Paddy field classification with MODIS-terra multi-temporal image transformation using phenological approach in Java Island (original) (raw)
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Remote Sensing Applications: Society and Environment, 2018
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Agricultural land use information is required to provide timely spatial information to formulate national food security policies. This study, focused on Paddy rice mapping in Kashmir Himalayan region using multi-temporal data from Moderate Resolution Imaging Spectroradiometer (MODIS) satellite. As part of process, the potential of using satellite spectral reflectance measurements to map and monitor paddy rice in region was evaluated. MODIS satellite data and ground-based field measurements were used to establish the efficacy of results based on remotely sensed indices. The two indices studied were the Normalized Difference Vegetation Index (NDVI) and the Normalized Difference Water Index (NDWI). The water inundation during the initial stage of paddy crop transplantation were used as inputs for Normalized Difference Water Index (NDWI) and absence of vegetation at latter stages of harvest were used as inputs for Normalized Difference Vegetation Index (NDVI) to identifying the initial ...
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Paddy field area and its cropping intensity are main information used to measure the crop production and the response of crop to changing climate conditions. Remote sensing technology has been used widely to map cropping pattern of paddy mostly using spectral analysis of multi temporal multispectral data of remote sensing. However, the cropping intensity of paddy was also influenced by the characteristics of planted land to paddy field which defines the level of land suitability for planting paddy. This research aimed to map paddy rotation by using single date ALI imagery by assessing the land and soil characteristics based on the land suitability parameters for planting paddy. Soil characteristics such as texture, acidity level, P205 (phosphor) and C-organic level collected from field work and terrain characteristics such as landform, surface water, and drainage density from visual delineation of SRTM 90 m was collected as inputs for the decision tree analysis to map the repetiti...
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Knowledge of the area and spatial distribution of paddy rice is important for assessment of food security, management of water resources, and estimation of greenhouse gas (methane) emissions. Paddy rice agriculture has expanded rapidly in northeastern China in the last decade, but there are no updated maps of paddy rice fields in the region. Existing algorithms for identifying paddy rice fields are based on the unique physical features of paddy rice during the flooding and transplanting phases and use vegetation indices that are sensitive to the dynamics of the canopy and surface water content. However, the flooding phenomena in high latitude area could also be from spring snowmelt flooding. We used land surface temperature (LST) data from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor to determine the temporal window of flooding and rice transplantation over a year to improve the existing phenology-based approach. Other land cover types (e.g., evergreen vegetation, permanent water bodies, and sparse vegetation) with potential influences on paddy rice identification were removed (masked out) due to their different temporal profiles. The accuracy assessment using high-resolution images showed that the resultant MODIS-derived paddy rice map of northeastern China in 2010 had a high accuracy (producer and user accuracies of 92% and 96%, respectively). The MODIS-based map also had a comparable accuracy to the 2010 Landsat-based National Land Cover Dataset (NLCD) of China in terms of both area and spatial pattern. This study demonstrated that our improved algorithm by using both thermal and optical MODIS data, provides a robust, simple and automated approach to identify and map paddy rice fields in temperate and cold temperate zones, the northern frontier of rice planting.
Mapping rice areas of South Asia using MODIS multitemporal data
Journal of Applied Remote Sensing, 2011
Our goal is to map the rice areas of six South Asian countries using moderateresolution imaging spectroradiometer (MODIS) time-series data for the time period 2000 to 2001. South Asia accounts for almost 40% of the world's harvested rice area and is also home to 74% of the population that lives on less than $2.00 a day. The population of the region is growing faster than its ability to produce rice. Thus, accurate and timely assessment of where and how rice is cultivated is important to craft food security and poverty alleviation strategies. We used a time series of eight-day, 500-m spatial resolution composite images from the MODIS sensor to produce rice maps and rice characteristics (e.g., intensity of cropping, cropping calendar) taking data for the years 2000 to 2001 and by adopting a suite of methods that include spectral matching techniques, decision trees, and ideal temporal profile data banks to rapidly identify and classify rice areas over large spatial extents. These methods are used in conjunction with ancillary spatial data sets (e.g., elevation, precipitation), national statistics, and maps, and a large volume of field-plot data. The resulting rice maps and statistics are compared against a subset of independent field-plot points and the best available subnational statistics on rice areas for the main crop growing season (kharif season). A fuzzy classification accuracy assessment for the 2000 to 2001 rice-map product, based on field-plot data, demonstrated accuracies from 67% to 100% for individual rice classes, with an overall accuracy of 80% for all classes. Most of the mixing was within rice classes. The derived physical rice area was highly correlated with the subnational statistics with R 2 values of 97% at the district level and 99% at the state level for 2000 to 2001. These results suggest that the methods, approaches, algorithms, and data sets we used are ideal for rapid, accurate, and large-scale mapping of paddy rice as well as for generating their statistics over large areas. C 2011 Society of Photo-Optical Instrumentation Engineers (SPIE). Use: http://spiedl.org/terms Gumma et al.: Mapping rice areas of South Asia using MODIS multitemporal data
