based on groundwater-influencing factors. The sub-features of every parameter were also ranked, using a pairwise comparison method, based on the groundwater-influencing factors and expert knowledge. The parameters selected for the analysis were geology, geomorphology, soil, land-use/land-cover, drainage density, lineament density, slope, rainfall, thickness of top-soil, thickness of fractured zone, thickness of weathered zone, topographic wetness index, roughness and curvature. The weighted overlay analysis method was adopted for all the thematic layers, with technical support from the AHP for assigning weightages. The results were classified into five categories viz., very high, high, moderate, low, and very low potential zones, and used to prepare a groundwater potential map. This map revealed a nuanced distribution of groundwater potential across the district, with varying degrees of potentiality in different regions. Very high potential zones occupy a minimal area (0.07%) but are strategically located in areas characterized by high weathered zone thickness and conducive soil characteristics. High potential zones cover a significant area (22.33%) and are dispersed throughout the district, with specific concentrations in regions featuring highly fractured zones and favorable geological attributes. Moderate potential zones (35.12%) are primarily located in the northeastern part of the study area, while low and very low potential regions cover 19.56% and 22.92% of the study area. The low and very low potential zones appear particularly in hilly terrains, with unfavorable top soil and geological conditions. The validation of the groundwater potential zone (GWPZ) map using the annual average water level data demonstrates a substantial match (60.53%) between the identified groundwater conditions and actual well depths, affirming the reliability and significance of the findings from this study.

Assessing Groundwater Vulnerability to Pollution in a Rapidly Urbanizing River Basin Using a Modified DRASTIC Land Use–Lineament Density Method

ABSTRACTGroundwater quality assessment is crucial for ensuring safe drinking water, protecting public health, and maintaining sustainable water resources for agricultural and industrial uses. The Awash River basin faces significant groundwater quality challenges due to rapid population growth, high urbanization, large‐scale irrigation, and industrial pollution. The main objective of this study is to evaluate the intrinsic vulnerability of aquifers to pollution in the Awash Basin and identify hotspots requiring urgent intervention using a modified DRASTIC overlay analysis method. In addition to the seven parameters considered in the generic DRASTIC overlay analysis (depth to the water table, recharge, aquifer media, slope, soil media, vadose zone, and hydraulic conductivity), we incorporated land use/land‐cover (LULC) and lineament density (LD) distributions (DRASTIC‐LU‐LD). This modification allowed us to produce more realistic groundwater vulnerability maps for the basin. To identify the most influential parameters in the overlay, sensitivity analysis was conducted using Map Removal Sensitivity Analysis (MRSA) and Single Parameter Sensitivity Analysis (SPSA). Initially, the generic DRASTIC index in the area ranges from 69 to 181, categorizing the area into four vulnerability zones: very low (21%), low (51%), medium (27%), and high (1%). After incorporating LU and LD, the index values ranged from 90 to 240. Based on the percentage of the total area studied, the inclusion of LU decreased the very low and low vulnerability zones from 72% to 44% and the inclusion of LD increased the high‐ and very‐high‐vulnerability zones from 14% to 27%. The areas most vulnerable to groundwater pollution are in the western (upper Awash), middle Awash, and northwestern regions, particularly in city centers where groundwater abstraction is significant. These high‐vulnerability zones coincide with municipal, industrial, and agricultural pollution sources. The vadose zone parameter has the highest impact in both MRSA and SPSA, with a variation index value of 3.08% and a mean effective weight of 27.93%, respectively. By identifying areas vulnerable to groundwater pollution, this study provides a valuable basis for informed decision‐making and the development of effective strategies for protecting groundwater from urban, industrial, and agricultural pollution sources.

