Extracting seasonal cropping patterns using multi-temporal vegetation indices from IRS LISS-III data in Muzaffarpur District of Bihar, India

Mondal, Saptarshi; Jeganathan, C.; Sinha, Nitish Kumar; Rajan, Harshit; Roy, Tapas; Kumar, Prabhat

doi:10.1016/j.ejrs.2014.09.002

Cited by 33 publications

(23 citation statements)

References 39 publications

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“…Destacase a imagem do ano de 2012 por ter sido obtida pelo satélite IRS-P6, o qual possui a bordo o sensor LISS3. Devido à falta de imagens dos satélites Landsat 5 e 7 a partir de novembro de 2011 e a inoperância à época do satélite Landsat 8, MONDAL et al (2014) testaram alguns índices de vegetação utilizando as imagens do satélite IRS-P6, sensor LISS3. Este sensor não possui as bandas 1 e 7 como os sensores Landsat, portanto possui menor resolução espectral, no entanto os autores consideraram o sensor LISS3 uma ferramenta viável, pois as imagens processadas permitem a detecção de alvos como estradas, danos no dossel e pequenas clareiras.…”

Section: Resultsunclassified

Técnicas De Sensoriamento Remoto No Âmbito Do Licenciamento De Plano De Manejo Florestal Madeireiro No Estado De Mato Grosso

Silva¹,

Côrte²,

Vasconcellos³

et al. 2017

Enci Bio

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Section: Resultsunclassified

Técnicas De Sensoriamento Remoto No Âmbito Do Licenciamento De Plano De Manejo Florestal Madeireiro No Estado De Mato Grosso

Silva¹,

Côrte²,

Vasconcellos³

et al. 2017

Enci Bio

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“…In savannah, high variability of the herbaceous layer is reflected in the overall phenological signal of a plot and this can affect the tree/grass separation method (Whitecross, Witkowski, and Archibald 2017a;Whitecross, Witkowski, and Archibald 2017b). Even though there can be small changes in the amplitude mostly contributed by the grass layer (Scanlon et al 2002), the use of a composite data set (Holben 1986;Hilker et al 2009) is more promising than a single-date data set (Mondal et al 2014) Although the lidar/SAR tree product, tree cover estimated using harmonic analysis (with estimated tree cover by Bucini), and the lidar/SAR data had strong correlation with field data, the level of uncertainty in the estimates from these products compared to field data is probably due to time gap between the remote-sensing data and the time of the field campaign. For example, the Bucini woody cover map was produced from the Landsat data acquired between 2000 and 2001 and the JERS-1 SAR scenes (L-band, HH polarization) were acquired between 1995 and 1996.…”

Section: Discussionmentioning

confidence: 99%

Estimating fractional cover of plant functional types in African savannah from harmonic analysis of MODIS time-series data

Ibrahim

Balzter

Tansey

et al. 2018

International Journal of Remote Sensing

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Assessments of tree/grass fractional cover in savannahs using remote sensing are challenging due to the heterogeneous mixture of the two plant functional types. Time-series decomposition models can be used to characterize vegetation phenology from satellite data, but have rarely been used for attributing phenological signal components to different plant functional types. Here, tree/ grass dynamics are assessed in savannah ecosystems using timeseries decomposition of 14 years of Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index data acquired from 2002 to 2015. The decomposition method uses harmonic analysis and tests the individual harmonic terms for statistical significance. Field data of fractional cover of trees and grasses were collected for 28 plots in Kruger National Park, South Africa. Matching MODIS pixels were analysed for their tree/grass phenological signals. Tree/grass annual and interannual variability were then assessed based on the harmonic models. In most harmonic cycles, grass-dominated sites had higher amplitudes than tree-dominated sites, while the tree green-up started earlier than grasses, before the start of the wet season. While changes in tree phenology are gradual, grasses present higher variability over time. Tree cover showed a significant correlation with the amplitude (r (correlation coefficient) = −0.59, p = 0.001) and phase of the first harmonic term (r = −0.73, p = 0.0001) and the number of cycles of the second harmonic term (r = 0. 56, p = 0.002). Grass cover was also significantly correlated with the amplitude (r = 0. 51, p = 0.005) and phase of the first harmonic term (r = 0.55, p = 0.002) and the number of cycles of the second harmonic term (r = −0.52, p = 0.005). The positive correlation of grass cover with phase and negative correlation with number of cycles is indicating a late greening period and higher variability, respectively. Tree cover estimated from the phase of the strongest harmonic term showed a positive correlation with field-measured tree cover (R 2 (coefficient of determination) = 0.55, p < 0.01, slope = 0.93, root mean square error = 13.26%). The estimated tree cover also had a strong correlation with the woody cover map (r = 0.78, p < 0.01) produced by Bucini. The results show that MODIS time-series data can be used to estimate the fractional tree cover in heterogeneous savannahs from the phase of the plant functional type's phenological behaviour. This study shows that harmonic analysis is able to discriminate between fractional cover by trees and grasses in savannahs. The quantitative analysis of tree/grass phenology from satellite time-series data enables a better understanding of the dynamics of the tree/grass competition and coexistence.

