Extraction of rice-planted area using a self-organizing feature map

Konishi, Tomohisa; Omatu, Sigeru; Suga, Yuki

doi:10.1007/s10015-007-0431-2

Cited by 10 publications

(4 citation statements)

References 3 publications

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“…Over the last three decades, different methods for rice mapping and monitoring have been developed using remote sensing data [16][17][18]. The spatial extents of these studies range from experimental plots to continental scales and employ unsupervised and supervised [19][20][21][22] rule-based [23], phenology-based [11,24], and time series algorithms [18,24,25]. The Moderate Resolution Imaging Spectroradiometer (MODIS) sensors have been one of the most widely used platforms for rice mapping and monitoring applications at larger regional scales because of the daily revisit, relatively small data size, and availability of spectral information that is particularly pertinent to agriculture [9,10,23,26,27].…”

Section: Introductionmentioning

confidence: 99%

Identifying Dry-Season Rice-Planting Patterns in Bangladesh Using the Landsat Archive

Shew

Ghosh

2019

Remote Sensing

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In many countries, in situ agricultural data is not available and cost-prohibitive to obtain. While remote sensing provides a unique opportunity to map agricultural areas and management characteristics, major efforts are needed to expand our understanding of cropping patterns and the potential for remotely monitoring crop production because this could support predictions of food shortages and improve resource allocation. In this study, we demonstrate a new method to map paddy rice using Google Earth Engine (GEE) and the Landsat archive in Bangladesh during the dry (boro) season. Using GEE and Landsat, dry-season rice areas were mapped at 30 m resolution for approximately 90,000 km2 annually between 2014 and 2018. The method first reconstructs spectral vegetation indices (VIs) for individual pixels using a harmonic time series (HTS) model to minimize the effect of any sensor inconsistencies and atmospheric noise, and then combines the time series indices with a rule-based algorithm to identify characteristics of rice phenology to classify rice pixels. To our knowledge, this is the first time an annual pixel-based time series model has been applied to Landsat at the national level in a multiyear analysis of rice. Findings suggest that the harmonic-time-series-based vegetation indices (HTS-VIs) model has the potential to map rice production across fragmented landscapes and heterogeneous production practices with comparable results to other estimates, but without local management or in situ information as inputs. The HTS-VIs model identified 4.285, 4.425, 4.645, 4.117, and 4.407 million rice-producing hectares for 2014, 2015, 2016, 2017, and 2018, respectively, which correlates well with national and district estimates from official sources at an average R-squared of 0.8. Moreover, accuracy assessment with independent validation locations resulted in an overall accuracy of 91% and a kappa coefficient of 0.83 for the boro/non-boro stable rice map from 2014 to 2018. We conclude with a discussion of potential improvements and future research pathways for this approach to spatiotemporal mapping of rice in heterogeneous landscapes.

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Section: Introductionmentioning

confidence: 99%

Identifying Dry-Season Rice-Planting Patterns in Bangladesh Using the Landsat Archive

Shew

Ghosh

2019

Remote Sensing

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“…Most of the current camouflage design methods use image segmentation technology to extract background texture features, which are used to design the camouflage spots [10,11]. During the actual process of painting the camouflage patterns onto clothes, trucks, and other equipment, pattern templates are used as painting cover to avoid deformation or chaos in the final camouflage pattern, and to achieve good camouflage effects.…”

Section: Camouflage Design Based On Pattern Template Distributionmentioning

confidence: 99%

Design of digital camouflage by recursive overlapping of pattern templates

Xue

Luo

et al. 2016

Neurocomputing

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“…In the meantime, researchers have been using both optical and SAR data with spatial extents for crop mapping (mainly rice). Some of their approaches have been: unsupervised and supervised (Cheema and Bastiaanssen 2010;Konishi et al 2007;Lin 2012;Turner and Congalton 1998), rule-based (Boschetti et al 2017), phenology-based (Dong et al 2015(Dong et al , 2016, and time-series classification algorithms (Dong et al 2016;Shew and Ghosh 2019). Besides, MODIS too has been effectively used for rice mapping and monitoring application scales (Burchfield et al 2016;Nelson et al 2014;Shapla et al 2015).…”

Section: Rice Crop In Bangladesh and Recent Efforts In Mappingmentioning

confidence: 99%

In-Season Crop-Area Mapping for Wheat and Rice in Afghanistan and Bangladesh

Tiwari

Qamer

Matin

et al. 2021

Earth Observation Science and Applications for Risk Reduction and Enhanced Resilience in Hindu Kush Himalaya Region

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Cereal grains are the most commonly grown crops in the world. Wheat and rice are important commodities which contribute to 50% of the world’s food-calorie intake (McKevith Nutr Bull 29(2): 111–142, 2004). These two cereals are critical to food security in the developing regions. In this context, crop-mapping services can be used for detailed monitoring of the cultivated areas; it can also provide the area statistics of specific crops and the data on their intensity across the landscape. This mapping process is also valuable for government agencies since it provides them with critical information that can be used to manage their stocks (for imports and exports). This chapter dwells on a crop-mapping service developed under the SERVIR-HKH program. In this regard, the needs assessment was carried out with the assistance of the governments of Bangladesh and Afghanistan through a consultation workshop. Wheat mapping in Afghanistan and rice mapping in Bangladesh were the top priorities for the respective governments. Here, we discuss two particular mapping exercises that were undertaken in these two countries: wheat mapping in Afghanistan at a national level and the mapping of Boro rice in selected districts of Bangladesh.

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Extraction of rice-planted area using a self-organizing feature map

Cited by 10 publications

References 3 publications

Identifying Dry-Season Rice-Planting Patterns in Bangladesh Using the Landsat Archive

Identifying Dry-Season Rice-Planting Patterns in Bangladesh Using the Landsat Archive

Design of digital camouflage by recursive overlapping of pattern templates

In-Season Crop-Area Mapping for Wheat and Rice in Afghanistan and Bangladesh

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