Classification of Herbaceous Vegetation Using Airborne Hyperspectral Imagery

L

Specific features of NDVI, NDWI and MNDWI as reflected in land cover categories

L

Comparative analysis of Landsat TM, ETM+, OLI and EO-1 ALI satellite images at the Tisza-tó area, Hungary

L

Fátlan vegetációtípusok azonosítása légi hiperspektrális távérzékelési módszerrel

Munkánk során egy szikes táj vegetációtípusainak osztályozását végeztük el, légi hiperspektrális adatok felhasználásával. A munka célja a hiperspektrális adatok alkalmazhatóságának vizsgálata volt e komplex társulásoknál, eltérő képosztályozási módszerek alkalmazásával. Vizsgálatunkban hagyományos osztályozó eljárások (Maximum Likelihood Classifier – MLC, Random Forest – RF és Support Vector Machine – SVM) eredményességét teszteltük 10 és 30 pixeles tanítóterületek felhasználásával. A mozaikolt hiperspektrális felvételen a zajszűrés és az információnyerés céljából MNF transzformációt alkalmaztunk. A légi hiperspektrális felvétel AISA EAGLE II szenzorral készült 1m terepi felbontásban. Társulástani besorolás és felszínborítás alapján összesen 20 vegetációosztályt alakítottunk ki. Az osztályokat további négy főbb élőhelykategóriába soroltuk: sztyeppék, nyílt szikes gyepek, szikes rétek, szikes és nem szikes mocsarak. Az SVM és az RF osztályozó eljárások, a pixelek számától függetlenül, majdnem minden vegetációosztálynál megbízhatóan működtek, nagy osztályozási pontosságot adtak. Az MLC bár nagy mintaszámnál nagy pontosságú osztályozást eredményezett, kis mintaszámnál számos osztály esetében alacsony megbízhatósággal működött. Az eredmények alapján elmondható, hogy a komplex fátlan táji környezetben a vegetáció osztályozásra az SVM megfelelő osztályozó lehet, mivel nagyobb pontosságot nyújt, mint az RF és az MLC. Az SVM bizonyult a legkevésbé érzékenynek a tanító területek mintáinak méretére, így alkalmas lehet azokban az esetekben, amikor néhány osztálynál az elérhető pixelek száma korlátozottan áll rendelkezésre

ESA SENTINEL 2 IMAGERY AND GBGEOAPP: INTEGRATED TOOLS FOR THE DEOSAI NATIONAL PARK MANAGEMENT PLAN

Deosai plateau, in the Gilgit-Baltistan Province of Pakistan, for its average elevation of 4,114 meters, is the second highest plateau in the world after Changtang Tibetan Plateau. Two biogeographically important mountain ranges merge in Deosai: the Himalayan and Karakorum–Pamir highlands. The Deosai National Park, with its first recognition in 1993, encompasses an area of about 1620 km2, with the altitude ranging from 3500 to 5200 meters a.s.l. It is known and visited by tourists for the presence of brown bear, but a large number of species of fauna and flora leave, and can be seen during the summer season. This high-altitude ecosystem is particularly fragile and can be considered a sentinel for the effects of climate changes. Due to its geographic position and high altitude, the area of Deosai has never been studied in all its ecosystem components, producing high resolution maps. The first land cover map of Deosai with 10 meters of resolution is discussed in this study. This map has been obtained from Sentinel-2 imagery and improved through the new tool developed in this study: the GBGEOApp. This application for mobile has been done with three main ambitions: the validation of the new land cover map, its improvement with land use information, and the collection of new data in the field. On the basis of the results, the use of the GBGEOApp, as a tool for validation and increasing of environmental data collection, seems to be completely applicable involving the local technicians in a process of data sharing

SPECIFIC FEATURES OF NDVI, NDWI AND MNDWI AS REFLECTED IN LAND COVER CATEGORIES

The remote sensing techniques provide a great possibility to analyze the environmental processes in local or global scale. Landsat images with their 30 m resolution are suitable among others for land cover mapping and change monitoring. In this study three spectral indices (NDVI, NDWI, MNDWI) were investigated from the aspect of land cover types: water body (W); plough land (PL); forest (F); vineyard (V); grassland (GL) and built-up areas (BU) using Landsat-7 ETM+ data. The range, the dissimilarities and the correlation of spectral indices were examined. In BU – GL – F categories similar NDVI values were calculated, but the other land cover types differed significantly. The water related indices (NDWI, MNDWI) were more effective (especially the MNDWI) to enhance water features, but the values of other categories ranged from narrower interval. Weak correlation were found among the indices due to the differences caused by the water land cover class. Statistically, most land cover types differed from each other, but in several cases similarities can be found when delineating vegetation with various water content. MNDWI was found as the most effective in highlighting water bodies

Comparative analysis of Landsat TM, ETM+, OLI and EO-1 ALI satellite images at the Tisza-tó area, Hungary

Satellite images are important information sources of land cover analysis or land cover change monitoring. We used the sensors of four different spacecraft: TM, ETM+, OLI and ALI. We classified the study area using the Maximum Likelihood algorithm and used segmentation techniques for training area selection. We validated the results of all sensors to reveal which one produced the most accurate data. According to our study Landsat 8’s OLI performed the best (96.9%) followed by TM on Landsat 5 (96.2%) and ALI on EO-1 (94.8%) while Landsat 7’s ETM+ had the worst accuracy (86.3%)

Oil palm age classification in Ladang Tereh Selatan,Johor,Malaysia using remote sensing technique

Determining and classifying the age of oil palm is important in predicting oil palm yield, planning replanting activities and oil palm age is also an important criteria in estimating the carbon sequestration and storage potential of oil palm trees.. Nevertheless, determining its age with conventional method is costly, time consuming and tedious process. Alternatively remote sensing methods are used with only a moderate success. Previous studies using remote sensing have shown limitations to classify more than five age classes of oil palm trees. This studyused SPOT-5 multispectral image to classify 12 different age classes of oil palm trees at Ladang Tereh Selatan, Kluang, Malaysia. . Three different classifiers namely Support Vector Machine (SVM), Artificial Neural Network (ANN), and Maximum Likelihood Classifier (MLC) were employed and it was found that all these techniques that rely on spectral information from the image could only classify the ages with low overall accuracy of 32.46%, 29.92% and 37.41% respectively. In order to improve the classification, Grey-Level Co-occurrence Matrix (GLCM) texture measurement was added into the MLC classifier. Various combinations of textures and window sizes were tested in order to find the optimum texture combination. The overall accuracy of the classification was improved to 89.6% with the incorporation of eight texture combinations with 39 × 39 window. This study also found that, window size is more important than the type of texture in determining the stand age of the palm trees, where all the window sizes were statistically significant at 95% confidence level. The method used in this study should be extended to other plantations to test the applicability of the technique in classifying more age classes

Machine Learning for Gully Feature Extraction Based on a Pan-Sharpened Multispectral Image: Multiclass vs. Binary Approach

K