Monitoring Agricultural Land and Land Cover Change from 2001–2021 of the Chi River Basin, Thailand Using Multi-Temporal Landsat Data Based on Google Earth Engine
In recent years, climate change has greatly affected agricultural activity, sustainability and production, making it difficult to conduct crop management and food security assessment. As a consequence, significant changes in agricultural land and land cover (LC) have occurred, mostly due to the intr...
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-09-01
|
| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/15/17/4339 |
| _version_ | 1797581904063496192 |
|---|---|
| author | Savittri Ratanopad Suwanlee Surasak Keawsomsee Morakot Pengjunsang Nudthawud Homtong Amornchai Prakobya Enrico Borgogno-Mondino Filippo Sarvia Jaturong Som-ard |
| author_facet | Savittri Ratanopad Suwanlee Surasak Keawsomsee Morakot Pengjunsang Nudthawud Homtong Amornchai Prakobya Enrico Borgogno-Mondino Filippo Sarvia Jaturong Som-ard |
| author_sort | Savittri Ratanopad Suwanlee |
| collection | DOAJ |
| description | In recent years, climate change has greatly affected agricultural activity, sustainability and production, making it difficult to conduct crop management and food security assessment. As a consequence, significant changes in agricultural land and land cover (LC) have occurred, mostly due to the introduction of new agricultural practices, techniques and crops. Earth Observation (EO) data, cloud-computing platforms and powerful machine learning methods can certainly support analysis within the agricultural context. Therefore, accurate and updated agricultural land and LC maps can be useful to derive valuable information for land change monitoring, trend planning, decision-making and sustainable land management. In this context, this study aims at monitoring temporal and spatial changes between 2001 and 2021 (with a four 5-year periods) within the Chi River Basin (NE–Thailand). Specifically, all available Landsat archives and the random forest (RF) classifier were jointly involved within the Google Earth Engine (GEE) platform in order to: (i) generate five different crop type maps (focusing on rice, cassava, para rubber and sugarcane classes), and (ii) monitoring the agricultural land transitions over time. For each crop map, a confusion matrix and the correspondent accuracy were computed and tested according to a validation dataset. In particular, an overall accuracy > 88% was found in all of the resulting five crop maps (for the years 2001, 2006, 2011, 2016 and 2021). Subsequently the agricultural land transitions were analyzed, and a total of 18,957 km<sup>2</sup> were found as changed (54.5% of the area) within the 20 years (2001–2021). In particular, an increase in cassava and para rubber areas were found at the disadvantage of rice fields, probably due to two different key drivers taken over time: the agricultural policy and staple price. Finally, it is worth highlighting that such results turn out to be decisive in a challenging agricultural environment such as the Thai one. In particular, the high accuracy of the five derived crop type maps can be useful to provide spatial consistency and reliable information to support local sustainable agriculture land management, decisions of policymakers and many stakeholders. |
| first_indexed | 2024-03-10T23:13:16Z |
| format | Article |
| id | doaj.art-511fe9e0ceb2485595ca6ce84cff1800 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T23:13:16Z |
| publishDate | 2023-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-511fe9e0ceb2485595ca6ce84cff18002023-11-19T08:47:50ZengMDPI AGRemote Sensing2072-42922023-09-011517433910.3390/rs15174339Monitoring Agricultural Land and Land Cover Change from 2001–2021 of the Chi River Basin, Thailand Using Multi-Temporal Landsat Data Based on Google Earth EngineSavittri Ratanopad Suwanlee0Surasak Keawsomsee1Morakot Pengjunsang2Nudthawud Homtong3Amornchai Prakobya4Enrico Borgogno-Mondino5Filippo Sarvia6Jaturong Som-ard7Department of Geography, Faculty of Humanities and Social Sciences, Mahasarakham University, Maha Sarakham 44150, ThailandDepartment of Geography, Faculty of Humanities and Social Sciences, Mahasarakham University, Maha Sarakham 44150, ThailandDepartment of Geography, Faculty of Humanities and Social Sciences, Mahasarakham University, Maha Sarakham 44150, ThailandDepartment of Geotechnology, Faculty of Technology, Khon Kaen University, Khon Kaen 40002, ThailandGeo-Informatics and Space Technology Development Agency (Public Organization), Chonburi 20230, ThailandDepartment of Agricultural, Forest and Food Sciences, University of Turin, 10095 Torino, ItalyDepartment of Agricultural, Forest and Food Sciences, University of Turin, 10095 Torino, ItalyDepartment of Geography, Faculty of Humanities and Social Sciences, Mahasarakham University, Maha Sarakham 44150, ThailandIn recent years, climate change has greatly affected agricultural activity, sustainability and production, making it difficult to conduct crop management and food security assessment. As a consequence, significant