scholarly journals Mapping Mangrove Forests Based on Multi-Tidal High-Resolution Satellite Imagery

2018 ◽  
Vol 10 (9) ◽  
pp. 1343 ◽  
Author(s):  
Qing Xia ◽  
Cheng-Zhi Qin ◽  
He Li ◽  
Chong Huang ◽  
Fen-Zhen Su
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Satellite Imagery ◽  
High Tide ◽  
Spectral Signature ◽  
Support Vector ◽  
Mangrove Forests ◽  
Remote Sensing Imagery ◽  
Object Based ◽  
High Resolution Satellite Imagery

Mangrove forests, which are essential for stabilizing coastal ecosystems, have been suffering from a dramatic decline over the past several decades. Mapping mangrove forests using satellite imagery is an efficient way to provide key data for mangrove forest conservation. Since mangrove forests are periodically submerged by tides, current methods of mapping mangrove forests, which are normally based on single-date, remote-sensing imagery, often underestimate the spatial distribution of mangrove forests, especially when the images used were recorded during high-tide periods. In this paper, we propose a new method of mapping mangrove forests based on multi-tide, high-resolution satellite imagery. In the proposed method, a submerged mangrove recognition index (SMRI), which is based on the differential spectral signature of mangroves under high and low tides from multi-tide, high-resolution satellite imagery, is designed to identify submerged mangrove forests. The proposed method applies the SMRI values, together with textural features extracted from high-resolution imagery and geographical features of mangrove forests, to an object-based support vector machine (SVM) to map mangrove forests. The proposed method was evaluated via a case study with GF-1 images (high-resolution satellites launched by China) in Yulin City, Guangxi Zhuang Autonomous Region of China. The results show that our proposed method achieves satisfactory performance, with a kappa coefficient of 0.86 and an overall accuracy of 94%, which is better than results obtained from object-based SVMs that use only single-date, remote sensing imagery.

Estimations for Percentage of Impervious Area by the Use of Satellite Remote Sensing Imagery

1994 ◽  
Vol 29 (1-2) ◽  
pp. 135-144 ◽  
Author(s):  
C. Deguchi ◽  
S. Sugio
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Satellite Imagery ◽  
Satellite Remote Sensing ◽  
Aerial Photographs ◽  
Estimation Errors ◽  
Remote Sensing Imagery ◽  
Impervious Area ◽  
Urbanized Areas

This study aims to evaluate the applicability of satellite imagery in estimating the percentage of impervious area in urbanized areas. Two methods of estimation are proposed and applied to a small urbanized watershed in Japan. The area is considered under two different cases of subdivision; i.e., 14 zones and 17 zones. The satellite imageries of LANDSAT-MSS (Multi-Spectral Scanner) in 1984, MOS-MESSR(Multi-spectral Electronic Self-Scanning Radiometer) in 1988 and SPOT-HRV(High Resolution Visible) in 1988 are classified. The percentage of imperviousness in 17 zones is estimated by using these classification results. These values are compared with the ones obtained from the aerial photographs. The percent imperviousness derived from the imagery agrees well with those derived from aerial photographs. The estimation errors evaluated are less than 10%, the same as those obtained from aerial photographs.

scholarly journals Detection of Tailings Dams Using High-Resolution Satellite Imagery and a Single Shot Multibox Detector in the Jing–Jin–Ji Region, China

2020 ◽  
Vol 12 (16) ◽  
pp. 2626 ◽  
Author(s):  
Qingting Li ◽  
Zhengchao Chen ◽  
Bing Zhang ◽  
Baipeng Li ◽  
Kaixuan Lu ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
High Resolution ◽  
Satellite Imagery ◽  
Spatial Scales ◽  
Detection Accuracy ◽  
Single Shot ◽  
Tailings Dams ◽  
High Resolution Satellite Imagery ◽  
Tailings Ponds

