Application of landscape metrics and a Markov chain model to assess land cover changes within a forested watershed, Taiwan

Yeh, Chun Kuo; Liaw, Shyue Cherng

doi:10.1002/hyp.10542

Cited by 8 publications

(6 citation statements)

References 52 publications

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“…Three remote sensing images with 8-m resolution, acquired in 2005, 2008, and 2011, were classified into four land cover classes: landslide patches, forest matrix, human-made patches, and channel corridors. More comprehensive information about the land cover classification can refer to the research by Yeh and Liaw (2015). In order to evaluate the accuracy of classification of remotely sensed data, the current study utilized stratified random sampling to produce 256 reference points, whose values conducted a consistency check with the class values of the classified image (Jensen 2005).…”

Section: Image Classification and Accuracy Assessmentmentioning

confidence: 99%

Applying spatial autocorrelation and logistic regression to analyze land cover change trajectory in a forested watershed

Yeh¹,

Liaw²

2021

Terr. Atmos. Ocean. Sci.

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Taiwan is more susceptible to earthquakes and typhoons because of the unique geographical position in the Northwestern Pacific Rim. It is necessary to understand the effects of such frequent natural disturbances on land cover change for watershed management and disaster mitigation. This study applied both global and local Moran's I statistics to analyze the spatial autocorrelation of landslide patches in a natural disturbed watershed in eastern Taiwan. The land cover maps extracted from FORMOSAT-2 satellite images acquired in 2005, 2008, and 2011. A logistic regression model validated to predict occurrence probability of change trajectory. Results showed that spatial pattern of homogeneous landslide patches presented on small scales; while heterogeneous landslide pattern was on larger scales. Landslide patches had higher positive spatial autocorrelation and indicated that as regional hotspots in study area. After the trajectory calculation and classification, two unchanged and five changed trajectories dominated the study area. The most significant transformation of land cover was from forest to landslide and channel. In addition, a fittest logistic regression model predicted the occurrence probability of change trajectory in the study area. Among these environmental variables for logistic regression, lithology was the most important spatial determinant for the change trajectories. Curvature and aspect variables were also significant. This spatial statistical model was helpful for predicting the occurrence probabilities of the change trajectories.

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Section: Image Classification and Accuracy Assessmentmentioning

confidence: 99%

Applying spatial autocorrelation and logistic regression to analyze land cover change trajectory in a forested watershed

Yeh¹,

Liaw²

2021

Terr. Atmos. Ocean. Sci.

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“…However, the current study explicitly localized and classified the significant anthropogenic triggering factors depending on the human footprint including forest fragmentation, forest conversion, timber harvesting, and mining within the forests. The influences of building forest roads [15,18,[21][22][23], logging [4,[11][12][13][14][15], deforestation [2-8], forest fragmentation [5], and mining [17] on the occurrence, frequency, and distribution of landslides have been demonstrated in the forest areas. For example, Guns and Vanacker [7] highlighted that anthropogenic activities such as forest conversion increased the occurrence of small landslides and sediment deposition in tropical forests.…”

Section: The Importance Of Conditioning Factors For Mapping Landslidementioning

confidence: 99%

“…However, the holistic understanding of the importance of conditioning and triggering factors that control the susceptibility of forest areas to landslides has not been appropriately customized yet. Anthropogenic triggering factors may reduce the resisting forces of forests to landslides by deforestation [2][3][4][5][6][7][8][9][10], logging [4,[11][12][13][14][15][16], and mining [17], or may increase the susceptibility of forest areas to landslides by the fragmentation induced by infrastructure development such as road-network expansion [4,5,16,[18][19][20][21][22][23] with the consequences of mass movements and slope failures. Likewise, natural triggering factors such as earthquake [24][25][26][27], rainfall [28][29][30][31][32], and flooding [33,34] may increase the propensity for occurring landslides by reducing the resisting forces in forest areas.…”

Section: Introductionmentioning

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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“…Landscape metrics are used to describe the composition and spatial configuration of landscape elements, and a large number of landscape metrics or indices have been developed (Yeh and Liaw 2015). The number of patches (NP), patch density (PD), percent of landscape (PLAND), landscape shape index (LSI), area-weighted mean fractal dimension index (FRA-C_AM), and landscape division index (DIVISION)…”

Section: Analysis Of Landscape Pattern Changementioning

confidence: 99%

Spatio-temporal characteristics and driving forces of landscape structure changes in the middle reach of the Heihe River Basin from 1990 to 2015

et al. 2019

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Context The oasis, desert-oasis ecotone (DOE) and desert compose the typical landscape structures in arid regions. The detection of landscape structure changes and quantification of the driving forces are vital to sustainable ecosystem management. Previous studies did not integrate the three landscape types, especially considering the role of the DOE, to detect landscape changes.Objectives (1) Detect the spatio-temporal dynamics of the landscape structure in the middle reach of the Heihe River Basin from 1990 to 2015. (2) Identify the key driving factors for landscape structure changes and quantify their contributions. Methods The decision tree classification algorithm was combined with visual interpretation to extract oasis, DOE and desert areas. Status and trend indicators, transition matrix and landscape metrics were used to characterize the landscape changes. Redundancy analysis and variation portioning analysis were integrated for quantification of driving forces. Results Landscape change was characterized by oasis expansion (? 37.21%) via a one-way transition with an unbalanced status. The contraction of DOE (-12.73%) and desert (-8.50%) areas mainly occurred on the periphery of the oasis. The landscape structure changes were mainly driven by the population, river flow and gross domestic product with unique contributions of 35.99, 9.01 and 7.68%, respectively, and a cumulative contribution of 96.64%. Conclusions The mutual transformations among landscapes showed distinct spatio-temporal variations. Human activities, especially demographic factors, drove the changes in landscape structure. The development of oasis areas should be controlled within a suitable scale, and the DOE should be protected to maintain the stability of ecosystems in arid regions.Keywords Desert-oasis ecotone Á Oasis expansion Á Landscape structure change Á Human activities Á Quantitative analysis Á Arid region

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Application of landscape metrics and a Markov chain model to assess land cover changes within a forested watershed, Taiwan

Cited by 8 publications

References 52 publications

Applying spatial autocorrelation and logistic regression to analyze land cover change trajectory in a forested watershed

Applying spatial autocorrelation and logistic regression to analyze land cover change trajectory in a forested watershed

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

Spatio-temporal characteristics and driving forces of landscape structure changes in the middle reach of the Heihe River Basin from 1990 to 2015

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