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- habitat; red deer (Cervus elaphus); remote sensing; ground-truth; landscape metrics; logistic regression; COPERNICUS; TERENO (1)
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The increasing number of severe storm events is threatening European forests. Besides the primary damages directly caused by storms, there are secondary damages such as bark beetle outbreaks and tertiary damages due to negative effects on the market. These subsequent damages can be minimized if a detailed overview of the affected area and the amount of damaged wood can be obtained quickly and included in the planning of clearance measures. The present work utilizes UAV-orthophotos and an adaptation of the U-Net architecture for the semantic segmentation and localization of windthrown stems. The network was pre-trained with generic datasets, randomly combining stems and background samples in a copy–paste augmentation, and afterwards trained with a specific dataset of a particular windthrow. The models pre-trained with generic datasets containing 10, 50 and 100 augmentations per annotated windthrown stems achieved F1-scores of 73.9% (S1Mod10), 74.3% (S1Mod50) and 75.6% (S1Mod100), outperforming the baseline model (F1-score 72.6%), which was not pre-trained. These results emphasize the applicability of the method to correctly identify windthrown trees and suggest the collection of training samples from other tree species and windthrow areas to improve the ability to generalize. Further enhancements of the network architecture are considered to improve the classification performance and to minimize the calculative costs.
The measurement of tree height has long been an important tree attribute for the purpose of calculating tree growth, volume, and biomass, which in turn deliver important ecological and economical information to decision makers. Tree height has traditionally been measured by indirect field-based techniques, however these methods are rarely contested. With recent advances in Unmanned Aerial Vehicle (UAV) remote sensing technologies, the possibility to acquire accurate tree heights semi-automatically has become a reality. In this study, photogrammetric and field-based tree height measurements of a Scots Pine stand were validated using destructive methods. The intensive forest monitoring site implemented for the study was configured with permanent ground control points (GCPs) measured with a Total Station (TS). Field-based tree height measurements resulted in a similar level of error to that of the photogrammetric measurements, with root mean square error (RMSE) values of 0.304 m (1.82%) and 0.34 m (2.07%), respectively (n = 34). A conflicting bias was, however, discovered where field measurements tended to overestimate tree heights and photogrammetric measurements were underestimated. The photogrammetric tree height measurements of all trees (n = 285) were validated against the field-based measurements and resulted in a RMSE of 0.479 m (2.78%). Additionally, two separate photogrammetric tree height datasets were compared (n = 251), and a very low amount of error was observed with a RMSE of 0.138 m (0.79%), suggesting a high potential for repeatability. This study shows that UAV photogrammetric tree height measurements are a viable option for intensive forest monitoring plots and that the possibility to acquire within-season tree growth measurements merits further study. Additionally, it was shown that negative and positive biases evident in field-based and UAV-based photogrammetric tree height measurements could potentially lead to misinterpretation of results when field-based measurements are used as validation.
The aim of this study was the development of a methodical processing line for estimating the log’s volume from scanning logs using a long-range high-speed personal laser scanner (PLS) GeoSLAM ZEB HORIZON and simultaneous localization and mapping (SLAM) technology in an open-source software framework of Cloud Compare. Additionally, the accuracy and precision of using PLS and the suggested method when measuring roundwood volume was examined compared to measurements made using a xylometer and estimations obtained from applying the formulae of Huber, Smalian and Newton. For this purpose, several parameters were measured in 50 logs of Norway spruce with an average length of 2.53 m and a mean diameter of 19.97 cm. Afterwards, the volume of these 50 logs was measured with a xylometer. The results of these measurements served as reference values. The same 50 logs were subsequently scanned with the PLS. The scans were converted into point clouds and were analyzed in Cloud Compare to estimate the volume of the scanned logs. Next, the root mean square error (RMSE) and the mean bias error (MBE) as well as their relative values were calculated for the volumes determined in Cloud Compare and estimated with the above mentioned formulae. The calculated RMSE (and relative RMSE) determined a deviation of the logs' volumes estimated in Cloud Compare from the xylometric volumes of 2.88 dm3 (3.54%) whereas the deviations of the log’s volumes calculated applying the formulae of Huber, Smalian and Newton from the xylometric volumes were respectively 9.63 dm3 (11.83%), 10.33 dm3 (12.69%) and 4.69 dm3 (5.76%). The calculated MBE (and relative MBE) showed that the estimated volumes in Cloud Compare and those using the formulae were overestimated, with the lowest overestimation of 0.10 dm3 (0.12%) in Cloud Compare and the highest, 5.50 dm3 (6.75%), using the Smalian formula. Therefore, it can be stated that results of suggested methodical processing line came closest to the logs' volumes obtained with the xylometer, i.e., they were more accurate and precise compared to the conventionally formulae for log’s volume estimation. The implementation of this method has the potential filling a gap towards a wall-to-wall complete digitization of the roundwood commercialization and ensure transparency and acceptance between the stakeholders involved in the wood supply chain.
