Wall-to-Wall Forest Mapping Based on Digital Surface Models from Image-Based Point Clouds and a NFI Forest Definition

Waser‬, Lars T.; Fischer, Christoph; Wang, Zuyuan; Ginzler, Christian

doi:10.3390/f6124386

Cited by 59 publications

(58 citation statements)

References 29 publications

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“…This example illustrates the crucial role of forest information for large scale hazard indication mapping in regions with protection forests. In the future, with better up-to-date forest information derived from remote sensing (Waser et al, 2015), this source of error might get less important .…”

Section: Automated Hazard Indication Mappingmentioning

confidence: 99%

Automated snow avalanche release area delineation – validation of existing algorithms and proposition of a new object-based approach for large scale hazard indication mapping

Bühler¹,

Rickenbach²,

Stoffel³

et al. 2018

Preprint

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Abstract. Snow avalanche hazard is threatening people and infrastructure in all alpine regions with seasonal or permanent snow cover around the globe. Coping with this hazard is a big challenge and during the past centuries, different strategies were 10 developed. Today, in Switzerland, experienced avalanche engineers produce hazard maps with a very high reliability based on avalanche cadastre information, terrain analysis, climatological datasets and numerical modelling of the flow dynamics for selected avalanche tracks that might affect settlements. However, for regions outside the considered settlement areas such areawide hazard maps are not available mainly because of the too high cost, in Switzerland and in most mountain regions around the world. Therefore, hazard indication maps, even though they are less reliable and less detailed, are often the only spatial 15 planning tool available. To produce meaningful and cost-effective avalanche hazard indication maps over large regions (regional to national scale), automated release area delineation has to be combined with volume estimations and state-of-theart numerical avalanche simulations.In this paper we validate existing potential release area (PRA) delineation algorithms, published in peer-reviewed journals, 20 that are based on digital terrain models and their derivatives such as slope angle, aspect, roughness and curvature. For validation, we apply avalanche cadastre data from three different ski resorts in the vicinity of Davos, Switzerland, where experienced ski-patrol staff mapped most avalanches in detail since many years. After calculating the best fit input parameters for every tested algorithm, we compare their performance based on the reference datasets. Because all tested algorithms do not provide meaningful delineation between individual potential release areas (PRA), we propose a new algorithm based on object-25 based image analysis (OBIA). In combination with an automatic procedure to estimate the average release depth (d0), defining the avalanche release volume, this algorithm enables the numerical simulation of thousands of avalanches over large regions applying the well-established avalanche dynamics model RAMMS. We demonstrate this for the region of Davos for two hazard scenarios, frequent (10 -30 years return period) and extreme (100 -300 years return period). This approach opens the door for large scale avalanche hazard indication mapping in all regions where high quality and resolution digital terrain models and 30 snow data are available.Nat. Hazards Earth Syst. Sci. Discuss., https://doi

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Section: Automated Hazard Indication Mappingmentioning

confidence: 99%

Automated snow avalanche release area delineation – validation of existing algorithms and proposition of a new object-based approach for large scale hazard indication mapping

Bühler¹,

Rickenbach²,

Stoffel³

et al. 2018

Preprint

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“…at 10% VCC (FAO 2010;Lindgren et al 2015), but also for use in studying habitat and biodiversity (Bergen et al 2009), which is linked to canopy cover (Müller and Vierling 2014). The raster-based method used by Waser et al (2015) in the production of a forest map resulted in overall accuracy of 97%, with lower accuracies along forest borders and at altitudes above 1400 m above sea level. The errors along forest borders were due to limitations in the classification method, while the errors at high altitude were caused by a combination of lower resolution of imagery, less distinct forest borders, and dominance of tree species with small and narrow crowns .…”

Section: Introductionmentioning

confidence: 99%

“…Apart from the studies by Waser et al (2015), canopy cover metrics derived from image-based point cloud data and ALS data have not been used for vegetation mapping in pasture areas, or in unmanaged forests in non-productive areas such as wetland or bedrock outcrops. These vegetation types are relatively common in a landscape context.…”

Section: Introductionmentioning

confidence: 99%

“…A recent study demonstrated the successful generation of a nationwide digital surface model (DSM) in Switzerland by image matching countrywide coverage of digital aerial stereo-imagery (Ginzler and Hobi 2015). Using this national DSM, normalized with a digital terrain model (DTM) from ALS data, Waser et al (2015) produced a wall-to-wall forest cover map for Switzerland via a workflow including classification of areas into forest or non-forest based on vegetation height. A similar approach could be used in Sweden, considering that the national coverage of digital stereo-imagery is at present updated every third year on average, and that Lantmäteriet (the National Land Survey of Sweden) is producing a DSM based on aerial images acquired in 2016 and onwards.…”

