THE PERFORMANCE OF LAND USE CHANGE CAUSATIVE FACTOR ON LANDSLIDE SUSCEPTIBILITY MAP IN UPPER UJUNG-LOE WATERSHEDS SOUTH SULAWESI, INDONESIA

The study aims to develop and apply land use change (LUC) performance on landslide susceptibility map using frequency ratio (FR), and Logistic regression (LR) method in a geographic information system. In the study area, Upper Ujung-loe Watersheds area of Indonesia, landslides were detected using field survey and air photography from time series data image of Google Earth Pro from 2012 to 2016 and LUC from 2004 to 2011. Landslide susceptibility map (LSM) was constructed using FR and LR with nine causative factors. The result indicated that LUC affect the production of LSM. Validation of landslide susceptibility was carried out in this study at both with and without LUC causative factors. First, performances of each landslide model were tested using AUC curve for success and predictive rate. The highest value of predictive rate at with LUC in both FR and LR method were 83.4 % and 85.2 %, respectively. In the second stage, the ratio of landslides falling on high to a very high class of susceptibility was obtained, which indicates the level of accuracy of the method.LR method with LUC had the highest accuracy of 80.24 %. Taken together, the results suggested that changing the vegetation to another landscape causes slopes unstable and increases probability to landslide occurrence.

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Weights of Evidence Modeling for Landslide Susceptibility Mapping of Kabi-Gebro Locality, Gundomeskel area, Central Ethiopia

10.21203/rs.3.rs-45713/v1 ◽

2020 ◽

Author(s):

Nega Getachew ◽

Matebie Meten

Keyword(s):

Landslide Susceptibility ◽

Agricultural Land ◽

Google Earth ◽

Weights Of Evidence ◽

Susceptibility Map ◽

Landslide Susceptibility Map ◽

Data Set ◽

Causative Factors ◽

Central Highland ◽

The Impact

Abstract Kabi-Gebro area is located within the Abay Basin at Dera District of North Shewa Zone near Gundomeskel town in the Central highland of Ethiopia and it is about 320 Km from Addis Ababa. This is characterized by undulating topography, intense rainfall, active erosion and highly cultivated area. Geologically characterized by weathered sedimentary and volcanic rocks. Currently, landslides are creating serious challenges in road construction, farming practices and affecting people in this area. Active landslides in this area damaged the gravel road, houses and agricultural land. The main objective of this research is to prepare the landslide susceptibility map. To overcome the landslide problem in this area, landslide susceptibility map was prepared using GIS- based Weights of Evidence model. Based on detailed field assessment and Google Earth image interpretation, 514 landslide locations were identified and classified randomly as training landslide (80%) and validation landslide (20%). The training landslide data set include nine landslide causative factors such as lithology, slope angle, aspect, curvature, land use/land cover, distance to stream, distance to lineament, distance to spring and rainfall inorder to prepare landslide susceptibility map in this study. The landslide susceptibility maps were prepared by adding the weights of contrast values of the nine causative factors using rater calculator in the spatial analyst tool of ArcGIS. The final landslide susceptibility map was reclassified as very low, low, moderate, high and very high landslide susceptiblity classes. This susceptibility map was validated using landslide density index and Area Under the Curve (AUC). The result from this validation showed a success rate and avalidaton rate accuracies of 82.4% and 83.4% respectively for this model. Finally, this study recommends application of appropriate mitigation or corrective measures in order to lessen the impact of landslide in the area.

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Implementing Landslide Susceptibility Map at Watershed Scale of Lompobattang Mountain South Sulawesi, Indonesia

Indonesian Journal of Geography ◽

10.22146/ijg.16632 ◽

2018 ◽

Vol 50 (2) ◽

pp. 197

Author(s):

Abdul Rachman Rasyid ◽

Netra Prakash Bhandary ◽

Ryuichi Yatabe

Keyword(s):

