AAFC Annual Crop Inventory

Transcription

1 AAFC Annual Crop Inventory Status and Challenges Fisette, T., Rollin, P., Aly, Z., Campbell, L., Daneshfar, B., Filyer, P., Smith, A., Davidson, A., Shang, J., Jarvis, I. Agriculture and Agri-Food Canada Ottawa, Canada Abstract Understanding the state and trends in agriculture production is essential to combat both short-term and long-term threats to stable and reliable access to food for all, and to ensure a profitable agricultural sector. In 2007, Agriculture and Agri- Food Canada (AAFC) took its first steps towards the development of an operational software system for mapping the crop types of individual fields using satellite observations. Focusing on the Prairie Provinces in 2009 and 2010, a Decision Tree (DT) based methodology was applied using optical (Landsat-5, AWiFS, DMC, SPOT) and radar (Radarsat-2) imagery. For the 2011 growing season and further years, this activity is extended to other provinces in support of a national crop inventory. At present, this approach can consistently deliver a crop inventory that meets the overall target accuracy of at least 85% at a final spatial resolution of 30m. To achieve full operational status, however, further development is required to optimize the data processing chain. Crop maps covering Canada s entire agricultural region are typically delivered eight months following the end of the growing season. To better meet the needs of AAFC and its partners, as well as those of potential new users, map delivery needs to be more timely. Indeed, there is considerable demand for two map products: an estimated within-season inventory (released during the growing season) as well as a final end-of-season inventory (released shortly after the end of the growing season). To this end, Earth Observation Service (EOS) staff is implementing a new and fully automated crop classifier that should significantly reduce production time. In 2012, the lack of affordable optical data forced AAFC to rely mostly on RADARSAT-2 data. This brings new challenges, given a doubling of the number of images as compared to In the coming years, new EO data (Landsat 8, Radarsat Constellation Mission, Sentinel-2) will have a significant positive impact on the quality of the AAFC crop inventory. Keywords Land Cover; Land Use; Remote Sensing; Agriculture; Classification I. INTRODUCTION Our ability to manage Canada s agriculture sector is only as good as the information available to inform decision making. The reliable flow of information among scientists, practitioners and policy makers is essential for identifying the challenges that threaten the stability, competitiveness and profitability of the Canadian agricultural sector. It is also critical for developing, supporting and evaluating the policies put in place to address these challenges, such as those relating to business risk management, flood and drought warning, pest management, environmental assessment, market access, food security, and public health and safety. The extent and complexity of the Canadian agricultural landscape means that earth-orbiting satellites are central to maintaining information flow by delivering timely and relevant information on Canadian agricultural production trends. In 2007, Agriculture and Agri-Food Canada (AAFC) took its first steps towards the operational delivery of this information by developing a software system for mapping crop types using satellite observations. This system is based on two decades of remote sensing research and applications development at AAFC and elsewhere, and has been used to map the agricultural land use of individual fields in Alberta, Saskatchewan and Manitoba for the years 2009 and 2010, and all but one of the Canadian Provinces in 2011 and 2012 (Newfoundland will be added in 2013). The crop inventory provides fundamental information on the state and changes in Canada s agricultural landscape, and its value is wide-ranging. For example, the 2011 inventory (Fig.1) included the identification of acreages that had been too wet to seed earlier in the year. These estimates fell within 3% of figures provided independently by the Provinces. The inventory has also been used to validate the practices of Canadian canola producers who wish to access the European bio-fuel feedstock market, estimated to be worth $500 million annually. This paper presents an overview of the current methodology and challenges the AAFC Crop Inventory currently faces. II. METHODS For operational purposes, AAFC needs a crop classification system that is efficient, economic, stable and repeatable, and one that can handle a large data volume capacity. The classification can be broken down into six generalized steps that are required to produce a digital crop type map (Fig. 2). A. Data Planning & Acquisition Successful crop identification relies on image acquisitions from multiple sensors during key crop phenological stages (reproduction, seed development and senescence). Multitemporal optical data are the primary data source for crop classification because the NIR/SWIR channels are vital to crop classification. Over a growing season, at least three optical

