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Restrepo, Carla; Vitousek, Peter M.; Neville, Paul

doi:10.1023/a:1023225419111

Cited by 58 publications

(13 citation statements)

References 46 publications

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“…1 Landscape context of ecological succession on landslides including disturbances that cause landslides, landscape inputs and outputs and interactions of developing soils and plant communities through succession. Modified from Walker et al (1996) weathering and soil infilling (Sidle and Ochiai 2006), impact regional carbon cycles (Stallard 1998;Restrepo et al 2003), reduce vegetative cover (Tang et al 1997) and shape landscape features such as altitudinal tree limits in Chile (Veblen et al 1977). In turn, the landscape context influences light conditions , the amount and impact of precipitation (Larsen and Simon 1993) and the amount and type of atmospheric deposition (McDowell et al 1990).…”

Section: The Landscape Contextmentioning

confidence: 99%

Applying lessons from ecological succession to the restoration of landslides

2009

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Landslides are excellent illustrations of the dynamic interplay of disturbance and succession. Restoration is difficult on landslide surfaces because of the high degree of spatial and temporal heterogeneity in soil stability and fertility. Principles derived from more than a century of study of ecological succession can guide efforts to reduce chronic surface soil erosion and restore both biodiversity and ecosystem function. Promotion of the recovery of selfsustaining communities on landslides is feasible by stabilization with native ground cover, applications of nutrient amendments, facilitation of dispersal to overcome establishment bottlenecks, emphasis on functionally redundant species and promotion of connectivity with the adjacent landscape. Arrested succession through resource dominance by a single species can be beneficial if that species also reduces persistent erosion, yet the tradeoff is often reduced biodiversity. Restoration efforts can be streamlined by using techniques that promote successional processes.

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Section: The Landscape Contextmentioning

confidence: 99%

Applying lessons from ecological succession to the restoration of landslides

2009

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“…The summation of above and below ground biomass was done to obtain total tree biomass. The total tree biomass was then converted to tons of CO 2 equivalent as given by Pearson et al (2007). The total tree biomass lost due to landslide was then obtained indirectly.…”

Section: Methodsmentioning

confidence: 99%

“…A study by Chambers et al (2007) on carbon footprint due to hurricane Katrina on Gulf coast forest showed that a total biomass loss of 105 TgC (1,05,000,000 tons) was incurred. Similarly, Restrepo et al (2003)'s research about influence of landslide on distribution of biomass in Hawaii, showed irreversible transformation of Ninole ecosystem within last 430 years with landslide disturbance rate, 15% per century, equivalent to 53 ton ha -1 biomass per century to exit through the system. In Nepal, the nationwide landslide inventory of 2015 had reported 5,003 landslides with total area of 126.34 km 2 (CDES-TU, 2015).…”

Section: Introductionmentioning

confidence: 99%

Loss of Tree Biomass in Jure Landslide, Sindhupalchowk, Nepal

Acharya

Khadka

2016

J. Inst. Sci. Tech.

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Landslide causes massive loss of lives and properties along with intangible losses in mountainous regions. Yet such intangible losses in ecosystems are rarely considered. The present study assesses the tree biomass lost due to Jure landslide in Sindhupalchowk that destroyed 71 hectare of land. Altogether, 12 plots (250 m 2 ) were sampled through systematic and purposive sampling technique. The total tree biomass was estimated using allometric equation. The study recorded 21 tree species in which Schimawallichiii (Korth.), Lagerstroemia parviflora (Roxb.), Shorea robusta (Gaertn.), Alnus nepalensis (D. Don), Phyllanthus emblica (Linn.) and Celtius australis (Linn.) were dominant. Schima wallichiii had the highest density (320 individual ha -1 ) and frequency (92%). The total biomass of tree species was 216 ton ha -1 in which Schima wallichiii constituted the highest total tree biomass (82 ton ha -1 ). In 71 ha landslide area, the landslide caused loss of 15,336 tons of total tree biomass, which equals to 56,283 tons CO 2 equivalents. These findings are relevant for assessing post-landslide impacts on the mountain environment. Furthermore, to reduce carbon emissions resulting from forest loss, mitigation of landslide is crucial.

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“…Lithological units greatly influence the landslide occurrence [77]. The lithologic units were divided into five groups ( Figure 3n, Table 1): the first group is quaternary (loess, silt), the second group is tertiary (mudstone, conglomerate), the third group is cretaceous (arkose), the fourth group is Jurassic (shale, sandstone, mudstone, conglomerate), and the fifth group is Triassic (mudstone, sandstone, conglomerate).…”

Section: Data Preparationmentioning

confidence: 99%

Landslide Susceptibility Evaluation Using Hybrid Integration of Evidential Belief Function and Machine Learning Techniques

Chen

2019

Water

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In this study, Random SubSpace-based classification and regression tree (RSCART) was introduced for landslide susceptibility modeling, and CART model and logistic regression (LR) model were used as benchmark models. 263 landslide locations in the study area were randomly divided into two parts (70/30) for training and validation of models. 14 landslide influencing factors were selected, such as slope angle, elevation, aspect, sediment transport index (STI), topographical wetness index (TWI), stream power index (SPI), profile curvature, plan curvature, distance to rivers, distance to road, soil, normalized difference vegetation index (NDVI), land use, and lithology. Finally, the hybrid RSCART model and two benchmark models were applied for landslide susceptibility modeling and the receiver operating characteristic curve method is used to evaluate the performance of the model. The susceptibility is quantitatively compared based on each pixel to reveal the system spatial pattern between susceptibility maps. At the same time, area under ROC curve (AUC) and landslide density analysis were used to estimate the prediction ability of landslide susceptibility map. The results showed that the RSCART model is the optimal model with the highest AUC values of 0.852 and 0.827, followed by LR and CART models. The results also illustrate that the hybrid model generally improves the prediction ability of a single landslide susceptibility model.Water 2020, 12, 113 2 of 29 weights of evidence [10][11][12], frequency ratio [13][14][15][16][17], logistic regression [18][19][20][21], linear multivariate regression, multivariate adaptive regression spline [22][23][24], and statistical index [25,26] have been widely used. However, these traditional statistical methods do not provide satisfactory evaluation of the correlation between landslide influencing factors [4,27].Therefore, machine learning technologies have drawn extensive attention, and many kinds of machine learning methods have been developed and used, such as classification and regression trees [28,29], adaptive neuro-fuzzy inference systems [30,31], fuzzy logic [32,33], alternating decision trees [34][35][36], support vector machine [37][38][39], artificial neural networks [40,41], and random forest [4,[42][43][44][45]. In particular, hybrid models are increasingly used, such as the rotation forest-based decision trees [46,47], frequency ratio-based ANFIS model [48], bagging-based reduced error pruning trees [49], and multiboost-based support vector machines [50].Spatial prediction of landslide is not only the first important step, but also one of the most difficult tasks [31,51]. Many modeling methods have been used in the construction of landslide susceptibility maps in the past, but the accuracy of these models has not been accepted by all researchers [52]. Youssef et al. used CART model to study landslide susceptibility in Wadi Tayyah Basin. The result of post-cart model is not optimal [53]. However, when Felicísimo et al. studied the landslide susceptibility, CART shows...

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Untitled

Cited by 58 publications

References 46 publications

Applying lessons from ecological succession to the restoration of landslides

Applying lessons from ecological succession to the restoration of landslides

Loss of Tree Biomass in Jure Landslide, Sindhupalchowk, Nepal

Landslide Susceptibility Evaluation Using Hybrid Integration of Evidential Belief Function and Machine Learning Techniques

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