Multiple goals should be considered while planning and executing forest management. This study proposes a new multidimensional framework for a precision forestry approach assisted by airborne laser scanning data (ALS). Therefore, successful management operations become a key element in the process of promoting protection through sustainable development. Thirteen relevant variables were derived from ALS data, such as: canopy height, terrain topography, relative vegetation density, forest gaps, slope restrictions, skidder restrictions, load truck restrictions, topographic wetness, flow accumulation, horizontal distance to drainage, vertical elevation from drainage, stream and headspring restrictions. Four different scenarios for the management plan optimization were studied: shortest distance, forest conservation, soil conservation and all combined. Results showed that the detailed forest information from ALS point clouds is useful to indicate regions not suitable for forest operations. Failure to properly consider the different factors involved may result in inadequate infrastructures, lower operational performance and constant re-planning requirements.
High-resolution recording and forecasting of peatlands under forests in northeast Brandenburg (Germany) using remotely sensed data (Radar ALOS-PALSAR and Sentinel 1). Peatlands are important in terms of CO2 storage, water cycling, biochemical cycles, biodiversity, and ecosystem services. Peatlands are constantly altered and damaged due to agriculture and drainage. Thus it is crucial to manage them sustainably. How? First step – locating and quantifying them with geospatial data and remote sensing methods since locating and collecting data in field is highly expensive and arduous in terms of labour and time. Our MAIN RESEARCH QUESTION therefore is: Is it possible to generate a method to record the location of peatlands under forests at high resolution using RS?.
The charcoal stock in a forest company is controlled based on the theoretical capacity of the masonry ovens (input) and shipped trucks (output). During the year, the company must monitor the stock for the purposes of accountability reports. This study proposes a more efficient and equally precise survey method that overcomes the challenges of the common monitoring system in Brazil. During this study, a new monitoring method based on digital stereoscopy from UAV images was developed, implemented and evaluated. The results were compared with a traditional topographic survey. A masonry oven's complex containing eight charcoal heaps was flown and surveyed using a multi-engine UAV, with an integrated Global Navigation Satellite System (GNSS) and RTK equipment. Two stereoscopic processing methods were applied: (1) very low quality and (2) high quality to image alignment, reconstruction of the dense cloud, face count and in three-dimensional mesh creation. Low quality products showed geometric deformities when compared to high quality, but resulted in similar estimation to the topographic survey. The results indicated that the charcoal heaps' volume estimation using UAV derived orthomosaics can replace the conventional method of GNSS RTK surveys with considerable gains in stockpile volume accuracy, inventory frequency, and safety. In the case of high accuracy parameterization, improvements in geometric precision and accuracy are also produced.
Research evidences that intact ecosystems within cities considerably improve the quality of citizens’ lives. The Cambodian-German project Build4People aims to include urban ecosystems into the long-term urban planning strategies of Cambodian cities. Jan-Peter Mund, Ravi Jayaweera, Hor Sanara, and Michael Waibel present the ambitious project.