A Study on Flood Hazard Zonation Mapping Based on GIS-driven Approach using Remote Sensing Data and Weighted Overlay Analysis (Woa) Model

Flood hazard mapping is crucial for identifying flood-prone areas and developing effective mitigation strategies. This study utilized a Weighted Overlay Analysis, which is one of the subjective model derived from Multi-Criteria Decision Support System (MCDA), to assess flood hazards in the Auranga watershed, India. Six key flood-inducing factors were selected, including topographic data such as elevation, slope, river distance, and flow length derived from remote sensing data of Digital Elevation Model and classified Land use/Land cover data, along with hydro-meteorological data of precipitation. Each dataset was standardized to a common scale to facilitate comparison and integration. Weights were assigned to the layers like elevation, slope, river distance, flow length, Land use/Land cover, and precipitation according to their significance in contributing to flood risk. These weighted layers were then combined using GIS tools like ArcGIS, resulting in a composite map that delineates areas with varying flood hazard zones. The final flood hazard map identified that approximately 914 sq. km (60.0%) of the study area was at high to very high flood risk, particularly in regions close to rivers, while around 97 sq. km (6.4%) exhibited very low flood hazard. The GIS-Weighted Overlay Analysis method proved effective in flood hazard zone mapping, especially when incorporating a greater number of parameters. This approach is widely recognized in environmental planning and risk assessment, especially in areas vulnerable to natural disasters like floods.

Spatiotemporal Dynamics and Evolution of Grain Cropping Patterns in Northeast China: Insights from Remote Sensing and Spatial Overlay Analysis

Understanding the spatiotemporal patterns and driving mechanisms of cropping patterns’ evolution tailored to local conditions is crucial for the effective allocation of black soil in northeast China and the advancement of agricultural development. This study utilized the Google Earth Engine platform to extract the spatial distribution data of major grain crops in northeast China for the year 2022. Using crop classification data from 2000 to 2022, the spatial overlay analysis method identified cropping pattern types based on spatial and temporal changes. The primary cropping patterns identified were continuous maize cropping, maize–soybean rotation, mixed cropping, and continuous soybean cropping. Simultaneously, this research constructed three distinct crop periods: Period I (2000–2002), Period II (2010–2012), and Period III (2020–2022). Over three periods, these patterns covered 94.73%, 88.76%, and 86.39% of the area, respectively. The evolution of the dominant cropping pattern from Period I to Period II involved the transition from continuous soybean cropping to continuous maize cropping, while from Period II to Period III, the main shift was from continuous maize cropping to maize–soybean mixed cropping. From a spatial perspective, since Period I, maize has increasingly replaced soybean as the dominant crop, with continuous maize cropping expanding northward and continuous soybean cropping contracting. The maize–soybean rotation area also migrated northward, particularly in the core area of the Songnen Plain, evolving mostly into continuous maize cropping. Maize cropping areas exhibited significant regional characteristics, being densely distributed in the Sanjiang Plain and Liaohe Plain, and along major tributaries in northeast China. Consequently, the interplay of the natural environment, economic policies, and agricultural technologies drove these changes. The findings offer valuable insights for optimizing cropping patterns and developing rotation systems in northeast China.

Spatiotemporal changes of orchards in the ancient canal of the Yellow River using Landsat data

As an important part of the Yellow River Basin, the ancient canal of the Yellow River plays a great role in promoting ecological protection and high-quality development in the Yellow River Basin. There is a need to explore the distribution of the available resources to optimally manage and utilize them more effectively. By analyzing multi-temporal characteristic of the remote sensing images, the spatial temporal distribution characteristics of orchards in the ancient canal of Yellow River were investigated. Time series of orchards were derived from the Landsat images over a nearly 30-year period at approximately 5-year intervals since 1990. And the spatial distribution characteristics were qualified basing on the spatial overlay analysis methods. The results show that from 1990 to 2020, the orchard area in the ancient canal of the Yellow River increased rapidly in the early, then slowed down after 2000; the area of orchards in 2020 was about 1268.30 km2. Dangshan County, Feng County and Xiao County were the major areas planting orchards; 86.84 % of the orchards distributed in flat terrain with an elevation of 21–60 m, 88.50 % distributing on a slope of less than 4° and 91.12 % of the orchards distributed within the distance of more than 280 km from the coastline; Moran's I index of orchard planting density was greater than 0 for all four spatial scales and was increasing when downscaling, which showed significant spatial positive correlation. These findings provide helpful information for the research in promoting ecological protection and high-quality development of the ancient canal of the Yellow River Basin.

