Forest Cover Mapping Based on a Combination of Aerial Images and Sentinel-2 Satellite Data Compared to National Forest Inventory Data

Selina Ganz,Petra Adler,Gerald Kändler

doi:10.3390/f11121322

Selina Ganz, Petra Adler + Show 1 more

Open Access

https://doi.org/10.3390/f11121322

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Abstract

Research Highlights: This study developed the first remote sensing-based forest cover map of Baden-Württemberg, Germany, in a very high level of detail. Background and Objectives: As available global or pan-European forest maps have a low level of detail and the forest definition is not considered, administrative data are often oversimplified or out of date. Consequently, there is an important need for spatio-temporally explicit forest maps. The main objective of the present study was to generate a forest cover map of Baden-Württemberg, taking the German forest definition into account. Furthermore, we compared the results to NFI data; incongruences were categorized and quantified. Materials and Methods: We used a multisensory approach involving both aerial images and Sentinel-2 data. The applied methods are almost completely automated and therefore suitable for area-wide forest mapping. Results: According to our results, approximately 37.12% of the state is covered by forest, which agrees very well with the results of the NFI report (37.26% ± 0.44%). We showed that the forest cover map could be derived by aerial images and Sentinel-2 data including various data acquisition conditions and settings. Comparisons between the forest cover map and 34,429 NFI plots resulted in a spatial agreement of 95.21% overall. We identified four reasons for incongruences: (a) edge effects at forest borders (2.08%), (b) different forest definitions since NFI does not specify minimum tree height (2.04%), (c) land cover does not match land use (0.66%) and (d) errors in the forest cover layer (0.01%). Conclusions: The introduced approach is a valuable technique for mapping forest cover in a high level of detail. The developed forest cover map is frequently updated and thus can be used for monitoring purposes and for assisting a wide range of forest science, biodiversity or climate change-related studies.

Highlights

Forest cover is a key variable of interest to science, management and reporting [1]
We agree with the statement of the authors of [1], who argued that the remotely sensed forest area agree with the statement of the authors of [1], who argued that the remotely sensed forest area mapping within the inventory cycles of an National Forest Inventories (NFIs) provides information that extends the sample-based mapping within the inventory cycles of an NFI provides information that extends the sample-based
The derivation of the forest cover map is almost completely automated, meaning that the applied methods are suitable for area-wide forest is almost completely automated, meaning that the applied methods are suitable for area-wide forest mapping

Summary

Introduction

Up-to-date and accurate information on the dynamics of forest cover is essential for the conservation and management of forests [2,3,4], carbon accounting efforts and the parameterization of a broad range of biogeochemical, hydrological, biodiversity and climate models [5]. A forest cover map provides information on the size, shape and spatial distribution of forests and assists in classifying the landscape into patterns. The spatial distribution of these landscape patterns is linked to the ecological functionality of the landscape [6] and provides new perspectives for ecological connectivity studies [7]. The assessment of forest cover is aimed at facilitating decisions on biodiversity conservation and reforestation programs [8]. Forest maps are crucial for global environmental change assessment and local forest management planning [7].

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