Monitoring rangeland ground cover vegetation using multitemporal MODIS data

The aim of the present research is to monitor changes in herbage production during the grazing season in the Semirom and Brojen regions, Iran, using multitemporal Moderate Resolution Imaging Spectroradiometer (MODIS) data. At first, various preprocessing steps were applied to a topography map. The a...

Full description

Saved in:
Bibliographic Details
Main Authors: Yeganeh, Hassan, Khajedein, Seyed Jamale, Amiri, Fazel, Mohamed Shariff, Abdul Rashid
Format: Article
Published: Springer Verlag 2014
Online Access:http://psasir.upm.edu.my/id/eprint/34596/
http://link.springer.com/article/10.1007%2Fs12517-012-0733-0
Tags: Add Tag
No Tags, Be the first to tag this record!
id my.upm.eprints.34596
record_format eprints
spelling my.upm.eprints.345962015-12-16T03:27:30Z http://psasir.upm.edu.my/id/eprint/34596/ Monitoring rangeland ground cover vegetation using multitemporal MODIS data Yeganeh, Hassan Khajedein, Seyed Jamale Amiri, Fazel Mohamed Shariff, Abdul Rashid The aim of the present research is to monitor changes in herbage production during the grazing season in the Semirom and Brojen regions, Iran, using multitemporal Moderate Resolution Imaging Spectroradiometer (MODIS) data. At first, various preprocessing steps were applied to a topography map. The atmospheric and topographic corrections were applied using subtraction of the dark object method and the Lambert method. Image processing, including false-color composite, principal component analysis, and vegetation indices were employed to produce land use and pasture production maps. Vegetation sampling was carried out over a period of 4 months during June–September 2008, using a stratified random sampling method. Twenty random sampling points were selected, and herbage production was estimated and verified with the double-checking method. Four MODIS data sets were used in this study. The models for image processing and integrating ground data with satellite images were processed, and the resulting images were categorized into seven classes. Finally, the land covers were verified for accuracy. A postclassification analysis was carried out to verify the seven class change detections. The results confirmed that Normalized Difference Vegetation Index (NDVI) and Soil-Adjusted Vegetation Index (SAVI) maps had a close relationship with the field data. The indices produced with shortwave infrared bands had a close relationship with field data where the ground cover and yields were high. The R 2 value observed was 0.85. The changes in the pasture vegetation were high during the growing season in more than 90 % of the pastures. During the growing season, most changes in the pastures belonged to class 5 and 2 in the NDVI and SAVI index maps, respectively. Springer Verlag 2014-01 Article PeerReviewed Yeganeh, Hassan and Khajedein, Seyed Jamale and Amiri, Fazel and Mohamed Shariff, Abdul Rashid (2014) Monitoring rangeland ground cover vegetation using multitemporal MODIS data. Arabian Journal of Geosciences, 7 (1). pp. 287-298. ISSN 1866-7511; ESSN: 1866-7538 http://link.springer.com/article/10.1007%2Fs12517-012-0733-0 10.1007/s12517-012-0733-0
institution Universiti Putra Malaysia
building UPM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Putra Malaysia
content_source UPM Institutional Repository
url_provider http://psasir.upm.edu.my/
description The aim of the present research is to monitor changes in herbage production during the grazing season in the Semirom and Brojen regions, Iran, using multitemporal Moderate Resolution Imaging Spectroradiometer (MODIS) data. At first, various preprocessing steps were applied to a topography map. The atmospheric and topographic corrections were applied using subtraction of the dark object method and the Lambert method. Image processing, including false-color composite, principal component analysis, and vegetation indices were employed to produce land use and pasture production maps. Vegetation sampling was carried out over a period of 4 months during June–September 2008, using a stratified random sampling method. Twenty random sampling points were selected, and herbage production was estimated and verified with the double-checking method. Four MODIS data sets were used in this study. The models for image processing and integrating ground data with satellite images were processed, and the resulting images were categorized into seven classes. Finally, the land covers were verified for accuracy. A postclassification analysis was carried out to verify the seven class change detections. The results confirmed that Normalized Difference Vegetation Index (NDVI) and Soil-Adjusted Vegetation Index (SAVI) maps had a close relationship with the field data. The indices produced with shortwave infrared bands had a close relationship with field data where the ground cover and yields were high. The R 2 value observed was 0.85. The changes in the pasture vegetation were high during the growing season in more than 90 % of the pastures. During the growing season, most changes in the pastures belonged to class 5 and 2 in the NDVI and SAVI index maps, respectively.
format Article
author Yeganeh, Hassan
Khajedein, Seyed Jamale
Amiri, Fazel
Mohamed Shariff, Abdul Rashid
spellingShingle Yeganeh, Hassan
Khajedein, Seyed Jamale
Amiri, Fazel
Mohamed Shariff, Abdul Rashid
Monitoring rangeland ground cover vegetation using multitemporal MODIS data
author_facet Yeganeh, Hassan
Khajedein, Seyed Jamale
Amiri, Fazel
Mohamed Shariff, Abdul Rashid
author_sort Yeganeh, Hassan
title Monitoring rangeland ground cover vegetation using multitemporal MODIS data
title_short Monitoring rangeland ground cover vegetation using multitemporal MODIS data
title_full Monitoring rangeland ground cover vegetation using multitemporal MODIS data
title_fullStr Monitoring rangeland ground cover vegetation using multitemporal MODIS data
title_full_unstemmed Monitoring rangeland ground cover vegetation using multitemporal MODIS data
title_sort monitoring rangeland ground cover vegetation using multitemporal modis data
publisher Springer Verlag
publishDate 2014
url http://psasir.upm.edu.my/id/eprint/34596/
http://link.springer.com/article/10.1007%2Fs12517-012-0733-0
_version_ 1643831201593556992
score 13.1944895