Effects of Inventory Bias on Landslide Susceptibility Calculations

Many landslide inventories are known to be biased, especially inventories for large regions such as Oregon's SLIDO or NASA's Global Landslide Catalog. These biases must affect the results of empirically derived susceptibility models to some degree. We evaluated the strength of the susceptibility model distortion from postulated biases by truncating an unbiased inventory. We generated a synthetic inventory from an existing landslide susceptibility map of Oregon, then removed landslides from this inventory to simulate the effects of reporting biases likely to affect inventories in this region, namely population and infrastructure effects. Logistic regression models were fitted to the modified inventories. Then the process of biasing a susceptibility model was repeated with SLIDO data. We evaluated each susceptibility model with qualitative and quantitative methods. Results suggest that the effects of landslide inventory bias on empirical models should not be ignored, even if those models are, in some cases, useful. We suggest fitting models in well-documented areas and extrapolating across the study region as a possible approach to modeling landslide susceptibility with heavily biased inventories

Mapping of landslide vulnerability in the build area based on Remote Sensing and GIS in Ambon City, Indonesia

Longsor lahan merupakan bahaya alam yang berupa pergerakan suatu massa batuan, pecahan batuan (debris), atau tanah pada lereng di bawah pengaruh gravitasi. Dalam mengkaji penelitian ini, metode kualitatif dan kuantitatif digunakan dengan pendekatan spatial untuk menganalisis data primer dan data sekunder yang diperoleh dari citra satelit, observasi, dan instansi terkait. Pengolahan data dilakukan dengan menggunakan software Global Mapper 20, Arcgis 10.8.1 dan Ermapper 8.1. Hasil yang diperoleh dari penelitian ini memperlihatkan kerawanan dari longsor lahan cukup tinggi dengan areal wilayah yang luas sekitar 51.63%, daerah ini tersebar pada wilayah perbukitan. Untuk lahan terbangun dengan tingkat kerawanan longsor lahan baik pada kategori tinggi dan sangat tinggi yaitu pada zona Z-4 dan Z-5, yang disebabkan oleh kemiringan lereng 25 sampai > 40%, dengan jenis batuan yang memiliki tingkat pelapukan tinggi, dan terdiri dari tutupan lahan berupa lahan terbangun yang menambah bobot yang lebih pada lereng. serta kapasitas menahan tanah yang rendah sehingga peka terhadap erosi yang terjadiLandslides are natural hazards characterized by rock mass, debris, or soil movement on slopes under gravity. This study employed qualitative and quantitative methods with a spatial approach to analyze primary and secondary data obtained from satellite imagery, observations, and relevant institutions. Data were processed using Global Mapper 20, ArcGIS 10.8.1, and ER Mapper 8.1 software. The results obtained from this study revealed that the majority of the Ambon City area (approximately 51.63 %) was classified as having high landslide vulnerability. Meanwhile, only approximately 16.26% of the total area had very low or low landslide vulnerability. The same pattern is observed in built-up areas, where most landslide vulnerability falls under the high category (Z-4), at approximately 39.01%. In contrast, very low landslide vulnerability (Z-1) accounted for approximately 35.09%, and low vulnerability (Z-2) accounted for approximately 11.89%. The level of landslide vulnerability in the built-up areas also highlights that most of the Ambon City area, with mountainous terrain accounting for approximately 89% of the total area, experienced relatively high occurrences. In response, the government and relevant authorities must undertake careful spatial planning, direct development towards safer places, and implement policies that support sustainable development

Mapping of landslide vulnerability in the build area based on Remote Sensing and GIS in Ambon City, Indonesia

Longsor lahan merupakan bahaya alam yang berupa pergerakan suatu massa batuan, pecahan batuan (debris), atau tanah pada lereng di bawah pengaruh gravitasi. Dalam mengkaji penelitian ini, metode kualitatif dan kuantitatif digunakan dengan pendekatan spatial untuk menganalisis data primer dan data sekunder yang diperoleh dari citra satelit, observasi, dan instansi terkait. Pengolahan data dilakukan dengan menggunakan software Global Mapper 20, Arcgis 10.8.1 dan Ermapper 8.1. Hasil yang diperoleh dari penelitian ini memperlihatkan kerawanan dari longsor lahan cukup tinggi dengan areal wilayah yang luas sekitar 51.63%, daerah ini tersebar pada wilayah perbukitan. Untuk lahan terbangun dengan tingkat kerawanan longsor lahan baik pada kategori tinggi dan sangat tinggi yaitu pada zona Z-4 dan Z-5, yang disebabkan oleh kemiringan lereng 25 sampai > 40%, dengan jenis batuan yang memiliki tingkat pelapukan tinggi, dan terdiri dari tutupan lahan berupa lahan terbangun yang menambah bobot yang lebih pada lereng. serta kapasitas menahan tanah yang rendah sehingga peka terhadap erosi yang terjadiLandslides are natural hazards characterized by rock mass, debris, or soil movement on slopes under gravity. This study employed qualitative and quantitative methods with a spatial approach to analyze primary and secondary data obtained from satellite imagery, observations, and relevant institutions. Data were processed using Global Mapper 20, ArcGIS 10.8.1, and ER Mapper 8.1 software. The results obtained from this study revealed that the majority of the Ambon City area (approximately 51.63 %) was classified as having high landslide vulnerability. Meanwhile, only approximately 16.26% of the total area had very low or low landslide vulnerability. The same pattern is observed in built-up areas, where most landslide vulnerability falls under the high category (Z-4), at approximately 39.01%. In contrast, very low landslide vulnerability (Z-1) accounted for approximately 35.09%, and low vulnerability (Z-2) accounted for approximately 11.89%. The level of landslide vulnerability in the built-up areas also highlights that most of the Ambon City area, with mountainous terrain accounting for approximately 89% of the total area, experienced relatively high occurrences. In response, the government and relevant authorities must undertake careful spatial planning, direct development towards safer places, and implement policies that support sustainable development

