Selçuk Reis

Analyzing Land Use/Land Cover Changes Using Remote Sensing and GIS in Rize, North-East Turkey

Mapping land use/land cover (LULC) changes at regional scales is essential for a wide range of applications, including landslide, erosion, land planning, global warming etc. LULC alterations (based especially on human activities), negatively effect the patterns of climate, the patterns of natural hazard and socio-economic dynamics in global and local scale. In this study, LULC changes are investigated by using of Remote Sensing and Geographic Information Systems (GIS) in Rize, North-East Turkey. For this purpose, firstly supervised classification technique is applied to Landsat images acquired in 1976 and 2000. Image Classification of six reflective bands of two Landsat images is carried out by using maximum likelihood method with the aid of ground truth data obtained from aerial images dated 1973 and 2002. The second part focused on land use land cover changes by using change detection comparison (pixel by pixel). In third part of the study, the land cover changes are analyzed according to the topographic structure (slope and altitude) by using GIS functions. The results indicate that severe land cover changes have occurred in agricultural (36.2%) (especially in tea gardens), urban (117%), pasture (-72.8%) and forestry (-12.8%) areas has been experienced in the region between 1976 and 2000. It was seen that the LULC changes were mostly occurred in coastal areas and in areas having low slope values.

Effects of land-use changes on landslides in a landslide-prone area (Ardesen, Rize, NE Turkey).

Various natural hazards such as landslides, avalanches, floods and debris flows can result in enormous property damages and human casualties in Eastern Black Sea region of Turkey. Mountainous topographic character and high frequency of heavy rain are the main factors for landslide occurrence in Ardesen, Rize. For this reason, the main target of the present study is to evaluate the landslide hazards using a sequence of historical aerial photographs in Ardesen (Rize), Turkey, by Photogrammetry and Geographical Information System (GIS). Landslide locations in the study area were identified by interpretation of aerial photographs dated in 1973 and 2002, and by field surveys. In the study, the selected factors conditioning landslides are lithology, slope gradient, slope aspect, vegetation cover, land class, climate, rainfall and proximity to roads. These factors were considered as effective on the occurrence of landslides. The areas under landslide threat were analyzed and mapped considering the landslide conditioning factors. Some of the conditioning factors were investigated and estimated by employing visual interpretation of aerial photos and topographic data. The results showed that the slope, lithology, terrain roughness, proximity to roads, and the cover type played important roles on landslide occurrence. The results also showed that degree of landslides was affected by the number of houses constructed in the region. As a consequence, the method employed in the study provides important benefits for landslide hazard mitigation efforts, because a combination of both photogrammetric techniques and GIS is presented.

Performance Analysis of Maximum Likelihood and Artificial Neural Network Classifiers for Training Sets with Mixed Pixels

This study evaluates the performance of an artificial neural network, specifically a multilayer perceptron, and a maximum likelihood algorithm to classify multitemporal Landsat ETM+ remote sensor data. The study area in Turkey is a mountainous region that contains many small scattered fields, usually 5-10 pixels in size. The classifiers were employed to identify eight land cover/use features covering the bulk of the study area using the same training and test datasets in order to avoid any difference resulting from sampling variations. Results show that the neural network approach performed better in extracting land cover information from multispectral and multitemporal images with training data sets including a large amount of mixed and atypical pixels. The maximum likelihood classifier was found to be ineffective, particularly in classifying spectrally similar categories and classes having subclasses.

Land cover identification for finding hazelnut fields using WorldView-2 imagery

Remote sensing imagery is currently used as an efficient tool for agricultural management. Particularly, very high spatial resolution (less than 1m) enables extraction of permanent crops (including nut orchards) by visual interpretation or automated methods based on mainly textural features representing the regular plantation pattern. For accurate detection of orchards (hazelnuts in particular), this study proposes a rule-based classification utilizing multi-scale Gabor features and spectral values. Thanks to its very high spatial (0.5m) and spectral (8-bands) resolution, WorldView-2 imagery is primarily used. The classification accuracies, obtained with features extracted from WorldView-2 and Quickbird imagery, are compared for a study area in Turkey (major hazelnut producer in the world). In addition, supplementary value of the new 4 bands (coastal, yellow, red edge, and NIR2) in WorldView-2 imagery is discussed.

Applicability of R statistics in analyzing landslides spatial patterns in Northern Turkey

Statistical analysis of rainfall-triggered landslides inventory patterns is a key for landslide hazard and risk prediction analysis of susceptible areas. The main objective of the study is to test if the landslides locations are spatially auto correlated, that could either be clustered (spatial attraction), dispersed or randomly distributed (spatially independent). Two categories of spatial distance functions were applied, first using, first-order distance analysis using Quadrat Counts function and kernel density analysis. The second category used second order distance analysis includes Diggles empty space F-function and nearest neighbor distance G-function, and also, more sophisticated Ripleys K-function, which evaluates the distribution of all neighbor distances within the space taking into consideration the edge correction effect. Based on the generated curves by the G, F and K functions, we observed that landslides locations clearly tend to be clustered in certain areas rather than randomly distributed. Eventually, Morans I autocorrelation function used to find where the highest amount of landslides are clustered using four conditioning factors (Elevation, Slope, Land-cover, and Geology). This study tests the landslides distribution pattern in landslide prone area of Trabzon city, northern turkey. The current study aims to facilitate the integration of spatial data and the coding in R environment through using the R extensive research tools and libraries.

Investigation of Availability of Remote Sensed Data in Cadastral Works

Cadastre, which defines legal status and rights specifying the boundaries of the immovable property on the land and map, is very important in relation to property (Kadastro 1987). In countries like Turkey that require high precision, in cadastral survey data is used in the cadastral work to ensure that precision plays an important role. Less costly and more efficient studies should be used to improve suitability to the original on cadastral maps (NAP 1980). Mainly terrestrial methods in cadastral mapping studies, photogrammetric and remote sensing methods are also used. The uses of these methods appear in differences like necessary equipment, used techniques, accuracy requirements, staff and cost. Surveying of parcel boundaries and other details in cadastral works are performed using terrestrial measurement methods generally called as traditional method.