Land Use ◽

Landslide Susceptibility ◽

Disaster Mitigation ◽

Weight Of Evidence ◽

Slope Aspect ◽

Susceptibility Map ◽

Watershed Scale ◽

Landslide Susceptibility Map ◽

Landslide Occurrence ◽

Predictive Rate

This study attempts to predict future landslide occurrence at watershed scale and calculate the potency of landslide for each sub-watershed at Lompobatang Mountain. In order to produce landslide susceptibility map (LSM) using the statistical model on the watershed scale, we identified the landslide with landslide inventories that occurred in the past, and predict the prospective future landslide occurrence by correlating it with landslide causal factors. In this study, six parameters were used namely, distance from fault, slope, aspect, curvature, distance from river and land use. This research proposed the weight of evidence (WoE) model to produce a landslide susceptibility map. Success and predictive rate were also used to evaluate the accuracy by using Area under curve (AUC) of Receiver operating characteristic (ROC). The result is useful for land use planner and decision makers, in order to devise a strategy for disaster mitigation.

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Landslide Susceptibility Mapping and Assessment Using Geospatial Platforms and Weights of Evidence (WoE) Method in the Indian Himalayan Region: Recent Developments, Gaps, and Future Directions

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi10030114 ◽

2021 ◽

Vol 10 (3) ◽

pp. 114

Author(s):

Amit Kumar Batar ◽

Teiji Watanabe

Keyword(s):

Land Use ◽

Landslide Susceptibility ◽

Susceptibility Mapping ◽

Landslide Inventory ◽

Susceptibility Map ◽

Landslide Susceptibility Map ◽

Future Directions ◽

Causative Factors ◽

Recent Developments ◽

Very High

The Himalayan region and hilly areas face severe challenges due to landslide occurrences during the rainy seasons in India, and the study area, i.e., the Rudraprayag district, is no exception. However, the landslide related database and research are still inadequate in these landslide-prone areas. The main purpose of this study is: (1) to prepare the multi-temporal landslide inventory map using geospatial platforms in the data-scarce environment; (2) to evaluate the landslide susceptibility map using weights of evidence (WoE) method in the Geographical Information System (GIS) environment at the district level; and (3) to provide a comprehensive understanding of recent developments, gaps, and future directions related to landslide inventory, susceptibility mapping, and risk assessment in the Indian context. Firstly, 293 landslides polygon were manually digitized using the BHUVAN (Indian earth observation visualization) and Google Earth® from 2011 to 2013. Secondly, a total of 14 landslide causative factors viz. geology, geomorphology, soil type, soil depth, slope angle, slope aspect, relative relief, distance to faults, distance to thrusts, distance to lineaments, distance to streams, distance to roads, land use/cover, and altitude zones were selected based on the previous study. Then, the WoE method was applied to assign the weights for each class of causative factors to obtain a landslide susceptibility map. Afterward, the final landslide susceptibility map was divided into five susceptibility classes (very high, high, medium, low, and very low classes). Later, the validation of the landslide susceptibility map was checked against randomly selected landslides using IDRISI SELVA 17.0 software. Our study results show that medium to very high landslide susceptibilities had occurred in the non-forest areas, mainly scrubland, pastureland, and barren land. The results show that medium to very high landslide susceptibilities areas are in the upper catchment areas of the Mandakini river and adjacent to the National Highways (107 and 07). The results also show that landslide susceptibility is high in high relative relief areas and shallow soil, near thrusts and faults, and on southeast, south, and west-facing steep slopes. The WoE method achieved a prediction accuracy of 85.7%, indicating good accuracy of the model. Thus, this landslide susceptibility map could help the local governments in landslide hazard mitigation, land use planning, and landscape protection.

