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ArticleCloud computing solution for monitoring dryland forests dynamics in Morocco
XV World Forestry Congress, 2-6 May 2022
2022Also available in:
No results found.The wide availability of free satellite imagery and the recent development of cloud-based geospatial analysis platforms dedicated to spatial Big Data that integrates both image archives from different providers, processing algorithms, distributed processing capabilities as well as an application programming interface that facilitate scripting and automation process, opened new perspectives for the use of vegetation observation time series over long time series and over large spatial scales. This work aims at harnessing these technologies and building up an automated solution to monitor forests rehabilitation dynamics in arid lands and to assess the effectiveness of stakeholder’s management or restoration strategies. Such solution is based on graphical user interface that facilitate the process and on the use of analysis functions relaying on analyzing temporal trajectories (time series) of different spectral indices derived from satellite images (Landsat, MODIS or Sentinel) at the required spatial analysis scale. The solution is implemented using java script language by the functions offered by Google Earth Engine (GEE) API. The graphical user interface of the first prototype is exploitable by the means of a standard web browser. It is accessible even to people without any background in regard to programming languages or to remote sensing skills and to overcome managers lack of technical skills or computing infrastructure capacities. The process was tested for two arid regions on Morocco mainly on sites recently rehabilitated: acacia ecosystems on the southern part of Morocco and the argan ecosystem which is an emblematic agrosilvopastoral system. The output has been qualified as promising solution and the prototype represent decision-support tool which contribute to managing and communicating forest information and management at different levels and facilitating the assessment of ecosystem trends and then to plan restoration interventions. Furthermore, field data collected by involving local communities can greatly facilitate results validation, which makes possible to compensate errors due to data sources. Key words: Google earth engine; remote sensing; monitoring; Dryland forests, Morocco ID: 3623147 -
DocumentAnalyzing the occurrence trend of sediment-related disasters and post-disaster recovery cases in mountain regions in North Korea based on a literature review and satellite image observations
XV World Forestry Congress, 2-6 May 2022
2022Also available in:
No results found.This study investigated spatiotemporal trends of sediment-related disasters in North Korea from 1960 to 2019 and post-disaster recovery cases based on a literature review and satellite images. Results showed that occurrence status of sediment-related disasters was initially externally reported in 1995 (during the Kim Jongil era); their main triggering factor was heavy summer rainfall. Furthermore, forest degradation rate was positively correlated with population density (R2 = 0.4347, p = 0.02) and occurrence number of sediment-related disasters was relatively high on the west coast region, where both variables showed high values. This indicates that human activity was a major cause of forest degradation and thus, significantly affected sediment-related disasters in mountain regions. Finally, sediment- related disasters due to shallow landslides, debris flow, and slow-moving landslides were observed in undisturbed forest regions and human-impacted forest regions, including terraced fields, opencast mines, forest roads, and post-wildfire areas, via satellite image analysis. These disaster-hit areas remained mostly abandoned without any recovery works, whereas hillside erosion control work (e.g., treeplanting with terracing) or torrent erosion control work (e.g., check dam, debris flow guide bank) were implemented in certain areas. These findings can provide reference information to expand inter-Korean exchange and cooperation in forest rehabilitation and erosion control works of North Korea. Keywords: Climate change, Deforestation and forest degradation, Sustainable forest management, Monitoring and data collection, Research ID: 3616353 -
DocumentPrecision monitoring of leaf-cutting ant nests in sub-orbital RGB images using deep learning
XV World Forestry Congress, 2-6 May 2022
2022Also available in:
No results found.Leaf-cutting ants are the main pests of plantation forests in South America, causing severe defoliation, leading to production losses, plant mortality and increased susceptibility to other insects. Chemical control with sulfluramid active ingredient is the most used method. This proposal aimed to develop an innovative method for leaf-cutting ants nest detection in sub-orbital RGB images using deep learning techniques. The study was carried out in a 6-month-old eucalyptus stand with 91.3 hectares in the municipality of Três Lagoas, in the state of Mato Grosso do Sul, Brazil. The images of the stand were collected by a DJI Phantom 4 Advanced aircraft with an RGB camera and processed to produce an orthomosaic with a ground-level resolution of 5.2 cm/pix. The final orthomosaic was cropped in sub-images of 98 x 81 pixels. Sub-images that contained ant nests were labelled using bounding boxes. The database used in experiments consists of 2465 images containing leaf-cutting ant nests and 2465 images of ants' nest absence (background). The detection algorithm used as the deep learning framework based on the YOLO convolutional neural network architecture. The quality of its predictions was evaluated by accuracy, Kappa, sensitivity, specificity, and absolute mean error (MAE) metrics between training and validation samples. YOLO achieved 98.45% accuracy and YOLO 0.49% MAE as the best performances in nests measuring task, demonstrating the high complexity of detecting this target type. Obtained results show that YOLO is a promising approach for precision monitoring of leaf-cutting ant nests in sub-orbital RGB images and can contribute to reduce and optimize insecticide use in plantation forests, which is aligned with the UN Sustainable Development Goals (SDGs), consisting of responsible consumption and production (Goal 12), and terrestrial life (Goal 15). Keywords: Monitoring and data collection, Innovation, Knowledge management ID: 3622337
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