Land cover classification in the tropics, solving the problem of cloud covered areas using topographic parameters

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Abstract

Optical remote sensing data has been extensively used since early seventies for mapping and monitoring land cover. But, cloud cover has always been hindering the optimal use of the data, especially in the tropical and temperate regions. Because of cloud cover, researchers are forced to use only cloud free images or generate a composite using data from different dates to fill in the cloud and cloud-shadow areas. It is not a problem if the filling image is from the same season and from the same year. If there is large time gap between the acquisition date of image with the clouds and that of the filling image it will have an effect on classification accuracy. In this paper, we describe an approach in land cover classification of areas covered by cloud and cloud shadows. We use the synergy of optical image classification, processing of SAR data taking advantage of its ability of penetrating clouds, and decision rules using terrain parameters. We hypothesize that surface roughness of different land cover types derived from processing of SAR data and in combination with other topographic parameters (slope and elevation) can be applied to classify areas covered by clouds and cloud-shadows. The method was applied in a case study in Mts. Wayang-Windu and Patuha areas, near Bandung in West Java, Indonesia. Landsat 8 OLI-TIRS, Sentinel-1 A C band and SRTM DEM data of the study area were obtained. Initial classification of the optical data was carried out using training samples and fuzzy classification. Class labelling was done by applying fuzzy c-means algorithm. The Sentinel-1 A C band data was processed to get surface roughness. Decision boundaries for surface roughness, terrain slope and elevation were generated for each land cover type, which were implemented in the classification of the areas covered by cloud and cloud shadows. The result shows that it is possible to solve the problem of cloud cover using terrain parameters.
LanguageEnglish
Pages84-93
Number of pages10
JournalInternational Journal of Applied Earth Observation and Geoinformation (JAG)
Volume77
Issue numberMay
DOIs
Publication statusPublished - 1 May 2019

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Tropics
land cover
surface roughness
cloud cover
Surface roughness
synthetic aperture radar
parameter
tropics
Shuttle Radar Topography Mission
Image classification
image classification
Processing
Labeling
Landsat
digital elevation model
Remote sensing

Keywords

  • ITC-ISI-JOURNAL-ARTICLE

Cite this

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title = "Land cover classification in the tropics, solving the problem of cloud covered areas using topographic parameters",
abstract = "Optical remote sensing data has been extensively used since early seventies for mapping and monitoring land cover. But, cloud cover has always been hindering the optimal use of the data, especially in the tropical and temperate regions. Because of cloud cover, researchers are forced to use only cloud free images or generate a composite using data from different dates to fill in the cloud and cloud-shadow areas. It is not a problem if the filling image is from the same season and from the same year. If there is large time gap between the acquisition date of image with the clouds and that of the filling image it will have an effect on classification accuracy. In this paper, we describe an approach in land cover classification of areas covered by cloud and cloud shadows. We use the synergy of optical image classification, processing of SAR data taking advantage of its ability of penetrating clouds, and decision rules using terrain parameters. We hypothesize that surface roughness of different land cover types derived from processing of SAR data and in combination with other topographic parameters (slope and elevation) can be applied to classify areas covered by clouds and cloud-shadows. The method was applied in a case study in Mts. Wayang-Windu and Patuha areas, near Bandung in West Java, Indonesia. Landsat 8 OLI-TIRS, Sentinel-1 A C band and SRTM DEM data of the study area were obtained. Initial classification of the optical data was carried out using training samples and fuzzy classification. Class labelling was done by applying fuzzy c-means algorithm. The Sentinel-1 A C band data was processed to get surface roughness. Decision boundaries for surface roughness, terrain slope and elevation were generated for each land cover type, which were implemented in the classification of the areas covered by cloud and cloud shadows. The result shows that it is possible to solve the problem of cloud cover using terrain parameters.",
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N2 - Optical remote sensing data has been extensively used since early seventies for mapping and monitoring land cover. But, cloud cover has always been hindering the optimal use of the data, especially in the tropical and temperate regions. Because of cloud cover, researchers are forced to use only cloud free images or generate a composite using data from different dates to fill in the cloud and cloud-shadow areas. It is not a problem if the filling image is from the same season and from the same year. If there is large time gap between the acquisition date of image with the clouds and that of the filling image it will have an effect on classification accuracy. In this paper, we describe an approach in land cover classification of areas covered by cloud and cloud shadows. We use the synergy of optical image classification, processing of SAR data taking advantage of its ability of penetrating clouds, and decision rules using terrain parameters. We hypothesize that surface roughness of different land cover types derived from processing of SAR data and in combination with other topographic parameters (slope and elevation) can be applied to classify areas covered by clouds and cloud-shadows. The method was applied in a case study in Mts. Wayang-Windu and Patuha areas, near Bandung in West Java, Indonesia. Landsat 8 OLI-TIRS, Sentinel-1 A C band and SRTM DEM data of the study area were obtained. Initial classification of the optical data was carried out using training samples and fuzzy classification. Class labelling was done by applying fuzzy c-means algorithm. The Sentinel-1 A C band data was processed to get surface roughness. Decision boundaries for surface roughness, terrain slope and elevation were generated for each land cover type, which were implemented in the classification of the areas covered by cloud and cloud shadows. The result shows that it is possible to solve the problem of cloud cover using terrain parameters.

AB - Optical remote sensing data has been extensively used since early seventies for mapping and monitoring land cover. But, cloud cover has always been hindering the optimal use of the data, especially in the tropical and temperate regions. Because of cloud cover, researchers are forced to use only cloud free images or generate a composite using data from different dates to fill in the cloud and cloud-shadow areas. It is not a problem if the filling image is from the same season and from the same year. If there is large time gap between the acquisition date of image with the clouds and that of the filling image it will have an effect on classification accuracy. In this paper, we describe an approach in land cover classification of areas covered by cloud and cloud shadows. We use the synergy of optical image classification, processing of SAR data taking advantage of its ability of penetrating clouds, and decision rules using terrain parameters. We hypothesize that surface roughness of different land cover types derived from processing of SAR data and in combination with other topographic parameters (slope and elevation) can be applied to classify areas covered by clouds and cloud-shadows. The method was applied in a case study in Mts. Wayang-Windu and Patuha areas, near Bandung in West Java, Indonesia. Landsat 8 OLI-TIRS, Sentinel-1 A C band and SRTM DEM data of the study area were obtained. Initial classification of the optical data was carried out using training samples and fuzzy classification. Class labelling was done by applying fuzzy c-means algorithm. The Sentinel-1 A C band data was processed to get surface roughness. Decision boundaries for surface roughness, terrain slope and elevation were generated for each land cover type, which were implemented in the classification of the areas covered by cloud and cloud shadows. The result shows that it is possible to solve the problem of cloud cover using terrain parameters.

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