Tracking dynamics characteristics of tidal flats using landsat time series and Google Earth Engine cloud platform

Tidal flats serve as vital spatial resources within coastal wetlands and play crucial roles in biological habitat preservation, environmental conservation, and human development. However, accurately delineating their extent from instantaneous remote sensing images poses challenges, owing to the dynamic interplay between seawater and land. Therefore, this study utilized the consistency of remote sensing satellites and developed a multi-feature decision approach based on the long-term remote sensing datasets and the Google Earth Engine (GEE) cloud platform for determining the spatial extent, classifying types, and mapping the spatial-temporal distribution of the tidal flats. Initially, a multi-temporal image stack was compiled from the filtered remote sensing images. Subsequently, the modified normalized difference water index (MNDWI) and automated water extraction index (AWEI) were calculated to establish the maximum and minimum water surfaces using the extreme value composite algorithm (EVCA). The Otsu algorithm and spatial overlay analysis method were then utilized to accurately delineate the spatial extent of the tidal flats. Additionally, the Normalized Difference Vegetation Index (NDVI) was utilized to accurately identify tidal flat types. Post-processing involves elevation data and mathematical morphological methods to mitigate the impact of permanent water bodies. Finally, the accuracy of the extracted spatial extent and types of tidal flats was evaluated. The efficacy and practicality of the proposed method were validated using the Zhoushan Archipelago and Landsat time series in China. The results revealed the following: (1) the acquired tidal flat information exhibited evident boundaries and accurate types, achieving an overall accuracy (OA) of 94.80 % and a Kappa coefficient of 0.89; (2) the area of tidal flats in the Zhoushan Archipelago increased from 2,182.19 ha in 1985 to 3,761.21 ha in 2020, with an average annual increase of 45.12 ha and an average annual increase rate of 1.57 %; and (3) over 35 periods, the area transformed from other land types to tidal flats amounted to 2,895.74 ha, while the area converted from tidal flats to other land types totaled 1,651.90 ha. The three islands with the most rapid growth in tidal flat area were Liuheng Island, Dachangtu Island, and Daishan Island, with changes of 675.68 ha, 380.47 ha, and 362.63 ha, respectively. This method can offer an automated, swift, and accurate approach for tidal flat extraction, demonstrating high precision and practical effectiveness, thereby providing robust technical support for coastal resource surveys.

Returning Cropland to Grassland as a Potential Method for Increasing Carbon Storage in Dry-Hot Valley Areas

A key aspect of mitigating global climate warming is enhancing the carbon storage capacity of terrestrial ecosystems. China’s Grain for Green Program (GFGP) is the largest ecological restoration project in the world, which is closely associated with land use change. A systematic assessment of the GFGP’s impact on regional carbon storage is of great significance for promoting regional development and maintaining ecosystem stability. Therefore, this study selects a typical dry-hot valley area—Yanjin County—as the study area, which serves as an ecological protection barrier in Southwest China. We employed the InVEST model and Geo-detector model based on land use data from three periods (2000, 2014, and 2019), combined with static overlay analysis methods, in order to evaluate the impact of the implementation of GFGP on the spatial and temporal distribution of carbon storage. We also explored the driving factors of the spatial differentiation of carbon storage. The results indicate that, since the implementation of the GFGP, a total of 180.03 km2 of cropland has been converted to forestland, increasing the forest cover rate from 81.83% to 83.37%. The project has contributed 5.88 × 105 t to regional carbon storage, effectively offsetting carbon emissions caused by human activities such as urban expansion while also promoting the growth of regional carbon storage. The implementation of the GFGP has led to changes in three types of land use. Among them, converting cropland to forestland (3262 t/km2) is the most effective carbon sequestration method, and converting cropland to grassland (2530 t/km2) has shown great potential in carbon sequestration. Additionally, the study found that elevation (0.038–0.059) is the main factor affecting the spatial differentiation of carbon storage, and the interaction between elevation and other factors can effectively enhance the carbon sequestration capacity of regional ecosystems. Overall, the GFGP not only plays a significant role in combating climate warming but also makes an important contribution to improving the stability and sustainability of regional ecosystems.

