Mapping of Land Cover with Optical Images, Supervised Algorithms, and Google Earth Engine
Crops and ecosystems constantly change, and risks are derived from heavy rains, hurricanes, droughts, human activities, climate change, etc. This has caused additional damages with economic and social impacts. Natural phenomena have caused the loss of crop areas, which endangers food security, destr...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-06-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/22/13/4729 |
| _version_ | 1827625416240660480 |
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| author | Fernando Pech-May Raúl Aquino-Santos German Rios-Toledo Juan Pablo Francisco Posadas-Durán |
| author_facet | Fernando Pech-May Raúl Aquino-Santos German Rios-Toledo Juan Pablo Francisco Posadas-Durán |
| author_sort | Fernando Pech-May |
| collection | DOAJ |
| description | Crops and ecosystems constantly change, and risks are derived from heavy rains, hurricanes, droughts, human activities, climate change, etc. This has caused additional damages with economic and social impacts. Natural phenomena have caused the loss of crop areas, which endangers food security, destruction of the habitat of species of flora and fauna, and flooding of populations, among others. To help in the solution, it is necessary to develop strategies that maximize agricultural production as well as reduce land wear, environmental impact, and contamination of water resources. The generation of crop and land-use maps is advantageous for identifying suitable crop areas and collecting precise information about the produce. In this work, a strategy is proposed to identify and map sorghum and corn crops as well as land use and land cover. Our approach uses Sentinel-2 satellite images, spectral indices for the phenological detection of vegetation and water bodies, and automatic learning methods: support vector machine, random forest, and classification and regression trees. The study area is a tropical agricultural area with water bodies located in southeastern Mexico. The study was carried out from 2017 to 2019, and considering the climate and growing seasons of the site, two seasons were created for each year. Land use was identified as: <i>water bodies</i>, <i>land in recovery</i>, <i>urban areas</i>, <i>sandy areas</i>, and <i>tropical rainforest</i>. The results in overall accuracy were: 0.99% for the support vector machine, 0.95% for the random forest, and 0.92% for classification and regression trees. The kappa index was: 0.99% for the support vector machine, 0.97% for the random forest, and 0.94% for classification and regression trees. The support vector machine obtained the lowest percentage of false positives and margin of error. It also acquired better results in the classification of soil types and identification of crops. |
| first_indexed | 2024-03-09T12:36:28Z |
| format | Article |
| id | doaj.art-f4ce52c38a2a4aef95d3faa3e7c33cd2 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T12:36:28Z |
| publishDate | 2022-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-f4ce52c38a2a4aef95d3faa3e7c33cd22023-11-30T22:24:44ZengMDPI AGSensors1424-82202022-06-012213472910.3390/s22134729Mapping of Land Cover with Optical Images, Supervised Algorithms, and Google Earth EngineFernando Pech-May0Raúl Aquino-Santos1German Rios-Toledo2Juan Pablo Francisco Posadas-Durán3Department of Computer Science, Instituto Tecnológico Superior de los Ríos, Balancán 86930, Tabasco, MexicoFaculty of Telematics, University of Colima, 333 University Avenue, Colima 28040, Colima, MexicoTecnológico Nacional de México Campus Tuxtla Gutiérrez, Tuxtla Gutiérrez 29050, Chiapas, MexicoInstituto Politécnico Nacional (IPN), Mexico City 07738, MexicoCrops and ecosystems constantly change, and risks are derived from heavy rains, hurricanes, droughts, human activities, climate change, etc. This has caused additional damages with economic and social impacts. Natural phenomena have caused the loss of crop areas, which endangers food security, destruction of the habitat of species of flora and fauna, and flooding of populations, among others. To help in the solution, it is necessary to develop strategies that maximize agricultural production as well as reduce land wear, environmental impact, and contamination of water resources. The generation of crop and land-use maps is advantageous for identifying suitable crop areas and collecting precise information about the produce. In this work, a strategy is proposed to identify and map sorghum and corn crops as well as land use and land cover. Our approach uses Sentinel-2 satellite images, spectral indices for the phenological detection of vegetation and water bodies, and automatic learning methods: support vector machine, random forest, and classification and regression trees. The study area is a tropical agricultural area with water bodies located in southeastern Mexico. The study was carried out from 2017 to 2019, and considering the climate and growing seasons of the site, two seasons were created for each year. Land use was identified as: <i>water bodies</i>, <i>land in recovery</i>, <i>urban areas</i>, <i>sandy areas</i>, and <i>tropical rainforest</i>. The results in overall accuracy were: 0.99% for the support vector machine, 0.95% for the random forest, and 0.92% for classification and regression trees. The kappa index was: 0.99% for the support vector machine, 0.97% for the random forest, and 0.94% for classification and regression trees. The support vector machine obtained the lowest percentage of false positives and margin of error. It also acquired better results in the classification of soil types and identification of crops.https://www.mdpi.com/1424-8220/22/13/4729remote sensing imagesland use with Sentinel-2Sentinel-2Sentinel-2 with Google Earth Engine |
| spellingShingle | Fernando Pech-May Raúl Aquino-Santos German Rios-Toledo Juan Pablo Francisco Posadas-Durán Mapping of Land Cover with Optical Images, Supervised Algorithms, and Google Earth Engine Sensors remote sensing images land use with Sentinel-2 Sentinel-2 Sentinel-2 with Google Earth Engine |
| title | Mapping of Land Cover with Optical Images, Supervised Algorithms, and Google Earth Engine |
| title_full | Mapping of Land Cover with Optical Images, Supervised Algorithms, and Google Earth Engine |
| title_fullStr | Mapping of Land Cover with Optical Images, Supervised Algorithms, and Google Earth Engine |
| title_full_unstemmed | Mapping of Land Cover with Optical Images, Supervised Algorithms, and Google Earth Engine |
| title_short | Mapping of Land Cover with Optical Images, Supervised Algorithms, and Google Earth Engine |
| title_sort | mapping of land cover with optical images supervised algorithms and google earth engine |
| topic | remote sensing images land use with Sentinel-2 Sentinel-2 Sentinel-2 with Google Earth Engine |
| url | https://www.mdpi.com/1424-8220/22/13/4729 |
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