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

Ganz, Selina; Adler, Petra; Kändler, Gerald

doi:10.3390/f11121322

Cited by 30 publications

(22 citation statements)

References 44 publications

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“…In the field of forest remote sensing, aerial imagery is less frequently used than satellite imagery, especially when it comes to forest type classification tasks [Holzwarth et al, 2020]. This may be driven by the low number of spectral bands (red, green, blue, and near-infrared (RGB+NIR)), which is considered to be a limitation in remote sensing applications [Fassnacht et al, 2016;Ganz et al, 2020]. On the other hand, many studies have shown the strengths of multi-spectral satellite imagery for forest classification tasks [Pasquarella et al, 2018;Grabska et al, 2019;Immitzer et al, 2019;Ottosen et al, 2020;Hemmerling et al, 2021;Kollert et al, 2021;Waser et al, 2021;Welle et al, 2022].…”

Section: State Of Researchmentioning

confidence: 99%

TreeSatAI Benchmark Archive: A multi-sensor, multi-label dataset for tree species classification in remote sensing

Ahlswede¹,

Schulz²,

Gava

et al. 2022

Preprint

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Abstract. Airborne and spaceborne platforms are the primary data sources for large-scale forest mapping, but visual interpretation for individual species determination is labour-intensive. Hence, various studies focusing on forests have investigated the benefits of multiple sensors for automated tree species classification. However, transferable deep learning approaches for large-scale applications are still lacking. This gap motivated us to create a novel dataset for tree species classification in Central Europe based on multi-sensor data from aerial, Sentinel-1 and Sentinel-2 imagery. In this paper, we introduce the TreeSatAI Benchmark Archive, which contains labels of 20 European tree species (i.e., 15 tree genera) derived from forest administration data of the federal state of Lower Saxony, Germany. We propose models and guidelines for the application of the latest machine learning techniques for the task of tree species classification with multi-label data. Finally, we provide various benchmark experiments showcasing the information which can be derived from the different sensors including artificial neural networks and tree-based machine learning methods. We found that residual neural networks (ResNet) perform sufficiently well with weighted precision scores up to 79 % only by using the RGB bands of aerial imagery. This result indicates that the spatial content present within the 0.2 m resolution data is very informative for tree species classification. With the incorporation of Sentinel-1 and Sentinel-2 imagery, performance improved marginally. However, the sole use of Sentinel-2 still allows for weighted precision scores of up to 74 % using either multi-layer perceptron (MLP) or Light Gradient Boosting Machine (LightGBM) models. Since the dataset is derived from real-world reference data, it contains high class imbalances. We found that this dataset attribute negatively affects the models' performances for many of the underrepresented classes (i.e., scarce tree species). However, the class-wise precision of the best performing late fusion model still reached values ranging from 54 % (Acer) to 88 % (Pinus). Based on our results, we conclude that deep learning techniques using aerial imagery could considerably support forestry administration in the provision of large-scale tree species maps at a very high resolution to plan for challenges driven by global environmental change. The original dataset used in this paper is shared via Zenodo (https://doi.org/10.5281/zenodo.6598390) [Schulz et al., 2022]. For citation of the dataset, we refer to this article.

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Section: State Of Researchmentioning

confidence: 99%

TreeSatAI Benchmark Archive: A multi-sensor, multi-label dataset for tree species classification in remote sensing

Ahlswede¹,

Schulz²,

Gava

et al. 2022

Preprint

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“…However, the production of up-to-date information in sufficient spatial detail for operative forest management and monitoring at forest district level still does not entirely satisfy data needs [1,5,11]. As very high-resolution (VHR) satellite imagery is currently only provided by commercial companies, or can only be accessed through open data programs for scientific use (i.e., the European Space Agency Third Party Missions [12]), aerial imagery remains a crucial dataset in multiple operational forestry applications on a local scale [13][14][15]. Indeed, VHR imagery provided in at least yearly cycles would be necessary to provide the capability to monitor changes and disturbances (i.e., wind fall), whereas intra-annual data in sufficient detail would be necessary for capturing dynamic changes caused by biotic and abiotic drivers (i.e., insect calamities, drought effects) [16,17].…”

Section: Introductionmentioning

confidence: 99%

Extension of an Open GEOBIA Framework for Spatially Explicit Forest Stratification with Sentinel-2

2022

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Spatially explicit information about forest cover is fundamental for operational forest management and forest monitoring. Although open-satellite-based earth observation data in a spatially high resolution (i.e., Sentinel-2, ≤10 m) can cover some information needs, spatially very high-resolution imagery (i.e., aerial imagery, ≤2 m) is needed to generate maps at a scale suitable for regional and local applications. In this study, we present the development, implementation, and evaluation of a Geographic Object-Based Image Analysis (GEOBIA) framework to stratify forests (needleleaf, broadleaved, non-forest) in Luxembourg. The framework is exclusively based on open data and free and open-source geospatial software. Although aerial imagery is used to derive image objects with a 0.05 ha minimum size, Sentinel-2 scenes of 2020 are the basis for random forest classifications in different single-date and multi-temporal feature setups. These setups are compared with each other and used to evaluate the framework against classifications based on features derived from aerial imagery. The highest overall accuracies (89.3%) have been achieved with classification on a Sentinel-2-based vegetation index time series (n = 8). Similar accuracies have been achieved with classification based on two (88.9%) or three (89.1%) Sentinel-2 scenes in the greening phase of broadleaved forests. A classification based on color infrared aerial imagery and derived texture measures only achieved an accuracy of 74.5%. The integration of the texture measures into the Sentinel-2-based classification did not improve its accuracy. Our results indicate that high resolution image objects can successfully be stratified based on lower spatial resolution Sentinel-2 single-date and multi-temporal features, and that those setups outperform classifications based on aerial imagery only. The conceptual framework of spatially high-resolution image objects enriched with features from lower resolution imagery facilitates the delivery of frequent and reliable updates due to higher spectral and temporal resolution. The framework additionally holds the potential to derive additional information layers (i.e., forest disturbance) as derivatives of the features attached to the image objects, thus providing up-to-date information on the state of observed forests.

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“…The paper regarding large juniper forests also introduces the concept of carbon sequestration, which is particularly important to our environment today [3]. The final three papers deal with higher spatial resolution remotely sensed imagery [4][5][6]. The paper by Ganz et al [4] demonstrates the benefits of having a detailed forest map of a German forest created from high spatial resolution imagery.…”

mentioning

confidence: 99%

“…The final three papers deal with higher spatial resolution remotely sensed imagery [4][5][6]. The paper by Ganz et al [4] demonstrates the benefits of having a detailed forest map of a German forest created from high spatial resolution imagery. They then introduce the concept of comparing or relating what can be determined on the detailed map created from the remotely sensed imagery with their forest inventory data collected on the ground.…”

mentioning

confidence: 99%

Mapping and Monitoring Forest Cover

Congalton¹

2021

Forests

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

Cited by 30 publications

References 44 publications

TreeSatAI Benchmark Archive: A multi-sensor, multi-label dataset for tree species classification in remote sensing

TreeSatAI Benchmark Archive: A multi-sensor, multi-label dataset for tree species classification in remote sensing

Extension of an Open GEOBIA Framework for Spatially Explicit Forest Stratification with Sentinel-2

Mapping and Monitoring Forest Cover

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