Satellite remote sensing detection of forest vegetation land cover changes and their potential drivers

2019 ◽  
Author(s):  
Dan M. Savastru ◽  
Maria A. Zoran ◽  
Roxana S. Savastru
Keyword(s):  
Remote Sensing ◽  
Land Cover ◽  
Satellite Remote Sensing ◽  
Forest Vegetation ◽  
Land Cover Changes

scholarly journals Land cover changes in the Air Telang Protected Forest, South Sumatra, Indonesia (1989-2013)

2017 ◽  
Vol 18 (4) ◽  
pp. 1538-1545 ◽  
Author(s):  
SYAIFUL EDDY ◽  
ISKHAQ ISKANDAR ◽  
MOH. RASYID RIDHO ◽  
ANDY MULYANA
Keyword(s):  
Remote Sensing ◽  
Land Cover ◽  
Image Classification ◽  
Oil Palm ◽  
Satellite Remote Sensing ◽  
Mangrove Forest ◽  
Remote Sensing Data ◽  
Land Cover Changes ◽  
Ground Truthing ◽  
Landsat Satellite

Eddy S, Iskandar I, Ridho MR, Mulyana A. 2017. Land cover changes in the Air Telang Protected Forest, South Sumatra, Indonesia (1989-2013). Biodiversitas 18: 1538-1545. The Air Telang Protected Forest (ATPF) is a mangrove forest in the Banyuasin District, South Sumatra, Indonesia. It has an area of about 12,660.87 ha. In fact, that the ATPF area has been converted into aquacultures, plantations, agricultural lands, settlements and ports during recent decades. The objective of this study is to identify the land cover changes in the ATPF from 1989 through 2013 using satellite remote sensing data. Three Landsat satellite imageries for 1989, 2001 and 2013 have been used to build maps and to predict the land cover changes in the study area. A ground-truthing verification was done to increase the accuracy of image classification in each region. The results showed that the anthropogenic forcing had caused significant degradation of primary mangrove forest in the ATPF from 1989 to 2013. This forcing was categorized as mangrove conversion into coconut plantations, oil palm plantations, aquacultures, farms, ports, and settlements. Of these six conversions, the coconut plantations, oil palm plantations and aquacultures have potential tendencies to increase construction that could threaten the existence of mangrove forest in ATPF. It was found that during 2013, the coconut plantations, oil palm plantations, and aquacultures accounted for about 18.0% (2,278.62 ha), 4.7% (591.87 ha) and 3.1% (386.18 ha) of mangrove forest changes, respectively.

scholarly journals A MACHINE LEARNING DATASET FOR LARGE-SCOPE HIGH RESOLUTION REMOTE SENSING IMAGE INTERPRETATION CONSIDERING LANDSCAPE SPATIAL HETEROGENEITY

2019 ◽  
Vol XLII-2/W13 ◽  
pp. 731-736
Author(s):  
Y. Xu ◽  
X. Hu ◽  
Y. Wei ◽  
Y. Yang ◽  
D. Wang
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Land Cover ◽  
Spatial Heterogeneity ◽  
Satellite Remote Sensing ◽  
Sampling Method ◽  
National Level ◽  
Remote Sensing Image ◽  
Training Sample ◽  
Large Scope

<p><strong>Abstract.</strong> The demand for timely information about earth’s surface such as land cover and land use (LC/LU), is consistently increasing. Machine learning method shows its advantage on collecting such information from remotely sensed images while requiring sufficient training sample. For satellite remote sensing image, however, sample datasets covering large scope are still limited. Most existing sample datasets for satellite remote sensing image built based on a few frames of image located on a local area. For large scope (national level) view, choosing a sufficient unbiased sampling method is crucial for constructing balanced training sample dataset. Dependable spatial sample locations considering spatial heterogeneity of land cover are needed for choosing sample images. This paper introduces an ongoing work on establishing a national scope sample dataset for high spatial-resolution satellite remote sensing image processing. Sample sites been chosen sufficiently using spatial sampling method, and divided sample patches been grouped using clustering method for further uses. The neural network model for road detection trained our dataset subset shows an increased performance on both completeness and accuracy, comparing to two widely used public dataset.</p>

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