A 30 m terrace mapping in China using Landsat 8 imagery and digital elevation model based on the Google Earth Engine
<p>The construction of terraces is a key soil conservation practice on agricultural land in China providing multiple valuable ecosystem services. Accurate spatial information on terraces is needed for both management and research. In this study, the first 30 m resolution terracing map of the e...
| Main Authors: | , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2021-06-01
|
| Series: | Earth System Science Data |
| Online Access: | https://essd.copernicus.org/articles/13/2437/2021/essd-13-2437-2021.pdf |
| _version_ | 1819085150298832896 |
|---|---|
| author | B. Cao L. Yu L. Yu V. Naipal P. Ciais W. Li W. Li Y. Zhao Y. Zhao W. Wei D. Chen Z. Liu P. Gong P. Gong |
| author_facet | B. Cao L. Yu L. Yu V. Naipal P. Ciais W. Li W. Li Y. Zhao Y. Zhao W. Wei D. Chen Z. Liu P. Gong P. Gong |
| author_sort | B. Cao |
| collection | DOAJ |
| description | <p>The construction of terraces is a key soil conservation
practice on agricultural land in China providing multiple valuable
ecosystem services. Accurate spatial information on terraces is needed for
both management and research. In this study, the first 30 m resolution
terracing map of the entire territory of China is produced by a supervised
pixel-based classification using multisource and multi-temporal data based
on the Google Earth Engine (GEE) platform. We extracted time-series spectral
features and topographic features from Landsat 8 images and the Shuttle
Radar Topography Mission digital elevation model (SRTM DEM) data,
classifying cropland area (cultivated land of Globeland30) into terraced and
non-terraced types through a random forest classifier. The overall accuracy
and kappa coefficient were evaluated by 10 875 test samples and achieved
values of 94 % and 0.72, respectively. For terrace class, the producer's
accuracy (PA) was 79.945 %, and the user's accuracy (UA) was 71.149 %.
The classification performed best in the Loess Plateau and southwestern
China, where terraces are most numerous. Some northeastern, eastern-central,
and southern areas had relatively high uncertainty. Typical errors in the
mapping results are from the sloping cropland (non-terrace cropland with a slope
of <span class="inline-formula">≥</span> 5<span class="inline-formula"><sup>∘</sup></span>), low-slope terraces, and non-crop vegetation.
Terraces are widely distributed in China, and the total terraced area was
estimated to be 53.55 Mha (i.e., 26.43 % of China's cropland area) by pixel
counting (PC) method and 58.46 <span class="inline-formula">±</span> 2.99 Mha (i.e., 28.85 % <span class="inline-formula">±</span> 1.48 % of China's cropland area) by error-matrix-based model-assisted
estimation (EM) method. Elevation and slope were identified as the main
features in the terrace/non-terrace classification, and multi-temporal
spectral features (such as percentiles of NDVI, TIRS2, and BSI) were also
essential. Terraces are more challenging to identify than other land use
types because of the intra-class feature heterogeneity, interclass feature
similarity, and fragmented patches, which should be the focus of future
research. Our terrace mapping algorithm can be used to map large-scale
terraces in other regions globally, and our terrace map will serve as a
landmark for studies on multiple ecosystem service assessments including
erosion control, carbon sequestration, and biodiversity conservation. The
China terrace map is available to the public at
<a href="https://doi.org/10.5281/zenodo.3895585">https://doi.org/10.5281/zenodo.3895585</a> (Cao et al., 2020).</p> |
| first_indexed | 2024-12-21T20:59:47Z |
| format | Article |
| id | doaj.art-f0ba3422296b414096e006fba4909e03 |
| institution | Directory Open Access Journal |
| issn | 1866-3508 1866-3516 |
| language | English |
| last_indexed | 2024-12-21T20:59:47Z |
| publishDate | 2021-06-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | Earth System Science Data |
