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Researching plant density classes of Tokat province by LANDSAT-7 ETM+ satellite images and Geographic Information Systems

Year 2014, Volume: 2014 Issue: 1, 46 - 53, 01.06.2014
https://doi.org/10.13002/jafag686

Abstract

Indices developed for vegetation hold important place in remote sensing technology and they are commonly used. One of them is Normalized Difference Vegetation Index (NDVI) which is developed for vegetation and accepted in worldwide. In this study, spatial distribution of plant density of Tokat province in 2000 was mapped by using LANDSAT-7 ETM+ images and Normalized Difference Vegetation Index (NDVI). Obtained NDVI map was classified as very weak, weak, moderate and intensive plant density classes for the first time by utilizing Braun Blanquet cover abundance classes (BB) and geographic information systems (GIS). The accuracy assessment of the created classes was performed by utilizing ground truth data collected from 103 points throughout the study area. The overall accuracy of NDVI (plant density) classes was found as 86.45 %. The results of the study indicated that the majority of the Tokat province takes place in the moderate class (47.56 %). This was followed by intense (40.36 %), low (7.57 %) and very weak (4.14 %) plant density classes. The remaining areas were evaluated as water surface (0.37 %). The results concretely demonstrated the high potential of Tokat province in terms of plant biological diversity and agriculture. BB assessments were also found to be usable to classify the NDVI values in a reliable way. Thus, a robust background reference was also formed to monitor vegetation cover change in the future.

References

  • Akman Y (1999). İklim ve Biyoiklim: Biyoiklim Metodları ve Türkiye İklimleri. Palme Yayıncılık, Ankara.
  • Akman Y, Ketenoğlu O, Kurt L, Güney K, Tuğ GM (2004). Bitki Ekolojisi. Palme Yayıncılık, Ankara.
  • Bonneau LR, Shields KS, Civco DL (1999). Using satellite images to classify and analyze the health of hemlock forests infested by the hemlock woolly adelgid. Biological Invasions 1: 255–267.
  • Braun-Blanquet J (1964). Plant Sociology: The Study of Plant Communities, 3rd Edn. Springer-Verlag, Berlin-Wien-New York [in German].
  • Davi H, Soudani K, Deckx T, Dufrene E, Le Dantec V, François C (2006). Estimation of forest leaf area index from SPOT imagery using NDVI distribution over forest stands. International Journal of Remote Sensing, 27: 885–902.
  • Dogan HM, Dogan M (2006). A new approach to diversity indices – modeling and mapping plant biodiversity of Nallihan (A3-Ankara) forest ecosystem in frame Biodiversity and Conservation, 15: 855–878.
  • Dogan HM (2008). Applications of remote sensing and geographic information systems to assess ferrous minerals and iron oxide of Tokat province in Turkey. International Journal of Remote Sensing, 29(1): 221-233.
  • Dogan HM, Celep F, Karaer F (2009). Evaluation of NDVI in plant community composition mapping: a case study for Tersakan Valley of Amasya county in Turkey. International Journal of Remote Sensing, 30(14): 3769 – 3798.
  • Dogan HM (2009). Mineral composite assessment of Kelkit River Basin by means of remote sensing. Journal of Earth System Science, 118(6): 701-710.
  • Dogan HM, Kılıç OM and Yılmaz DS (2013) Preparing and analyzing the thematic map layers of great soil groups, erosion classes and land capability classes of Tokat Province by GIS. Journal of Agricultural Faculty of Gaziosmanpasa University, 30(2): 18- 29.
  • Dogan HM and Kılıç OM (2013). Modeling and mapping some soil surface properties of Central Kelkit Basin in Turkey by using LANDSAT-7 ETM+ images. International Journal of Remote Sensing, 34(15): 5623-5640.
  • Duran C (2007). Uzaktan algılama teknikleri ile bitki örtüsü analizi. DOA Dergisi, 13: 45-67.
  • Edwards MC, Wellens J, Al-Eisawi D (1999). Monitoring the grazing resources of the Badia region, Jordan, using remote sensing. Applied Geography, 19: 385–398.
  • Emekli NY, Topakçı M (2009). Precision agriculture in technologies
  • Agricultural Faculty of Gaziosmanpasa University, 26(2), 9-17. irrigation. Journal of
  • ERDAS (2003). Erdas Field Guide, 7th Edn. Leica Geosystems, GIS and Mapping LLC: Atlanta, Georgia.
  • ESRI (2004). ArcGIS 9, Geoprocessing in ArcGIS. Environmental Systems Research Institute Press: Redlands California.
  • Franklin KA, Lyons K, Nagler PL, Derrick L, Glenn EP, Molinafreaner F, Markow T, Huete AR (2006). Buffelgrass (Pennisetum ciliare) land conversion and productivity in the plains of Sonora, Mexico. Biological Conservation, 127: 62–71.
  • Kumar M, Monteith JL (1981). Remote sensing of crop growth. In H Smith (Ed.), Plants and the Daylight Spectrum, Academic Press London, pp. 134–144.
  • Schino G, Borfecchia F, De Cecco L, Dibari C, Iannetta M, Martini S, Pedrotti F (2003). Satellite estimate of grass biomass in a mountainous range in central Italy. Agroforestry Systems, 59: 157–162.
  • Susam T and Oğuz İ (2006). Determination of slope and aspect interpretation on agricultural scopes. Journal of Tokat city and Faculty of Gaziosmanpasa
  • Tucker CJ, Vanpraet CL, Sharman MJ, Van Ittersum G (1985). Satellite herbaceous biomass production in the Senegalese Sahel: Environment, 17: 233–249. sensing of total 1980–1984. Remote Sensing of USGS (2006). LANDSAT Project. Available: http://landsat7.usgs.gov/index.php
  • Wessels KJ, Prince SD, Zambatis N, Macfadyen S, Frost PE, Van Zyl D (2006). Relationship between herbaceous biomass and 1-km2 Advanced Very High Resolution Radiometer (AVHRR) NDVI in Kruger National Park, South Africa. International Journal of Remote Sensing, 27: 951–973.
  • Wylie BK, Harrington JA, Prince SD, Denda I (1991). Satellite and groundbased pasture production assessment in Niger 1986–1988. International Journal of Remote Sensing, 12: 1281–1300.