Section: Introductionmentioning

confidence: 99%

“…Regularly updated nationwide image-based point cloud data would provide data for vegetation mapping (e.g. Granholm et al 2015;Reese et al 2015;Waser et al 2015) and change detection (e.g. Waser et al 2008;Wang, Ginzler, and Waser 2015).…”

Section: Introductionmentioning

confidence: 99%

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Estimating vertical canopy cover using dense image-based point cloud data in four vegetation types in southern Sweden

Granholm

Lindgren

Olofsson

et al. 2017

International Journal of Remote Sensing

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This study had the aim of investigating the utility of image-based point cloud data for estimation of vertical canopy cover (VCC). An accurate measure of VCC based on photogrammetric matching of aerial images would aid in vegetation mapping, especially in areas where aerial imagery is acquired regularly. The test area is located in southern Sweden and was divided into four vegetation types with sparse to dense tree cover: unmanaged coniferous forest; pasture areas with deciduous tree cover; wetland; and managed coniferous forest. Aerial imagery with a ground sample distance of 0.24 m was photogrammetrically matched to produce dense image-based point cloud data. Two different image matching software solutions were used and compared: MATCH-T DSM by Trimble and SURE by nFrames. The image-based point clouds were normalized using a digital terrain model derived from airborne laser scanner (ALS) data. The canopy cover metric vegetation ratio was derived from the image-based point clouds, as well as from raster-based canopy height models (CHMs) derived from the point clouds. Regression analysis was applied with vegetation ratio derived from near nadir ALS data as the dependent variable and metrics derived from image-based point cloud data as the independent variables. Among the different vegetation types, vegetation ratio derived from the image-based point cloud data generated by using MATCH-T resulted in relative root mean square errors (rRMSE) of VCC ranging from 6.1% to 29.3%. Vegetation ratio based on point clouds from SURE resulted in rRMSEs ranging from 7.3% to 37.9%. Use of the vegetation ratio based on CHMs generated from the image-based point clouds resulted in similar, yet slightly higher values of rRMSE. ARTICLE HISTORY

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Countrywide classification of permanent grassland habitats at high spatial resolution

Huber

Ginzler

Pazúr

et al. 2022

Remote Sens Ecol Conserv

Self Cite

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European grasslands face strong declines in extent and quality. Many grassland types are priority habitats for national and European conservation strategies. Countrywide, high spatial resolution maps of their distribution are often lacking. Here, we modelled the spatial distribution of 20 permanent grassland habitats at the level of phytosociological alliances across Switzerland at 10x10 m resolution. First, we applied ensemble models to provide distribution maps of the individual habitat types, using training data from various sources. Copernicus Sentinel satellite imagery and variables describing climate, soil and topography were used as predictors. The performance of these models was assessed based on the true skill statistics with a split-sampling of the data. Second, the individual maps were combined into countrywide maps of the most and second most likely habitat type, respectively, using an expert-based weighting approach. The performance of the combined map for the most likely habitat type was assessed via an independent testing dataset and a comparison of the predicted habitat-type proportions with extrapolations from field surveys. Most individual maps had useful to excellent predictive performance (TSS ≥ 0.6). For most grid cells in the combined maps, the most and second most likely habitat types were either ecologically closely related or representing two grassland types along a nutrient gradient. The same was true for omission errors. We found good agreement between the predicted and estimated proportions from field surveys. The area of raised bogs appears to be underestimated, while dry grasslands showed highest agreement. This work highlights the potential of earth observation data at fine spatial and temporal resolution to map habitats at broad scales, thereby providing the foundation for diverse conservation applications. A particular challenge remains in capturing the transition from nutrient-poor to nutrient-rich grasslands, which is highly important for biodiversity conservation.

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Wall-to-Wall Forest Mapping Based on Digital Surface Models from Image-Based Point Clouds and a NFI Forest Definition

Cited by 59 publications

References 29 publications

Automated snow avalanche release area delineation – validation of existing algorithms and proposition of a new object-based approach for large scale hazard indication mapping

Automated snow avalanche release area delineation – validation of existing algorithms and proposition of a new object-based approach for large scale hazard indication mapping

Estimating vertical canopy cover using dense image-based point cloud data in four vegetation types in southern Sweden

Countrywide classification of permanent grassland habitats at high spatial resolution

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