Cropland Mapping Research Articles

Cropland for sub-Saharan Africa: A synergistic approach using five land cover data sets

This paper presents a methodology for the creation of a cropland map for Africa through the combination of five existing land cover products: GLC-2000, MODIS Land Cover, GlobCover, MODIS Crop Likelihood and AfriCover. A synergy map is created in which the products are ranked by experts, which reflects the likelihood or probability that a given pixel is cropland. The cropland map is then calibrated with national and sub-national crop statistics using a novel approach. Preliminary validation of the map was undertaken and the results are presented. The resulting cropland map has an accuracy of 83%, which is higher than the accuracy of any of the individual maps. The cropland map is freely available at agriculture.geo-wiki.org.

Mapping of summer crops in the State of Paraná, Brazil, through the 10-day spot vegetation NDVI composites

The search for low subjectivity area estimates has increased the use of remote sensing for agricultural monitoring and crop yield prediction, leading to more flexibility in data acquisition and lower costs comparing to traditional methods such as census and surveys. Low spatial resolution satellite images with higher frequency in image acquisition have shown to be adequate for cropland mapping and monitoring in large areas. The main goal of this study was to map the Summer crops in the State of Paraná, Brazil, using 10-day composition of NDVI SPOT Vegetation data for 2005/2006, 2006/2007 and 2007/2008 cropping seasons. For this, a supervised digital classification method with Parallelepiped algorithm in multitemporal RGB image composites was used, in order to generate masks of Summer cultures for each 10-day composition. Accuracy assessment was performed using Kappa index, overall accuracy and Willmott's concordance index, resulting in good levels of accuracy. This methodology allowed the accomplishment, with free and low resolution data, of the mapping of Summer cultures at State level.

Mapping Cropland and Major Crop Types across the Great Lakes Basin using MODIS-NDVI Data

This research evaluated the potential for using the MODIS Normalized Difference Vegetation Index (NDVI) 16-day composite (MOD13Q) 250 m time-series data to develop an annual crop type mapping capability throughout the 480,000 km 2 Great Lakes Basin (GLB). An ecoregion-stratified approach was developed using a two-step processing approach that included an initial differentiation of cropland versus non-cropland and subsequent identification of individual crop types. Major crop types were mapped for the calendar years of 2002 and 2007. National Agricultural Statistics Service (NASS) census data were used to assess county level accuracies on a unit area basis (2002), and the NASS Crop Data Layer (C DL ) was used to generate 231,616 reference data points to support a pixel-wise assessment of the MODIS crop type classification (2007) accuracy across the US portion of the CLB. County level comparisons for 2002 indicated 2.2, ―6.8, ―6.0, and ―5.8 percent of area bias errors for corn, soybeans, wheat, and hay, respectively. Detailed pixel-wise accuracy assessments resulted in an overall crop type classification accuracy of 84 percent (Kappa = 0.73) for 2007. Kappa coefficients ranged from 0.74 to 0.69 for individual ecoregions. The user's accuracies for corn, soybean, wheat, and hay were 87, 82, 81, and 70 percent, respectively. There were spatial variations of classification performances across ecoregions, especially for soybean and hay. Field sizes had a direct impact on the variable classification performances across the GLB.

Spatial dynamics of cropland and cropping pattern change analysis using landsat TM and IRS P6 LISS III satellite images with GIS

The study of the spatial patterns and temporal changes of cropland is important to understand the underlying factors and the functional effects of the agricultural landscape. On the other hand, crop dynamics mapping is essential to know the overall agro-spatial diversity of the area. Therefore, this paper addressed a spatio-temporal analysis of cropland and cropping pattern change in the Bogra district of Bangladesh over the last 16 years (between 1988/89 and 2004/05). In this paper, crop mapping from multi-temporal and multi-sensor satellite images was described. Landsat TM and IRS P6 LISS III satellite images were used with GIS for spatial dynamics of cropland and cropping pattern change analysis. First, seasonal cropland maps were derived from object-based classification of satellite images, then two-date classified image differencing with GIS overlay technique and decision rules were applied. Cropping pattern change was analyzed in a spatial and quantitative way for the 16 years and for this, Integrated Land and Water Information System (ILWIS) and Land Change Modular (LCM) of IDRISI Andes were used. The results showed that in the area, mono crop cultivation was found in summer, but in winter, areas under different crop cultivation had changed dramatically. Change analysis showed that the changes mainly occurred in the north northwest and southwest of the areas, and during the time the highest change area was found under the rice-potato pattern.