Mapping paddy rice agriculture in South and Southeast Asia using multi-temporal MODIS images
Remote Sensing of Environment, 2006
In this paper, we developed a new geospatial database of paddy rice agriculture for 13 countries in South and Southeast Asia. These countries have¨30% of the world population and¨2/3 of the total rice land area in the world. We used 8-day composite images (500-m spatial resolution) in 2002 from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor onboard the NASA EOS Terra satellite. Paddy rice fields are characterized by an initial period of flooding and transplanting, during which period a mixture of surface water and rice seedlings exists. We applied a paddy rice mapping algorithm that uses a time series of MODIS-derived vegetation indices to identify the initial period of flooding and transplanting in paddy rice fields, based on the increased surface moisture. The resultant MODIS-derived paddy rice map was compared to national agricultural statistical data at national and subnational levels. Area estimates of paddy rice were highly correlated at the national level and positively correlated at the subnational levels, although the agreement at the national level was much stronger. Discrepancies in rice area between the MODIS-derived and statistical datasets in some countries can be largely attributed to: (1) the statistical dataset is a sown area estimate (includes multiple cropping practices); (2) failure of the 500-m resolution MODIS-based algorithm in identifying small patches of paddy rice fields, primarily in areas where topography restricts field sizes; and (3) contamination by cloud. While further testing is needed, these results demonstrate the potential of the MODIS-based algorithm to generate updated datasets of paddy rice agriculture on a timely basis. The resultant geospatial database on the area and spatial distribution of paddy rice is useful for irrigation, food security, and trace gas emission estimates in those countries. D
Earth Science …, 2012
Moderate-Resolution Imaging Spectroradiometer (MODIS) satellite data has been widely employed for many applications. It has fine temporal, spectral and spatial resolution. This feature can be used for monitoring earth condition continuously, such as the rice field distribution. Rice fields can be known by detecting the rice plant in that area. Enhanced Vegetation Index 2 (EVI2) is one of the indexes, which describe vegetation conditions and was employed to mapping the rice field. Rice field area was identified using growth curve recognition of EVI2 MODIS data based on rice plant temporal dynamic characteristics. The rice field distributions in Bali that were estimated from MODIS data in 2009 show reasonable spatial agreement with rice field distribution from land use map of 2008. The total area of rice field from MODIS data is 101,218.75 hectare (accuracy 88.21% of reference data). The southern part of Bali has wider rice coverage compared to northern part of Bali because of the topographic condition in southern Bali is suitable for rice cultivation. The regency and district level comparison of rice field area in Bali Province showed a good spatial agreement of accuracy. This indicates that MODIS EVI2 250 m data can be used to mapping homogeneous areas in the small region of district scale. The accuracy identify rice field are affected by several factors, such as spatial resolution and cloud cover of satellite data, elevation of rice field area, and type of rice field.
Assessment the seasonal dynamics of the Java paddy field using MODIS satellite images IJGI2014
Abstract: Accurate information of paddy fields over wide areas is essential to support sustainable agricultural and a food security program. Monitoring of these lands continuously, using remote sensing technology, will provide information related to the cropping intensity in the field, as well as its dynamics change. We characterized seasonal vegetation dynamics from long-term multi-temporal MODIS satellite datasets in order to determine cropping intensity and to analyze the dynamics change in paddy field of Java. The results indicate that the methodology employed in this research distinguished many specific uses in paddy fields as means of their cropping intensity. Moreover, the seasons were the most important factor affected the dynamics change in the agricultural system. Extreme climate variability caused many paddy fields, especially in non-irrigated land, to remain barren as well the planting time was postponed. Indeed, characterizing the long-term vegetation dynamics of paddy field provides information about the characteristics and trends in these land use types, either caused by natural factors or human activities.
Assessing the seasonal dynamics of the Java paddy field using MODIS satellite images IJGI2014
Accurate information of paddy fields over wide areas is essential to support sustainable agricultural and a food security program. Monitoring of these lands continuously, using remote sensing technology, will provide information related to the cropping intensity in the field, as well as its dynamics change. We characterized seasonal vegetation dynamics from long-term multi-temporal MODIS satellite datasets in order to determine cropping intensity and to analyze the dynamics change in paddy field of Java.