Exploring Land Cover Classification Accuracy of Landsat 8 Image Using Spectral Index Layer Stacking in Hilly Region of South Korea

Lee, Jae Kang; Acharya, Tri Dev; Lee, Dong Ha

doi:10.18494/sam.2018.1934

Cited by 26 publications

(20 citation statements)

References 31 publications

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“…Accurate and up-to-date information of the spatial distribution of surface water is a backbone for numerous scientific tasks, such as surface water inventory mapping, water estimation for drinking and irrigation purposes, land use/land cover (LULC) mapping and change, etc. Remote sensing is a rapidly growing technology that can provide low-cost and reliable information for environmental changes at local, regional, and global scales, with their long-collected repeatable, and even real-time, data [ 3 , 4 ]. Numerous water extraction algorithms have been developed and applied for remotely sensed imageries.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Water Indices for Surface Water Extraction in a Landsat 8 Scene of Nepal

Acharya

Subedi

Lee

2018

Sensors

219

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Accurate and frequent updates of surface water have been made possible by remote sensing technology. Index methods are mostly used for surface water estimation which separates the water from the background based on a threshold value. Generally, the threshold is a fixed value, but can be challenging in the case of environmental noise, such as shadow, forest, built-up areas, snow, and clouds. One such challenging scene can be found in Nepal where no such evaluation has been done. Taking that in consideration, this study evaluates the performance of the most widely used water indices: Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), Modified NDWI (MNDWI), and Automated Water Extraction Index (AWEI) in a Landsat 8 scene of Nepal. The scene, ranging from 60 m to 8848 m, contains various types of water bodies found in Nepal with different forms of environmental noise. The evaluation was conducted based on measures from a confusion matrix derived using validation points. Comparing visually and quantitatively, not a single method was able to extract surface water in the entire scene with better accuracy. Upon selecting optimum thresholds, the overall accuracy (OA) and kappa coefficient (kappa) was improved, but not satisfactory. NDVI and NDWI showed better results for only pure water pixels, whereas MNDWI and AWEI were unable to reject snow cover and shadows. Combining NDVI with NDWI and AWEI with shadow improved the accuracy but inherited the NDWI and AWEI characteristics. Segmenting the test scene with elevations above and below 665 m, and using NDVI and NDWI for detecting water, resulted in an OA of 0.9638 and kappa of 0.8979. The accuracy can be further improved with a smaller interval of categorical characteristics in one or multiple scenes.

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Section: Introductionmentioning

confidence: 99%

Evaluation of Water Indices for Surface Water Extraction in a Landsat 8 Scene of Nepal

Acharya

Subedi

Lee

2018

Sensors

219

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“…The required coefficients and values were obtained from the Landsat response function, metadata file, and SRTM DEM. Google Earth has been widely used in various studies, (5,(24)(25)(26)(27)(28) even in North Korea, (17) as a reference and validation data. Because access to the area is limited, this study used Google Earth imagery for training and validation purposes in this study.…”

Section: Datamentioning

confidence: 99%

“…Remote sensing is a powerful and cost-effective technology that allows us to collect data and assess the spatial and temporal dynamics of Earth's surface processes and hazards. (4)(5)(6)(7)(8) Remotely sensed data from legacy satellites such as Landsat have been continuously obtained for the past forty years. With such a large collected redundant database, it is possible to monitor deforestation and thus erosion-prone areas in remote and inaccessible areas.…”

Section: Introductionmentioning

confidence: 99%

Decision-tree-based Mapping of Erosion-prone Areas in Hilly Regions of Kangwon Province, North Korea