Tokat ili bitki yoğunluk sınıflarının LANDSAT-7 ETM+ uydu görüntüleri ve Coğrafi Bilgi Sistemleri ile araştırılması

Year 2014, Volume: 2014 Issue: 1, 46 - 53, 01.06.2014
https://doi.org/10.13002/jafag686

Abstract

Uzaktan algılama teknolojilerinde bitki örtüsü için geliştirilen indeksler önemli bir yer tutmakta ve sıkça kullanılmaktadır. Bunlardan biri de vejetasyon için geliştirilen ve dünyada kabul gören Normalize Edilmiş Fark Bitki Örtüsü İndeksi`dir (NDVI). Bu çalışmada Tokat ili bitki yoğunluğunun 2000 yılındaki dağılımı LANDSAT-7 ETM+ görüntüleri ve NDVI kullanılarak haritalanmıştır. Elde edilen NDVI haritası bitki sosyolojisinde kullanılan Braun Blanquet örtüş bolluğu sınıfları (BB) ve coğrafi bilgi sistemlerinden (CBS) yararlanılarak çok zayıf, zayıf, orta ve yoğun olarak ilk kez sınıflandırılmıştır. NDVI sınıflandırılmasının doğruluk analizi çalışma alanının genelinde 103 noktadan toplanan yersel veriler kullanılarak yapılmıştır. Doğruluk değerlendirmesi NDVI sınıflarına ait genel doğruluğun % 86.45 olduğunu göstermiştir. Bu sınıflandırmaya göre Tokat ilinin büyük bir kısmı orta (% 47.56) bitki yoğunluğu sınıfına girmiştir. Bunu sırasıyla yoğun (% 40.36), zayıf (% 7.57) ve çok zayıf (% 4.14) bitki yoğunluğu sınıfları izlemiştir. Geriye kalan alanlar su yüzeyi (% 0.37) olarak değerlendirilmiştir. Sonuçlar bitki biyolojik çeşitliliği ve tarımsal faaliyetler yönünden Tokat ilinin yüksek potansiyelini somut bir şekilde ortaya koymuştur. BB değerlendirmelerinin de NDVI değerlerinin sınıflandırılmasında güvenilir olarak kullanılabileceği bulunmuştur. Böylece bitki örtüsünün gelecekteki değişiminin izlenmesi için de sağlam bir referans oluşturulmuştur.

