Similarity Metrics Enforcement in Seasonal Agriculture Areas Classification
Accurate identification of agriculture areas is a key piece in the building blocks strategy of environment and economics resources management. The challenge requires one to deal with landscape complexity, sensors and data acquisition limitations through a proper computational approach to timely deli...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2020-06-01
|
| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/12/11/1791 |
| _version_ | 1797566386743017472 |
|---|---|
| author | Marcio A. S. Santos Eduardo D. Assad Angelo C. Gurgel Nizam Omar |
| author_facet | Marcio A. S. Santos Eduardo D. Assad Angelo C. Gurgel Nizam Omar |
| author_sort | Marcio A. S. Santos |
| collection | DOAJ |
| description | Accurate identification of agriculture areas is a key piece in the building blocks strategy of environment and economics resources management. The challenge requires one to deal with landscape complexity, sensors and data acquisition limitations through a proper computational approach to timely deliver accurate information. In this paper, a Machine Learning (ML) based method to enhance the classification process of areas dedicated to seasonal crops (row crops) is proposed. To this objective, a broad exploration of data from Moderate Resolution Imaging Spectro-radiometer sensors (MODIS) was made using pixel time-series combined with time-series similarity metrics. The experiment was performed in Brazil, covered 61% of the total agriculture areas, five different states specifically selected to demonstrate biome differences and the country’s diversity. The validation was made against independent data from EMBRAPA (Brazilian Agriculture Research Corporation), RapidEye Sensor Scene Maps. For the eight tested algorithms, the results were enhanced and demonstrate that the method can rate the classification accuracy up to 98.5%, average value for the tested algorithms. The process can be used to timely monitor large areas dedicated to row crops and enables the application of state of art classification techniques, two levels classification process, to identify crops according to each specific need within the areas. |
| first_indexed | 2024-03-10T19:26:08Z |
| format | Article |
| id | doaj.art-b219d371783546429cd962a722206934 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T19:26:08Z |
| publishDate | 2020-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-b219d371783546429cd962a7222069342023-11-20T02:33:35ZengMDPI AGRemote Sensing2072-42922020-06-011211179110.3390/rs12111791Similarity Metrics Enforcement in Seasonal Agriculture Areas ClassificationMarcio A. S. Santos0Eduardo D. Assad1Angelo C. Gurgel2Nizam Omar3Computing and Informatics School, Mackenzie Presbyterian University—Rua da Conslação, 930 Consolação, São Paulo SP 01302-90, BrazilEmbrapa Informatica Agropecuária, Environmental Modeling Lab—Av. Andre Tosello, 209 Unicamp Campus, Campinas SP 13083-886, BrazilGetúlio Vargas Foundation/São Paulo School of Economics—Rua Itapeva, 474 Bela Vista, São Paulo SP 01332-000, BrazilComputing and Informatics School, Mackenzie Presbyterian University—Rua da Conslação, 930 Consolação, São Paulo SP 01302-90, BrazilAccurate identification of agriculture areas is a key piece in the building blocks strategy of environment and economics resources management. The challenge requires one to deal with landscape complexity, sensors and data acquisition limitations through a proper computational approach to timely deliver accurate information. In this paper, a Machine Learning (ML) based method to enhance the classification process of areas dedicated to seasonal crops (row crops) is proposed. To this objective, a broad exploration of data from Moderate Resolution Imaging Spectro-radiometer sensors (MODIS) was made using pixel time-series combined with time-series similarity metrics. The experiment was performed in Brazil, covered 61% of the total agriculture areas, five different states specifically selected to demonstrate biome differences and the country’s diversity. The validation was made against independent data from EMBRAPA (Brazilian Agriculture Research Corporation), RapidEye Sensor Scene Maps. For the eight tested algorithms, the results were enhanced and demonstrate that the method can rate the classification accuracy up to 98.5%, average value for the tested algorithms. The process can be used to timely monitor large areas dedicated to row crops and enables the application of state of art classification techniques, two levels classification process, to identify crops according to each specific need within the areas.https://www.mdpi.com/2072-4292/12/11/1791remote sensingagriculturetime series similarity metricsmachine learningland use dynamics |
| spellingShingle | Marcio A. S. Santos Eduardo D. Assad Angelo C. Gurgel Nizam Omar Similarity Metrics Enforcement in Seasonal Agriculture Areas Classification Remote Sensing remote sensing agriculture time series similarity metrics machine learning land use dynamics |
| title | Similarity Metrics Enforcement in Seasonal Agriculture Areas Classification |
| title_full | Similarity Metrics Enforcement in Seasonal Agriculture Areas Classification |
| title_fullStr | Similarity Metrics Enforcement in Seasonal Agriculture Areas Classification |
| title_full_unstemmed | Similarity Metrics Enforcement in Seasonal Agriculture Areas Classification |
| title_short | Similarity Metrics Enforcement in Seasonal Agriculture Areas Classification |
| title_sort | similarity metrics enforcement in seasonal agriculture areas classification |
| topic | remote sensing agriculture time series similarity metrics machine learning land use dynamics |
| url | https://www.mdpi.com/2072-4292/12/11/1791 |
| work_keys_str_mv | AT marcioassantos similaritymetricsenforcementinseasonalagricultureareasclassification AT eduardodassad similaritymetricsenforcementinseasonalagricultureareasclassification AT angelocgurgel similaritymetricsenforcementinseasonalagricultureareasclassification AT nizamomar similaritymetricsenforcementinseasonalagricultureareasclassification |