The timely and accurate mapping and monitoring of mine tailings dams is crucial to the improvement of management practices by decision makers and to the prevention of disasters caused by failures of these dams. Due to the complex topography, varying geomorphological characteristics, and the diversity of ore types and mining activities, as well as the range of scales and production processes involved, as they appear in remote sensing imagery, tailings dams vary in terms of their scale, color, shape, and surrounding background. The application of high-resolution satellite imagery for automatic detection of tailings dams at large spatial scales has been barely reported. In this study, a target detection method based on deep learning was developed for identifying the locations of tailings ponds and obtaining their geographical distribution from high-resolution satellite imagery automatically. Training samples were produced based on the characteristics of tailings ponds in satellite images. According to the sample characteristics, the Single Shot Multibox Detector (SSD) model was fine-tuned during model training. The results showed that a detection accuracy of 90.2% and a recall rate of 88.7% could be obtained. Based on the optimized SSD model, 2221 tailing ponds were extracted from Gaofen-1 high resolution imagery in the Jing–Jin–Ji region in northern China. In this region, the majority of tailings ponds are located at high altitudes in remote mountainous areas. At the city level, the tailings ponds were found to be located mainly in Chengde, Tangshan, and Zhangjiakou. The results prove that the deep learning method is very effective at detecting complex land-cover features from remote sensing images.

Durum wheat in-field monitoring and early-yield prediction: assessment of potential use of high resolution satellite imagery in a hilly area of Tuscany, Central Italy

2013 ◽  
Vol 153 (1) ◽  
pp. 68-77 ◽  
Author(s):  
A. DALLA MARTA ◽  
D. GRIFONI ◽  
M. MANCINI ◽  
F. ORLANDO ◽  
F. GUASCONI ◽  
...  
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Durum Wheat ◽  
Satellite Imagery ◽  
Technological Development ◽  
Stem Elongation ◽  
Central Italy ◽  
Area Index ◽  
High Resolution Satellite Imagery ◽  
Potential Use

SUMMARYModern agriculture is based on the control of in-field variability, which is determined by the interactions of numerous factors such as soil, climate and crop. For this reason, the use of remote sensing is becoming increasingly important, thanks to the technological development of satellites able to supply information with high spatial resolution and revisit frequency. Despite the large number of studies on the use of remote sensing for crop monitoring, very few have addressed the problem of spatial variability at field scale or the early prediction of crop yield and grain quality. The aim of the current research was to assess the potential use of high resolution satellite imagery for monitoring durum wheat growth and development, addressing forecast grain yield and protein content, through vegetation indices at two stages of crop development. To best represent the natural variability of agricultural production, the study was conducted in wheat fields managed by local farmers. As regards dry weight, leaf area index and nitrogen (N) content, the possibility of describing the crop state is evident at stem elongation, while at anthesis this potential is completely lost. However, satellites seem to be unable to estimate the N concentration. Aboveground biomass accumulated from emergence to stem elongation is strictly related to the final yield, while it has been confirmed that the crop parameters observed at anthesis are less informative, despite approaching harvesting time.

A Novel Region-Based Approach for Automatic Road Extraction from High Resolution Satellite Images

2013 ◽  
Vol 284-287 ◽  
pp. 2998-3003
Author(s):  
Young Gi Byun
Keyword(s):  
High Resolution ◽  
Satellite Imagery ◽  
Satellite Image ◽  
Road Surface ◽  
Support Vector ◽  
Support Vector Data Description ◽  
Road Extraction ◽  
High Resolution Satellite Images ◽  
High Resolution Satellite Imagery ◽  
One Class Classification

With the constantly increasing public availability of high resolution satellite imagery, interest in automatic road extraction from this imagery has recently increased. Road extraction from high resolution satellite imagery refers to reliable road surface extraction instead of road line extraction because roads in the imagery mostly correspond to an elongated region with a locally constant spectral signature rather than traditional thin lines. This paper proposes a novel automatic road extraction approach that is based on a combination of image segmentation and one-class classification and consists of two main steps. First, the image is segmented using a modified previous segmentation algorithm to achieve more reliable segmentation for road extraction. The key road objects are then automatically extracted from the segmented image to obtain road training samples. Then one-class classification, based on a support vector data description classifier, is carried out to extract the road surface area from the image. The experimental results from a pan-sharpened KOMPSAT-2 satellite image demonstrate the correctness and efficiency of the proposed method for its application to road extraction from high resolution satellite image.

scholarly journals Application of Object Based Classification and High Resolution Satellite Imagery for Savanna Ecosystem Analysis

2010 ◽  
Vol 2 (12) ◽  
pp. 2748-2772 ◽  
Author(s):  
Cerian Gibbes ◽  
Sanchayeeta Adhikari ◽  
Luke Rostant ◽  
Jane Southworth ◽  
Youliang Qiu
Keyword(s):  
High Resolution ◽  
Satellite Imagery ◽  
Object Based ◽  
High Resolution Satellite Imagery
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