Fuzzy clustering algorithm to identify the effects of some soil parameters on mechanical aspects of soil and wheat yield

In this paper, site-specific management zones (MZs) were delineated in three fields belonging to a farm in the center of Italy and characterized by different soil texture. Crop yield and various soil parameters, both physical (soil structural stability, clay fraction, water content, and organic matter) and mechanical (shear strength and penetration resistance) were monitored. Yield data were acquired by means of a combine harvester equipped with a precision land management system during three consecutive growing seasons. At the end of the third growing season, soil properties were investigated by means of georeferenced soil sampling. After data gathering, a fuzzy clustering algorithm was applied to define management zones. Results highlighted spatial variability between the three fields and temporal variability between the three consecutive growing seasons. Whilst the latter could be ascribed to the rainfall distribution (therefore moisture could be considered as a limiting factor in wheat growth), the delineated MZs suggest that clay content and organic matter could affect both mechanical parameters of soil and crop yield. The defined MZs can serve as a basis to generate prescription maps for variable-rate application inputs and variable tillage

Landslide susceptibility map using certainty factor for hazard mitigation in mountainous areas of Ujung-loe watershed in South Sulawesi

This study aims to build a landslide susceptibility map (LSM) by using certainty factor (CF) models for mitigation of landslide hazards and mitigation for people who live near to the forest. In the study area, the mountainous area of the Ujung-loe watersheds of South Sulawesi, Indonesia, information on landslides were derived from aerial photography using time series data images from Google Earth Pro© from 2012 to 2016 and field surveys. The LSM was built by using a CF model with eleven causative factors. The results indicated that the causative factor with the highest impact on the probability of landslide occurrence is the class of change from dense vegetation to sparse vegetation (4-1), with CF value 0.95. The CF method proved to be an excellent method for producing a landslide susceptibility map for mitigation with an area under curve (AUC) success rate of 0.831, and AUC predictive rate 0.830 and 85.28% of landslides validation fell into the high to very high class. In conclusion, correlations between landslide occurrence with causative factors shows an overall highest LUC causative factor related to the class of change from dense vegetation to sparse vegetation, resulting in the highest probability of landslide occurrence. Thus, forest areas uses at these locations should prioritize maintaining dense vegetation and involving the community in protection measures to reduce the occurrence of landslide risk. LSM models that apply certainty factors can serve as guidelines for mitigation of people living in this area to pay attention to landslide hazards with high and very high landslide vulnerability and to be careful to avoid productive activities at those locations

Assessment of Landslide Vulnerability in Urban Areas Using GIS and Remote Sensing: A Study in Ambon City

Slope stability and land movements, commonly referred to as landslides, are natural hazards that involve the shifting of materials like soil, rock, and debris, primarily caused by the force of gravity. This research utilized both qualitative and quantitative approaches, focusing on spatial analysis by examining primary and secondary data derived from satellite imagery, observations, and pertinent institutions. Processing of the collected data using specialized software like Global Mapper 20, ArcGIS 10.8.1, and ER Mapper 8.1. The findings of this investigation disclosed that a significant portion of Ambon City, roughly 51.63% of its area, exhibited high susceptibility to landslides. Conversely, only about 16.26% of the total area demonstrated very low or low vulnerability. Similar trends were observed in urbanized regions, where the majority, around 39.01%, were classified as highly vulnerable (Z-4). In contrast, approximately 35.09% showed very low vulnerability (Z-1), and 11.89% depicted low vulnerability (Z-2). The study's findings clearly highlight a critical situation in Ambon City, where a substantial 89% of its territory, characterized by mountainous landscapes, is experiencing a markedly increased frequency of landslides. Given these concerning insights, it is absolutely essential for government authorities to engage in rigorous spatial planning. This should involve redirecting development efforts towards areas identified as safer, away from high-risk zones. Furthermore, the government must enforce and adhere to policies that not only mitigate landslide risks but also promote sustainable development, ensuring the long-term safety and resilience of Ambon City against such natural disasters. Keywords: Mapping, Landslide, Vulnerability, Build Are