| spelling | doaj.art-f0ba3422296b414096e006fba4909e032022-12-21T18:50:28ZengCopernicus PublicationsEarth System Science Data1866-35081866-35162021-06-01132437245610.5194/essd-13-2437-2021A 30 m terrace mapping in China using Landsat 8 imagery and digital elevation model based on the Google Earth EngineB. Cao0L. Yu1L. Yu2V. Naipal3P. Ciais4W. Li5W. Li6Y. Zhao7Y. Zhao8W. Wei9D. Chen10Z. Liu11P. Gong12P. Gong13Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing 100084, ChinaMinistry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing 100084, ChinaMinistry of Education Ecological Field Station for East Asian Migratory Birds, Beijing 100084, ChinaDepartment of Geography, Faculty of Geosciences, Ludwig-Maximilian University, Munich, GermanyLaboratoire des Sciences du Climat et de l'Environnement, CEA-CNRS-UVSQ, UMR8212, Gif-sur-Yvette, FranceMinistry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing 100084, ChinaMinistry of Education Ecological Field Station for East Asian Migratory Birds, Beijing 100084, ChinaCollege of Land Science and Technology, China Agricultural University, Beijing 100083, ChinaKey Laboratory of Remote Sensing for Agri-Hazards, Ministry of Agriculture and Rural Affairs, Beijing 100083, ChinaState Key Laboratory of Urban and Regional Ecology, Research Center for Eco-environmental Sciences, Chinese Academy of Sciences, Beijing 100085, ChinaState Key Laboratory of Urban and Regional Ecology, Research Center for Eco-environmental Sciences, Chinese Academy of Sciences, Beijing 100085, ChinaMinistry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing 100084, ChinaMinistry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing 100084, ChinaMinistry of Education Ecological Field Station for East Asian Migratory Birds, Beijing 100084, China<p>The construction of terraces is a key soil conservation practice on agricultural land in China providing multiple valuable ecosystem services. Accurate spatial information on terraces is needed for both management and research. In this study, the first 30 m resolution terracing map of the entire territory of China is produced by a supervised pixel-based classification using multisource and multi-temporal data based on the Google Earth Engine (GEE) platform. We extracted time-series spectral features and topographic features from Landsat 8 images and the Shuttle Radar Topography Mission digital elevation model (SRTM DEM) data, classifying cropland area (cultivated land of Globeland30) into terraced and non-terraced types through a random forest classifier. The overall accuracy and kappa coefficient were evaluated by 10 875 test samples and achieved values of 94 % and 0.72, respectively. For terrace class, the producer's accuracy (PA) was 79.945 %, and the user's accuracy (UA) was 71.149 %. The classification performed best in the Loess Plateau and southwestern China, where terraces are most numerous. Some northeastern, eastern-central, and southern areas had relatively high uncertainty. Typical errors in the mapping results are from the sloping cropland (non-terrace cropland with a slope of <span class="inline-formula">≥</span> 5<span class="inline-formula"><sup>∘</sup></span>), low-slope terraces, and non-crop vegetation. Terraces are widely distributed in China, and the total terraced area was estimated to be 53.55 Mha (i.e., 26.43 % of China's cropland area) by pixel counting (PC) method and 58.46 <span class="inline-formula">±</span> 2.99 Mha (i.e., 28.85 % <span class="inline-formula">±</span> 1.48 % of China's cropland area) by error-matrix-based model-assisted estimation (EM) method. Elevation and slope were identified as the main features in the terrace/non-terrace classification, and multi-temporal spectral features (such as percentiles of NDVI, TIRS2, and BSI) were also essential. Terraces are more challenging to identify than other land use types because of the intra-class feature heterogeneity, interclass feature similarity, and fragmented patches, which should be the focus of future research. Our terrace mapping algorithm can be used to map large-scale terraces in other regions globally, and our terrace map will serve as a landmark for studies on multiple ecosystem service assessments including erosion control, carbon sequestration, and biodiversity conservation. The China terrace map is available to the public at <a href="https://doi.org/10.5281/zenodo.3895585">https://doi.org/10.5281/zenodo.3895585</a> (Cao et al., 2020).</p>https://essd.copernicus.org/articles/13/2437/2021/essd-13-2437-2021.pdf |