Urban mobility analytics amid COVID-19 pandemic: A framework for promoting work resumption based on mobile phone data

The Corona Virus Disease 2019 (COVID-19) pandemic had a pernicious influence on the whole world, so that the international community implemented various travel policies to inhibit the viral spread. With the COVID-19 pandemic under control, the overly lenient measures allowed the new variant to take advantage. Also, it is difficult to determine the geographical scope of transport policy that has the least impact on work resumption. Therefore, it is necessary to find policies to promote work resumption while preventing and controlling the pandemic in a geographical perspective. In this paper, a framework based on mobile phone data is proposed for promoting work resumption during the pandemic. First, human mobility networks are constructed through the origin-destination flow extracted from mobile phone data. Second, the singular value decomposition (SVD) algorithm is utilized to recognize the mobility patterns and explore the urban mobility dynamics of work-resumption-related travel during the pandemic. Third, the nonoverlapping community detection algorithm is used to generate policy zoning for work-resumption-related travel. Finally, the policy zoning is validated by public health data based on the overlay analysis and spatial statistics method, and policy implications are proposed. A case study of Shanghai, China is applied to verify the proposed framework. The results show that the framework can capture the mobility patterns and implement the policy zoning based on mobile phone data to balance the economic activity and public health during the pandemic, and the proposed framework can be applied to other cities with future public health events.

Analyzing the impact of Jakarta’s reclamation on the distribution of TSS using remote sensing technology

Jakarta Bay is a bay supporting the economy of thousands of nearby fishermen. Therefore, the reclamation in Jakarta Bay raises many environmental, economic, and social problems. During this reclamation, dredging and stockpiling activities increase the turbidity of the waters. This research aims to determine the changes in the distribution of total suspended solids (TSS) after reclamation using Sentinel 2A satellite imagery from 2016 to 2022. This research implemented the previous algorithm to produce the distribution of TSS in Jakarta Bay for attaining TSS values from Sentinel 2A satellite imagery processed by Google Earth Engine with overlay analysis method in a geographic information system. Spatial data were obtained by extracting TSS values using the previous estimation algorithm, namely the Budhiman algorithm, with a correlation coefficient of -0.745 and NMAE of 0.167. Based on the data processing results, it was found that the distribution of TSS from 2016 to 2022 was at a moderate level, which had a relatively significant influence on fisheries, requiring the fishermen to go to the open sea farther from the coast. This shows that reclamation carries no impact on the life of marine biota in Jakarta Bay.

IDENTIFIKASI TIPOLOGI JALUR PEJALAN KAKI DI KORIDOR JALAN BANDUNG – CIREBON DESA TANJUNGSARI

Pedestrian paths or pedestrian paths are facilities that must exist in a city. Pedestrian paths generally function to facilitate pedestrian movement from one place to another easily, smoothly, safely, comfortably and independently. Pedestrian paths are created so that road users, especially pedestrians, can avoid accidents and can enjoy walking activities safely, relaxed and comfortably without having to worry about vehicles crossing them. The unavailability of pedestrian paths on the Jalan Bandung - Cirebon corridor in Tanjungsari Village, Tanjungsari District, Sumedang Regency has caused various problems related to pedestrian safety with many traffic accidents occurring on this route. As a first step in creating a concept plan for pedestrian routes in the Jalan Bandung - Cirebon corridor, it is necessary to identify the typology of pedestrian routes using an overlay analysis method between the land cover map and the spatial pattern map. The study findings in the form of segments will become a land plan for planning pedestrian paths that comply with safety and comfort standards. The results of the analysis state that there are 3 (three) segments that can be used as pedestrian path plans, namely road segment 1 (one) in the trade and services zone in the south with a segment length of 450 meters, road segment 2 (two) in the mixed zone with segment length of 476 meters and segment 3 (three) in the trade and services zone in the north with a segment length of 374 meters, which will be the basis for determining further analysis.

Estimation of forest canopy density through Geospatial Technology-a case study on Sathyamangalam Forest, Erode District, Tamil Nadu.

