A5012494631- Remote Sensing in Agriculture
- Soil Moisture and Remote Sensing
- Synthetic Aperture Radar (SAR) Applications and Techniques
- Land Use and Ecosystem Services
- Soil Geostatistics and Mapping
- Remote Sensing and Land Use
- Plant Water Relations and Carbon Dynamics
- Remote Sensing and LiDAR Applications
- Geology and Paleoclimatology Research
- Geological Studies and Exploration
- Species Distribution and Climate Change
- Precipitation Measurement and Analysis
- Ecology and Vegetation Dynamics Studies
- Remote-Sensing Image Classification
- Hydrology and Drought Analysis
- Atmospheric and Environmental Gas Dynamics
- Geochemistry and Geologic Mapping
- Conservation, Biodiversity, and Resource Management
- Rangeland and Wildlife Management
- Climate variability and models
- Climate change and permafrost
- Air Quality and Health Impacts
- Geological Modeling and Analysis
- Climate change impacts on agriculture
- Rice Cultivation and Yield Improvement
Agriculture and Agri-Food Canada
2011-2024
Pfizer (United States)
2023
National Aeronautics and Space Administration
2022
Air Canada
2022
Japan Aerospace Exploration Agency
2022
University of Toronto
2001-2022
Carleton University
2009-2021
Ottawa Research and Development Centre
2018-2020
University of California, Davis
2011-2017
Natural Resources Canada
1969-2017
Early warning information on crop yield and production are very crucial for both farmers decision-makers. In this study, we assess the skill reliability of Integrated Canadian Crop Yield Forecaster (ICCYF), a regional forecasting tool, at different temporal (i.e. 1–3 months before harvest) spatial census agricultural region – CAR, provincial national) scales across Canada. A distinct feature ICCYF is that it generates in-season forecasts well end growing season provides probability...
Crop yield forecasting plays a vital role in coping with the challenges of impacts climate change on agriculture. Improvements timeliness and accuracy by incorporating near real-time remote sensing data use sophisticated statistical methods can improve our capacity to respond effectively these challenges. The objectives this study were (i) investigate derived vegetation indices for spring wheat (Triticum aestivum L.) from Moderate resolution Imaging Spectroradiometer (MODIS) at ecodistrict...
Understanding the state and trends in agriculture production is essential to combat both short-term long-term threats stable reliable access food for all, ensure a profitable agricultural sector. In 2007, Agriculture Agri-Food Canada (AAFC) took its first steps towards development of an operational software system mapping crop types individual fields using satellite observations. Focusing on Prairie Provinces 2009 2010, Decision Tree (DT) based methodology was applied optical (Landsat-5,...
The ability of the Canadian agriculture sector to make better decisions and manage its operations more competitively in long term is only as good information available inform decision-making. At all levels Government, a reliable flow between scientists, practitioners, policy-makers, commodity groups critical for developing supporting agricultural policies programs. Given vastness complexity Canada’s regions, space-based remote sensing one most approaches get detailed describing evolving...
Abstract Understanding factors that influence population connectivity and the spatial distribution of genetic variation is a major goal in molecular ecology. Improvements availability high‐resolution geographic data have made it increasingly possible to quantify effects landscape features on dispersal structure. However, most studies examining such been conducted at very fine (e.g. genetics) or broad phylogeography) scales. Thus, extent which processes operating scales are linked patterns...
We present parallel algorithms and implementations of a bzip2-like lossless data compression scheme for GPU architectures. Our approach parallelizes three main stages in the bzip2 pipeline: Burrows-Wheeler transform (BWT), move-to-front (MTF), Huffman coding. In particular, we utilize two-level hierarchical sort BWT, design novel scan-based MTF algorithm, implement reduction to build tree. For each perform detailed performance analysis, discuss its strengths weaknesses, suggest future...
