Relation between landform and vegetation in alpine regions of Wallis, Switzerland. A multiscale remote sensing and GIS approach

Hoersch, Bianca; Braun, Gerald; Schmidt, U.

doi:10.1016/s0198-9715(01)00039-4

Cited by 78 publications

(60 citation statements)

References 27 publications

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“…MCS60 represents an integrated slope averaging a bigger area and is, therefore, an indicator of the landscape variability on a larger scale [49]. In this way, the landforms have already been highlighted as relevant indicators able to explain the spatial pattern of species distribution [53]. The digital crown height model (DCM90), which represents the only forest structural variable, was also of considerable relative importance in the predictive model, which was also found by Dufour et al [18], Bässler et al [47], and Féret & Asner [43].…”

Section: Ecological Implicationsmentioning

confidence: 99%

Comparison of Airborne LiDAR and Satellite Hyperspectral Remote Sensing to Estimate Vascular Plant Richness in Deciduous Mediterranean Forests of Central Chile

et al. 2015

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Abstract:The Andes foothills of central Chile are characterized by high levels of floristic diversity in a scenario, which offers little protection by public protected areas. Knowledge of the spatial distribution of this diversity must be gained in order to aid in conservation management. Heterogeneous environmental conditions involve an important number of niches closely related to species richness. Remote sensing information derived from satellite hyperspectral and airborne Light Detection and Ranging (LiDAR) data can be used as proxies to generate a spatial prediction of vascular plant richness. This study aimed to estimate the spatial distribution of plant species richness using remote sensing in the Andes foothills of the Maule Region, Chile. This region has a secondary deciduous forest dominated by Nothofagus obliqua mixed with sclerophyll species. Floristic measurements were performed using a nested plot design with 60 plots of 225 m 2 each. Multiple predictors were evaluated: 30 topographical and vegetation structure indexes from LiDAR data, and 32 spectral indexes and band transformations from the EO1-Hyperion sensor. A random forest algorithm was used to identify relevant variables in richness prediction, and these variables were used in turn to obtain a final multiple linear regression predictive model (Adjusted R 2 = 0.651; RSE = 3.69). An independent validation survey was performed with significant results (Adjusted R 2 = 0.571, RMSE = 5.05). Selected variables were statistically significant: OPEN ACCESSRemote Sens. 2015, 7 2693 catchment slope, altitude, standard deviation of slope, average slope, Multiresolution Ridge Top Flatness index (MrRTF) and Digital Crown Height Model (DCM). The information provided by LiDAR delivered the best predictors, whereas hyperspectral data were discarded due to their low predictive power.

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Section: Ecological Implicationsmentioning

confidence: 99%

Comparison of Airborne LiDAR and Satellite Hyperspectral Remote Sensing to Estimate Vascular Plant Richness in Deciduous Mediterranean Forests of Central Chile

et al. 2015

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“…Bolstad, Swank and Vose (1998) noted the importance of the physical environment in determining the spatial diversity of the land surface of mountainous regions. Höersch et al (2002) and Abbate, Cavalli, Pascucci, Pignatti and Poscolieri (2006) concluded that there is a relationship between the vegetation types and topography in mountain areas. Hence, land surface parameters such as slope, aspect, profile curvature and plan curvature can be considered as important inputs in spatial analysis and estimation of the distribution of vegetation cover in mountainous environments.…”

Section: Introductionmentioning

confidence: 97%

“…Three main methodological approaches to analysing relationships between vegetation and environmental site factors are discernible in current Geographical Information Systems (GIS) literature (Höersch, Braun & Schmidt, 2002). These are those that analyse relationships between vegetation and (i) direct influence of environmental site factors, (ii) entire set of environmental site factors, be it direct or indirect, and (iii) direct influence of environmental site factors based on the assumption that such influence can be reveal by land surface parameters.…”

Section: Introductionmentioning

confidence: 99%

Spatial Analysis of Land Surface - Vegetation Relationship in Mountainous Areas of the Tropics Using Srtm-3 Dem

Efiong¹,

Digha²,

Asouzu³

2016

JGG

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Digital elevation models (DEMs) have shown much potential for use in the extraction of land surface parameters and analysis of the relationship between land surface units and vegetation cover. However, there is lack of studies on the use of SRTM-3 DEM in vegetation studies of mountainous regions. This study is therefore an attempt to relate land surface parameters to vegetation cover in the Obudu mountain region using SRTM-3 DEM and Landsat data. Geomorphometric classification of the land surface was done using an unsupervised ISOCLUST algorithm while vegetation cover classification was done using the supervised approach based on the Maximum Likelihood algorithm. The resultant land surface units and vegetation cover maps were then related using grid-based statistic within the geographic information systems. The overall measure of difference between the two maps yielded a chi-square (d.f. = 24) = 1.9154, p > 0.05. This implies that there is no significant difference between the land surface units and the vegetation cover in the study area. This findings support the use of SRTM-3 for land surface and vegetation mapping where there is no higher quality data, or the cost of obtaining one is inhibitive; a situation that is faced by many developing economies like Nigeria. However, this results should be interpreted and used within the context of the uncertainty that is contained in the SRTM-3 DEM.

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“…In particular, landform classification has been proved a valuable processing tool for studies related to archaeology, ecology, agriculture, forestry, rural planning, hazards, etc. (Ho and Umitsu, 2011;Hoersch et al, 2002;Macmillan et al, 2003;Martin-Duque et al, 2003;Mcnab, 1993;Verhagen and Dragut, 2012).…”

Section: Introductionmentioning

confidence: 99%

Landform Analysis Using Terrain Attributes. A Gis Application on the Island of Ikaria (Aegean Sea, Greece)

Skentos

Anagnostopoulou

2017

Annals of Valahia University of Targoviste, Geographical Series

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The main objective of this study is to classify the landforms of Ikaria Island by conducting morphometric analysis. The whole classification process is based on the calculation of the Topographic Position Index (TPI). The delivered TPI landform classes are spatially correlated with the geology, slope, valley depth and the topographic ruggedness of the island. The results of this study indicate the presence of two distinctive landform units, affected mainly by the local geological setting.

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Relation between landform and vegetation in alpine regions of Wallis, Switzerland. A multiscale remote sensing and GIS approach

Cited by 78 publications

References 27 publications

Comparison of Airborne LiDAR and Satellite Hyperspectral Remote Sensing to Estimate Vascular Plant Richness in Deciduous Mediterranean Forests of Central Chile

Comparison of Airborne LiDAR and Satellite Hyperspectral Remote Sensing to Estimate Vascular Plant Richness in Deciduous Mediterranean Forests of Central Chile

Spatial Analysis of Land Surface - Vegetation Relationship in Mountainous Areas of the Tropics Using Srtm-3 Dem

Landform Analysis Using Terrain Attributes. A Gis Application on the Island of Ikaria (Aegean Sea, Greece)

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