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    EVALUATION OF FIRE LOOKOUT TOWERS USING GIS-BASED SPATIAL VISIBILITY AND SUITABILITY ANALYZES
    (Croatian Forestry Soc, 2020) Akay, Abdullah Emin; Wing, Michael; Buyuksakalli, Halit; Malkocglu, Salih
    Effective forest fire fighting involves alerting firefighting teams immediately in the case of a tire so that teams can promptly arrive the fire scene. The most effective way for an early detection of forest tires is monitoring of forest lands from fire lookout towers. Especially in fire sensitive forest lands, towers should be systematically located in such a way that fire lookout personnel can monitor the largest amount of forest land as possible. In this study, the visibility capabilities of lookout towers located in Koycegiz Forest Enterprise Directorate (FED)in the city of Mugla in Turkey were evaluated by using Geographical Information System (GIS) based visibility and suitability analysis. The results of visibility analysis indicated that 77.12% of forest land were visible from the current towers. To extend the proportion of visible forest lands, locations of additional lookout towers were evaluated using spatial visibility and suitability analysis in which the tower locations were examined by considering specific criteria (i.e. distance to roads, elevation, ground slope, topographic features). Suitability analysis results identified five new towers in addition to current towers in the study area. The results indicated that visible forest lands increased to 81.47% by locating new towers, and increase of almost 4.35%. In addition, over half of the forests became visible by at least two towers when including five towers suggested by suitability analysis. The GIS-based method developed in this study can assist fire managers to determine the optimal locations for fire lookout towers for effective fire management activities.
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    Land Use and Land Cover (LULC) Mapping Accuracy Using Single-Date Sentinel-2 MSI Imagery with Random Forest and Classification and Regression Tree Classifiers
    (Mdpi, 2025) Gulci, Sercan; Wing, Michael; Akay, Abdullah Emin
    The use of Google Earth Engine (GEE), a cloud-based computing platform, in spatio-temporal evaluation studies has increased rapidly in natural sciences such as forestry. In this study, Sentinel-2 satellite imagery and Shuttle Radar Topography Mission (SRTM) elevation data and image classification algorithms based on two machine learning techniques were examined. Random Forest (RF) and Classification and Regression Trees (CART) were used to classify land use and land cover (LULC) in western Oregon (USA). To classify the LULC from the spectral bands of satellite images, a composition consisting of vegetation difference indices NDVI, NDWI, EVI, and BSI, and a digital elevation model (DEM) were used. The study area was selected due to a diversity of land cover types including research forest, botanical gardens, recreation area, and agricultural lands covered with diverse plant species. Five land classes (forest, agriculture, soil, water, and settlement) were delineated for LULC classification testing. Different spatial points (totaling 75, 150, 300, and 2500) were used as training and test data. The most successful model performance was RF, with an accuracy of 98% and a kappa value of 0.97, while the accuracy and kappa values for CART were 95% and 0.94, respectively. The accuracy of the generated LULC maps was evaluated using 500 independent reference points, in addition to the training and testing datasets. Based on this assessment, the RF classifier that included elevation data achieved an overall accuracy of 92% and a kappa coefficient of 0.90. The combination of vegetation difference indices with elevation data was successful in determining the areas where clear-cutting occurred in the forest. Our results present a promising technique for the detection of forests and forest openings, which was helpful in identifying clear-cut sites. In addition, the GEE and RF classifier can help identify and map storm damage, wind damage, insect defoliation, fire, and management activities in forest areas.