Object-oriented land cover information extraction in emigration area of Zigui county using high resolution imagery

With the improvement of remote sensing technology, the spatial, structural and texture information of land covers are present clearly in high resolution imagery, which enhances the ability of crop mapping. Since the satellite RapidEye was launched in 2009, high resolution multispectral imagery together with wide red edge band has been utilized in vegetation monitoring. Broad red edge band related vegetation indices improved land use classification and vegetation studies. RapidEye high resolution imagery acquired on May 29 and August 9th of 2012 was used in this study to evaluate the potential of red edge band in agricultural land cover/use mapping using an objected-oriented classification approach. A new object-oriented decision tree classifier was introduced in this study to map agricultural lands in the study area. Besides the five bands of RapidEye image, the vegetation indexes derived from spectral bands and the structural and texture features are utilized as inputs for agricultural land cover/use mapping in the study. The optimization of input features for classification by reducing redundant information improves the mapping precision over 9% for AdaTree. WL, and 5% for SVM, the accuracy is over 90% for both approaches. Time phase characteristic is much important in different agricultural lands, and it improves the classification accuracy 7% for AdaTree.WL and 6% for SVM.

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Assessing Riyadh’s Urban Change Utilizing High-Resolution Imagery

Land ◽

10.3390/land8120193 ◽

2019 ◽

Vol 8 (12) ◽

pp. 193

Author(s):

Ali Alghamdi ◽

Anthony R. Cummings

Keyword(s):

Maximum Likelihood ◽

High Resolution ◽

Accuracy Assessment ◽

Middle Eastern ◽

Object Oriented ◽

Training Data ◽

High Resolution Imagery ◽

Significant Research ◽

Desert Areas ◽

Spot 5

The implications of change on local processes have attracted significant research interest in recent times. In urban settings, green spaces and forests have attracted much attention. Here, we present an assessment of change within the predominantly desert Middle Eastern city of Riyadh, an understudied setting. We utilized high-resolution SPOT 5 data and two classification techniques—maximum likelihood classification and object-oriented classification—to study the changes in Riyadh between 2004 and 2014. Imagery classification was completed with training data obtained from the SPOT 5 dataset, and an accuracy assessment was completed through a combination of field surveys and an application developed in ESRI Survey 123 tool. The Survey 123 tool allowed residents of Riyadh to present their views on land cover for the 2004 and 2014 imagery. Our analysis showed that soil or ‘desert’ areas were converted to roads and buildings to accommodate for Riyadh’s rapidly growing population. The object-oriented classifier provided higher overall accuracy than the maximum likelihood classifier (74.71% and 73.79% vs. 92.36% and 90.77% for 2004 and 2014). Our work provides insights into the changes within a desert environment and establishes a foundation for understanding change in this understudied setting.

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