The term forest canopy density (FCD) refers to one of the important criteria used to evaluate forest's ecological health. It plays a significant role in assessing the health of the forest and serves as a key landmark for potential management actions. The canopy coverage or crown cover is referred to the percentage of the forest floor that is covered by the vertical projection of tree crowns and necessary for monitoring the condition of the forest. The present study aims to estimate the forest canopy density (FCD) through Geospatial Techniques for Sathyamangalam Forest for the period between 2016 and 2022 with SENTINEL 2A satellite data. The weighted overlay analysis method was implemented with biophysical parameters, namely, Normalize Difference Vegetation Index (NDVI), Advanced Vegetation Index (AVI), Shadow Index (SI), and Soil Bareness Index (SBI) to analyze the state of the forest and its activity. The results observed significantly that the forest canopy with 158.60 km2 in 2016 which is increased to 190.37 km2 in 2018 (1.14%) then suddenly decreased to 134.85 km2 in 2020 (2.47%). The forest canopy has recovered some of its original area with 168.83 km2 through better environmental conditions during 2021-2022 (1.52%). Therefore, Geospatial Technology plays a significant role in estimating recent changes in regional forest.

The estimated suitability of land for hemp cultivation in the western region of Romania

In any region of the globe, the cultivation of industrial crops, including hemp, imposes certain conditions related to relief, climate, hydrology, or soil. The interrelations between these factors lead to differentiations regarding the establishment of cultivation zones and implicitly in terms of productivity. This study aimed to identify suitable areas for hemp cultivation and generate favorability maps in western Romania, based on GIS and MCDA techniques. Ten factors, significantly important for hemp cultivation, related to relief, climatic conditions, soil, and human intervention were considered. Following spatial analyses using the Weighted Overlay Analysis (WOA) method, the territory was classified into four zones: Zone I, with highly suitable lands (26%), located in the western half, overlapping with plain areas and river valleys; Zone II, with moderately suitable lands (28%), located in regions of high plains and hilly areas; Zone III, with marginal lands (3%), located in some depression areas, hilly areas, or at the contact with pre-mountainous areas; Zone IV, with unsuitable lands for hemp cultivation, in mountainous areas, under restrictive environmental conditions. The obtained results can be used in land management strategies or by farmers for estimating available land resources.

Application of geospatial tools in the assessment of Flood hazard impact on social vulnerability of Malda district, West Bengal, India

Social vulnerability assessment is a dynamic process, which varies from place to place. In the present study, the social vulnerability index (SVI) of Malda district has been prepared because of several impacts of flood inundation. The flood inundation layer has been generated using multi-temporal remote sensing data. The flood inundation layer is prepared from real-time Synthetic Aperture Radar (SAR) data. For social vulnerability assessment, the most efficient indicators are household composition, age & sex composition, and underprivileged population (SC& ST). Economic and educational data has been collected from the Census of India Handbook 2011. All these data are combined with the district's village database on the GIS platform. The weightage overlay analysis method is applied to generate the social vulnerability index of the study area, where the multi-influencing factor (MIF) technique has been used for determining the influencing factors. The social vulnerability index has categories into Very High (4%), High (37%), Moderate (32%) and Low (27%). The social vulnerability index is being further intersected with the flood inundation layer to build a database for the most vulnerable area of this district. It has been observed that 70 villages are in Very High zones, 662 villages are in High, 578 villages are in Moderate and 479 villages are in Low zones. This study will help the disaster manager and stakeholders about the vulnerable situation of the study area and also depict the importance of geospatial techniques in disaster management.

Analysis of Land Use Developments Along the LRT Line (Case Study: Polresta, Jakabaring and DJKA Station’s)

Transportation has become a basic need that plays an important role in people's lives. To reach a place, a high level of accessibility is required. A high level of accessibility will affect the number of people coming to an area. Many people will affect the number of activities that require land. Therefore, this study aims to analyze land use trends that occurred in the time period before and after the South Sumatra LRT. LRT or Light Rail Transit is a type of public transportation in the form of light rail operating in urban areas. LRT is the first light rail transportation in Indonesia right in Palembang City. Therefore, it is interesting to see its relation to land change along the LRT route. This research was conducted at three stations which were case studies, namely Polresta, Jakabaring and DJKA stations. This station was chosen because it is in Jakabaring. Jakabaring was chosen as the research area because it is a sub-district which is located on the border with other regencies but is still passed by the LRT. This research is a qualitative research using the overlay before-after analysis method through spatial data. This research uses the help of Archgis software, through Intersect tools and multiple ring buffers. The results of the analysis are then used as data for further analysis using a pivot table in excel. The results of this analysis prove that there is a tendency for changes in land use within a certain radius. The trend is in the form of changes in land from swamps or paddy fields to deserted, trade and services as well as offices.

