Suitable map analysis for wind energy projects using remote sensing and GIS: a case study in Turkey

UZAR DİNLEMEK A. M., Şener Z.

Environmental Monitoring and Assessment, cilt.191, sa.459, ss.1-17, 2019 (SCI-Expanded) identifier identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 191 Sayı: 459
  • Basım Tarihi: 2019
  • Doi Numarası: 10.1007/s10661-019-7551-8
  • Dergi Adı: Environmental Monitoring and Assessment
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.1-17
  • Anahtar Kelimeler: LiDAR, Rule-based classification, FAHP, Wind energy, AUTOMATIC BUILDING EXTRACTION, LIDAR DATA FUSION, POINT, CLASSIFICATION, IMAGERY
  • Yıldız Teknik Üniversitesi Adresli: Evet

Özet

The aim of the study is to create a suitable

map for wind energy projects in a rural area. The primary

goal here is to show a methodology using automatic

object extraction of the target classes of buildings,

vegetation, and ground. The secondary goal is to identify

the potential effects for wind turbine sites based on

four criteria: Wind speed, Slope, Building, and Vegetation

using the fuzzy analytical hierarchy process

(FAHP). This paper discusses two important situations

for wind energy projects. The first strategy is to just

determine the best suitable site locations of wind turbines,

while the second strategy determines the locations

of wind turbines with minimal negative effects on the

rural area. The proposed approach is tested using the

data obtained from a multi-sensor system in Evrencik,

Turkey. In preliminary phases of renewable energy projects,

successful results are dependent on evaluating the

potential site’s suitability with criteria such as social,

environmental, physical, and economic conditions. Furthermore,

an accuracy analysis is performed on the

automatically extracted target classes for the study area,

yielding a value of 89% in the remote sensing section of

the study. Moreover, for the GIS section of the study,

suitable and unsuitable areas are identified, and the

suitability levels of the remaining areas are determined

for the two strategies. According to the results, 11% of

the areas are found to have high, moderate, and low

suitability levels, and 89% are unsuitable for the first

strategy, whereas these rates are, respectively, 2% and

98% for the second strategy.