References

  • Akman Y (1999). İklim ve Biyoiklim: Biyoiklim Metodları ve Türkiye İklimleri. Palme Yayıncılık, Ankara.
  • Akman Y, Ketenoğlu O, Kurt L, Güney K, Tuğ GM (2004). Bitki Ekolojisi. Palme Yayıncılık, Ankara.
  • Bonneau LR, Shields KS, Civco DL (1999). Using satellite images to classify and analyze the health of hemlock forests infested by the hemlock woolly adelgid. Biological Invasions 1: 255–267.
  • Braun-Blanquet J (1964). Plant Sociology: The Study of Plant Communities, 3rd Edn. Springer-Verlag, Berlin-Wien-New York [in German].
  • Davi H, Soudani K, Deckx T, Dufrene E, Le Dantec V, François C (2006). Estimation of forest leaf area index from SPOT imagery using NDVI distribution over forest stands. International Journal of Remote Sensing, 27: 885–902.
  • Dogan HM, Dogan M (2006). A new approach to diversity indices – modeling and mapping plant biodiversity of Nallihan (A3-Ankara) forest ecosystem in frame Biodiversity and Conservation, 15: 855–878.
  • Dogan HM (2008). Applications of remote sensing and geographic information systems to assess ferrous minerals and iron oxide of Tokat province in Turkey. International Journal of Remote Sensing, 29(1): 221-233.
  • Dogan HM, Celep F, Karaer F (2009). Evaluation of NDVI in plant community composition mapping: a case study for Tersakan Valley of Amasya county in Turkey. International Journal of Remote Sensing, 30(14): 3769 – 3798.
  • Dogan HM (2009). Mineral composite assessment of Kelkit River Basin by means of remote sensing. Journal of Earth System Science, 118(6): 701-710.
  • Dogan HM, Kılıç OM and Yılmaz DS (2013) Preparing and analyzing the thematic map layers of great soil groups, erosion classes and land capability classes of Tokat Province by GIS. Journal of Agricultural Faculty of Gaziosmanpasa University, 30(2): 18- 29.
  • Dogan HM and Kılıç OM (2013). Modeling and mapping some soil surface properties of Central Kelkit Basin in Turkey by using LANDSAT-7 ETM+ images. International Journal of Remote Sensing, 34(15): 5623-5640.
  • Duran C (2007). Uzaktan algılama teknikleri ile bitki örtüsü analizi. DOA Dergisi, 13: 45-67.
  • Edwards MC, Wellens J, Al-Eisawi D (1999). Monitoring the grazing resources of the Badia region, Jordan, using remote sensing. Applied Geography, 19: 385–398.
  • Emekli NY, Topakçı M (2009). Precision agriculture in technologies
  • Agricultural Faculty of Gaziosmanpasa University, 26(2), 9-17. irrigation. Journal of
  • ERDAS (2003). Erdas Field Guide, 7th Edn. Leica Geosystems, GIS and Mapping LLC: Atlanta, Georgia.
  • ESRI (2004). ArcGIS 9, Geoprocessing in ArcGIS. Environmental Systems Research Institute Press: Redlands California.
  • Franklin KA, Lyons K, Nagler PL, Derrick L, Glenn EP, Molinafreaner F, Markow T, Huete AR (2006). Buffelgrass (Pennisetum ciliare) land conversion and productivity in the plains of Sonora, Mexico. Biological Conservation, 127: 62–71.
  • Kumar M, Monteith JL (1981). Remote sensing of crop growth. In H Smith (Ed.), Plants and the Daylight Spectrum, Academic Press London, pp. 134–144.
  • Schino G, Borfecchia F, De Cecco L, Dibari C, Iannetta M, Martini S, Pedrotti F (2003). Satellite estimate of grass biomass in a mountainous range in central Italy. Agroforestry Systems, 59: 157–162.
  • Susam T and Oğuz İ (2006). Determination of slope and aspect interpretation on agricultural scopes. Journal of Tokat city and Faculty of Gaziosmanpasa
  • Tucker CJ, Vanpraet CL, Sharman MJ, Van Ittersum G (1985). Satellite herbaceous biomass production in the Senegalese Sahel: Environment, 17: 233–249. sensing of total 1980–1984. Remote Sensing of USGS (2006). LANDSAT Project. Available: http://landsat7.usgs.gov/index.php
  • Wessels KJ, Prince SD, Zambatis N, Macfadyen S, Frost PE, Van Zyl D (2006). Relationship between herbaceous biomass and 1-km2 Advanced Very High Resolution Radiometer (AVHRR) NDVI in Kruger National Park, South Africa. International Journal of Remote Sensing, 27: 951–973.
  • Wylie BK, Harrington JA, Prince SD, Denda I (1991). Satellite and groundbased pasture production assessment in Niger 1986–1988. International Journal of Remote Sensing, 12: 1281–1300.
There are 24 citations in total.

Details

Primary Language Turkish
Journal Section Research Articles
Authors

Hakan Mete Doğan This is me

Orhan Mete Kılıç This is me

Doğaç Sencer Yılmaz This is me

Publication Date June 1, 2014
Published in Issue Year 2014 Volume: 2014 Issue: 1

Cite

APA Doğan, H. M., Kılıç, O. M., & Yılmaz, D. S. (2014). Tokat ili bitki yoğunluk sınıflarının LANDSAT-7 ETM+ uydu görüntüleri ve Coğrafi Bilgi Sistemleri ile araştırılması. Journal of Agricultural Faculty of Gaziosmanpaşa University (JAFAG), 2014(1), 46-53. https://doi.org/10.13002/jafag686