Land cover changes in Bila Watershed, South Sulawesi, Indonesia

Changes in land cover are characterized by changes in land use conversion. One of the factors that cause changes in land closure is the increase in the number of residents so the need for residential land also increases. This study aims to identify changes in land cover in 2017 and 2021 in the Bila River Basin. Land cover change analysis is carried out using the Geographic Information System approach using the overlay analysis method in ArcGIS software. The results showed that in 2017-2021, secondary dryland forest land cover experienced a reduction in the area of 915.96 hectares or from the total area of watersheds converted to mixed dryland agriculture. Meanwhile, the largest addition occurred in the dry land closure class covering an area of 634.08 ha of the total watershed area converted to dryland farming. This is due to the increasing need and dependence of the community on land, with community activities in managing land and utilizing the land to increase agricultural yields as well as a source of livelihood for the community.

The evolution of spatiotemporal patterns and influencing factors of high-level tourist attractions in the Yellow River Basin

Introduction: High-level tourist attractions (HLTAs) are essential components of high-quality tourism development in the Yellow River Basin. In the context of holistic tourism and mass tourism, it is necessary to re-examine the spatial pattern of HLTAs.Methods: Selecting the Qing–Gan–Ning region of the Yellow River Basin as a case study site, based on the data on 590 high-level tourist attractions in 2009, 2015, and 2021, and with the help of ArcGIS10.8 spatial analysis tools, the nearest neighbor index, kernel density analysis, and standard deviation ellipse methods, such as ellipse and ESDA spatial exploratory analysis, were used to analyze the spatiotemporal pattern of the spatial distribution of high-level tourist attractions in the study area from the aspects of type, density, and spatial autocorrelation. Overlay analysis, buffer analysis, and other methods were used to select the influencing factors, and finally, the influencing factors were verified with the help of GeoDetector.Conclusions: The conclusions are as follows: the NNI values for the Qing–Gan–Ning area of the Yellow River Basin are 0.699, 0.7, and 0.618, and the spatial structure type was clustered. The distribution density showed an evolutionary trend of point-like agglomeration and linear expansion, with the provincial capital as the core and the Yellow River as the axis. The distribution density of high-level tourist attractions is 27, 44, and 74 per 10,000 km2. In terms of the dynamic distribution direction of the center of mass, there was little interannual variation, showing a northeast–southwest direction, which is consistent with the flow direction of the Yellow River in the region. Furthermore, the analysis of Moran’s I index showed clear spatial autocorrelation at the county scale. HLTAs exhibited clustering and wider distribution in H–H and L–L zones, while the L–H and H–L zones displayed a more dispersed and narrower distribution. The order of factors affecting the spatial distribution of HLTAs was economic factors (0.5257) > social factors (0.5235) > natural factors (0.491), and interactive detection showed that there were two-factor enhancements and nonlinear enhancements in the factors.Dicussions: This study contributes to the conservation development and sustainable development of ecotourism resources in the Yellow River Basin.

Analysis of Hotspot Distribution on Land Use Patterns in Rasau Jaya District in 2022

Rasau Jaya Sub-District has six villages that experience frequent forest and land fires every year. Forest and land fires are caused by climatic and human factors that have an impact on health, socio-economic locally and globally, as well as damage to land cover vegetation. Forest and land fires can be identified by monitoring hotspots on satellite imagery. This research was conducted to identify the distribution of hotspots on land use patterns in the Kecamatan Rasau Jaya. The method used is quantitative descriptive with overlay analysis method using intersect tools on hotspot maps, land-use, and administrative maps. The distribution of hotspots in the Rasau Jaya District in 2022 is relatively large in the western part of the Rasau Jaya District, namely Pematang Tujuh Village and Bintang Mas Village. The type of land use that has a high potential for forest and land fires is agricultural land with 134 hotspots and 91 land use shrubs. Overall, the villages that have the potential for forest and land fires are Pematang Tujuh Village, Bintang Mas Village, and Rasau Jaya Umum Village.

