The application and research of anomaly extraction process by remote sensing in the vegetation-covered area: an example in Chengmenshan of JiuJiang district

2010 ◽  
Author(s):  
Yan Chen ◽  
Peng Tao Zhang ◽  
Ping Xu
Keyword(s):  
Remote Sensing ◽  
Extraction Process ◽  
Covered Area ◽  
Anomaly Extraction

scholarly journals A Spectral Analysis of Snow in Mt. Rainier

2018 ◽  
Vol 10 (3) ◽  
pp. 20
Author(s):  
Shrinidhi Ambinakudige ◽  
Pushkar Inamdar ◽  
Aynaz Lotfata
Keyword(s):  
Remote Sensing ◽  
Snow Cover ◽  
Thermal Emission ◽  
Major Influence ◽  
Landsat 8 ◽  
Run Off ◽  
Snow Covered Area ◽  
Covered Area ◽  
Local Water ◽  
Normalized Difference Snow Index

Snow cover helps regulate the temperature of the Earth's surface. Snowmelt recharges groundwater, provides run-off for rivers and creeks, and acts as a major source of local water for many communities around the world. Since 2000, there has been a significant decrease in the snow-covered area in the Northern Hemisphere. Climate change is the major factor influencing the change in snow cover amount and distribution. We analyze spectral properties of the remote sensing sensors with respect to the study of snow and examine how data from some of the major remote sensing satellite sensors, such as (Advanced Spaceborne Thermal Emission and Reflection Radiometer) ASTER, Landsat-8, and Sentinel-2, can be used in studying snow. The study was conducted in Mt. Rainier. Although reflectance values recorded were lower due to the timing of the data collection and the aspect of the study site, data can still be used calculate normalized difference snow index (NDSI) to clearly demarcate the snow from other land cover classes. NDSI values in all three satellites ranged from 0.94 to 0.97 in the snow-covered area of the study site. Any pollutants in snow can have a major influence on spectral reflectance in the VIS spectrum because pollutants absorb more than snow.

scholarly journals Impact of varying debris cover thickness on ablation: a case study for Koxkar Glacier in the Tien Shan

2014 ◽  
Vol 8 (2) ◽  
pp. 377-386 ◽  
Author(s):  
M. Juen ◽  
C. Mayer ◽  
A. Lambrecht ◽  
H. Han ◽  
S. Liu
Keyword(s):  
Remote Sensing ◽  
Field Measurements ◽  
Tien Shan ◽  
Shielding Effect ◽  
Glacier Surface ◽  
Glacier Terminus ◽  
Covered Area ◽  
Debris Cover ◽  
High Resolution Satellite Imagery ◽  
Cover Thickness

Abstract. To quantify the ablation processes on a debris covered glacier, a simple distributed ablation model has been developed and applied to a selected glacier. For this purpose, a set of field measurements was carried out to collect empirical data. A morphometric analysis of the glacier surface enables us to capture statistically the areal distribution of topographic features that influence debris thickness and consequently ablation. Remote-sensing techniques, using high-resolution satellite imagery, were used to extrapolate the in situ point measurements to the whole ablation area and to map and classify melt-relevant surface types. As a result, a practically applicable method is presented that allows the estimation of ablation on a debris covered glacier by combining field data and remote-sensing information. The sub-debris ice ablation accounts for about 24% of the entire ice ablation, while the percentage of the moraine covered area accounts for approximately 32% of the entire glacierized area. Although the ice cliffs occupy only 1.7% of the debris covered area, the melt amount accounts for approximately 12% of the total sub-debris ablation and 2.5% of the total ablation respectively. Our study highlights the influence of debris cover on the response of the glacier terminus in a particular climate setting. Due to the fact that melt rates beyond 0.1 m of moraine cover are highly restricted, the shielding effect of the debris cover dominates over the temperature and elevation dependence of the ablation in the bare ice case.

scholarly journals Discriminating Wet Snow and Firn for Alpine Glaciers Using Sentinel-1 Data: A Case Study at Rofental, Austria

Geosciences ◽  
2019 ◽  
Vol 9 (2) ◽  
pp. 69 ◽  
Author(s):  
Achim Heilig ◽  
Anna Wendleder ◽  
Andreas Schmitt ◽  
Christoph Mayer
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Data ◽  
Optical Data ◽  
Optical Remote Sensing ◽  
Mountain Ranges ◽  
Snow Covered Area ◽  
Covered Area ◽  
Wet Snow ◽  
Sar Data ◽  
Sar Analysis

