Identifying Areas Prone to Windthrow Damage and Generating Susceptibility Maps Utilizing a Novel Vegetation Index Extracted from Sentinel-2A Imagery
Küçük Resim Yok
Tarih
2023
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Springer
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
Forests can be significantly affected by windthrow damage, which negatively impacts the process of forest utilization. Therefore, it is important to identify areas with potential windthrow damage and include them in the planning processes. Based on this idea, a windthrow susceptibility map was created using windthrow data obtained from the extraordinary yield reports of Turkiye-Zonguldak Forest Regional Directorate (FRD) for the years 2017-2022. Firstly, Sentinel-2A satellite images from one week before (pre-windthrow) and one week after (post-windthrow) the occurrence dates of each of the 325 windthrow events were acquired. Subsequently, a cloud mask was applied using the Python programming language in Google Earth Engine (GEE), and the Normalized Difference Fraction Index (NDFI) was calculated. Each identified damage area was saved as a polygon vector data format, and within each polygon, a point was assigned for every 100 m2, resulting in a total of 929 windthrow areas. Data related to wind speed, slope, precipitation, elevation, and distance-to-road variables were obtained for each point. Then, the component values of the axis with the highest variance explanation ratio were modeled using the Random Forest (RF) method. Ultimately, the predictive values of the model were extrapolated to the study area to generate the susceptibility windthrow map. The predictive map revealed that the southern parts of the study area had relatively higher windthrow potential. In this study, for the first time, the detection of windthrow areas was performed using NDFI, and the coefficients of environmental parameters were determined to generate a susceptibility mapping.
Açıklama
Anahtar Kelimeler
Remote Sensing, Sentinel-2A, Principal component analysis, Random forest, Windthrow, Boreal Forests, Soil, Productivity, Stands, Temperate, Dynamics, Trees, Storm, Pine
Kaynak
Journal of the Indian Society of Remote Sensing
WoS Q Değeri
Q3
Scopus Q Değeri
Q2
Cilt
51
Sayı
12
