Over 150 Years of Change: Object-Oriented Analysis of Historical Land Cover in the Main River Catchment, Bavaria/Germany

Ulloa-Torrealba, Yrneh Zarit; Stahlmann, Reinhold; Wegmann, Martin; Koellner, Thomas

doi:10.3390/rs12244048

Cited by 6 publications

(4 citation statements)

References 23 publications

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“…The confusion matrix, as an important index in machine learning, is often used to evaluate the performance of models. In many studies on land cover, forest classification, and remote sensing information extraction, confusion matrices have proved to be feasible in evaluating model performance [62][63][64]. Of course, in contrast, K-fold cross-validation can reduce the contingency of calculation results.…”

Section: Discussionmentioning

confidence: 99%

Object-Oriented Canopy Gap Extraction from UAV Images Based on Edge Enhancement

et al. 2022

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Efficient and accurate identification of canopy gaps is the basis of forest ecosystem research, which is of great significance to further forest monitoring and management. Among the existing studies that incorporate remote sensing to map canopy gaps, the object-oriented classification has proved successful due to its merits in overcoming the problem that the same object may have different spectra while different objects may have the same spectra. However, mountainous land cover is unusually fragmented, and the terrain is undulating. One major limitation of the traditional methods is that they cannot finely extract the complex edges of canopy gaps in mountainous areas. To address this problem, we proposed an object-oriented classification method that integrates multi-source information. Firstly, we used the Roberts operator to obtain image edge information for segmentation. Secondly, a variety of features extracted from the image objects, including spectral information, texture, and the vegetation index, were used as input for three classifiers, namely, random forest (RF), support vector machine (SVM), and k-nearest neighbor (KNN). To evaluate the performance of this method, we used confusion matrices to assess the classification accuracy of different geo-objects. Then, the classification results were screened and verified according to the area and height information. Finally, canopy gap maps of two mountainous forest areas in Yunnan Province, China, were generated. The results show that the proposed method can effectively improve the segmentation quality and classification accuracy. After adding edge information, the overall accuracy (OA) of the three classifiers in the two study areas improved to more than 90%, and the classification accuracy of canopy gaps reached a high level. The random forest classifier obtained the highest OA and Kappa coefficient, which could be used for extracting canopy gap information effectively. The research shows that the combination of the object-oriented method integrating multi-source information and the RF classifier provides an efficient and powerful method for extracting forest gaps from UAV images in mountainous areas.

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Section: Discussionmentioning

confidence: 99%

Object-Oriented Canopy Gap Extraction from UAV Images Based on Edge Enhancement

et al. 2022

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“…This approach yielded highly satisfactory results, as verified against high-resolution RGB images. The multiresolution segmentation algorithm, a region-growing technique with parameters set at 35 Scale, 0.8 Shape, and 0.5 Compactness using eCognition, was used to generate the image objects [50] as shown in Figure 3.…”

Section: Data Collectionmentioning

confidence: 99%

Utilizing Remote Sensing and GIS Techniques for Flood Hazard Mapping and Risk Assessment

Al-Omari,

Shatnawi,

Shbeeb

et al. 2024

Civ Eng J

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In this paper, a comprehensive flood hazard map for the vicinity of King Talal Dam in Jordan, utilizing advanced remote sensing (RS) and GIS methodologies, is developed. Key geographical and environmental factors, encompassing terrain slope, elevation, aspect, proximity to water streams, drainage density, and land use/land cover, are integrated to highlight areas with increased flood risk. This study, by employing a novel theoretical approach, harnesses the synergistic capabilities of RS and GIS to collect and analyze geospatial data. The Analytic Hierarchy Process (AHP) is applied to assign weights to various flood-conditioning factors, quantifying their relative importance in flood risk assessment. Through the weighted sum overlay technique, the aforementioned factors are integrated to categorize flood risk levels from very low to very high. This study successfully maps flood hazards, identifying areas near main water channels, ravines, and lower-elevation areas prone to flooding. This research provides a robust framework for flood risk assessment, contributing valuable knowledge to the fields of environmental management and disaster mitigation. It underscores the importance of continuous monitoring and updating of flood hazard maps to accommodate changing land use, climate, and hydrological conditions. The innovative application offers crucial insights for urban planners and policymakers, emphasizing the need for proactive strategies in flood-prone areas and serving as a model for similar geographical regions. Doi: 10.28991/CEJ-2024-010-05-05 Full Text: PDF

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“…Our planet is losing biodiversity at unprecedented rates due to land-use change, direct exploitation, climate change, pollution and the invasion of exotic species (Cardinale et al, 2012;IPBES, 2019;Millennium Ecosystem Assessment, 2005;Tilman, 1999). In Western countries, for example, this loss had started already in the second half of the 18th century, with the onset of industrialisation and modern agriculture (Krausmann & Haberl, 2002;Lambin & Geist, 2006;Ulloa-Torrealba et al, 2020). Ecosystems and their biodiversity contribute to human wellbeing and the functioning of societal subsystems (IPBES, 2019;Millennium Ecosystem Assessment, 2005) in various ways.…”

Section: Introductionmentioning

confidence: 99%