| spellingShingle | B. Cao L. Yu L. Yu V. Naipal P. Ciais W. Li W. Li Y. Zhao Y. Zhao W. Wei D. Chen Z. Liu P. Gong P. Gong A 30 m terrace mapping in China using Landsat 8 imagery and digital elevation model based on the Google Earth Engine Earth System Science Data |
| title | A 30 m terrace mapping in China using Landsat 8 imagery and digital elevation model based on the Google Earth Engine |
| title_full | A 30 m terrace mapping in China using Landsat 8 imagery and digital elevation model based on the Google Earth Engine |
| title_fullStr | A 30 m terrace mapping in China using Landsat 8 imagery and digital elevation model based on the Google Earth Engine |
| title_full_unstemmed | A 30 m terrace mapping in China using Landsat 8 imagery and digital elevation model based on the Google Earth Engine |
| title_short | A 30 m terrace mapping in China using Landsat 8 imagery and digital elevation model based on the Google Earth Engine |
| title_sort | 30 thinsp m terrace mapping in china using landsat 8 imagery and digital elevation model based on the google earth engine |
| url | https://essd.copernicus.org/articles/13/2437/2021/essd-13-2437-2021.pdf |
| work_keys_str_mv | AT bcao a30thinspmterracemappinginchinausinglandsat8imageryanddigitalelevationmodelbasedonthegoogleearthengine AT lyu a30thinspmterracemappinginchinausinglandsat8imageryanddigitalelevationmodelbasedonthegoogleearthengine AT lyu a30thinspmterracemappinginchinausinglandsat8imageryanddigitalelevationmodelbasedonthegoogleearthengine AT vnaipal a30thinspmterracemappinginchinausinglandsat8imageryanddigitalelevationmodelbasedonthegoogleearthengine AT pciais a30thinspmterracemappinginchinausinglandsat8imageryanddigitalelevationmodelbasedonthegoogleearthengine AT wli a30thinspmterracemappinginchinausinglandsat8imageryanddigitalelevationmodelbasedonthegoogleearthengine AT wli a30thinspmterracemappinginchinausinglandsat8imageryanddigitalelevationmodelbasedonthegoogleearthengine AT yzhao a30thinspmterracemappinginchinausinglandsat8imageryanddigitalelevationmodelbasedonthegoogleearthengine AT yzhao a30thinspmterracemappinginchinausinglandsat8imageryanddigitalelevationmodelbasedonthegoogleearthengine AT wwei a30thinspmterracemappinginchinausinglandsat8imageryanddigitalelevationmodelbasedonthegoogleearthengine AT dchen a30thinspmterracemappinginchinausinglandsat8imageryanddigitalelevationmodelbasedonthegoogleearthengine AT zliu a30thinspmterracemappinginchinausinglandsat8imageryanddigitalelevationmodelbasedonthegoogleearthengine AT pgong a30thinspmterracemappinginchinausinglandsat8imageryanddigitalelevationmodelbasedonthegoogleearthengine AT pgong a30thinspmterracemappinginchinausinglandsat8imageryanddigitalelevationmodelbasedonthegoogleearthengine AT bcao 30thinspmterracemappinginchinausinglandsat8imageryanddigitalelevationmodelbasedonthegoogleearthengine AT lyu 30thinspmterracemappinginchinausinglandsat8imageryanddigitalelevationmodelbasedonthegoogleearthengine AT lyu 30thinspmterracemappinginchinausinglandsat8imageryanddigitalelevationmodelbasedonthegoogleearthengine AT vnaipal 30thinspmterracemappinginchinausinglandsat8imageryanddigitalelevationmodelbasedonthegoogleearthengine AT pciais 30thinspmterracemappinginchinausinglandsat8imageryanddigitalelevationmodelbasedonthegoogleearthengine AT wli 30thinspmterracemappinginchinausinglandsat8imageryanddigitalelevationmodelbasedonthegoogleearthengine AT wli 30thinspmterracemappinginchinausinglandsat8imageryanddigitalelevationmodelbasedonthegoogleearthengine AT yzhao 30thinspmterracemappinginchinausinglandsat8imageryanddigitalelevationmodelbasedonthegoogleearthengine AT yzhao 30thinspmterracemappinginchinausinglandsat8imageryanddigitalelevationmodelbasedonthegoogleearthengine AT wwei 30thinspmterracemappinginchinausinglandsat8imageryanddigitalelevationmodelbasedonthegoogleearthengine AT dchen 30thinspmterracemappinginchinausinglandsat8imageryanddigitalelevationmodelbasedonthegoogleearthengine AT zliu 30thinspmterracemappinginchinausinglandsat8imageryanddigitalelevationmodelbasedonthegoogleearthengine AT pgong 30thinspmterracemappinginchinausinglandsat8imageryanddigitalelevationmodelbasedonthegoogleearthengine AT pgong 30thinspmterracemappinginchinausinglandsat8imageryanddigitalelevationmodelbasedonthegoogleearthengine |