Acharya¹,

Kim²,

Lee³

et al. 2019

Sensors and Materials

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After the division of the Korean peninsula, North Korea overexploited their natural resources especially the forest. It lost about 23% of the total forest from 1990 to 2011, which continues today. However, the country is inaccessible to monitor such changes. Hence, in this study, we aim to use Landsat 8 imagery with the aid of Google Earth to map erosion-prone areas in a subset area of Kangwon Province, North Korea. Pruned Decision Tree (DT) modeling was used in selecting the optimum ratio/index and threshold based on ground truth points extracted for Landsat scenes from May, October, and both months combined. Pruned DT resulted in applying the normalized green, near-infrared (NIR), green ratio vegetation index (GRVI), red-green ratio index (RGRI), infrared percentage vegetation index (IPVI), and slope with the optimum threshold for the segmentation of the study area with reasonable accuracy. The result shows that combining the ground truths from different seasons resulted in rules giving higher overall accuracy (OA) and kappa coefficient than the individual rule results. However, interchanging ground truths of different months is not effective. On average, out of the total land, high and medium erosion-prone areas are 15 and 20%, respectively. The remaining 65% is covered by forest. The result can be useful for estimating loss and restoring resources such as forest and land in the future.

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“…This option enabled several researchers, for example (Pal and Antil, 2017;Leroux et al, 2018;Gašparović et al, 2019), to expect a better classification mapping of the land cover/use. Likewise, other researches focused on urban area detection such as the works of (Xu, 2007;Patel and Mukherjee, 2014;Bramhe et al, 2018;Lee et al, 2018;Nur Hidayati et al, 2018;Ettehadi Osgouei et al, 2019;Lynch and Blesius, 2019). However, the study of (Ettehadi Osgouei et al, 2019) applied the Normalized Difference Tillage Index (NDTI) -developed in (Deventer, 1997) and also used in (Daughtry et al, 2010;Eskandari et al, 2016) -which has used SWIR bands of the Sentinel-2 images and succeeded in differentiating bare land and built-up area classes better than the other spectral indices used in the study.…”

Section: Introductionmentioning

confidence: 99%

Applying Multi-Index approach from Sentinel-2 Imagery to Extract Urban Area in dry season (Semi-Arid Land in North East Algeria)

Rouibah

Belabbas

2020

Rev. Teledetec.

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<p>The mapping of urban areas mostly presents a big difficulty, particularly, in arid and semi-arid environments. For that reason, in this research, we expect to increase built up accuracy mapping for Bordj Bou Arreridj city in semi-arid regions (North-East Algeria) by focusing on the identification of appropriate combination of the remotely sensed spectral indices. The study applies the ‘k–means’ classifier. In this regard, four spectral indexes were selected, namely normalized difference tillage index (NDTI) for built-up, and both bare soil index (BSI) and dry bare-soil index (DBSI), which are related to bare soil, as well as the normalized difference vegetation index (NDVI). All previous spectral indices mentioned were derived from Sentinel-2 data acquired during the dry season. Two combinations of them were generated using layer stack process, keeping both of NDTI and NDVI index constant in both combinations so that the multi-index NDTI/BSI/NDVI was the first single dataset combination, and the multi-index NDTI/DBSI/NDVI as the second component. The results show that BSI index works better with NDTI index compared to the use of DBSI index. Therefore, BSI index provides improvements: bare soil classes and built-up were better discriminated, where the overall accuracy increased by 5.67% and the kappa coefficient increased by 12.05%. The use of k-means as unsupervised classifier provides an automatic and a rapid urban area detection. Therefore, the multi-index dataset NDTI/ BSI / NDVI was suitable for mapping the cities in dry climate, and could provide a better urban management and future remote sensing applications in semi-arid areas particularly.</p>

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Exploring Land Cover Classification Accuracy of Landsat 8 Image Using Spectral Index Layer Stacking in Hilly Region of South Korea

Abstract: His research interests are in geodesy, surveying, geospatial information, and natural hazard analysis.

Cited by 26 publications

References 31 publications

Evaluation of Water Indices for Surface Water Extraction in a Landsat 8 Scene of Nepal

Evaluation of Water Indices for Surface Water Extraction in a Landsat 8 Scene of Nepal

Decision-tree-based Mapping of Erosion-prone Areas in Hilly Regions of Kangwon Province, North Korea

Applying Multi-Index approach from Sentinel-2 Imagery to Extract Urban Area in dry season (Semi-Arid Land in North East Algeria)

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