Fuzzy land element classification from DTMs based on geometry and terrain position

Schmidt, Jochen; Hewitt, Allan E.

doi:10.1016/j.geoderma.2003.10.008

Cited by 198 publications

(175 citation statements)

References 13 publications

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“…A similar scheme was put forward by Dikau [24] and modified by Wood [14]. These classification schemes havesometimes in adapted or extended form -often been applied to derive both crisp [25,26,27,28] and fuzzy [29,30] classifications. Wood [14] proposed multi-scale classification which was extended into fuzzy multi-scale classification based on a range of crisp classifications at different scales [2].…”

Section: Describing Landscapes In Terms Of Surface Formmentioning

confidence: 99%

Delineation of Valleys and Valley Floors

Straumann

Purves

2008

Geographic Information Science

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Methods to automatically derive landforms have typically focused on pixel-based, bottom-up approaches and most commonly on the derivation of topographic eminences. In this paper we describe an object-based, top-down algorithm to identify valley floors. The algorithm is based on a region growing approach, seeded by thalwegs with pixels added to the region according to a threshold gradient value. Since such landforms are fiat we compare the results of our algorithm for a particular valley with a numberof textual sources describing that valley. In a further comparison, we computed a pixel-based six-fold morphometric classification for regions we classified as either being, or not being, valley floor. The regions classified as valley floor are dominated by pla nar slopes and channels,though the algorithm is robust enough to allow local convexities to be classified as within the valley floor. Future work will explore the delineation of valley sides, and thus complete valleys. Abstract. Methods to automatically derive landforms have typically focused on pixel-based, bottom-up approaches and most commonly on the derivation of topographic eminences. In this paper we describe an object-based, top-down algorithm to identify valley floors. The algorithm is based on a region growing approach, seeded by thalwegs with pixels added to the region according to a threshold gradient value. Since such landforms are fiat we compare the results of our algorithm for a particular valley with a number of textual sources describing that valley. In a further comparison, we computed a pixel-based sixfold morphometric classification for regions we classified as either being, or not being, valley floor. The regions classified as valley floor are dominated by planar slopes and channels, though the algorithm is robust enough to allow local convexities to be classified as within the valley floor. Future work will explore the delineation of valley sides, and thus complete valleys. Delineation of Valleys and Valley Floors

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Section: Describing Landscapes In Terms Of Surface Formmentioning

confidence: 99%

Delineation of Valleys and Valley Floors

Straumann

Purves

2008

Geographic Information Science

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“…It has been successfully used in geohydrology, soil science and vegetation mapping (Vriend et al 1988, de Bruin, Stein 1998, Burrough. McDonnell 1998, Burrough et al 2000, 2001, Schmidt, Hewitt 2004.…”

Section: K-means Unsupervised Classification Algorithmmentioning

confidence: 99%

“…There are many approaches and classification schemes for the landform classification in digital geomorphology (Pennock et al 1987, Dikau 1989, Wood 1996, Irvin et al 1997, Ventura, Irvin 2000, Burrough et al 2001, Shary et al 2002, Schmidt, Hewitt 2004, Dragut, Blaschke 2006, Ehsani, Quiel 2008, MacMillan, Shary 2009). Here a combination of the classification criterias of Dikau (1989), and Dragut, Blaschke (2006) with small modifications is used.…”

Section: Landform Classificationmentioning

confidence: 99%

Semi-Automated Classification of Landform Elements in Armenia Based on SRTM DEM using K-Means Unsupervised Classification

Piloyan

Konečný

2017

Quaestiones Geographicae

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PiloyAn A., Konečný M., 2017. Semi-automated classification of landform elements in Armenia based on SRTM DEM using k-means unsupervised classification. Quaestiones Geographicae 36(1), Bogucki Wydawnictwo Naukowe, Poznań, pp. 93-103, 5 figs, 4 tables.AbstrAct: Land elements have been used as basic landform descriptors in many science disciplines, including soil mapping, vegetation mapping, and landscape ecology. This paper presents a semi-automatic method based on k-means unsupervised classification to analyze geomorphometric features as landform elements in Armenia. First, several data layers were derived from DEM: elevation, slope, profile curvature, plan curvature and flow path length. Then, k-means algorithm has been used for classifying landform elements based on these morphomertic parameters. The classification has seven landform classes. Overall, landform classification is performed in the form of a three-level hierarchical scheme. The resulting map reflects the general topography and landform character of Armenia.

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“…Barilotti et al [2007a] have developed automated extraction of individual trees from CHM: they have been detected through a sequence of morphological transformations by a Top Hat algorithm implemented under open-source GrassGIS environment [Neteler and Mitašova, 2004]. Top Hat is a mathematical function of image processing that allows to highlight the relief structures [Schmidt and Hewitt, 2004], which in the case of forest stands are constituted by the tree apexes. Barilotti et al [2007b] have also developed the automatic delineation of tree crowns by the subsequent use of a segmentation algorithm able to classify the laser points in subsets belonging to the individual crowns.…”

Section: Als-assisted Assessment Of Forest Stand Structurementioning

confidence: 99%

Airborne Laser Scanning to support forest resource management under alpine, temperate and Mediterranean environments in Italy

Corona

Cartisano

Salvati

et al. 2012

European Journal of Remote Sensing

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This paper aims to provide general considerations, in the form of a scientific review, with reference to selected experiences of ALS applications under alpine, temperate and Mediterranean environments in Italy as case studies. In Italy, the use of ALS data have been mainly focused on the stratification of forest stands and the estimation of their timber volume and biomass at local scale. Potential for ALS data exploitation concerns their integration in forest inventories on large territories, their usage for silvicultural systems detection and their use for the estimation of fuel load in forest and pre-forest stands. Multitemporal ALS may even be suitable to support the assessment of current annual volume increment and the harvesting rates.

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Fuzzy land element classification from DTMs based on geometry and terrain position

Cited by 198 publications

References 13 publications

Delineation of Valleys and Valley Floors

Delineation of Valleys and Valley Floors

Semi-Automated Classification of Landform Elements in Armenia Based on SRTM DEM using K-Means Unsupervised Classification

Airborne Laser Scanning to support forest resource management under alpine, temperate and Mediterranean environments in Italy

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