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Weights of evidence modeling for landslide susceptibility mapping of Kabi-Gebro locality, Gundomeskel area, Central Ethiopia

Geoenvironmental Disasters ◽

10.1186/s40677-021-00177-z ◽

2021 ◽

Vol 8 (1) ◽

Author(s):

Nega Getachew ◽

Matebie Meten

Keyword(s):

Landslide Susceptibility ◽

Google Earth ◽

Weights Of Evidence ◽

Slope Aspect ◽

Susceptibility Map ◽

Landslide Susceptibility Map ◽

Causative Factors ◽

Central Highland ◽

Slide Debris ◽

The Impact

AbstractKabi-Gebro locality of Gundomeskel area is located within the Abay Basin at Dera District of North Shewa Zone in the Central highland of Ethiopia and it is about 320Km from Addis Ababa. This is characterized by undulating topography, intense rainfall, active erosion and highly cultivated area. Geologically, it comprises weathered sedimentary and volcanic rocks. Active landslides damaged the gravel road, houses and agricultural land. The main objective of this research is to prepare the landslide susceptibility map using GIS-based Weights of Evidence model. Based on detailed field assessment and Google Earth image interpretation, 514 landslides were identified and classified randomly into training landslides (80%) and validation landslides (20%). The most common types of landslides in the study area include earth slide (rotational and translational slide), debris slide, debris flow, rock fall, topple, rock slide, creep and complex. Nine landslide causative factors such as lithology, slope, aspect, curvature, land use/land cover, distance to stream, distance to lineament, distance to spring and rainfall were used to prepare a landslide susceptibility map of the study area by adding the weights of contrast values of these causative factors using a rater calculator of the spatial analyst tool in ArcGIS. The final landslide susceptibility map was reclassified as very low, low, moderate, high and very high susceptibility classes. This susceptibility map was validated using landslide density index and area under the curve (AUC). The result from this model validation showed a success rate and a validation rate accuracy of 82.4% and 83.4% respectively. Finally, implementing afforestation strategies on bare land, constructing surface drainage channels & ditches, providing engineering reinforcements such as gabion walls, retaining walls, anchors and bolts whenever necessary and prohibiting hazardous zones can be recommended in order to lessen the impact of landslides in this area.

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Production of Landslide Susceptibility Map using Bayesian Probability Model

International Journal of 3-D Information Modeling ◽

10.4018/ij3dim.2015040102 ◽

2015 ◽

Vol 4 (2) ◽

pp. 16-33 ◽

Cited By ~ 1

Author(s):

Halil Akıncı ◽

Ayşe Yavuz Özalp ◽

Mehmet Özalp ◽

Sebahat Temuçin Kılıçer ◽

Cem Kılıçoğlu ◽

...

Keyword(s):

Land Use ◽

Land Use Planning ◽

Landslide Susceptibility ◽

Probability Model ◽

Bayesian Probability ◽

Slope Aspect ◽

Susceptibility Map ◽

Model Parameters ◽

Operating Characteristics ◽

Landslide Susceptibility Map

Artvin is one of the provinces in Turkey where landslides occur most frequently. There have been numerous landslides characterized as natural disaster recorded across the province. The areas sensitive to landslides across the province should be identified in order to ensure people's safety, to take the necessary measures for reducing any devastating effects of landslides and to make the right decisions in respect to land use planning. In this study, the landslide susceptibility map of the Central district of Artvin was produced by using Bayesian probability model. Parameters including lithology, altitude, slope, aspect, plan and profile curvatures, soil depth, topographic wetness index, land cover, and proximity to the road and stream were used in landslide susceptibility analysis. The landslide susceptibility map produced in this study was validated using the receiver operating characteristics (ROC) based on area under curve (AUC) analysis. In addition, control landslide locations were used to validate the results of the landslide susceptibility map and the validation analysis resulted in 94.30% accuracy, a reliable outcome for this map that can be useful for general land use planning in Artvin.

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Reconstructing Three Decades of Land Use and Land Cover Changes in Brazilian Biomes with Landsat Archive and Earth Engine

Remote Sensing ◽

10.3390/rs12172735 ◽

2020 ◽

Vol 12 (17) ◽

pp. 2735 ◽

Cited By ~ 16

Author(s):

Carlos M. Souza ◽

Julia Z. Shimbo ◽

Marcos R. Rosa ◽

Leandro L. Parente ◽

Ane A. Alencar ◽

...

