Detecting and Assessing Nondominant Farmland Area with Long-Term MODIS Time Series Images
While most land use and land cover (LULC) studies have focused on modeling, change detection and driving forces at the class or categorical level, few have focused on the subclass level, especially regarding the quality change within a class such as farmland. The concept of nondominant farmland area...
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MDPI AG
2020-07-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/12/15/2441 |
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| author | Shengnan Yu Xiaokang Zhang Xinle Zhang Huanjun Liu Jiaguo Qi Yankun Sun |
| author_facet | Shengnan Yu Xiaokang Zhang Xinle Zhang Huanjun Liu Jiaguo Qi Yankun Sun |
| author_sort | Shengnan Yu |
| collection | DOAJ |
| description | While most land use and land cover (LULC) studies have focused on modeling, change detection and driving forces at the class or categorical level, few have focused on the subclass level, especially regarding the quality change within a class such as farmland. The concept of nondominant farmland area (NAF) is proposed in this study to assess within class variability and quantify farmland areas where poor environmental conditions, unsuitable natural factors, natural disasters or unsustainable management practices lead to poor crop growth and thus low yield. A 17-year (2000–2016) time series of the Normalized Difference Vegetation Index (NDVI) was used to develop a NAF extraction model with abnormal features in the NDVI curves and subsequently applied to Heilongjiang province in China. The NAF model was analyzed and assessed from three aspects: agricultural disasters, soil types and medium- and low-yield fields, to determine dominant factors of the NAF patterns. The results suggested that: (1) the NAF model was able to extract a variety of NAF types with an overall accuracy of ~80%. The NAF area accumulated more than 8 years in 17 years is 6.20 thousand km<sup>2</sup> in Heilongjiang Province, accounting for 3.75% of the total cultivated land area; (2) the NAF had significant spatial clustering characteristics and temporal variability. 53.24% of the NAF accumulated more than 8 years in 17 years is mainly concentrated in the west of Heilongjiang Province. The inter-annual NAF variability was related with meteorological variations, topography and soil properties; and (3) the spatial and temporal NAF patterns seem to reflect a cumulative impact of meteorological disasters, poor farmland quality, and soil degradation on crop growth. The determinant factors of the observed NAF patterns differed across regions, and must be interpreted in the local context of topography, soil properties and meteorological environment. Spatial and temporal NAF variability could provide useful, diagnostic information for precision farmland management. |
| first_indexed | 2024-03-10T18:06:19Z |
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| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T18:06:19Z |
| publishDate | 2020-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-8e79f784d51b4aa182a588567995286f2023-11-20T08:26:57ZengMDPI AGRemote Sensing2072-42922020-07-011215244110.3390/rs12152441Detecting and Assessing Nondominant Farmland Area with Long-Term MODIS Time Series ImagesShengnan Yu0Xiaokang Zhang1Xinle Zhang2Huanjun Liu3Jiaguo Qi4Yankun Sun5College of Resources and Environment Sciences, Northeast Agricultural University, Harbin 150030, ChinaInternational Institute for Earth System Sciences, Nanjing University, Nanjing 210023, ChinaCollege of Resources and Environment Sciences, Northeast Agricultural University, Harbin 150030, ChinaCollege of Resources and Environment Sciences, Northeast Agricultural University, Harbin 150030, ChinaAsia Hub, Nanjing Agricultural University, Nanjing 210095, ChinaCollege of Resources and Environment Sciences, Northeast Agricultural University, Harbin 150030, ChinaWhile most land use and land cover (LULC) studies have focused on modeling, change detection and driving forces at the class or categorical level, few have focused on the subclass level, especially regarding the quality change within a class such as farmland. The concept of nondominant farmland area (NAF) is proposed in this study to assess within class variability and quantify farmland areas where poor environmental conditions, unsuitable natural factors, natural disasters or unsustainable management practices lead to poor crop growth and thus low yield. A 17-year (2000–2016) time series of the Normalized Difference Vegetation Index (NDVI) was used to develop a NAF extraction model with abnormal features in the NDVI curves and subsequently applied to Heilongjiang province in China. The NAF model was analyzed and assessed from three aspects: agricultural disasters, soil types and medium- and low-yield fields, to determine dominant factors of the NAF patterns. The results suggested that: (1) the NAF model was able to extract a variety of NAF types with an overall accuracy of ~80%. The NAF area accumulated more than 8 years in 17 years is 6.20 thousand km<sup>2</sup> in Heilongjiang Province, accounting for 3.75% of the total cultivated land area; (2) the NAF had significant spatial clustering characteristics and temporal variability. 53.24% of the NAF accumulated more than 8 years in 17 years is mainly concentrated in the west of Heilongjiang Province. The inter-annual NAF variability was related with meteorological variations, topography and soil properties; and (3) the spatial and temporal NAF patterns seem to reflect a cumulative impact of meteorological disasters, poor farmland quality, and soil degradation on crop growth. The determinant factors of the observed NAF patterns differed across regions, and must be interpreted in the local context of topography, soil properties and meteorological environment. Spatial and temporal NAF variability could provide useful, diagnostic information for precision farmland management.https://www.mdpi.com/2072-4292/12/15/2441nondominant farmland areaNDVI time seriesmeteorological disastersmiddle- and low-yield fieldsplanting structureHeilongjiang Province |
| spellingShingle | Shengnan Yu Xiaokang Zhang Xinle Zhang Huanjun Liu Jiaguo Qi Yankun Sun Detecting and Assessing Nondominant Farmland Area with Long-Term MODIS Time Series Images Remote Sensing nondominant farmland area NDVI time series meteorological disasters middle- and low-yield fields planting structure Heilongjiang Province |
| title | Detecting and Assessing Nondominant Farmland Area with Long-Term MODIS Time Series Images |
| title_full | Detecting and Assessing Nondominant Farmland Area with Long-Term MODIS Time Series Images |
| title_fullStr | Detecting and Assessing Nondominant Farmland Area with Long-Term MODIS Time Series Images |
| title_full_unstemmed | Detecting and Assessing Nondominant Farmland Area with Long-Term MODIS Time Series Images |
| title_short | Detecting and Assessing Nondominant Farmland Area with Long-Term MODIS Time Series Images |
| title_sort | detecting and assessing nondominant farmland area with long term modis time series images |
| topic | nondominant farmland area NDVI time series meteorological disasters middle- and low-yield fields planting structure Heilongjiang Province |
| url | https://www.mdpi.com/2072-4292/12/15/2441 |
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