Geospatial Technology for Land Cover Analysis
| Main Author: | Lemenkova Polina |
|---|---|
| Format: | Article Journal |
| Terbitan: |
, 2013
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| Subjects: | |
| Online Access: |
https://zenodo.org/record/2309997 |
Daftar Isi:
- The Izmir region in Turkey is extremely valuable for the environment while being used immensely for tourism, agriculture and recreation. The geographic area of western Turkey is known for its unique physical and environmental settings. Due to its location in the Mediterranean climate, the region has certain typical features and is influenced by the local and regional climatic setting. The geography of the region combines diverse landforms, various geomorphological features, natural landscapes, species and vegetation richness: mountainous and hilly landscapes, caves, islands in the coastal area and lakes. The geology of the area of Western Anatolia is characterized by the neotectonic active faulting and high seismicity, which leads to the geomorphic instabilities and landslide formation in the research area. The vegetation in the study area is dominated by typical Mediterranean flora. The data used in the article consists of the following types: Landsat TM Image from the EarthSat producer, WRS-2, Path 180, Row 033, acquired on 1987.06.05, distributed by GLCF in GeoTIFF format. Landsat ETM+ image from the USGS producer, WRS-2, Path 180, Row 033 acquired on 2000.06.16, distributed by GLCF in GeoTIFF format. Spatial analysis Development of spectral signatures for land cover classes The classification of Landsat TM imagery for the same study area taken at different years allowed the recording of gradual land degradation. The last one is mainly caused by intensive construction of the second house summer cottages, and tourist activities. Recent changes in land use / land cover types in selected regions of Turkey have been proved by Evrendilek et al., (2011) who performed historical land use change detection using measurements of carbon that indirectly indicate the extent of peatlands within the landscapes. The use of the Landsat TM images was applied to assess changes in the land cover types over time. The thematic mapping of the land cover types in the selected area has been done using methods of supervised classification. The legend representing land cover classes include a variety of land types that exist in the Izmir surroundings. After determining the land cover characteristics, separating images into diverse homogeneous areas, a compilation of the following land cover type parameters was done: Urban areas: residential and built-up regions, roads; 2) Croplands-1 (wheat); 3) Croplands-2 (barley and other cereals); 4) Agricultural lands (e.g. cotton); 5) Pastures; 6) Grassland; 7) Shrubland; 8) Broadleaf forest; 9) Evergreen needleleaf forest, pine (Pinus brutia, P.nigra, P.pinea, P.silvestris); 10) Evergreen coniferous forests, firs (Abies); 11) Mixed forests; 12) Wetlands; 13) Sparsely vegetated areas; 14) Water bodies (natural and artificial lakes, rivers); 15) Coppice Training sites were digitised as areas of known land cover type identity. The creation of training polygons was performed using manual digitising available in 'Drawing' function of Erdas Imagine, by specifying the corner points of each polygon. The attributive characteristics on the areas were entered using appropriate columns with 'land cover types'. In such a way, specific training areas were identified for each, from the 15 land use classes. The land cover classes were identified as sets of pixels that best represent each land type and region according to their spectral characteristics and topological information: location, neighborhood, type and size of the polygons, etc. Supervised classification: Minimal Distance and Maximal Likelihood approaches The traditional methods of classification can roughly be divided into two approaches: unsupervised and supervised. The current study was done using supervised methods, since it better corresponds to the target task. The unsupervised approach categorises pixels into spectral groups that may be mixed with other lands cover types, or do not clearly specify as to which class group do they belong to. Supervised classification enables much more user control on the process. During supervised classification, the pixel classification process is carefully supervised by the user, via creation of training polygons and then assigning of pixels into these groups by the specific algorithm approach. Training sites have been created for the respective land cover types that are typical for the study area in Izmir surroundings (Fig.6). The representative sample sites of known 12 cover types, were recognized on the image and manually digitised. There areas are used as a digital interpretation key that indicated the 'ideal' spectral signatures for the respective land cover classes, according to their individual characteristics. Subsequently, the whole image was classified using Erdas Imagine. It was done using automatic comparison of each pixel within the image to each 'ideal' land category in the training sites, and then assigning all pixels to the classes to which they best corresponded. The difference between the MD and ML methods consists in the mathematical approaches of this comparison and the method of the class categorisation. The logical idea of the MD classification approach is in calculation of the distance in Euclidean coordinate systems from the values of every pixel to the value of mean vector. Mathematically, the calculation is based on the Pythagorean Theorem. The ML classifier has more statistical than geometrical character. It estimates the greatest probability of each pixel to approach the model group, which is represented by the normally distributed Gaussian Curve, i.e. core training areas.
- Geospatial Technology for Land Cover Analysis. Middle East and Africa (MEA) Geospatial Digest (Nov. 2013). e-magazine. doi: 10.6084/m9.figshare.7439228. url: https://www. geospatialworld.net/article/geospatial-technology-for-land-cover-analysis/.