Using variance analysis of multitemporal MODIS images for rice field mapping in Bali Province, Indonesia

Nuarsa, I Wayan; Nishio, Fumihiko; Hongo, Chuki; Mahardika, I Gusti Ngurah Kade

doi:10.1080/01431161.2012.661091

Cited by 52 publications

(22 citation statements)

References 27 publications

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“…Finally, we determined a pixel to be labeled as boro rice cultivation, if it satisfied the following set of formula. Note that these were similar to that of Nuarsa et al [17].…”

Section: Formulating Mathematical Model For Extracting the Boro Rice supporting

confidence: 77%

“…In most of the abovementioned studies (see Table 1 [4,[13][14][15][16][17][18][19][20]), the spatial resolution of the input images (i.e., MODIS, AVHRR, and SPOT-VGT) were having relatively low (i.e., in the range 500 m to 1.1 km) [4,[13][14][15][16][17][18]. This particular issue would be critical as the dimension of some rice fields might be smaller than those of the employed spatial resolution.…”

Section: Introductionmentioning

confidence: 99%

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Development of a Remote Sensing-Based “Boro” Rice Mapping System

Mosleh

Hassan

2014

Remote Sensing

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Rice is one of the staple foods across the world, thus information about its production is essential for ensuring food security. Here, our objective was to develop a method for mapping -boro‖ rice (i.e., cultivated during the months January to May) in a Bangladeshi context. In this paper, we used a Moderate Resolution Imaging Spectroradiometer (MODIS)-derived 16-day composite of normalized difference vegetation index (NDVI) at 250 m spatial resolution in conjunction with ancillary datasets (i.e., land use map, crop calendar, and ground-based rice production information) during the period 2007-2012. The proposed method consisted of three procedures: (i) ISODATA clustering and determining the boro rice signatures in temporal dimension using data from the period 2007-2009; (ii) formulating a mathematical model for extracting the boro rice areas using data from the period 2007-2009; and (iii) model calibration using data from the period 2007-2009 and its validation using data from the period 2010-2012. The implementation of the abovementioned procedures revealed reasonable agreements between the model (i.e., MODIS-based) and ground-based estimates of boro rice area at both country (i.e., percentage error in the range −0.83-1.42%) and district levels (i.e., r 2 in the range 0.69-0.89) during the period 2010-2012. Our proposed method demonstrated its effectiveness in mapping rice system at the regional/country scale.

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“…Finally, we determined a pixel to be labeled as boro rice cultivation, if it satisfied the following set of formula. Note that these were similar to that of Nuarsa et al [17].…”

Section: Formulating Mathematical Model For Extracting the Boro Rice supporting

confidence: 77%

Section: Introductionmentioning

confidence: 99%

Development of a Remote Sensing-Based “Boro” Rice Mapping System

Mosleh

Hassan

2014

Remote Sensing

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“…However, using the spectral characteristics (or vegetation indices) determined by the key phenologies of a specific crop species, i.e., multi-temporal remote sensing imageries, is a promising way to improve the classification accuracy [62,63]. To effectively discriminate the rice field in eastern plain region of China, where the rice field is generally fragmented and irregular due to the topography and widely distributed water bodies and road networks, a specifically designed stepwise remote sensing classification strategy was applied in this study.…”

Section: Discussionmentioning

confidence: 99%

Rice Fields Mapping in Fragmented Area Using Multi-Temporal HJ-1A/B CCD Images

et al. 2015

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Rice is one of the most important crops in the world; meanwhile, the rice field is also an important contributor to greenhouse gas methane emission. Therefore, it is important to get an accurate estimation of rice acreage for both food production and climate change related studies. The eastern plain region is one of the major single-cropped rice (SCR) growing areas in China. Subjected to the topography and intensified human activities, the rice fields are generally fragmented and irregular. How remote sensing can meet this challenge to accurately estimate the acreage of the rice in this region using medium-resolution imagery is the topic of this study. In this study, the applicability of the Chinese HJ-1A/B satellites and a two-band enhanced vegetation index (EVI2) was investigated. Field campaigns were carried out during the rice growing season and ground-truth data were collected for classification accuracy assessments in 2012. A stepwise classification strategy utilizing the EVI2 signatures during key phenology stages, i.e., the transplanting and the vegetative to reproductive transition phases, of the SCR was proposed, and the overall classification accuracy was 91.7%. The influence of the mixed pixel and boundary effects to classification accuracy was also investigated. This work demonstrates OPEN ACCESSRemote Sens. 2015, 7 3468 that the Chinese HJ-1A/B data are suitable data source to estimating SCR cropping area under complex land cover composition.

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“…The forest land use type is also dominated by perennial plants and mangroves. Nuarsa et al [66] showed that annual, perennial and mangrove plants in the tropics have relatively stable vegetation indices throughout the year and are unaffected by the monsoon seasons. The agriculture land use type exhibits a seasonal pattern that is influenced by monsoons due to the planting period in which the dominant plant are paddy crops [67].…”

Section: Methodsmentioning

confidence: 99%

Changes in Gross Primary Production (GPP) over the Past Two Decades Due to Land Use Conversion in a Tourism City

Nuarsa

As-syakur

Gunadi

et al. 2018

IJGI

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Understanding the changes in gross primary production (GPP), which is the total carbon fixation by terrestrial ecosystems through vegetation photosynthesis, due to land use conversion in a tourism city is important for carbon cycle studies. Satellite data from Landsat 5, Landsat 7 and Landsat 8 and meteorological data are used to calculate annual GPP for 1995, 2003 and 2014, respectively, using the vegetation production model (VPM) in the tourism city Denpasar, Bali, Indonesia. Five land use types generated from topographic maps in three different years over the past two decades are used to quantify the impacts of land use changes on GPP estimation values. Analysis was performed for two periods to determine changes in land use and GPP value as well as their speed. The results demonstrated that urban land development, namely, the increase of settlement areas due to tourism activity, had overall negative effects on terrestrial GPP. The total GPP of the whole area decreased by 7793.96 tC year −1 (12.65%) during the study period. The decline is due to the conversion of agriculture and grassland area into settlements, which caused the city to lose half of its ability to uptake carbon through vegetation. However, although forest area is declining, forest maintenance and restoration by making them protection areas has been helpful in preventing a drastic decline in GPP value over the past two decades. This study provides information that is useful for carbon resource management, tourism, policy making and scholars concerned about carbon reduction in a tourism city.

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Using variance analysis of multitemporal MODIS images for rice field mapping in Bali Province, Indonesia

Cited by 52 publications

References 27 publications

Development of a Remote Sensing-Based “Boro” Rice Mapping System

Development of a Remote Sensing-Based “Boro” Rice Mapping System

Rice Fields Mapping in Fragmented Area Using Multi-Temporal HJ-1A/B CCD Images

Changes in Gross Primary Production (GPP) over the Past Two Decades Due to Land Use Conversion in a Tourism City

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