2 images are required to successfully identify crops. To the optical data, we add dual-polarization RADARSAT-2 data. The ScanSAR mode, with its large swath (300 km) and moderate resolution (50 m), fits the agricultural landscape of the Prairie provinces. Elsewhere, the finer resolution of the Wide mode (30 m) is better suited to narrow fields. In 2012, we processed more than 2500 combinations of imagery in our classifier. Overall accuracy for each combination was calculated and summarized (Table I). Results have shown that a classification containing only one or two optical images can have its overall accuracy improved by up to 16% by adding dual-polarization radar images. after a color-balance, if necessary. This process allows the creation of large classification region with more training sites. Fig. 2. AAFC crop mapping flowchart. Fig. 1. The 2011 crop inventory. Annual crop insurance data are the most accurate, detailed and complete sources of information for crop types in Canada. As such, AAFC cooperates with crop insurance agencies to use their data for the training and validation of satellite data analysis. For provinces where insurance data cannot be accessed, ground-truth information is provided by point observations from AAFC staff or other provincial sources. The NLWIS circa 2000 land cover map [1] is used to define the agricultural extent under each annual crop inventory. This map is updated to the current year through an automated process. B. Data Pre-Processing Optical data are not atmospherically corrected. Results from other studies [2,3] demonstrate that an atmospheric correction is unnecessary when classifying multi-temporal data, assuming that the training data comes from the same images as those being classified. If not already geometrically corrected, images were orthorectified using a 3-D multi-sensor physical model developed at the Canada Centre for Remote Sensing [4] and implemented in PCI Geomatica software. Ground control points (GCPs) used for the geometric correction came from the National Road Network database of Geobase, an initiative overseen by the Canadian Council on Geomatics. A Tasseled Cap (TC) algorithm, in which Bands 1 to 5 and 7 were converted into three channels (brightness, greenness, wetness) was applied to each Landsat image pair. The TC transformation reduces data processing while maintaining accuracy [5,6]. A semi-automated cloud and shadow masking technique was applied to every optical image. A gamma maximum-a-posteriori filter is applied on radar data to remove frequency noise (speckle) and resampled to 16-bit. Along-track images of the same date are mosaicked together C. Define Classification Regions The country is divided into regions that are classified independently (Fig. 3). Each of these regions combines several optical and radar dates. Around 100 regions are defined nationally and the combination of imagery per region depends on many factors including: image overlap, cloud cover, agricultural extent and training site distribution. D. Classification Process Classification is performed on a region-by-region basis because the dynamic nature of crop rotations, crop growth and harvest patterns create significant reflectance differences between adjacent satellite scenes within the temporal period encompassed by scene availability. All classifications are conducted using digital number (DN) values. Time series images are used for each classification in order to separate the various crops classes, as they have varying spectral characteristics over the growing season. The DT method, as implemented in See5 software, is a multivariate model based on a set of decision rules defined by combinations of features and a set of linear discriminant functions that are applied at each test node. Decision boundaries and coefficients for the linear discriminate function are estimated empirically from the training data. The DT method was chosen because of its ability to handle discrete data, its processing speed, its independence of the distribution of class signatures, its interpretable classification rules [7,8] and its cost effectiveness. Advanced options such as pruning and boosting have also been incorporated into the decision tree classification process to improve the accuracy of the algorithm. TABLE I. AVG. OVERALL CROP CLASSIFICATION ACCURACIES (2012) Number of Optical Dates Number of Radar Dates % 44.6% 48.9% 52.4% 54.1% % 65.8% 69.5% 72.1% 74.3% 74.7% % 70.3% 73.7% 75.9% 78.4% 75.7%

3 A Graphical User Interface (GUI) developed by AAFC automatically manages the classification processes through See5, PCI Geomatica and ArcGIS. The training sites and spectral information were entered into the See5 software. A two-level hierarchical classification scheme is implemented in the methodology. This hierarchical approach allows the agriculture landscape to be segmented into general classes within which more detailed classes can be discriminated. Fig. 3. Division of the 2012 agricultural extent of Canada into regions. The classification process is divided into three iterations. The first iteration is updating the NLWIS circa 2000 land cover to the current year. Random selection of training sites is performed across the land cover map and use as input to the classifier. To reduce the error, only some changes are allowed between the year 2000 and the current year; for example, a pixel identified as urban in 2000 could not be changed to another land cover class. With current year training sites as input to the classifier, the second iteration is mapping only two classes: agriculture and grassland. The three maps (the NLWIS circa 2000, the new updated land cover from random training sites and the agriculture vs grassland map), are combined together through a voting or majority approach. The final iteration is the actual crop classification that is restricted to the agriculture extent of the previously created map. Within a region, clouds, overlapping and nonoverlapping areas must be processed independently as they will generate different rulesets. One of the See5 process output is a thematic layer partitioning the scene into homogenous zones representing these areas. Zones also contain crop accuracy values estimated from the validation data. E. Classification Post-Processing For each classified region, the multi-temporal optical bands were input to the ecognition segmentation algorithm that derives polygons (object primitives). A spatial scale of 10 was found to be a good compromise between the number of objects and individual field representation. The polygons were imported into Esri ArcGIS Spatial Analyst module and a majority zonal statistic was calculated on the per pixel classification. This assigned the majority class value within each object primitive to the entire polygon. That step improves the overall accuracy by about 5%. Classification regions are then mosaicked together through an automated process. Zones with higher accuracies within a region will get priority over lower accuracy zones of another region. A per-province final accuracy assessment is made with the validation data. F. Data Publication Data are distributed in raster format by UTM zones and by province. It can be downloaded on request from an AAFC ftp link. Annual crop maps are typically delivered eight months following the end of growing season. III. CHALLENGES Multi-spectral data (Landsat, SPOT, DMC, AWiFS, etc.) can adequately classify crops if data are available during critical time periods. Accuracy can be significantly degraded if gaps due to clouds in data collection occur. To mitigate that risk and to reduce data costs associated with some optical data, AAFC is working to develop a radar-only approach to crop classification. For major crops such as canola, spring wheat, lentil, and field peas, over 85% accuracies can be reached early in the growing season (early July) when multitemporal multi-beam RADARSAT-2 Fine Quad (FQ) data are used [9]. However, for our large area mapping, using the very small 25 by 25 km FQ scene size is not an option. The next generation sensors such as Canada s RADARSAT- Constellation will enable maintenance of a larger swath coverage than actual fully polarimetric SAR systems [10]. Tests are on-going but results to date have been very encouraging. Research has also demonstrated that with 2 or more frequencies (X+C, C+L, X+C+L), no optical data are required [11]. For now, the operational use of multi-frequency radar data over large areas is difficult considering the price and availability of data. There is a growing demand for the crop inventory to be made available immediately following the growing season. Many users would also like to see an estimated inventory published within the season. Achieving these objectives will require the optimization of our classification process that could take up to a week for a single region. A new and fully automated crop classifier that should significantly reduce production time is under production. As a major upgrade, classification regions will be defined automatically based on images extent, cloud coverage, training site distribution and preliminary accuracies. In addition to reducing analyst intervention, it will also optimize product accuracy. In 2012, the lack of affordable optical data forced AAFC to rely mostly on RADARSAT-2 observations (Fig. 4). This brings new challenges, such as a doubling of the number of images as compared to In 2013, the total number of scenes will be around 2000, including 1200 RADARSAT-2 scenes and 800 Landsat-8 images. The increase in imagery forces us to reconsider some aspects of our methodology. For our next inventory, control and execution of the various production steps, from the data downloading to the map publication will entirely be done through a database. Acquisition conflicts between RADARSAT-2 data users represent an important problem for EOS. These conflicts create data gaps in some areas such as coastal zones and petroleum mining areas. This problem was amplified recently with the malfunction of RADARSAT-1. Over the next 2 years, modifications to our classification system will be made to ingest and process new satellite data such as the Radarsat Constellation Mission (RCM), ESA s Sentinel and Landsat 8.

