A laboratory for conceiving Essential Biodiversity Variables (EBVs)—The ‘Data pool initiative for the Bohemian Forest Ecosystem’

Effects of climate change-induced events on forest ecosystem dynamics of composition, function and structure call for increased long-term, interdisciplinary and integrated research on biodiversity indicators, in particular within strictly protected areas with extensive non-intervention zones. The long-established concept of forest supersites generally relies on long-term funds from national agencies and goes beyond the logistic and financial capabilities of state-or region-wide protected area administrations, universities and research institutes. We introduce the concept of data pools as a smaller-scale, user-driven and reasonable alternative to co-develop remote sensing and forest ecosystem science to validated products, biodiversity indicators and management plans. We demonstrate this concept with the Bohemian Forest Ecosystem Data Pool, which has been established as an interdisciplinary, international data pool within the strictly protected Bavarian Forest and Šumava National Parks and currently comprises 10 active partners. We demonstrate how the structure and impact of the data pool differs from comparable cases. We assessed the international influence and visibility of the data pool with the help of a systematic literature search and a brief analysis of the results. Results primarily suggest an increase in the impact and visibility of published material during the life span of the data pool, with highest visibilities achieved by research conducted on leaf traits, vegetation phenology and 3D-based forest inventory. We conclude that the data pool results in an efficient contribution to the concept of global biodiversity observatory by evolving towards a training platform, functioning as a pool of data and algorithms, directly communicating with management for implementation and providing test fields for feasibility studies on earth observation missions.publishedVersio

Deep Learning-based classification of tree species and standing dead trees using Silvi-Net

1041601623German Federal Ministry of Education and Research (BMBF

Silvi-Net – A dual-CNN approach for combined classification of tree species and standing dead trees from remote sensing data

Forest managers and nature conservationists rely on precise mapping of single trees from remote sensing data for efficient estimation of forest attributes. In recent years, additional quantification of dead wood in particular has garnered interest. However, tree-level approaches utilizing segmented single trees are still limited in accuracy and their application is therefore mostly restricted to research studies. Furthermore, the combined classification of presegmented single trees with respect to tree species and health status is important for practical use but has been insufficiently investigated so far. Therefore, we introduce Silvi-Net, an approach based on convolutional neural networks (CNNs) fusing airborne lidar data and multispectral (MS) images for 3D object classification. First, we segment single 3D trees from the lidar point cloud, render multiple silhouette-like side-view images, and enrich them with calibrated laser echo characteristics. Second, projected outlines of the segmented trees are used to crop and mask the MS orthomosaic and to generate MS image patches for each tree. Third, we independently train two ResNet-18 networks to learn meaningful features from both datasets. This optimization process is based on pretrained CNN weights and recursive retraining of model parameters. Finally, the extracted features are fused for a final classification step based on a standard multi-layer perceptron and majority voting. We analyzed the network’s performance on data captured in two study areas, the Chernobyl Exclusion Zone (ChEZ) and the Bavarian Forest National Park (BFNP). For both study areas, the lidar point density was approximately 55 points/m2 and the ground sampling distance values of the true orthophotos were 10 cm (ChEZ) and 20 cm (BFNP). In general, the trained models showed high generalization capacity on independent test data, achieving an overall accuracy (OA) of 96.1% for the classification of pines, birches, alders, and dead trees (ChEZ) - and 91.5% for coniferous, deciduous, snags, and dead trees (BFNP). Interestingly, lidar-based imagery increased the OA by 2.5% (ChEZ) and 5.9% (BFNP) compared to experiments only utilizing MS imagery. Moreover, Silvi-Net also demonstrated superior OA compared to the baseline method PointNet++ by 11.3% (ChEZ) and 2.2% (BFNP). Overall, the effectiveness of our approach was proven using 2D and 3D datasets from two natural forest areas (400–530 trees/ha), acquired with different sensor models, and varying geometric and spectral resolution. Using the technique of transfer learning, Silvi-Net facilitates fast model convergence, even for datasets with a reduced number of samples. Consequently, operators can generate reliable maps that are of major importance in applications such as automated inventory and monitoring projects

