Effects of different land use and land cover data on the landslide susceptibility zonation of road networks

Meneses, Bruno M.; Pereira, Susana; Reis, Eusébio

doi:10.5194/nhess-19-471-2019

Cited by 51 publications

(25 citation statements)

References 59 publications

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“…The Sen is also considered the true positive rate, and the value (1 − Spe) is the rate of false positives (Melchiorre et al, 2008). Generally, high sensitivity indicates a high number of correct predictions, whereas high specificity (low 1 − Spe difference) indicates a low number of false positives (Mohammady et al, 2012). Hence, the Sen of the model is plotted against 1 − Spe to obtain the ROC curve, and in most cases the area under the curve (AUC) is utilized to evaluate the prediction ability of models.…”

Section: Receiver Operating Characteristic (Roc) Curvementioning

confidence: 99%

The influence of land use and land cover change on landslide susceptibility: a case study in Zhushan Town, Xuan'en County (Hubei, China)

Chen

Guo

Yin

et al. 2019

Nat. Hazards Earth Syst. Sci.

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Abstract. Land use and land cover change can increase or decrease landslide susceptibility (LS) in the mountainous areas. In the hilly and mountainous part of southwestern China, land use and land cover change (LUCC) has taken place in the last decades due to infrastructure development and rapid economic activities. This development and activities can worsen the slope susceptible to sliding due to mostly the cutting of slopes. This study, taking Zhushan Town, Xuan'en County, as the study area, aims to evaluate the influence of land use and land cover change on landslide susceptibility at a regional scale. Spatial distribution of landslides was determined in terms of visual interpretation of aerial photographs and remote sensing images, supported by field surveys. Two types of land use and land cover (LUC) maps, with a time interval covering 21 years (1992–2013), were prepared: the first was obtained by the neural net classification of images acquired in 1992 and the second by the object-oriented classification of images in 2002 and 2013. Landslide-susceptible areas were analyzed using the logistic regression model (LRM) in which six influencing factors were chosen as the landslide susceptibility indices. In addition, the hydrologic analysis method was applied to optimize the partitioning of the terrain. The results indicated that the LUCC in the region was mainly the transformation from the grassland and arable land to the forest land, which is increased by 34.3 %. An increase of 1.9 % is shown in the area where human engineering activities concentrate. The comparison of landslide susceptibility maps among different periods revealed that human engineering activities were the most important factor in increasing LS in this region. Such results emphasize the requirement of a reasonable land use planning activity process.

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Section: Receiver Operating Characteristic (Roc) Curvementioning

confidence: 99%

The influence of land use and land cover change on landslide susceptibility: a case study in Zhushan Town, Xuan'en County (Hubei, China)

Chen

Guo

Yin

et al. 2019

Nat. Hazards Earth Syst. Sci.

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“…The CLC project largely fulfills the expectations of researchers and other users [6], having two main advantages: it provides a seamless geometry usable for macro-regional studies and an internal classification of the land use/cover categories that allows time traceability of changes, for a reasonable period . The facility of tracking the changes in the use of the land has allowed a multitude of applications and correlations between CLC datasets and different parameters [7][8][9]. There is an intense use of the CLC datasets for local or regional studies; however, the intermediate scales are systematically neglected by other researches [10][11][12].…”

Section: Introductionmentioning

confidence: 99%

Structural Changes in the Romanian Economy Reflected through Corine Land Cover Datasets

et al. 2020

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During the last 30 years, the Romanian economy has faced different challenges due to structural readjustments, overcoming crisis and globalization. The share of primary and secondary sectors in the gross domestic product have strongly decreased, while the services have taken off. The main objective for this study is to observe how these economic readjustments can be assessed and measured using the Corine Land Cover datasets from 1990, 2000, 2006, 2012 and 2018 (with special observation on the range 2006 and 2018, after Romania was included in European Union). Despite some of the methodological limitations (like the minimum surface change), the Corine Land Cover turned out to be a powerful tool and it allowed us to detect an intense correlation between the socioeconomic and the structural trends in land use, in specific spatial contexts. The artificial surfaces are constantly increasing and this trend is rather visible as a distance function to the major Romanian cities. The most interesting changes occurred in the case of the agricultural polygons. The main trend emphasized by our analysis regards the redeployment of large farms in areas of agronomic and environmental territorial optimum. Such is the case for vineyards (after a decline during 2000–2006) and for annual cultures. All these changes in land-use patterns are too complex to be encompassed by a single methodology, which is why we used different tools, ranging from spatial analysis to geo-economic modeling, in order to detect how the Corine Land Cover datasets might be used for a better understanding of the Romanian economic readjustments.

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“…Most previous studies have frequently pointed to the anthropogenic triggering factors such as distance to roads [51,53,78,105,130], road density [76,105,135], land-use/land-cover types [42,47,49,58,78,106], and land-use changes [3,136,137] for the mapping of landslide susceptibility.…”

Section: The Importance Of Conditioning Factors For Mapping Landslidementioning

confidence: 99%

A Holistic Analysis for Landslide Susceptibility Mapping Applying Geographic Object-Based Random Forest: A Comparison between Protected and Non-Protected Forests

Shirvani

2020

Remote Sensing

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Despite recent progress in landslide susceptibility mapping, a holistic method is still needed to integrate and customize influential factors with the focus on forest regions. This study was accomplished to test the performance of geographic object-based random forest in modeling the susceptibility of protected and non-protected forests to landslides in northeast Iran. Moreover, it investigated the influential conditioning and triggering factors that control the susceptibility of these two forest areas to landslides. After surveying the landslide events, segment objects were generated from the Landsat 8 multispectral images and digital elevation model (DEM) data. The features of conditioning factors were derived from the DEM and available thematic layers. Natural triggering factors were derived from the historical events of rainfall, floods, and earthquake. The object-based image analysis was used for deriving anthropogenic-induced forest loss and fragmentation. The layers of logging and mining were obtained from available historical data. Landslide samples were extracted from field observations, satellite images, and available database. A single database was generated including all conditioning and triggering object features, and landslide samples for modeling the susceptibility of two forest areas to landslides using the random forest algorithm. The optimal performance of random forest was obtained after building 500 trees with the area under the receiver operating characteristics (AUROC) values of 86.3 and 81.8% for the protected and non-protected forests, respectively. The top influential factors were the topographic and hydrologic features for mapping landslide susceptibility in the protected forest. However, the scores were loaded evenly among the topographic, hydrologic, natural, and anthropogenic triggers in the non-protected forest. The topographic features obtained about 60% of the importance values with the domination of the topographic ruggedness index and slope in the protected forest. Although the importance of topographic features was reduced to 36% in the non-protected forest, anthropogenic and natural triggering factors remarkably gained 33.4% of the importance values in this area. This study confirms that some anthropogenic activities such as forest fragmentation and logging significantly intensified the susceptibility of the non-protected forest to landslides.

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Effects of different land use and land cover data on the landslide susceptibility zonation of road networks

Cited by 51 publications

References 59 publications

The influence of land use and land cover change on landslide susceptibility: a case study in Zhushan Town, Xuan'en County (Hubei, China)

The influence of land use and land cover change on landslide susceptibility: a case study in Zhushan Town, Xuan'en County (Hubei, China)

Structural Changes in the Romanian Economy Reflected through Corine Land Cover Datasets

A Holistic Analysis for Landslide Susceptibility Mapping Applying Geographic Object-Based Random Forest: A Comparison between Protected and Non-Protected Forests

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