Assessment of classification methods and elaboration of the potential of coarse resolution satellite ımagery for forest cover mapping at the continental region
Başlık çevirisi mevcut değil.
- Tez No: 873788
- Danışmanlar: PROF. DR. IOANNIS GITAS
- Tez Türü: Yüksek Lisans
- Konular: Ormancılık ve Orman Mühendisliği, Forestry and Forest Engineering
- Anahtar Kelimeler: Belirtilmemiş.
- Yıl: 2011
- Dil: İngilizce
- Üniversite: The International Centre for Advanced Mediterranean Agronomic Studies (CIHEAM)
- Enstitü: Yurtdışı Enstitü
- Ana Bilim Dalı: Belirtilmemiş.
- Bilim Dalı: Belirtilmemiş.
- Sayfa Sayısı: 88
Özet
Environmental spatio-temporal heterogeneity characterizes major parts of the Continental biogeographical region's vegetation, which extends across transition zones between humid and dry climates. Continental forest areas in particular are dynamic and changing through time and space with respect to composition and stand structure due to the Continental region's main characteristics. Satellites provide a general view of the whole Earth that is unavailable to any other forest measurement method. To measure forests globally, satellite imagery is a practical necessity, especially on a global and/or regional scale. Satellite remote sensing is a unique data source for wide regional monitoring as a result of its information content and its frequent coverage. This study evaluates the current and future technical capacity of Proba-V satellite imagery to measure and monitor global forests. MODIS, SPOT-Vegetation and simulated Proba-V imagery are going to be used for assessing the classification methods and elaboration of the potential of coarse resolution satellite imagery for forest cover mapping at the Continental region. Land-cover classifications were performed on each satellite data using-pixel based classification techniques, more specifically, the Maximum Likelihood (ML), Support Vector Machines (SVM) and Artificial Neural Networks classifiers. The stratified random sampling approach was used in order to derive a reference map from Landsat 5 (TM). All the results were evaluated using the reference map derived from Landsat 5 (TM) and ground truth data from Google Earth with high spatial resolution imagery. The aim of this work was to map forest/non-forest in Continental Europe by employing medium and low resolution satellite imagery and different image analysis techniques. The low spatial resolution of the Proba-V has a negative effect in the classification results. The results show that the Maximum Likelihood classification performed better in Landsat 5 (TM) and in the simulated Proba-V imagery. On the other hand Support Vector Machines outperformed higher overall accuracy on MODIS and SPOT- Vegetation imagery. However, the ANN classification performed invalid results for MODIS and SPOT-Vegetation imagery and gave the lowest overall accuracy for Proba- V imagery.
Özet (Çeviri)
Environmental spatio-temporal heterogeneity characterizes major parts of the Continental biogeographical region's vegetation, which extends across transition zones between humid and dry climates. Continental forest areas in particular are dynamic and changing through time and space with respect to composition and stand structure due to the Continental region's main characteristics. Satellites provide a general view of the whole Earth that is unavailable to any other forest measurement method. To measure forests globally, satellite imagery is a practical necessity, especially on a global and/or regional scale. Satellite remote sensing is a unique data source for wide regional monitoring as a result of its information content and its frequent coverage. This study evaluates the current and future technical capacity of Proba-V satellite imagery to measure and monitor global forests. MODIS, SPOT-Vegetation and simulated Proba-V imagery are going to be used for assessing the classification methods and elaboration of the potential of coarse resolution satellite imagery for forest cover mapping at the Continental region. Land-cover classifications were performed on each satellite data using-pixel based classification techniques, more specifically, the Maximum Likelihood (ML), Support Vector Machines (SVM) and Artificial Neural Networks classifiers. The stratified random sampling approach was used in order to derive a reference map from Landsat 5 (TM). All the results were evaluated using the reference map derived from Landsat 5 (TM) and ground truth data from Google Earth with high spatial resolution imagery. The aim of this work was to map forest/non-forest in Continental Europe by employing medium and low resolution satellite imagery and different image analysis techniques. The low spatial resolution of the Proba-V has a negative effect in the classification results. The results show that the Maximum Likelihood classification performed better in Landsat 5 (TM) and in the simulated Proba-V imagery. On the other hand Support Vector Machines outperformed higher overall accuracy on MODIS and SPOT- Vegetation imagery. However, the ANN classification performed invalid results for MODIS and SPOT-Vegetation imagery and gave the lowest overall accuracy for Proba- V imagery.
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