Evaluation of the NDVI in plant community composition mapping: a case study of Tersakan Valley, Amasya County, Turkey

DOĞAN, Hakan Mete
Celep, Ferhat
Karaer, Fergan
Mapping the composition of plant community types requires reliable spatial data obtained from field surveys and satellite-derived indices. The normalized difference vegetation index (NDVI) is the simplest and most frequently used index in plant applications. If relationships between the NDVI and plant cover abundance are determined, this information can be used in the mapping process. In this study, we investigated these possible connections for mapping the plant community composition of Tersakan Valley in Turkey. We collected georeferenced cover-abundance (Braun-Blanquet, BB) data from 1077 quadrats in the field, and derived an NDVI raster map from a Landsat Enhanced Thematic Mapper Plus (ETM+) image. Then we classified this NDVI map by using various methods and class numbers. Using bivariate correlation analysis, we explored the relationships between 26 classified NDVI maps and BB, and chose the classified NDVI map with the strongest correlation. We further examined this relationship by using scatter plots, histograms and paired samples t-tests. According to the results, the NDVI (equal-interval 15) classes between 4 and 8 corresponded with the BB classes between 1 and 5, respectively. Using this relationship, the spatial distribution of 43 different plant community compositions were determined in geographic information systems (GIS). The results indicate that the NDVI has the potential to map plant community composition reliably.


Integration of environmental variables with satellite images in regional scale vegetation classification
Domaç, Ayşegül; Süzen, Mehmet Lütfi; Bilgin, Cemal Can (Informa UK Limited, 2006-04-01)
The difficulty of collecting information at conventional field studies and relatively coarse spatial and spectral resolution of Landsat images forced the use of environmental variables as ancillary data in vegetation mapping. The aim of this study is to increase the accuracy of species level vegetation classification incorporating environmental variables in the Amanos Mountains region of southern central Turkey. In the first part of the study, ordinary vegetation classification is attained by using a maximu...
Evaluation of cross-track illumination in EO-1 Hyperion imagery for lithological mapping
San, B. Taner; Süzen, Mehmet Lütfi (Informa UK Limited, 2011-01-01)
Hyperspectral remote sensing data is a powerful tool for discriminating lithological units and for the preparation of mineral maps for alteration studies. The spaceborne hyperspectral Hyperion sensor, despite its narrow swath width (similar to 7.5 km), possesses great potential with its 196 channels within the wavelength range 426.82-2395.50 nm. Although it has many advantages such as low cost and on-demand coverage, much uncertainty exists in the utility of its applications. For example, poor signal-to-noi...
Evaluation of Remotely-Sensed and Model-Based Soil Moisture Products According to Different Soil Type, Vegetation Cover and Climate Regime Using Station-Based Observations over Turkey
Bulut, Burak; Yılmaz, Mustafa Tuğrul; Afshar, Mahdı Hesamı ; Sorman, A. Unal; Yücel, İsmail; Cosh, Michael H.; Simsek, Osman (MDPI AG, 2019-08-01)
This study evaluates the performance of widely-used remotely sensed- and model-based soil moisture products, including: The Advanced Scatterometer (ASCAT), the Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E), the European Space Agency Climate Change Initiative (ESA-CCI), the Antecedent Precipitation Index (API), and the Global Land Data Assimilation System (GLDAS-NOAH). Evaluations are performed between 2008 and 2011 against the calibrated station-based soil moisture observations coll...
Hierarchical classification of Sentinel 2-a images for land use and land cover mapping and its use for the CORINE system
Demirkan, Doga C.; Koz, Alper; Duzguna, H. Sebnem (SPIE-Intl Soc Optical Eng, 2020-06-01)
The aim of this study is to investigate the potential of the Sentinel-2 satellite for land use and land cover (LULC) mapping. The commonly known supervised classification algorithms, support vector machines (SVMs), random forest (RF), and maximum likelihood (ML) classification are adopted for investigation along with a proposed hierarchical classification model based on a coordination of information on the environment land cover system. The main classes for land cover and mapping in the proposed hierarchica...
Evaluating a mesoscale atmosphere model and a satellite-based algorithm in estimating extreme rainfall events in northwestern Turkey
Yücel, İsmail (Copernicus GmbH, 2014-01-01)
Quantitative precipitation estimates are obtained with more uncertainty under the influence of changing climate variability and complex topography from numerical weather prediction (NWP) models. On the other hand, hydrologic model simulations depend heavily on the availability of reliable precipitation estimates. Difficulties in estimating precipitation impose an important limitation on the possibility and reliability of hydrologic forecasting and early warning systems. This study examines the performance o...
Citation Formats
H. M. DOĞAN, F. Celep, and F. Karaer, “Evaluation of the NDVI in plant community composition mapping: a case study of Tersakan Valley, Amasya County, Turkey,” INTERNATIONAL JOURNAL OF REMOTE SENSING, pp. 3769–3798, 2009, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/66431.