Continuous monitoring of glacier changes supports our understanding of climate related glacier behavior. Remote sensing data offer the unique opportunity to observe individual glaciers as well as entire mountain ranges. In this study, we used synthetic aperture radar (SAR) data to monitor the recession of wet snow area extent per season for three different glacier areas of the Rofental, Austria. For four glaciological years (GYs, 2014/2015–2017/2018), Sentinel-1 (S1) SAR data were acquired and processed. For all four GYs, the seasonal snow retreated above the elevation range of perennial firn. The described processing routine is capable of discriminating wet snow from firn areas for all GYs with sufficient accuracy. For a short in situ transect of the snow—firn boundary, SAR derived wet snow extent agreed within an accuracy of three to four pixels or 30–40 m. For entire glaciers, we used optical remote sensing imagery and field data to assess reliability of derived wet snow covered area extent. Differences in determination of snow covered area between optical data and SAR analysis did not exceed 10% on average. Offsets of SAR data to results of annual field assessments are below 10% as well. The introduced workflow for S1 data will contribute to monitoring accumulation area extent for remote and hazardous glacier areas and thus improve the data basis for such locations.

A Spectral Analysis of Snow in Mt. Rainier

2018 ◽  
Vol 10 (3) ◽  
pp. 18
Author(s):  
Shrinidhi Ambinakudige ◽  
Pushkar Inamdar ◽  
Aynaz Lotfata
Keyword(s):  
Remote Sensing ◽  
Snow Cover ◽  
Thermal Emission ◽  
Major Influence ◽  
Landsat 8 ◽  
Run Off ◽  
Snow Covered Area ◽  
Covered Area ◽  
Local Water ◽  
Normalized Difference Snow Index

Snow cover helps regulate the temperature of the Earth's surface. Snowmelt recharges groundwater, provides run-off for rivers and creeks, and acts as a major source of local water for many communities around the world. Since 2000, there has been a significant decrease in the snow-covered area in the Northern Hemisphere. Climate change is the major factor influencing the change in snow cover amount and distribution. We analyze spectral properties of the remote sensing sensors with respect to the study of snow and examine how data from some of the major remote sensing satellite sensors, such as (Advanced Spaceborne Thermal Emission and Reflection Radiometer) ASTER, Landsat-8, and Sentinel-2, can be used in studying snow. The study was conducted in Mt. Rainier. Although reflectance values recorded were lower due to the timing of the data collection and the aspect of the study site, data can still be used calculate normalized difference snow index (NDSI) to clearly demarcate the snow from other land cover classes. NDSI values in all three satellites ranged from 0.94 to 0.97 in the snow-covered area of the study site. Any pollutants in snow can have a major influence on spectral reflectance in the VIS spectrum because pollutants absorb more than snow.

scholarly journals Long-term analysis of snow-covered area in the Moroccan High-Atlas through remote sensing

2010 ◽  
Vol 12 ◽  
pp. S109-S115 ◽  
Author(s):  
A. Boudhar ◽  
B. Duchemin ◽  
L. Hanich ◽  
L. Jarlan ◽  
A. Chaponnière ◽  
...  
Keyword(s):  
Remote Sensing ◽  
High Atlas ◽  
Snow Covered Area ◽  
Covered Area ◽  
Term Analysis

scholarly journals Corn Residue Covered Area Mapping with a Deep Learning Method Using Chinese GF-1 B/D High Resolution Remote Sensing Images

2021 ◽  
Vol 13 (15) ◽  
pp. 2903
Author(s):  
Wancheng Tao ◽  
Zixuan Xie ◽  
Ying Zhang ◽  
Jiayu Li ◽  
Fu Xuan ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
Classification Accuracy ◽  
Crop Residue ◽  
Black Soil ◽  
Support Vector ◽  
Learning Method ◽  
Remote Sensing Images ◽  
Covered Area ◽  
Corn Residue