This policy brief introduces preliminary research results of the
Build4People project, funded by the German government as part
of the multi-phase focus programme “Sustainable Development
of Urban Regions”. Furthermore, the research agenda of the
upcoming main four-year Research and Development phase (RD
phase) will be introduced based on insights gained in previous
shorter funding periods. The Build4People research consortium
consists of representatives from four German universities and two
German companies with their respective research, implementation and dissemination partners in the Kingdom of Cambodia.
Build4People’s rationale is based on Phnom Penh‘s dynamic
economic growth, which is coupled with a local urbanisation and
construction boom. However, new buildings and neighbourhoods
are rarely constructed in a sustainable way with direct and
indirect negative environmental impacts. Issues of sustainability
are hardly addressed by decision-makers in the construction
sector, related ministries, city administrations or building users.
The overall aim of the multi- and trans-disciplinary Build4People
project during the RD phase is to support and analyse the
transformative shift in Phnom Penh’s current business-as-usual
urban development pathway towards a pathway with higher
sustainability and liveability levels. The entry points for the
research are the building and neighbourhood planning sectors.
Methods, tools and key instruments to achieve the objectives of
the RD phase are collaborative planning workshops, strategic
niche management approaches, transition management approaches and subsequent experimental implementation. All of these measures will generate applied knowledge for local stakeholders and a basis for evidence-based decision-making.
Urban quality of life as a people-driven approach serves as the
integrating link for all of our scientific, conceptional, analytical
and normative dimensions. During our research, we will jointly
conceptualize, measure and model urban quality of life and
consider its political dimension and its often-ambiguous
relationship to sustainability.
The destruction of habitats has not only reduced biological diversity but also affected essential ecosystem services of the Central European cultural landscape. Therefore, in the further development of the cultural landscape and in the management of natural resources, special importance must be attached to the habitat demands of species and the preservation of ecosystem services. The study of ecosystem services has extended its influence into spatial planning and landscape ecology, the integration of which can offer an opportunity to enhance the saliency, credibility, and legitimacy of landscape ecology in spatial planning issues. Objective: This paper proposes a methodology to detect red deer habitats for e.g. huntable game. The model is established on remote sensing based value-added information products, the derived landscape structure information and the use of spatially and temporally imprecise in-situ data (e.g. available hunting statistics). In order to realize this, four statistical model approaches were developed and their predictive performance assessed. Methods: Altogether, our results indicate that based on the data mentioned above, modeling of habitats is possible using a coherent statistical model approach. All four models showed an overall classification of > 60% and in the best case 71,4%. The models based on logistic regression using preference data derived from 5-year hunting statistics, which has been interpreted as habitat suitability. The landscape metrics (LSM) will be calculated on the basis of the Global Forest Change dataset (HANSEN et al. 2013b ). The interpolation of landcover data into landscape-level was made with the software FRAGSTAT and the moving window approach. Correlation analysis is used to identify relevant LSM serving as inputs; logistic regression was used to derive a final binary classifier for habitat suitability values. Three model variations with different sets of LSM are tested using the unstandardized regression coefficient. Results lead to an insight of the effect of each LSM but not on the strength of the effect. Furthermore, the predicted outcome is rather difficult to interpret as different units and scales for each LSM are used. Hence, we calculated the fourth model using the standardized regression coefficient. It harmonized the measurement units of the LSM and thus allowed a better comparison, interpretation, and evaluation.Conclusion: Our research reveals that applying a statistical model using coarse data is effective to identify potential red deer habitats in a significant qualitative manner. The presented approach can be analogously applied to other mammals if the relevant structural requirements and empirical habitat suitability data (e.g. home range, biotopes, and food resources) are known. The habitat preferences of red deer are best described by LSM concerning area-relation and wildlifeedge relations. Most important are edges between meadows, pastures or agricultural field and forest, as well as short paths between those elements for food resources. A large proportion of forest is important for species survival and positively influences the occurrence of red deer. Outcomes help to understand species habitat relation and on which scale wildlife perceives the landscape. In addition, they support the practical habitat management and thus the overall species diversity.