4 product accuracy, and significantly reducing the delivery time of the final product. A better data distribution mechanism through a web portal is also under development. Fig. 4. Number and types of images used for classification (2010 to 2012). Recently, new classifier algorithms, such as the Random Forest (RF) classifier, have become available. When compared to our traditional DT classifier, the RF classifier delivered several advantages: (i) overall and class specific accuracies were generally higher, (ii) a supplementary variable importance measure was provided, and (iii) in tests of the classification speed, the RF outperformed the DT by classifying 18 times faster [12]. For six regions across the country, the RF classifier was tested against the EOS DT classifier. In all cases, the RF algorithm was more accurate than the DT (Table II). Each year, AAFC staff collects tens of thousands of points identifying crops across the country. These points are used as training or reference sites and needs to be associated to a field contour. Much effort is required to digitize from high resolution imagery. To reduce the time allocated to this task, an automatic method based on the use of cadastral data and image segmentation is under development. At the scale of the country, the crop type legend is not homogeneous. In some provinces, such as Alberta, Saskatchewan and Quebec, we have been able to divide the cereal crops in sub-categories (Barley, oats, wheat, etc.). For other provinces, the cereals class has not been subdivided. The lack of training sites and, in some cases, the limited availability of spectral data does not allow to differentiate cereals into sub-categories with sufficient precision. This results in class discontinuities between provinces. In 2013, the use of Landsat-8 and the increase of sampling sites in targeted regions should resolve the problem. TABLE II. Region 1 (Maritime) Region 2 (Ontario) Region 3 (Manitoba) Region 4 (Saskatchewan) Region 5 (Saskatchewan) Region 6 (Quebec) DT VS RF OVERALL CLASSIFICATION ACCURACIES. DT Overall Accuracy (%) RF Overall Accuracy (%) Difference (%) Significant headway needs to be made in the development of methods for the annual crop inventory. This includes new approaches for assimilating and processing the unprecedented volume of EO data required to map nationally, and the development of new classification methodology, increasing IV. CONCLUSIONS A Decision Tree (DT) method has been developed to successfully classify Canadian agricultural lands. Post filtering and the combination of images into mosaics make use of object segmentation. PCI Geomatica, See5, Esri, and ecognition software were integrated in to a custom GUI written in VB.Net. In addition to being a flexible land cover classification tool, the DT approach permits the integration of disparate data sources. The AAFC methodology takes advantage of the segmentation process of ecognition, which reduces the speckle effect found in pixel-based classifiers, relying on a more statistically robust algorithm to automate class separation. At the national scale, our 85% target accuracy was reached. For some regions, however, the level of precision is much lower. Considering Canada s diverse agricultural landscape, we ll have to better understand the variables that impact the classification accuracy in some sectors and adjust our methodology accordingly. Our intent is not to build a static methodology but rather to constantly improve it as our classification work evolves. Integration of other earth observation imagery and geospatial data within the classifier will continue to be explored. REFERENCES [1] T. Fisette, R. Chenier, M. Maloley, P.Y. Gasser, T. Huffman, L. White, R. Ogston and A. 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