The Effect of NDVI Time Series Density Derived from Spatiotemporal Fusion of Multisource Remote Sensing Data on Crop Classification Accuracy

Sun, Rui; Chen, Shaohui; Su, Haijun; Mi, Chunrong; Jin, Ning

doi:10.3390/ijgi8110502

Cited by 28 publications

(18 citation statements)

References 49 publications

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“…Sentinel-2 imagery can greatly contribute to more detailed mapping of cropping patterns at regional and local scales. More recent studies have used Sentinel-2 data to map cropping patterns in different landscapes [59,74,115,136]. Moreover, studies using microwave sensors are also increasing and as a result, there is a steady increase in studies using multi-source data.…”

Section: Sensor Types and Properties And Their Relation To The Mappin...mentioning

confidence: 99%

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Cropping Patterns of Annual Crops: A Remote Sensing Review

2022

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Cropping patterns are defined as the sequence and spatial arrangement of annual crops on a piece of land. Knowledge of cropping patterns is crucial for crop production and land-use intensity. While cropping patterns are related to crop production and land use intensity, they are rarely reported in agricultural statistics, especially those relating to small farms in developing countries. Remote sensing has enabled mapping cropping patterns by monitoring crops’ spatial and temporal dynamics. In this paper, we reviewed remote sensing studies of single, sequential and intercropping patterns of annual crops practiced at local and regional scales. A total of 89 studies were selected from 753 publications based on their cropping pattern types and relevance to the scope of this review. The review found that despite the increase in single cropping pattern studies due to the Sentinel missions, studies on intercropping patterns are rare, suggesting that mapping intercropping is still challenging. More so, microwave remote sensing for mapping intercropping has not been fully explored. Given the complexities in mapping intercropping, our review highlights how less frequently used vegetation indices (VIs) that benefit from red-edge and SWIR spectral bands may improve intercropping mapping.

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Section: Sensor Types and Properties And Their Relation To The Mappin...mentioning

confidence: 99%

“…Utilizing microwave sensors can help fill data gaps and provide useful information on crop canopies structures to improve the mapping of cropping patterns. A few reviewed studies have shown that a multi-source approach improved classification [117,136].…”

Section: Research Gaps Future Scope and Opportunities In Mapping Crop...mentioning

confidence: 99%

Cropping Patterns of Annual Crops: A Remote Sensing Review

2022

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“…NDVI offer great potential on regional scale vegetation monitoring especially on measuring loss of vegetation land. The NDVI density correspond to earth surface and higher density NDVI indicate high concentration of forest and dense agricultural, moderates value represents lower concentration of vegetation such as shrubs and grassland while low NDVI density represents non vegetation areas (Sun et al, 2019). NDBI provide detection of density of urban and built-up area.…”

Section: Introductionmentioning

confidence: 99%

Landsat observation of urban growth and land use change using NDVI and NDBI analysis

Yasin

Abdullah

Yusoff

et al. 2022

IOP Conf. Ser.: Earth Environ. Sci.

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Landsat observation has numerous potentials as a quantitative approach in regional scale monitoring of urban growth and environmental change. To achieve this approach, three Landsat data of year 1991 (TM), 2005 (ETM+) and 2019 (OLI-TIRS) has been acquired, classified, and accurately assessed. The research assesses spatio-temporal urban growth, its pattern and land use land cover (LULC) changes of using Normalized Difference Vegetation Index (NDVI) and Normalized Difference Building Index (NDBI) analysis. NDVI were performed for vegetation monitoring especially on loss of vegetation land while NDBI were performed for identification of dense urban and built-up areas. The NDVI and NDBI density results show a significant decreased of vegetation land and a leap up increased of urban and built-up land use. This indicates a significant rapid growth development and a vast transformation of agricultural and forest land into low density development. A rapid urban growth of regional development corridor has significant influence on environment change especially to their periphery. The utilization of both NDVI and NDBI as surrogates has the capability to provide dynamic view and improve the accuracy of land use land cover change analysis. The study showed urban growth has quadrupled from 1991 to 2019 with most rapid growth was from 1991 to 2005 due to greater low-density development and a discontinue growth pattern in the past years compare with much sustainable higher-density development in the recent years.

