European Journal of Forest Engineering最新文献

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Assessing Two Decades of Land Use/Land Cover Changes in the Uluabat Lake Ramsar Site using Multi-Temporal Satellite Imagery 利用多时卫星图像评估乌鲁阿巴特湖拉姆萨尔湿地二十年的土地利用/土地覆盖变化情况

European Journal of Forest Engineering Pub Date : 2024-07-08 DOI: 10.33904/ejfe.1318353

Emre Kılınçarslan, B. Gencal, I. Tas

{"title":"Assessing Two Decades of Land Use/Land Cover Changes in the Uluabat Lake Ramsar Site using Multi-Temporal Satellite Imagery","authors":"Emre Kılınçarslan, B. Gencal, I. Tas","doi":"10.33904/ejfe.1318353","DOIUrl":"https://doi.org/10.33904/ejfe.1318353","url":null,"abstract":"The Ramsar Convention on Wetlands designates over 2,000 sites of international importance, providing crucial habitats for diverse species. Uluabat Lake, faces anthropogenic pressures such as urbanization, agriculture, and industrialization, affecting its ecological integrity. Using multi-temporal Landsat 7 and Landsat 9 satellite images from 2002 and 2022, along with 2019 management plans, we assessed land use/land cover (LULC) changes in the lake's catchment area. Data were pre-processed with ENVI and stored in ERDAS Imagine. We employed pixel-based image analysis with maximum likelihood classification (MLC) to generate LULC maps and evaluated classification accuracy using ground truth data and the kappa coefficient. Our findings revealed a 15.8% reduction in lake area, from 136.1 km² in 2002 to 114.5 km² in 2022, primarily due to sediment transport from surrounding agricultural land and tributary streams. Urban-agricultural and reed-swamp areas increased by 74.7% and 59.6%, respectively, while shrubs and forests declined by 35.64%, largely from reed conversion to agriculture in the Mustafakemalpaşa River delta. Overall classification accuracy ranged from 88.2% to 91% with a kappa coefficient of 0.81 to 0.82. These transformations highlight the increase in reed and swamp areas and the decrease in lake area, emphasizing the need for effective conservation and management practices.","PeriodicalId":509558,"journal":{"name":"European Journal of Forest Engineering","volume":" 14","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141668494","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

The effect of pre- and post-processing techniques on tree detection in young forest stands from images of snow cover using YOLO neural networks 使用 YOLO 神经网络从积雪覆盖图像中检测幼林林分中树木的前后处理技术的影响

European Journal of Forest Engineering Pub Date : 2024-06-06 DOI: 10.33904/ejfe.1462335

Aleksey Portnov, Andrey Shubin, Gulfina Frolova

{"title":"The effect of pre- and post-processing techniques on tree detection in young forest stands from images of snow cover using YOLO neural networks","authors":"Aleksey Portnov, Andrey Shubin, Gulfina Frolova","doi":"10.33904/ejfe.1462335","DOIUrl":"https://doi.org/10.33904/ejfe.1462335","url":null,"abstract":"A neural network model for individual tree detection was developed based on the YOLOv4 architecture, which underwent additional preprocessing and postprocessing steps. The preprocessing step involved expanding the dataset by randomly cutting fragments from images, calculating anchor frame sizes using the K-means clustering algorithm, and discarding anchor frames that were too small a priori. The existing post-processing block of the YOLO architecture was modified by giving more weight to false positives in the error function and using the non-maximum suppression algorithm. Baseline neural networks from the YOLOv4 and YOLOv5 architectures, each in two versions (pre-trained and not pre-trained on the MS COCO dataset), were used for comparison without any additional modifications. In the overgrown experimental field, multi-season aerial copter surveys and ground counts were conducted on several sample plots to gather data. Comparison of multi-season aerial photographs with ground-count data showed that the best images in terms of the percentage of visually identifiable trees were those taken during the snowy season and when there was no foliage. Using these images and some additional images, we manually created a dataset on which we trained and tested neural network models. The model we developed showed significantly better results (2 to 10 times better) on the mAP 0.5 metric compared to the alternatives we considered.","PeriodicalId":509558,"journal":{"name":"European Journal of Forest Engineering","volume":"6 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141380402","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Spatio-temporal analysis of carbon storage in urban areas after wildfires: the case of Marmaris fire 野火后城市地区碳储存的时空分析：马尔马里斯火灾案例

European Journal of Forest Engineering Pub Date : 2024-05-09 DOI: 10.33904/ejfe.1467509

Zennure Uçar

{"title":"Spatio-temporal analysis of carbon storage in urban areas after wildfires: the case of Marmaris fire","authors":"Zennure Uçar","doi":"10.33904/ejfe.1467509","DOIUrl":"https://doi.org/10.33904/ejfe.1467509","url":null,"abstract":"Cities and urban areas are the primary source of CO2 worldwide by using around 70% of global energy and emitting more than 71% of CO2. Urban vegetation, referring to all trees and shrubs, are important components of urban environments. They provide many ecosystem services to human beings both directly and indirectly. Especially, they play a key role in reducing carbon emissions in urban areas by storing and capturing the carbon. However, recently, an increase in the number and intensity of wildfires that occur within urban areas has been observed. It resulted in losing stored carbon, releasing GHG to the atmosphere. Hence, quantifying above-ground carbon stored by urban trees and its distribution is essential to better understanding urban vegetation's role in urban environments and to better urban vegetation management. This study aimed to examine how forest fire affects the amount and distribution of stored carbon in the urban environment for the case of the Marmaris fire in the Summer of 2021 in Türkiye. For the study, urban forest carbon storage maps were generated before and after the Marmaris forest fire using remote sensing-based methodology with freely available remote sensing (RS) data. The results indicated that using the existing methodology could be rapid and cost-effective in monitoring the carbon storage change after an anthropogenic and natural disaster. However, for precise and reliable estimation of total carbon storage and the change in total urban carbon storage, the methodology needs to be developed at a local scale using field sampling along with RS data.","PeriodicalId":509558,"journal":{"name":"European Journal of Forest Engineering","volume":" 30","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140995869","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0