Keyword(s):

Land Use ◽

Time Series ◽

Land Cover ◽

Time Series Data ◽

Google Earth ◽

Series Data ◽

Data Series ◽

Forest Conversion ◽

Pixel Resolution ◽

Historical Land Use

Brazil has a monitoring system to track annual forest conversion in the Amazon and most recently to monitor the Cerrado biome. However, there is still a gap of annual land use and land cover (LULC) information in all Brazilian biomes in the country. Existing countrywide efforts to map land use and land cover lack regularly updates and high spatial resolution time-series data to better understand historical land use and land cover dynamics, and the subsequent impacts in the country biomes. In this study, we described a novel approach and the results achieved by a multi-disciplinary network called MapBiomas to reconstruct annual land use and land cover information between 1985 and 2017 for Brazil, based on random forest applied to Landsat archive using Google Earth Engine. We mapped five major classes: forest, non-forest natural formation, farming, non-vegetated areas, and water. These classes were broken into two sub-classification levels leading to the most comprehensive and detailed mapping for the country at a 30 m pixel resolution. The average overall accuracy of the land use and land cover time-series, based on a stratified random sample of 75,000 pixel locations, was 89% ranging from 73 to 95% in the biomes. The 33 years of LULC change data series revealed that Brazil lost 71 Mha of natural vegetation, mostly to cattle ranching and agriculture activities. Pasture expanded by 46% from 1985 to 2017, and agriculture by 172%, mostly replacing old pasture fields. We also identified that 86 Mha of the converted native vegetation was undergoing some level of regrowth. Several applications of the MapBiomas dataset are underway, suggesting that reconstructing historical land use and land cover change maps is useful for advancing the science and to guide social, economic and environmental policy decision-making processes in Brazil.

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

Forest and Society ◽

10.24259/fs.v2i1.3594 ◽

2018 ◽

Vol 2 (1) ◽

pp. 79 ◽

Cited By ~ 2

Author(s):

Andang Suryana Soma ◽

Tetsuya Kubota

Keyword(s):

Landslide Susceptibility ◽

Causative Factor ◽

Susceptibility Map ◽

Landslide Susceptibility Map ◽

Landslide Occurrence ◽

Sparse Vegetation ◽

Dense Vegetation ◽

South Sulawesi ◽

Landslide Hazards ◽

Causative Factors

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.

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Landslide Susceptibility Mapping Based on the Combination of Bivariate Statistics and Modified Analytic Hierarchy Process Methods: A Case Study of Tinh Tuc Town, Nguyen Binh District, Cao Bang Province, Vietnam

Journal of Disaster Research ◽

10.20965/jdr.2021.p0521 ◽

2021 ◽

Vol 16 (4) ◽

pp. 521-528

Author(s):

Nguyen Trung Kien ◽

The Viet Tran ◽

Vy Thi Hong Lien ◽

Pham Le Hoang Linh ◽

Nguyen Quoc Thanh ◽

...

Keyword(s):

Land Use ◽

Landslide Susceptibility ◽

Water Storage ◽

Landslide Inventory ◽

Landslide Susceptibility Mapping ◽

Susceptibility Map ◽

Landslide Susceptibility Map ◽

The Road ◽

Hierarchy Process ◽

Very High

Tinh Tuc town, Cao Bang province, Vietnam is prone to landslides due to the complexity of its climatic, geological, and geomorphological conditions. In this study, in order to produce a landslide susceptibility map, the modified analytical hierarchy process and landslide susceptibility analysis methods were used together with the layers, including: landslide inventory, slope, weathering crust, water storage, geology, land use, and distance from the road. In the study area, 98% of landslides occurred in highly or completely weathered units. Geology, land use, and water storage data layers were found to be important factors that are closely related with the occurrence of landslides. Although the weight of the “distance from the road” factor has a low value, the weight of layer “<100 m” has a high value. Therefore, the landslide susceptibility index very high is concentrated along the roads. For the validation of the predicted result, the landslide susceptibility map was compared with the landslide inventory map containing 47 landslides. The outcome shows that about 90% of these landslides fall into very high susceptibility zones.

