Wall-to-Wall Tree Type Mapping from Countrywide Airborne Remote Sensing Surveys

Abstract

Abstract:Although wall-to-wall, accurate, and up-to-date forest composition maps at the stand level are a fundamental input for many applications, ranging from global environmental issues to local forest management planning, countrywide mapping approaches on the tree type level remain rare. This paper presents and validates an innovative remote sensing based approach for a countrywide mapping of broadleaved and coniferous trees in Switzerland with a spatial resolution of 3 m. The classification approach incorporates a random forest classifier, explanatory variables from multispectral aerial imagery and a Digital Terrain Model (DTM) from Airborne Laser Scanning (ALS) data, digitized training polygons and independent validation data from the National Forest Inventory (NFI). The methodological workflow was optimized for an area of 41,285 km 2 that is characterized by temperate forests within a complex topography. Whereas high model overall accuracies (0.99) and kappa (0.98) were achieved, the comparison of the tree type map with independent NFI data revealed significant deviations that are related to underestimations of broadleaved trees (median of −3.17%). Constraints of the tree type mapping approach are mostly related to the acquisition date and time of the imagery and the topographic (negative) effects on the prediction. A comparison with the most recent High Resolution Layers (HRL) forest 2012 from the European Environmental Agency revealed that the tree type map is superior regarding spatial resolution, level of detail and accuracy. The high-quality map achieved with the approach presented here is of great value for optimizing forest management and planning activities and is also an important information source for applications outside the forestry sector.

Using Multitemporal Sentinel-1 C-band Backscatter to Monitor Phenology and Classify Deciduous and Coniferous Forests in Northern Switzerland

“…Nevertheless, our achieved accuracies were comparable to other studies classifying deciduous and coniferous forests using even fully polarimetric C-band SAR data [16,83,84]. Compared with other technologies such as airborne laser scanning (OA of 89-96% and κ = 0.61 − 0.92) [85][86][87] or imaging spectrometer data (OA of 83-99% and κ = 0.73 − 0.98) [88][89][90], our forest type classification performance (OA of 86% and κ = 0.73) was not as competitive. This could be due to the lower spatial resolution used in this instance.…”

Large‐scale early‐wilting response of Central European forests to the 2018 extreme drought

“…At the Central European scale, we derived a forest mask based on the Tree Cover Density map 2015 from Copernicus (https://land.coper nicus.eu/), which has an original resolution of 20 m. Areas with less than 80% tree coverage were considered nonforested, and removed from the analyses. For forest mixture (fraction of broadleaved trees relative to needle-leaved trees), we used the tree-type map for Switzerland (Waser et al, 2017) (Ginzler & Hobi, 2015) that we aggregated to 10 × 10 m by average and standard deviation, respectively. Distance to forest edge was calculated from the 10 × 10 m aggregate of the Swiss forest mask.…”

Are we ready for a National Forest Information System? State of the art of forest maps and airborne laser scanning data availability in Italy

“…Forest mapping is an important source of information for assessing woodland resources and a key issue for any National Forest Inventory (NFI) programme (Waser et al 2017). Nowadays, global and nationwide wall-to-wall, raster-type maps of forest resources, based on satellite images, laser scanning, aerial orthomosaic and pho-togrammetric point cloud data are considered essential to monitor and quantify forest variables (Hansen et al 2013, Waser et al 2017, Kangas et al 2018. Forest maps are currently produced on the basis of remote sensing technologies at different spatial scales for global or continental forest resources.…”