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An automated cropland classification algorithm (ACCA) by combining MODIS, Landsat, and Secondary Data for the State of California. Photogramm. Eng. Remote Sensing 2012
"... Abstract: The overarching goal of this research was to develop and demonstrate an automated Cropland Classification Algorithm (ACCA) that will rapidly, routinely, and accurately classify agricultural cropland extent, areas, and characteristics (e.g., irrigated vs. rainfed) over large areas such as a ..."
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Abstract: The overarching goal of this research was to develop and demonstrate an automated Cropland Classification Algorithm (ACCA) that will rapidly, routinely, and accurately classify agricultural cropland extent, areas, and characteristics (e.g., irrigated vs. rainfed) over large areas such as a country or a region through combination of multi-sensor remote sensing and secondary data. In this research, a rule-based ACCA was conceptualized, developed, and demonstrated for the country of Tajikistan using mega file data cubes (MFDCs) involving data from Landsat Global Land Survey (GLS), Landsat Enhanced Thematic Mapper Plus (ETM+) 30 m, Moderate Resolution Imaging Spectroradiometer (MODIS) 250 m time-series, a suite of secondary data (e.g., elevation, slope, precipitation, temperature), and in situ data. First, the process involved producing an accurate reference (or truth) cropland layer (TCL), consisting of cropland extent, areas, and irrigated vs. rainfed cropland areas, for the entire country of Tajikistan based on MFDC of year 2005 (MFDC2005). The methods involved in producing TCL included using ISOCLASS clustering, Tasseled Cap bi-spectral plots, spectro-temporal characteristics from MODIS 250 m monthly normalized difference vegetation index (NDVI) maximum value composites
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, 2011
"... Land cover study in Iowa: analysis of classification methodology and its impact on scale, accuracy, and landscape metrics ..."
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Land cover study in Iowa: analysis of classification methodology and its impact on scale, accuracy, and landscape metrics
NDVI TIME SERIES MODELING IN THE PROBLEM OF CROP IDENTIFICATION BY SATELLITE IMAGES
, 2016
"... Abstract. The paper deals with the problem of NDVI time series modeling and application of simulated data in task of crop identification by satellite images. The simulation is performed for six types (classes) of crops in each agricultural zone, situated in the territory of the Samara region. Simul ..."
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Abstract. The paper deals with the problem of NDVI time series modeling and application of simulated data in task of crop identification by satellite images. The simulation is performed for six types (classes) of crops in each agricultural zone, situated in the territory of the Samara region. Simulation parameters for each class are calculated from the coefficients of approximation which are obtained by approximating the time series of real agricultural fields by the function of a certain kind. The generated sets of simulated time series are used for crop recognition on real fields, located on the territory of the Samara region. Keywords: time series, vegetation index, NDVI, satellite images, crops identification, crops recognition, algorithm for calculating estimates, time series approximation, time series modeling. Citation: Vorobiova NS, Chernov AV. NDVI time series modeling in the problem of crop identification by satellite images.
Article Capability of Integrated MODIS Imagery and ALOS for Oil Palm, Rubber and Forest Areas Mapping in Tropical Forest Regions
, 2014
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Article A Framework for Defining Spatially Explicit Earth Observation Requirements for a Global Agricultural Monitoring Initiative (GEOGLAM)
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Article MODIS-Based Fractional Crop Mapping in the U.S. Midwest with Spatially Constrained Phenological Mixture Analysis
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RESEARCH ARTICLE Census Parcels Cropping System Classification fromMultitemporal Remote Imagery: A Proposed Universal Methodology
"... A procedure named CROPCLASS was developed to semi-automate census parcel crop as-sessment in any agricultural area using multitemporal remote images. For each area, CROPCLASS consists of a) a definition of census parcels through vector files in all of the images; b) the extraction of spectral bands ..."
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A procedure named CROPCLASS was developed to semi-automate census parcel crop as-sessment in any agricultural area using multitemporal remote images. For each area, CROPCLASS consists of a) a definition of census parcels through vector files in all of the images; b) the extraction of spectral bands (SB) and key vegetation index (VI) average val-ues for each parcel and image; c) the conformation of a matrix data (MD) of the extracted in-formation; d) the classification of MD decision trees (DT) and Structured Query Language (SQL) crop predictive model definition also based on preliminary land-use ground-truth work in a reduced number of parcels; and e) the implementation of predictive models to classify unidentified parcels land uses. The software named CROPCLASS-2.0 was devel-oped to semi-automatically perform the described procedure in an economically feasible manner. The CROPCLASS methodology was validated using seven GeoEye-1 satellite im-ages that were taken over the LaVentilla area (Southern Spain) from April to October 2010 at 3- to 4-week intervals. The studied region was visited every 3 weeks, identifying 12 crops and others land uses in 311 parcels. The DT training models for each cropping system were assessed at a 95 % to 100 % overall accuracy (OA) for each crop within its corresponding cropping systems. The DT training models that were used to directly identify the individual crops were assessed with 80.7 % OA, with a user accuracy of approximately 80 % or higher for most crops. Generally, the DT model accuracy was similar using the seven images that were taken at approximately one-month intervals or a set of three images that were taken during early spring, summer and autumn, or set of two images that were taken at about 2 to 3 months interval. The classification of the unidentified parcels for the individual crops was achieved with an OA of 79.5%.