Mapping of landslide vulnerability in the build area based on Remote Sensing and GIS in Ambon City, Indonesia

Landslides are natural hazards characterized by rock mass, debris, or soil movement on slopes under gravity. This study employed qualitative and quantitative methods with a spatial approach to analyze primary and secondary data obtained from satellite imagery, observations, and relevant institutions. Data were processed using Global Mapper 20, ArcGIS 10.8.1, and ER Mapper 8.1 software. The results obtained from this study revealed that the majority of the Ambon City area (approximately 51.63 %) was classified as having high landslide vulnerability. Meanwhile, only approximately 16.26% of the total area had very low or low landslide vulnerability. The same pattern is observed in built-up areas, where most landslide vulnerability falls under the high category (Z-4), at approximately 39.01%. In contrast, very low landslide vulnerability (Z-1) accounted for approximately 35.09%, and low vulnerability (Z-2) accounted for approximately 11.89%. The level of landslide vulnerability in the built-up areas also highlights that most of the Ambon City area, with mountainous terrain accounting for approximately 89% of the total area, experienced relatively high occurrences. In response, the government and relevant authorities must undertake careful spatial planning, direct development towards safer places, and implement policies that support sustainable development

Metodologías para la evaluación de la amenaza por movimientos en masa como parte de los estudios básico de amenaza: caso de estudio municipio de Andes, Antioquia, Colombia

Los estudios básicos de susceptibilidad y amenaza por la ocurrencia de movimientos en masa son un elemento fundamental para la actualización de los planes de ordenamiento de los municipios del territorio colombiano. Dado lo anterior, la Ley 1523 de 2012 establece la política marco, y el Decreto 1807 de 2014, compilado en el 1077 de 2015, establece los lineamientos técnicos que tales estudios deben seguir y las condiciones mínimas que se deben cumplir. Por este motivo, se realizó una selección de algunas metodologías reconocidas en la literatura, que, al ser adecuadas y validadas según las condiciones propias de cada municipalidad, pueden ser utilizadas para la realización de tales estudios, sean tanto para el área rural y para suelo urbano y de expansión, como para cada uno de los factores que pueden detonar estos eventos.Landslide susceptibility and hazard studies are essential for updating land use plans in the municipalities of the Colombian territory. Thereby, the law 1523 of 2012 that established the political framework and the decrees 1807 of 2014 and 1077 of 2015 have established the technical guidelines that such studies must follow and the minimum conditions that must be achieved. Therefore, a selection of some known methodologies in literature was done, which, being adapted and validated according to the specific conditions of each municipality, they are used to carry out these studies, for both rural, urban and expansion areas and for each factor that can detonate these events

Application of a Spatially Explicit, Agent-Based Land Use Conversion Model to Assess Water Quality Outcomes under Buffer Policies

Land use changes within watersheds have spatially explicit dynamics and involve decision making by individuals. The role of the spatial dimension of human behavior and its impact on land use change has been analyzed using agent-based modelling approaches. Agent-based land use change has received a significant theoretical attention; however, these models lack empirical implementation and testing due to the lack of spatial modelling tools and data that can capture human land use dynamics.;This research presents a methodology for projecting land use conversions through the implementation of a spatially explicit agent-based simulation model in the Opequon Creek watershed of Berkeley County, West Virginia. Empirical estimates for factors that influence the land use conversion probability are captured using a spatial logistic regression model. Then, agentbased probabilistic land use conversion (APLUC) model is programmed on Python language within a geographic information system (GIS) to explore the impacts of policies on land use conversion decisions using estimates from actual land use change from 2001-2011. A series of model runs are executed under buffer policy scenarios. Three policy scenarios are developed: (1) a scenario where there is no policy implemented, (2) a scenario where 50 ft buffer zones are applied to all streams, and (3) a scenario where 50 ft buffers are applied only on critical source areas (CSAs) watersheds. The land use patterns project in APLUC model are driven by individual land conversion decisions over 50 model runs of 10 iterations each under each policy scenario. The APLUC model is validated at sub-basin level and outcomes are analyzed to identify the influence of various land use policies on land use patterns. The results show that a 50 ft buffer policy everywhere in watershed, greatly reduced the residential land use conversions. Spatial patterns generated under a 50 ft buffer policy in CSAs only showed that future projected land use changes occurred close to major highways. In the baseline policy, most conversions occurred near existing residential land use and urban centers. Results from the APLUC model also suggests that forest is serving as distant amenity for residential land conversion.;Finally, the impacts of these three policies on water quality are estimated using an ArcSWAT model, a graphical user interface for SWAT (Soil and Water Assessment Tool). This model indicates that the 50 ft buffer policy in CSAs is most effective among the three policies in reducing the pollutant loads. This study suggests that carefully designed policies, which discourage residential land use conversion in CSAs, result in less pollutant loads by shifting the location of residential conversion to less critical areas where agricultural land is dominant in the watershed