Mapping regional cropping patterns by using GF-1 WFV sensor data

The successful launched Gaofen satellite no. 1 wide field-of-view (GF-1 WFV) camera is characterized by its high spatial resolution and may provide some potential for regional crop mapping. This study, taking the Bei'an City, Northeast China as the study area, aims to investigate the potential of GF-1 WFV images for crop identification and explore how to fully use its spectral, textural and temporal information to improve classification accuracy. In doing so, an object-based and Random Forest (RF) algorithm was used for crop mapping. The results showed that classification based on an optimized single temporal GF-1 image can achieve an overall accuracy of about 83%, and the addition of textural features can improve the accuracy by 8.14%. Moreover, the multi-temporal GF-1 data can produce a classification map of crops with an overall accuracy of 93.08% and the introduction of textural variables into multi-temporal GF-1 data can only increase the accuracy by about 1%, which suggests the importance of temporal information of GF-1 for crop mapping in comparison with single temporal data. By comparing classification results of GF-1 data with different feature inputs, it is concluded that GF-1 WFV data in general can meet the mapping efficiency and accuracy requirements of regional crop. But given the unique spectral characteristics of the GF-1 WFV imagery, the use of textual and temporal information is needed to yield a satisfactory accuracy.

[1]  André Stumpf,et al.  Object-oriented mapping of landslides using Random Forests , 2011 .

[2]  Mathew R. Schwaller,et al.  On the blending of the Landsat and MODIS surface reflectance: predicting daily Landsat surface reflectance , 2006, IEEE Transactions on Geoscience and Remote Sensing.

[3]  Lei Wang,et al.  Comparative Analysis of GF-1 WFV, ZY-3 MUX, and HJ-1 CCD Sensor Data for Grassland Monitoring Applications , 2015, Remote. Sens..

[4]  Huajun Tang,et al.  How Could Agricultural Land Systems Contribute to Raise Food Production Under Global Change , 2014 .

[5]  L. You,et al.  Interpretation of Climate Change and Agricultural Adaptations by Local Household Farmers: a Case Study at Bin County, Northeast China , 2014 .

[6]  Francisca López-Granados,et al.  Object- and pixel-based analysis for mapping crops and their agro-environmental associated measures using QuickBird imagery , 2009 .

[7]  Pedro Antonio Gutiérrez,et al.  Object-Based Image Classification of Summer Crops with Machine Learning Methods , 2014, Remote. Sens..

[8]  Bing Zhang,et al.  Object-Based Crop Classification with Landsat-MODIS Enhanced Time-Series Data , 2015, Remote. Sens..

[9]  Li Wang,et al.  Feature Selection of Time Series MODIS Data for Early Crop Classification Using Random Forest: A Case Study in Kansas, USA , 2015, Remote. Sens..

[10]  Huajun Tang,et al.  Climate change and the food production system: impacts and adaptation in China , 2014, Regional Environmental Change.

[11]  Francisco Javier Gallego,et al.  Efficiency assessment of using satellite data for crop area estimation in Ukraine , 2014, Int. J. Appl. Earth Obs. Geoinformation.

[12]  Brian Brisco,et al.  Crop-type identification potential of Radarsat-2 and MODIS images for the Canadian prairies , 2011 .

[13]  A. Brenning,et al.  Assessing fruit-tree crop classification from Landsat-8 time series for the Maipo Valley, Chile , 2015 .

[14]  Dirk Tiede,et al.  ESP: a tool to estimate scale parameter for multiresolution image segmentation of remotely sensed data , 2010, Int. J. Geogr. Inf. Sci..

[15]  Daniel L. Civco,et al.  Evaluation of data fusion and image segmentation in earth observation based rapid mapping workflows , 2014 .

[16]  J. Six,et al.  Object-based crop identification using multiple vegetation indices, textural features and crop phenology , 2011 .

[17]  Mustafa Turker,et al.  Field-based crop classification using SPOT4, SPOT5, IKONOS and QuickBird imagery for agricultural areas: a comparison study , 2011 .

[18]  Mutlu Ozdogan,et al.  The spatial distribution of crop types from MODIS data: Temporal unmixing using Independent Component Analysis , 2010 .

[19]  Thomas J. Jackson,et al.  Crop condition and yield simulations using Landsat and MODIS , 2004 .

[20]  Y. Ban.  Synergy of multitemporal ERS-1 SAR and Landsat TM data for classification of agricultural crops , 2003 .

[21]  B. Wardlow,et al.  Analysis of time-series MODIS 250 m vegetation index data for crop classification in the U.S. Central Great Plains , 2007 .

[22]  Kenichi Tatsumi,et al.  Crop classification of upland fields using Random forest of time-series Landsat 7 ETM+ data , 2015, Comput. Electron. Agric..

[23]  Mingquan Wu,et al.  Combining HJ CCD, GF-1 WFV and MODIS Data to Generate Daily High Spatial Resolution Synthetic Data for Environmental Process Monitoring , 2015, International journal of environmental research and public health.

[24]  P. Gong,et al.  Efficient corn and soybean mapping with temporal extendability: A multi-year experiment using Landsat imagery , 2014 .

[25]  Heather Reese,et al.  Mapping Tree Canopy Cover and Aboveground Biomass in Sudano-Sahelian Woodlands Using Landsat 8 and Random Forest , 2015, Remote. Sens..

[26]  Chris Roelfsema,et al.  Multi-temporal mapping of seagrass cover, species and biomass: A semi-automated object based image analysis approach , 2014 .

[27]  D. Lobell,et al.  Cropland distributions from temporal unmixing of MODIS data , 2004 .

[28]  Olena Dubovyk,et al.  Derivation of temporal windows for accurate crop discrimination in heterogeneous croplands of Uzbekistan using multitemporal RapidEye images , 2014 .

[29]  Xiao Zheng,et al.  Mapping Spatial Distribution of Larch Plantations from Multi-Seasonal Landsat-8 OLI Imagery and Multi-Scale Textures Using Random Forests , 2015, Remote. Sens..

[30]  B. Wardlow,et al.  A Comparative Analysis of Phenological Curves for Major Crops in Kansas , 2010 .

[31]  Clement Atzberger,et al.  Object Based Image Analysis and Data Mining applied to a remotely sensed Landsat time-series to map sugarcane over large areas , 2012 .

[32]  Qian Song,et al.  Exploring the Use of Google Earth Imagery and Object-Based Methods in Land Use/Cover Mapping , 2013, Remote. Sens..

[33]  Steven E. Franklin,et al.  Multi-scale object-based image analysis and feature selection of multi-sensor earth observation imagery using random forests , 2012 .

[34]  Alexis J. Comber,et al.  Random forest classification of salt marsh vegetation habitats using quad-polarimetric airborne SAR, elevation and optical RS data , 2014 .