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“…The reason being that during this time, other agricultural crops are not grown by most of the farmers. Most fields then are without crops, which helped to isolate the spectral responses of other vegetation or crops and map area under horticulture in the region with a high degree of accuracy (Mondal et al 2014). Settlement areas, water bodies and forest boundaries were digitised, with an overlay of classified image as an area of interest (AOI) file, and the area converted into the correct class using fill algorithm in ERDAS software.…”

Section: Methodsmentioning

confidence: 99%

Study on Land Use Dynamics: Appropriate Methods for Change Estimation in Social Science Research

et al. 2017

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Background Studies on dynamics of land use change still rely on survey questionnaires to collect the data. This study was undertaken to explore the possibility of an appropriate method for estimating land use land cover (LULC) changes for temporal periods for which there is no direct evidence of change spatially explicit. Purpose The hypothesis of this study is that the data on land use generated by official records, empirical field studies, and analysis of satellite imagery resemble with each other. However, in case of inconsistencies the aim of the present study was to identify a scientific and effective method for obtaining precise data on land use dynamics as part of developing appropriate strategies for a sustainable land use management. Methods The land use information on taluk (block) level was collected from the official records. A questionnaire-based survey was done for collecting household data on land use during the period 2014-2016. Furthermore, satellite data were analysed for land use change estimation. In this study, the conventional methods as well as advanced techniques have been used for estimating crop area at various spatial scales. Per pixel classification techniques were used for generating thematic maps from raster data sets. Data collected from GPS were converted into a shape file for generating a signature file for classification of satellite data sets. While generating thematic maps, the maximum likelihood and parallelepiped classification methods were used. Results The inconsistencies were found between land use data obtained from field studies/official records and the data obtained from analysis of remote-sensing imagery. The satellite data outputs are validated using ground-truth data. Various accuracy assessment metrics such as overall accuracy and kappa coefficient (K hat ) are computed from the validation procedure. The overall accuracy and K hat of the generated thematic maps on LULC are found to be ranging from 79 to 84% and 0.76 to 0.83 for various points of time. However, data obtained from official records and results obtained through questionnaire-based survey could not be cross-checked for an accuracy assessment as the physical conditions of land significantly differ year by year. Conclusion An overall spatial and temporal assessment of land use data, using only a questionnaire-based survey, is not scientifically appropriate. Therefore, this study advocates a trans-disciplinary research in social science based on the convergence of social science and space science and technology.

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Extracting seasonal cropping patterns using multi-temporal vegetation indices from IRS LISS-III data in Muzaffarpur District of Bihar, India

Cited by 33 publications

References 39 publications

Técnicas De Sensoriamento Remoto No Âmbito Do Licenciamento De Plano De Manejo Florestal Madeireiro No Estado De Mato Grosso

Técnicas De Sensoriamento Remoto No Âmbito Do Licenciamento De Plano De Manejo Florestal Madeireiro No Estado De Mato Grosso

Estimating fractional cover of plant functional types in African savannah from harmonic analysis of MODIS time-series data

Study on Land Use Dynamics: Appropriate Methods for Change Estimation in Social Science Research

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