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Short-Term Forecasting of Land Use Change Using Recurrent Neural Network Models

Sustainability ◽

10.3390/su11195376 ◽

2019 ◽

Vol 11 (19) ◽

pp. 5376 ◽

Cited By ~ 1

Author(s):

Cong Cao ◽

Suzana Dragićević ◽

Songnian Li

Keyword(s):

Neural Network ◽

Land Use ◽

Land Use Change ◽

Recurrent Neural Network ◽

Planning Process ◽

Time Series Data ◽

Sustainable Land Use ◽

Series Data ◽

Short Term ◽

Lower Accuracy

Land use change (LUC) is a dynamic process that significantly affects the environment, and various approaches have been proposed to analyze and model LUC for sustainable land use management and decision making. Recurrent neural network (RNN) models are part of deep learning (DL) approaches, which have the capability to capture spatial and temporal features from time-series data and sequential data. The main objective of this study was to examine variants of the RNN models by applying and comparing them when forecasting LUC in short time periods. Historical land use data for the City of Surrey, British Columbia, Canada were used to implement the several variants of the RNN models. The land use (LU) data for years 1996, 2001, 2006, and 2011 were used to train the DL models to enable the short-term forecast for the year 2016. For the 2011 to 2016 period, only 4.5% of the land use in the study area had changed. The results indicate that an overall accuracy of 86.9% was achieved, while actual changes in each LU type were forecasted with a relatively lower accuracy. However, only 25% of changed raster cells correctly forecasted the land use change. This research study demonstrates that RNN models provide a suite of valuable tools for short-term LUC forecast that can inform and complement the traditional long-term planning process; however, further additional geospatial data layers and considerations of driving factors of LUC need to be incorporated for model improvements.

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Land Use Changes, Disturbances, and Their Interactions on Future Forest Aboveground Biomass Dynamics in the Northern US

Forests ◽

10.3390/f10070606 ◽

2019 ◽

Vol 10 (7) ◽

pp. 606 ◽

Cited By ~ 3

Author(s):

Wu Ma ◽

Grant M. Domke ◽

Christopher W. Woodall ◽

Anthony W. D’Amato

Keyword(s):

Land Use ◽

Land Use Change ◽

Aboveground Biomass ◽

Time Series Data ◽

Forest Harvesting ◽

National Forest Inventory ◽

Series Data ◽

C Stocks ◽

Undisturbed Forest ◽

Forest C

Land use change (LUC), disturbances, and their interactions play an important role in regional forest carbon (C) dynamics. Here we quantified how these activities and events may influence future aboveground biomass (AGB) dynamics in forests using national forest inventory (NFI) and Landsat time series data in the Northern United States (US). Total forest AGB predictions were based on simulations of diameter growth, mortality, and recruitment using matrix growth models under varying levels of LUC and disturbance severity (low (L), medium (M), and high (H)) every five years from 2018 to 2098. Land use change included the integrated effects of deforestation and reforestation/afforestation (forest [F]→agriculture [A], settlements [S, urbanization/other], and A&S→F), specifically, conversion from F→A, F→S, F→A&S, A→F, S→F, and A&S→F. Disturbances included natural and anthropogenic disturbances such as wildfire, weather, insects and disease, and forest harvesting. Results revealed that, when simultaneously considering both medium LUC and disturbances, total forest AGB predictions of LUC + fire, LUC + weather, LUC + insect & disease, and LUC + harvest indicated substantial increases in regional C stocks (± standard deviation) from 1.88 (±0.13) to 3.29 (±0.28), 3.10 (±0.24), 2.91 (±0.19), and 2.68 (±0.17) Pg C, respectively, from 2018 to 2098. An uncertainty analysis with fuzzy sets suggested that medium LUC under disturbances would lead to greater forest AGB C uptake than undisturbed forest C uptake with high certainty, except for LUC + harvest. The matrix models in this study were parameterized using NFI and Landsat data from the past few decades. Thus, our results imply that if recent trends persist, LUC will remain an important driver of forest C uptake, while disturbances may result in C emissions rather than undisturbed forest C uptake by 2098. The combined effects of LUC and disturbances may serve as an important driver of C uptake and emissions in the Northern US well into the 21st century.

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