Cycle 7 (2018 Deadline)
Establishing a cropland database in Cambodia from remote sensing satellite data
PI: Sanara Hor (email@example.com), Royal University of Agriculture
U.S. Partner: Robert Hijmans, University of California, Davis
Dates: February 2019 - January 2020
Agriculture is a crucial sector in Cambodia, contributing more than one-quarter of the country’s gross domestic product and employing more than 8 million Cambodians. Having accurate information regarding land use in a user-friendly format would be a valuable tool for farmers and government planners alike. The overall purpose of this study is to establish cropland database in Cambodia using remote sensing satellite data. The specific objectives include (1) identifying appropriate satellite images and derived remote sensing products for cropland mapping; (2) developing a rapid cropland mapping tool based on cloud services for multiple time periods; (3) mapping different crop types across lowland and highland cropland regions; (4) exploring pathways to use the research results and technology to benefit Cambodian farmers; and (5) strengthening the research capacity of junior Cambodian researchers. Working in cooperation with their U.S. partners, the principal investigator and his team will examine remote sensing data from multiple sources including Landsat, MODIS, Sentinel-1, and Sentinel-2 and select the appropriate data for estimating cropland area in Cambodia based on availability, coverage, and cost. They will compile the data into a database that will cover specific sites representing complex cropping practices ranging from lowland to upland regions in the provinces located around Tonle Sap Lake. The team will present their results to various stakeholders through training programs, seminars, and lectures. The results and findings from the project will also be incorporated into the teaching curriculum at the Royal University of Agriculture.
In the past, cropland mapping has been unavailable to countries like Cambodia due to the cost. This project will provide an inexpensive means to map and monitor cropland from satellite images. The research will also provide basic cropland data that can be used in subsequent analyses like crop yield estimations or environmental impact assessments enabling better agricultural planning. Rapid crop mapping and measuring will also support the implementation of crop insurance products in Cambodia, which will be extremely beneficial for Cambodian farmers at risk of extreme weather events. In terms of facilitating better land use management, the project will also benefit government officials from various departments. The team will convey the results to them in an interactive way, using overlays in Google Maps, to assist in their planning efforts and encourage the diversification of agricultural land uses.
Summary of Recent Activities:
Due to administrative delays in setting up the project at the Royal University of Agriculture and getting the funds disbursed, the PI Dr. Sanara Hor and his team could only begin their activities in April 2019. Since then, they have completed their preparations for data collection, but before that could begin they also had to wait for the dry season to end in June, as prior to that that time there would be few agriculture activities to observe. Beginning in August the researchers will collect aerial images of croplands in Battambang Province using a drone provided by their U.S. partners at the University of California, Davis. Additional high-resolution satellite imagery will be obtained through USAID for image classification and analysis. As the drone survey campaign progresses, the team will also conduct ground-truthing in the field.
Dr. Hor’s project also works closely with two other current PEER-supported projects. On the project by Dr. Sok Serey, they trained the PI’s team to use an Open Data Kit application and lent them some tablets for use in their field data collection activities. On the project by Mr. Nareth Nut, they provided training and mentorship to the PI regarding analysis of remote sensing data.
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