Black soil is one of the most productive soils with high organic matter content. Crop residue covering is important for protecting black soil from alleviating soil erosion and increasing soil organic carbon. Mapping crop residue covered areas accurately using remote sensing images can monitor the protection of black soil in regional areas. Considering the inhomogeneity and randomness, resulting from human management difference, the high spatial resolution Chinese GF-1 B/D image and developed MSCU-net+C deep learning method are used to mapping corn residue covered area (CRCA) in this study. The developed MSCU-net+C is joined by a multiscale convolution group (MSCG), the global loss function, and Convolutional Block Attention Module (CBAM) based on U-net and the full connected conditional random field (FCCRF). The effectiveness of the proposed MSCU-net+C is validated by the ablation experiment and comparison experiment for mapping CRCA in Lishu County, Jilin Province, China. The accuracy assessment results show that the developed MSCU-net+C improve the CRCA classification accuracy from IOUAVG = 0.8604 and KappaAVG = 0.8864 to IOUAVG = 0.9081 and KappaAVG = 0.9258 compared with U-net. Our developed and other deep semantic segmentation networks (MU-net, GU-net, MSCU-net, SegNet, and Dlv3+) improve the classification accuracy of IOUAVG/KappaAVG with 0.0091/0.0058, 0.0133/0.0091, 0.044/0.0345, 0.0104/0.0069, and 0.0107/0.0072 compared with U-net, respectively. The classification accuracies of IOUAVG/KappaAVG of traditional machine learning methods, including support vector machine (SVM) and neural network (NN), are 0.576/0.5526 and 0.6417/0.6482, respectively. These results reveal that the developed MSCU-net+C can be used to map CRCA for monitoring black soil protection.

scholarly journals A Study on Deformation of Land and Loss of Vegetation Due to Mining at Barapukuria Coal Mine

2019 ◽  
Vol 11 (3) ◽  
pp. 293-301
Author(s):  
I. Abedin ◽  
M. A. N. Abir ◽  
A. Hussain ◽  
G. Hasan
Keyword(s):  
Remote Sensing ◽  
Adverse Effect ◽  
Surface Area ◽  
Coal Mine ◽  
Extraction Process ◽  
Major Portion ◽  
Remote Sensing Technology ◽  
Barapukuria Coal Mine ◽  
Severe Effect ◽  
Sensing Technology

Barapukuria coal mine is the biggest coal mine in Bangladesh discovered in 1985. It has lease area around 10.53 square km with the total reserve of 390 million tons. Though it fulfils the major portion of demand of coal in Bangladesh nevertheless it has severe effect like deformation of topography of that surface area while extracting coal from mine and causes severe impact on vegetation on the coal mine vicinity. This is due to some poor design of extraction process. The intent and purpose of this research is to make an analysis of the influence of topography changes due to mining and impact on vegetation. To assess the effect of mining on vegetation and topography remote sensing technology, GIS and Landsat image were used. Our study confirms the deformation of topography and loss of vegetation due to mining. If this mining process continue it will create adverse effect on environment. The result is expected to be a reference for various parties to perform activities related to the mining of coal in Barapukuria coal mine.

scholarly journals Impact of varying debris cover thickness on catchment scale ablation: a case study for Koxkar glacier in the Tien Shan

2013 ◽  
Vol 7 (6) ◽  
pp. 5307-5332 ◽  
Author(s):  
M. Juen ◽  
C. Mayer ◽  
A. Lambrecht ◽  
H. Haidong ◽  
L. Shiyin
Keyword(s):  
Remote Sensing ◽  
Field Measurements ◽  
Ground Truth ◽  
Tien Shan ◽  
Shielding Effect ◽  
Catchment Scale ◽  
Glacier Surface ◽  
Glacier Terminus ◽  
Covered Area ◽  
Debris Cover

Abstract. To quantify the ablation processes on a debris covered glacier, a simple distributed ablation model has been developed and applied to a selected glacier. For this purpose, a bundle of field measurements was carried out to collect empirical data. A morphometric analysis of the glacier surface enables us to statistically capture the areal distribution of topographic features that influence debris thickness and consequently ablation. Remote sensing techniques, using high resolution satellite imagery, were used to extrapolate the ground truth results to the whole ablation area and to map and classify melt-relevant surface types. As a result, a practically applicable method is presented, that allows the estimation of ablation on a debris covered glacier by combining field data and remote sensing information. The sub-debris ice ablation accounts for about 19% of the entire ice ablation, while the percentage of the moraine covered area accounts for approximately 32% of the entire glacerized area. Although the ice cliffs occupy only 1.7% of the debris covered area the melt amount accounts for approximately 15% of the total sub-debris ablation and 2.7% of the total ablation respectively. Our study highlights the influence of debris cover on the response of the glacier terminus to climate warming. Due to the fact that melt rates beyond 0.1m of moraine cover are highly restricted the shielding effect of the debris cover dominates over the temperature- and elevation dependence of the ablation in the bare ice case.

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