changes in agricultural land and land cover (LC) have occurred, mostly due to the introduction of new agricultural practices, techniques and crops. Earth Observation (EO) data, cloud-computing platforms and powerful machine learning methods can certainly support analysis within the agricultural context. Therefore, accurate and updated agricultural land and LC maps can be useful to derive valuable information for land change monitoring, trend planning, decision-making and sustainable land management. In this context, this study aims at monitoring temporal and spatial changes between 2001 and 2021 (with a four 5-year periods) within the Chi River Basin (NE–Thailand). Specifically, all available Landsat archives and the random forest (RF) classifier were jointly involved within the Google Earth Engine (GEE) platform in order to: (i) generate five different crop type maps (focusing on rice, cassava, para rubber and sugarcane classes), and (ii) monitoring the agricultural land transitions over time. For each crop map, a confusion matrix and the correspondent accuracy were computed and tested according to a validation dataset. In particular, an overall accuracy > 88% was found in all of the resulting five crop maps (for the years 2001, 2006, 2011, 2016 and 2021). Subsequently the agricultural land transitions were analyzed, and a total of 18,957 km<sup>2</sup> were found as changed (54.5% of the area) within the 20 years (2001–2021). In particular, an increase in cassava and para rubber areas were found at the disadvantage of rice fields, probably due to two different key drivers taken over time: the agricultural policy and staple price. Finally, it is worth highlighting that such results turn out to be decisive in a challenging agricultural environment such as the Thai one. In particular, the high accuracy of the five derived crop type maps can be useful to provide spatial consistency and reliable information to support local sustainable agriculture land management, decisions of policymakers and many stakeholders.https://www.mdpi.com/2072-4292/15/17/4339agricultureland coverchange detectionEarth Observationmulti-temporal imageLandsat |
| spellingShingle | Savittri Ratanopad Suwanlee Surasak Keawsomsee Morakot Pengjunsang Nudthawud Homtong Amornchai Prakobya Enrico Borgogno-Mondino Filippo Sarvia Jaturong Som-ard Monitoring Agricultural Land and Land Cover Change from 2001–2021 of the Chi River Basin, Thailand Using Multi-Temporal Landsat Data Based on Google Earth Engine Remote Sensing agriculture land cover change detection Earth Observation multi-temporal image Landsat |
| title | Monitoring Agricultural Land and Land Cover Change from 2001–2021 of the Chi River Basin, Thailand Using Multi-Temporal Landsat Data Based on Google Earth Engine |
| title_full | Monitoring Agricultural Land and Land Cover Change from 2001–2021 of the Chi River Basin, Thailand Using Multi-Temporal Landsat Data Based on Google Earth Engine |
| title_fullStr | Monitoring Agricultural Land and Land Cover Change from 2001–2021 of the Chi River Basin, Thailand Using Multi-Temporal Landsat Data Based on Google Earth Engine |
| title_full_unstemmed | Monitoring Agricultural Land and Land Cover Change from 2001–2021 of the Chi River Basin, Thailand Using Multi-Temporal Landsat Data Based on Google Earth Engine |
| title_short | Monitoring Agricultural Land and Land Cover Change from 2001–2021 of the Chi River Basin, Thailand Using Multi-Temporal Landsat Data Based on Google Earth Engine |
| title_sort | monitoring agricultural land and land cover change from 2001 2021 of the chi river basin thailand using multi temporal landsat data based on google earth engine |
| topic | agriculture land cover change detection Earth Observation multi-temporal image Landsat |
| url | https://www.mdpi.com/2072-4292/15/17/4339 |
| work_keys_str_mv | AT savittriratanopadsuwanlee monitoringagriculturallandandlandcoverchangefrom20012021ofthechiriverbasinthailandusingmultitemporallandsatdatabasedongoogleearthengine AT surasakkeawsomsee monitoringagriculturallandandlandcoverchangefrom20012021ofthechiriverbasinthailandusingmultitemporallandsatdatabasedongoogleearthengine AT morakotpengjunsang monitoringagriculturallandandlandcoverchangefrom20012021ofthechiriverbasinthailandusingmultitemporallandsatdatabasedongoogleearthengine AT nudthawudhomtong monitoringagriculturallandandlandcoverchangefrom20012021ofthechiriverbasinthailandusingmultitemporallandsatdatabasedongoogleearthengine AT amornchaiprakobya monitoringagriculturallandandlandcoverchangefrom20012021ofthechiriverbasinthailandusingmultitemporallandsatdatabasedongoogleearthengine AT enricoborgognomondino monitoringagriculturallandandlandcoverchangefrom20012021ofthechiriverbasinthailandusingmultitemporallandsatdatabasedongoogleearthengine AT filipposarvia monitoringagriculturallandandlandcoverchangefrom20012021ofthechiriverbasinthailandusingmultitemporallandsatdatabasedongoogleearthengine AT jaturongsomard monitoringagriculturallandandlandcoverchangefrom20012021ofthechiriverbasinthailandusingmultitemporallandsatdatabasedongoogleearthengine |