Comparison of AHP and BWM methods based on ArcGIS for ecological suitability assessment of Panax notoginseng in Yunnan Province, China

Panax notoginseng (P. notoginseng) is a traditional Chinese medicinal material with high economic and medicinal value. There is an urgent need to find new cultivation areas due to continuous cropping barriers and the effects of climate change. This study integrated the geographical environment of Yunnan with the necessary requirements for a suitable habitat for P. notoginseng and comprehensively analyzed climatic, topographical and soil factors. Furthermore, the criteria were weighted and prioritized by the analytic hierarchy process (AHP) and the best worst method (BWM) respectively, and the overlay analysis method was used to present the suitability map in the GIS environment. Two ecological suitability assessment models were established and compared to explore the ecological suitability regionalization of P. notoginseng in Yunnan Province, China. The results showed that:(1) Among the 12 driving factors, precipitation in the hottest season is the most important, followed by average temperature in the coldest season, average temperature in the hottest season, altitude, and annual precipitation. (2) The most suitable, suitable, and unsuitable regions obtained by the AHP-GIS model accounted for respective 17.98%, 33.41%, and 48.61% of the total area of Yunnan. The values for BWM-GIS model were 13.93%, 40.25%, and 45.81%, respectively. (3) The suitable regions obtained by the two models were overlayed and concentrated in Wenshan, Qujing, Kunming, Chuxiong, Baoshan, Dali, Lijiang, Honghe and Yuxi in central Yunnan, while the unsuitable regions were distributed in Xishuangbanna, Puer, Dehong, Diqing, and southwestern Zhaotong. The sensitivity of the assessment model was analyzed, and its reliability was verified by receiver operating characteristic (ROC) and area under curve (AUC). The BWM-GIS model (AUC=0.85) had higher accuracy than AHP-GIS model (AUC=0.822), and was more suitable for ecological suitability analysis of P. notoginseng. Our research results provide a theoretical basis for the rational expansion of cultivation regions of P. notoginseng and agricultural layout planning in Yunnan Province of China.

Geospatial assessment of urban green space using multi-criteria decision analysis in Debre Markos City, Ethiopia

Urban green space (UGS) serves as a lung for city dwellers to breathe and is critical for fostering the sustainable growth of urban ecology and improving residents’ quality of life. Debre Markos City (DMC) lacks natural recreational areas such as lakes and artificial green spaces. A notable UGS study utilizing multiple contributing factors integrated with remote sensing and GIS has a significant advantage in making residents more comfortable. This study, therefore, aimed to assess the conventional methods (existing and proposed UGS) against remote sensing and GIS-based, and to identify, quantify and allocate the proper suitable site for UGS development.. Hence, ten UGS influencing factors were selected. Each criterion was analyzed with the use of its proper algorithm. Multicriteria decision-making (MCDM) analysis integrated with Analytical Hierarchy Process (AHP) method and Remote Sensing and GIS were applied. The weighted sum overlay analysis method of spatial analysis was implemented based on the criteria weight of each factor. A 90.6% and 0.8855 accuracy assessment and kappa coefficient were recorded for land use/land cover classification, respectively. The value of the consistency ratio was 0.043, which is < 1 and acceptable. Results revealed that 13.12% (2201 ha), 25.47% (4271.7 ha), 30.89% (5180.8 ha), 22.49% (3772.7 ha), and 8.2% (1345.8 ha) of the total area coverage had very high, highly, moderately, poorly and unsuitable for UGS development activity at DMC, respectively. In this study, a resident can access UGS with less than 1 km radius distance, closer to the national standard of Ethiopia (i.e. 500 m). However, in the existing UGS, a person is forced to walk or travel more than 8.4 km to get the service. In conclusion, remote sensing and GIS can make life easy to access UGS in the study area with minimum distance, cost, and a short period. It is recommended that policymakers, planners, and executive bodies in Ethiopia consider the future urban demand of dwellers with proper studies earlier to establish other urban infrastructures.