Detection of radioactive waste sites in the Chornobyl exclusion zone using UAV-based lidar data and multispectral imagery

The severe accident at the Chornobyl Nuclear Power Plant (ChNPP) in 1986 resulted in extraordinary contamination of the surrounding territory, which necessitated the creation of the Chornobyl Exclusion Zone (ChEZ). During the accident, liquidation materials contaminated by radioactive fallout (e.g., contaminated soil and trees) were buried in so-called Radioactive Waste Temporary Storage Places (RWTSPs). The exact locations of these burials were not always sufficiently documented. However, for safety management, including eventual remediation works, it is crucial to know their locations and rely on precise hazard maps. Over the past 34 years, most of these so-called trenches and clamps have been exposed to natural processes. In addition to settlement and erosion, they have been overgrown with dense vegetation. To date, more than 700 burials have been thoroughly investigated, but a large number of burial sites (approximately 300) are still unknown. In the past, numerous burials were identified based on settlement or elevation in the decimeter range, and vegetation anomalies that tend to appear in the immediate vicinity. Nevertheless, conventional detection methods are time-, effort- and radiation dose-intensive. Airborne gamma spectrometry and visual ground inspection of morphology and vegetation can provide useful complementary information, but it is insufficient for precisely localizing unknown burial sites in many cases. Therefore, sensor technologies, such as UAV-based lidar and multispectral imagery, have been identified as potential alternative solutions. This paper presents a novel method to detect radioactive waste sites based on a set of prominent features generated from high-resolution remote sensing data in combination with a random forest (RF) classifier. Initially, we generate a digital terrain model (DTM) and 3D vegetation map from the data and derive tree-based features, including tree density, tree height, and tree species. Feature subsets compiled from normalized DTM height, fast point feature histograms (FPFH), and lidar metrics are then incorporated. Next, an RF classifier is trained on reference areas defined by visual interpretation of the DTM grid. A backward feature selection strategy reduces the feature space significantly and avoids overfitting. Feature relevance assessment clearly demonstrates that the members of all feature subsets represent a final list of the most prominent features. For three representative study areas, the mean overall accuracy (OA) is 98.2% when using area-wide test data. Cohens’ kappa coefficient ranges from 0.609 to 0.758. Additionally, we demonstrate the transferability of a trained classifier to an adjacent study area (OA = 93.6%, = 0.452). As expected, when utilizing the classifier on geometrically incorrect and incomplete reference data, which were generated from old maps and orthophotos based on visual inspection, the OA decreases significantly to 65.1% ( = 0.481). Finally, detection is verified through 38 borings that successfully confirm the existence of previously unknown buried nuclear materials in classified areas. These results demonstrate that the proposed methodology is applicable to detecting area-wide unknown radioactive biomass burials in the ChEZ

Sensitivity Analysis of 3D Individual Tree Detection from LiDAR Point Clouds of Temperate Forests

Light detection and ranging (LiDAR) sampling or full-area coverage is deemed as favorable means to achieve timely and robust characterizations of forests. Recently, a 3D segmentation approach was developed for extracting single trees from LiDAR data. However, key parameters for modules used in the strategy had to be empirically determined. This paper highlights a comprehensive study for the sensitivity analysis of 3D single tree detection from airborne LiDAR data. By varying key parameters, their influences on results are to be quantified. The aim of the study is to enlighten the optimal combination of parameter values towards new applications. For the experiment, a number of sample plots from two temperate forest sites in Europe were selected. LiDAR data with a point density of 25 pts/m2 over the first site in the Bavarian forest national park were captured with under both leaf-on and leaf-off conditions. Moreover, a Riegl scanner was used to acquire data over the Austrian Alps forest with four-fold point densities of 5 pts/m2, 10 pts/m2, 15 pts/m2 and 20 pts/m2, respectively, under leaf-off conditions. The study results proved the robustness and efficiency of the 3D segmentation approach. Point densities larger than 10 pts/m2 did not seem to significantly contribute to the improvement in the performance of 3D tree detection. The performance of the approach can be further examined and improved by optimizing the parameter settings with respect to different data properties and forest structures