We present a multi-stage method for solving large tridiagonal systems on the GPU. Previously cannot be efficiently solved due to limitation of on-chip shared memory size. tackle this problem by splitting into smaller ones and then them on-chip. The characteristic our method, together with various workloads GPUs different capabilities, obligates an auto-tuning strategy carefully select switch points between computation stages. In particular, we show two ways effectively prune tuning space...
Abstract A multi‐index drought (MID) model was developed to combine the strengths of various indices for agricultural risk assessment on Canadian prairies, as related spring wheat crop yield. The automatically selects and combines optimum derived from preceding current months they become available better match conditions (both spatially temporally) where work well. cross‐validation results showed that (1) prediction accuracy MID is than (or occasionally equal to) using any single index all...
We evaluated the utility of Terra/MODIS-derived crop metrics for yield estimation across Canadian Prairies. This study was undertaken at Census Agriculture Region (CAR) and Rural Municipality (RM) province Saskatchewan, in three prairie agro-climate zones. compared MODIS-derived vegetation indices, gross primary productivity (GPP), net (NPP) to known yields barley, canola, spring wheat. Multiple linear regressions were used assess relationships between CAR RM levels years 2000 2016. Models...
The water cloud model (WCM) can be inverted to estimate leaf area index (LAI) using the intensity of backscatter from synthetic aperture radar (SAR) sensors. Published studies have demonstrated that WCM accurately LAI if is effectively calibrated. However, calibration this requires access field measures as well soil moisture. In contrast, machine learning (ML) algorithms trained satellite data, even moisture are not available. study, a support vector (SVM) was for corn, soybeans, rice, and...
AbstractGenerating historical AVHRR 1 km baseline satellite data records over Canada suitable for climate change studies Rasim Latifovic, Alexander P. Trishchenko, Ji Chen, William B. Park, Konstantin V. Khlopenkov, Richard Fernandes, Darren Pouliot, Calin Ungureanu, Yi Luo, Shusen Wang, Andrew Davidson, and Josef Cihlar Pages 324-346 Abstract. Satellite are an important component of the global observing system (GCOS). To serve purpose monitoring, these should satisfy certain criteria in...
Petter, M., S. Mooney, M. Maynard, A. Davidson, Cox, and I. Horosak. 2012. A methodology to map ecosystem functions support services assessments. Ecology Society 18(1): 31. https://doi.org/10.5751/ES-05260-180131
Drought is a natural climatic phenomenon that occurs throughout the world and impacts many sectors of society. To help decision-makers reduce drought, it important to improve monitoring tools provide relevant timely information in support drought mitigation decisions. Given complex hazard manifests different forms, can be improved by integrating various types (e.g., remote sensing climate) region specific identify where when droughts are occurring. The Vegetation Response Index for Canada...
Abstract Soil moisture from Moisture Ocean Salinity (SMOS) passive microwave satellite data was assessed as an information source for identifying regions experiencing climate-related agricultural risk a period 2010 to 2013. Both absolute soil and anomalies compared 4-yr SMOS baseline were used in the assessment. The operational of wetter than 30-yr climate normal many locations, particularly late summer most spring province Manitoba. This leads somewhat unrepresentative that skews anomaly...
Accurate crop-type classification is a challenging task due, primarily, to the high within-class spectral variations of individual crops during growing season (phenological development) and, second, between-class similarity crop types. Utilizing within-season multi-temporal optical and multi-polarization synthetic aperture radar (SAR) data, this study introduces combined object- pixel-based image methodology for accurate classification. Particularly, investigates improvement by using least...
Few countries are using space-based Synthetic Aperture Radar (SAR) to operationally produce national-scale maps of their agricultural landscapes. For the past ten years, Canada has integrated C-band SAR with optical satellite data map what crops grown in every field, for entire country. While advantages well understood, barriers its operational use include lack familiarity by end-user agencies and a 'blueprint' on how implement an SAR-based mapping system. This research reviewed order...