Land use and land cover classification over a large area in Iran based on single date analysis of satellite imagery

Saadat, Hossein; Adamowski, Jan; Bonnell, R. B.; Sharifi, Forood; Namdar, Mohammad; Ale-Ebrahim, Sasan

doi:10.1016/j.isprsjprs.2011.04.001

Cited by 141 publications

(78 citation statements)

References 29 publications

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“…Pan-sharpening techniques are useful for enhancing the process and results of image processing, and help provide a better understanding of the observed earth surface [14,25]. There are numerous pan-sharpening methods to use on satellite images: high pass filter (HPF), modified intensity-hue-saturation (M-IHS), Ehlers and Gram-Schmidt (GS) [25][26][27][28].…”

Section: Image Preprocessing and Pan-sharpeningmentioning

confidence: 99%

“…For example, In [13] author used artificial neural networks to provide LCLU maps and detect tree cover changes in Golestan province, Iran, using TM and ETM+ images from 1987 and 2001 to identify forest cover changes. Furthermore, [14] introduced a method to create accurate LCLU maps using ancillary data, while [15] provided a LULC map in 1998 for the Dough watershed (a small upstream sub-basin of the GW), for use in flood analysis. However, despite the global interest in LCLU change, few researchers have studied LCLU conditions in the Gorganrood region and only [13] investigated the LCLU changes, though exclusively in terms tree cover.…”

Section: Introductionmentioning

confidence: 99%

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Watershed Land Cover/Land Use Mapping Using Remote Sensing and Data Mining in Gorganrood, Iran

Minaei

Kainz

2016

IJGI

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Abstract:The Gorganrood watershed (GW) is experiencing considerable environmental change in the form of natural hazards and erosion, as well as deforestation, cultivation and development activities. As a result of this, different types of Land Cover/Land Use (LCLU) change are taking place on an intensive level in the area. This research study investigates the LCLU conditions upstream of this watershed for the years 1972, 1986, 2000 and 2014, using Landsat MSS, TM, ETM+ and OLI/TIRS images. LCLU maps for 1972, 1986, and 2000 were produced using pixel-based classification methods. For the 2014 LCLU map, Geographic Object-Based Image Analysis (GEOBIA) in combination with the data-mining capabilities of Gini and J48 machine-learning algorithms were used. The accuracy of the maps was assessed using overall accuracy, quantity disagreement and allocation disagreement indexes. The overall accuracy ranged from 89% to 95%, quantity disagreement from 2.1% to 6.6%, and allocation disagreement from 2.1% for 2014 to 2.7% for 2000. The results of this study indicate that a significant amount of change has occurred in the region, and that this has as a consequence affected ecosystem services and human activity. This knowledge of the LCLU status in the area will help managers and decision makers to develop plans and programs aimed at effectively managing the watershed into the future.

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Section: Image Preprocessing and Pan-sharpeningmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Watershed Land Cover/Land Use Mapping Using Remote Sensing and Data Mining in Gorganrood, Iran

Minaei

Kainz

2016

IJGI

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“…Given the advent of climate change, these problems will likely become more acute in the future [SAADAT et al 2011;ARAGHI et al 2015]. Accurate forecasting of short-term water demand can contribute to the efficient operation and management of urban water supply systems, resulting in demand being met efficiently and sustainably CAMPISI-PINTO et al 2012;TIWARI, ADAMOWSKI 2014].…”

Section: Introductionmentioning

confidence: 99%

Water demand forecasting using extreme learning machines

Tiwari¹,

Adamowski²,

Adamowski³

2016

Journal of Water and Land Development

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The capacity of recently-developed extreme learning machine (ELM) modelling approaches in forecasting daily urban water demand from limited data, alone or in concert with wavelet analysis (W) or bootstrap (B) methods (i.e., ELM, ELM W , ELM B ), was assessed, and compared to that of equivalent traditional artificial neural network-based models (i.e., ANN, ANN W , ANN B ). The urban water demand forecasting models were developed using 3-year water demand and climate datasets for the city of Calgary, Alberta, Canada. While the hybrid ELM B and ANN B models provided satisfactory 1-day lead-time forecasts of similar accuracy, the ANN W and ELM W models provided greater accuracy, with the ELM W model outperforming the ANN W model. Significant improvement in peak urban water demand prediction was only achieved with the ELM W model. The superiority of the ELM W model over both the ANN W or ANN B models demonstrated the significant role of wavelet transformation in improving the overall performance of the urban water demand model.

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“…These two concepts present two distinctive concepts, because LU maps explain human activities happening on the land, such as artificial surface construction, farming, and forestry that represent the usage of land (Ellis 2007;Wästfelt & Arnberg 2013), while LC maps present the physical cover on the ground (De Sherbinin 2002). Traditionally, applying image processing algorithms on remotely sensed data elaborated with ground-truth measurements and other complementary archive data have been the main source of collecting LU and LC features (Qi, Yeh, Li & Lin 2012;Saadat et al 2011). Although remote sensing images and techniques often facilitate earth observation efforts, in-field surveying as well as personal interviews with local residents are required for the sake of results' validation, i.e.…”

Section: Introductionmentioning

confidence: 99%

On the Contribution of Volunteered Geographic Information to Land Monitoring Efforts

Arsanjani

Fonte

2016

European Handbook of Crowdsourced Geographic Information

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Land-related inventories are important sources of geoinformation for environmentalists, researchers, policy-makers, practitioners, and ecologists. Traditionally, a considerable amount of energy, time, and money have been dedicated to map global/regional/local land use datasets. While remote sensing images and techniques along with field surveying have been the main sources of data for determining land use features, field measurements of ground truth have always amplified the required time and money, as well as information credibility. Nowadays, volunteered geographic information (VGI) has shown its great contributions to different scientific disciplines. This was made possible thanks to Web 2.0 technologies and GPS-enabled devices, which have advanced citizens knowledge-based projects and made them user-friendly for volunteered citizens to collect and share their knowledge about geographical objects. OpenStreetMap as one of those leading VGI projects has shown its great potential for collecting and providing land use information. The collaboratively collected 270 European Handbook of Crowdsourced Geographic Information land use features from diverse citizens could greatly back up the challenging element of land use mapping, which is in-field data gathering. Hence, in this literature we will look at the completeness, thematic accuracy and fitness for use of OpenStreetMap features for land mapping purposes over European countries. The empirical findings reveal that the degree of completeness varies widely ranging from 2% to 96% and overall and per-class thematic accuracies goes up to 80% and 96%, respectively compared to the European GMESUA datasets. Furthermore, more than 50% of land use features of eight European countries are mapped. This messages that the harnessing citizens' knowledge can play a great role in land mapping as an alternative and complementary data source.

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Land use and land cover classification over a large area in Iran based on single date analysis of satellite imagery

Cited by 141 publications

References 29 publications

Watershed Land Cover/Land Use Mapping Using Remote Sensing and Data Mining in Gorganrood, Iran

Watershed Land Cover/Land Use Mapping Using Remote Sensing and Data Mining in Gorganrood, Iran

Water demand forecasting using extreme learning machines

On the Contribution of Volunteered Geographic Information to Land Monitoring Efforts

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