Remotely sensed estimation of cropland in China: a comparison of the maps derived from four global land cover datasets

the University of Maryland Dataset (UMD), the International Geosphere-Biosphere Programme Data and Information System Cover (IGBP-DISCover), the Moderate Resolution Imaging Spectrometer (MODIS) dataset, and the Global Land Cover 2000 (GLC2000). Although produced primarily for global applications, these global land cover datasets are often used for national or regional applications. The objective of this study was to examine the similarities and differences between these datasets in mapping Chinese cropland and to highlight their strengths and weaknesses when used at the national and regional scales. First, the total areas (in ha) of the cropland mapped by the four datasets were compared at the Chinese national, regional, and provincial levels. Second, we performed a pixel-by-pixel comparison of the spatial locations of cropland mapped by the four datasets. These datasets were then separately compared with the Chinese 1996 National Land Survey Dataset (NLSD-1996) to assess their accuracy in cropland mapping. The results show that, despite some agreement, there are discrepancies between the datasets, and the type and extent varies between regions. There are many reasons for the discrepancies, the main ones being the diverse land cover classification schemes, coarse spatial resolution, per-pixel classification approach, and landscape heterogeneity.

Validation and comparison of 1 km global land cover products in China

Four 1 km global land cover products are currently available to the scientific community: the University of Maryland (UMD) global land cover product, the International Geosphere–Biosphere Programme Data and Information System Cover (IGBP‐DISCover), the MODerate resolution Imaging Spectrometer (MODIS) global land cover product and Global Land Cover 2000 (GLC2000). Because of differences in data sources, temporal scales, classification systems and methodologies, it is important to compare and validate these global maps before using them for a variety of studies at regional to global scales. This study aimed to perform the validation and comparison of the four global land cover datasets, and to examine the suitability and accuracy of different coarse spatial resolution datasets in mapping and monitoring cropland across China. To meet this objective, we compared the four global land cover products with the National Land Cover Dataset 2000 (NLCD‐2000) at three scales to evaluate the accuracy of estimates of aggregated cropland areas in China. This was followed by a spatial comparison to assess the accuracies of the four products in estimating the spatial distribution of cropland across China. A comparative analysis showed that there are varying levels of apparent discrepancies in estimating the cropland of China between these four global land cover datasets, and that both area totals and spatial (dis)agreement between them vary from region to region. Among these, the MODIS dataset has the best fit in depicting China's croplands. The coarse spatial resolution and the per pixel classification approach, as well as landscape heterogeneity, are the main reasons for the large discrepancies between the global land cover datasets tested and the reference data.

Landscape-scale characterization of cropland in China using Vegetation and Landsat TM images

In this landscape-scale study we explored the potential for multitemporal 10-day composite data from the Vegetation sensor to characterize land cover types, in combination with Landsat TM image and agricultural census data. The study area (175 km by 165 km) is located in eastern Jiangsu Province, China. The Normalized Difference Vegetation Index (NDVI ) and the Normalized Difference Water Index (NDWI ) were calculated for seven 10-day composite (VGT-S10) data from 11 March to 20 May 1999. Multi-temporal NDVI and NDWI were visually examined and used for unsupervised classification. The resultant VGT classification map at 1 km resolution was compared to the TM classification map derived from unsupervised classification of a Landsat 5 TM image acquired on 26 April 1996 at 30 m resolution to quantify percent fraction of cropland within a 1 km VGT pixel; resulting in a mean of 60% for pixels classified as cropland, and 47% for pixels classified as cropland/natural vegetation mosaic. The estimates of cropland area from VGT data and TM image were also aggregated to county-level, using an administrative county map, and then compared to the 1995 county-level agricultural census data. This landscape-scale analysis incorporated image classification (e.g. coarse-resolution VGT data, fineresolution TM data), statistical census data (e.g. county-level agricultural census data) and a geographical information system (e.g. an administrative county map), and demonstrated the potential of multi-temporal VGT data for mapping of croplands across various spatial scales from landscape to region. This analysis also illustrated some of the limitations of per-pixel classification at the 1 km resolution for a heterogeneous landscape.

Three Tests of Agricultural Remote Sensing for Crop Inventory in Eastern Canada: Results, Problems and Prospects

SUMMARYLANDSAT digital data and several types of airborne remote sensing imagery have been tested for determining white bean area in Ontario, potato area in New Brunswick, and potato and cropland distribution in Prince Edward Island. White bean area was determined with 85–91% accuracy using 1:104,000 colour infra-red photography and 91–96% accuracy using LANDSAT digital data. Potato area was 94% accurate using 1:112,000 true colour imagery and 85% accurate (for the province) using digital LANDSAT data. LANDSAT data were used in PEI to provide cropland maps — cloud prevented an attempt at a potato inventory.A major contribution of these three tests is in the experience which was gained and which is now shared in this paper. Discussed are the importance of ground based expertise in the crop being studied, test site selection, field data collection and its subsequent organization. Simple methods for handling overlap of spectral signatures, and boundary pixels, accuracy assessment, signature extension, and de...