Tracking the dynamics of paddy rice planting area in 1986–2010 through time series Landsat images and phenology-based algorithms
Jinwei Dong a,b
, Xiangming Xiao a,b,c,
⁎, Weili Kou a,b,d
, Yuanwei Qin a,b
, Geli Zhang a,b
, Li Li a,b,e
, Cui Jin a,b
, Yuting Zhou a,b
, Jie Wang a,b
, Chandrashekhar Biradar f
, Jiyuan Liu g
, Berrien Moore III h
a Department of Microbiology and Plant Biology, University of Oklahoma, Norman, OK 73019, USA
b Center for Spatial Analysis, University of Oklahoma, Norman, OK 73019, USA
c Institute of Biodiversity Science, Fudan University, Shanghai 200433, China
d School of Computer Science and Information, Southwest Forestry University, Kunming, 650224 China
e College of Information & Electrical Engineering, China Agricultural University, Beijing 100083, China
f International Center for Agricultural Research in Dry Areas, Amman 11195, Jordan
g Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
h College of Atmospheric and Geographic Sciences, University of Oklahoma, Norman, OK 73019, USA
Dong, J., et al., Tracking the dynamics of paddy rice planting area in 1986–2010 through time series Landsat images and phenology-based algorithms, Remote Sensing of Environment (2015), http://dx.doi.org/10.1016/j.rse.2015.01.004
abstract
Agricultural land use change substantially affects climate, water, ecosystems, biodiversity, and human welfare. In recent decades, due to increasing population and food demand and the backdrop of global warming, croplands have been expanding into higher latitude regions. One such hotspot is paddy rice expansion in northeast China. However, there are no maps available for documenting the spatial and temporal patterns of continuous paddy rice expansion. In this study, we developed an automated, Landsat-based paddy rice mapping (LandsatRICE) system that uses time series Landsat images and a phenology-based algorithm based on the unique spectral characteristics of paddy rice during the flooding/transplanting phase. As a pilot study, we analyzed all the available Landsat images from 1986 to 2010 (498 scenes) in one tile (path/row 113/27) of northeast China, which tracked paddy rice expansion in epochs with five-year increments (1986–1990, 1991–1995, 1996–2000, 2001–2005, and 2006–2010). Several maps of land cover types (barren land and built-up land; evergreen, deciduous and sparse vegetation types; and water-related land cover types such as permanent water body, mixed pixels of water and vegetation, spring flooded wetlands and summer flooded land) were generated as masks. Air temperature was used to define phenology timing and crop calendar, which were then used to select Landsat images in the phenology-based algorithms for paddy rice and masks. The resultant maps of paddy rice in the five epochs were evaluated using validation samples from multiple sources, and the overall accuracies and Kappa coefficients ranged from 84 to 95% and 0.6–0.9, respectively. The paddy rice area in the study area substantially increased from 1986 to 2010, particularly after the 1990s. This study demonstrates the potential of the Landsat-RICE system and time series Landsat images for tracking agricultural land use changes at 30-m resolution in the temperate zone with single crop cultivation.
Keywords: Paddy rice, Landsat-RICE’ Phenology, Land use change, Northeast China
5. Conclusions
Landsat is the only remote sensing data source to track continuous regional land use change back to the 1980s at 30-m spatial resolution; however, the use of time series Landsat imagery in LCLUC studies faces a series of challenges, including data quality issues (e.g., clouds, cloud shadows, and SLC-off), uneven data availability at temporal and spatial scales, incomplete datasets from various ground receiving stations, as well as relatively big data size and computation requirements. To our knowledge, this study is the first to analyze all of the available time series Landsat data in a path/row to quantify the dynamics of paddy rice planting area in northeast China. The results clearly demonstrate the value and potential for the data mining of all available time series Landsat images for long-term LCLUC studies. The Landsat-RICE system has the potential to effectively track paddy rice planting area changes in the temperate zones of northeast Asia, where there is one single cropping season and the temperature-based plant growing season can effectively simplify and improve image selection and the extraction of the rice flooding and transplanting signals. However, the extended application of the system to other regions, e.g., the subtropical and tropical regions, needs additional case studies and algorithm improvement. Future studies are needed to further develop the time series Landsat data processing system and phenology-based algorithms in an effort to improve the classification of other land cover types, which includes comprehensive study of phenology of individual land cover types and development of better phenological metrics and rules.
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