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“…To address the aforementioned research gaps, this paper integrated Support Vector Machine (SVM) and Random Forest (RF) machine learning models due to their impressive functionalities in analysis and achieving local minima and generalization with a small sample size [42,43] to classify polluted and nonpolluted vegetation and wetland. This will be followed by a comprehensive assessment of the impacts and recovery trend of the polluted vegetation and wetland over an extended period.…”

Section: Introductionmentioning

confidence: 99%

Spatio-Temporal Analysis of Oil Spill Impact and Recovery Pattern of Coastal Vegetation and Wetland Using Multispectral Satellite Landsat 8-OLI Imagery and Machine Learning Models

et al. 2020

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Oil spills are a global phenomenon with impacts that cut across socio-economic, health, and environmental dimensions of the coastal ecosystem. However, comprehensive assessment of oil spill impacts and selection of appropriate remediation approaches have been restricted due to reliance on laboratory experiments which offer limited area coverage and classification accuracy. Thus, this study utilizes multispectral Landsat 8-OLI remote sensing imagery and machine learning models to assess the impacts of oil spills on coastal vegetation and wetland and monitor the recovery pattern of polluted vegetation and wetland in a coastal city. The spatial extent of polluted areas was also precisely quantified for effective management of the coastal ecosystem. Using Johor, a coastal city in Malaysia as a case study, a total of 49 oil spill (ground truth) locations, 54 non-oil-spill locations and Landsat 8-OLI data were utilized for the study. The ground truth points were divided into 70% training and 30% validation parts for the classification of polluted vegetation and wetland. Sixteen different indices that have been used to monitor vegetation and wetland stress in literature were adopted for impact and recovery analysis. To eliminate similarities in spectral appearance of oil-spill-affected vegetation, wetland and other elements like burnt and dead vegetation, Support Vector Machine (SVM) and Random Forest (RF) machine learning models were used for the classification of polluted and nonpolluted vegetation and wetlands. Model optimization was performed using a random search method to improve the models’ performance, and accuracy assessments confirmed the effectiveness of the two machine learning models to identify, classify and quantify the area extent of oil pollution on coastal vegetation and wetland. Considering the harmonic mean (F1), overall accuracy (OA), User’s accuracy (UA), and producers’ accuracy (PA), both models have high accuracies. However, the RF outperformed the SVM with F1, OA, PA and UA values of 95.32%, 96.80%, 98.82% and 95.11%, respectively, while the SVM recorded accuracy values of F1 (80.83%), OA (92.87%), PA (95.18%) and UA (93.81%), respectively, highlighting 1205.98 hectares of polluted vegetation and 1205.98 hectares of polluted wetland. Analysis of the vegetation indices revealed that spilled oil had a significant impact on the vegetation and wetland, although steady recovery was observed between 2015-2018. This study concludes that Chlorophyll Vegetation Index, Modified Difference Water Index, Normalized Difference Vegetation Index and Green Chlorophyll Index vegetation indices are more sensitive for impact and recovery assessment of both vegetation and wetland, in addition to Modified Normalized Difference Vegetation Index for wetlands. Thus, remote sensing and Machine Learning models are essential tools capable of providing accurate information for coastal oil spill impact assessment and recovery analysis for appropriate remediation initiatives.

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The Effect of NDVI Time Series Density Derived from Spatiotemporal Fusion of Multisource Remote Sensing Data on Crop Classification Accuracy

Cited by 28 publications

References 49 publications

Cropping Patterns of Annual Crops: A Remote Sensing Review

Cropping Patterns of Annual Crops: A Remote Sensing Review

Landsat observation of urban growth and land use change using NDVI and NDBI analysis

Spatio-Temporal Analysis of Oil Spill Impact and Recovery Pattern of Coastal Vegetation and Wetland Using Multispectral Satellite Landsat 8-OLI Imagery and Machine Learning Models

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