Developing Remote Sensing Protocols for Inventory of Nutrient Management Practices

Date: 
Apr 2014

Issue

An accurate inventory of in-field and off-site nutrient management practices is essential to establish baseline conditions and document implementation of the Iowa Nutrient Reduction Strategy. For some practices, such as crop rotation, available satellite sensor data already has been processed into crop rotation datasets. For most practices — cover crops, residue cover, riparian buffers, flow or erosion control structures — more research is needed to refine methodologies and calibrate results.

Objective

This project will develop standard remote sensing protocols to inventory cover crops and residue cover. These protocols will be validated in the Turkey River, Cedar River and Skunk River watersheds.

Approach

Remote detection of cover crops will use Landsat-7 and 8 satellite sensor data, plus other high resolution/high repeat datasets. Landsat imagery will be acquired in late fall and spring to detect greenness at each of these times, and used to determine the presence of a cover crop over the winter period.

Estimates of residue cover at the field scale will be evaluated using Landsat-7 and 8 satellite sensor data to produce a normalized difference tillage index (NDTI). The NDTI is a promising methodology to estimate residue cover prior to crop emergence.

Project Updates

March 2017

Residue cover photograph classification and data assignment to field boundaries has been completed. Work to develop the ArcGIS and Python script that will enable evaluating measured residue cover versus the resulting satellite imagery/residue index correlations is still underway. Extra cover crop model validation work continues as well. The IDALS cover crop cost share data has been transferred to the PLSS (US Public Land Survey System) QQQ (Quarter Quarter Quarter; 10 acre squares) polygon boundaries. Work is underway to calculate the NDVI (Normalized Difference Vegetation Index) values for these polygons to further validate the cover crop detection techniques developed and supported by this project.

December 2016

Residue cover photograph classification has been completed and the data now must be assigned to field boundaries. This will make it possible to evaluate measured residue cover versus the resulting satellite imagery/residue index correlations. This process will be done for multiple imagery dates across data from 2014, 2015, and 2016. Validation work on alternative cover crop detection techniques using remote sensing data will continue through the academic year.

 

September 2016

Collection of spring 2016 residue cover photographs was completed in early June. Analysis of the resulting satellite imagery/residue index correlations and residue cover photos began when students returned to campus in late August. Validation work on alternative cover crop detection techniques using remote sensing data is underway. This will use additional cover crop location data obtained from IDALS cover crop cost share. 

June 2016

During this quarter, work continued to investigate and account for sources of error in residue cover and cover crop estimation procedures. Additional residue cover photographs were collected for 2016 in more than 10 Iowa counties, and photographs are being manually analyzed for residue cover. This analysis should be complete by the end of 2016 and final accuracy analysis of residue cover classification will begin in early 2017. For cover crops, the project analysis is complete. The major finding was that cover crops can be reliably detected better than 80 percent of the time using a classification tree approach with monthly median field NDVI values. The NDVI (normalized differenced vegetation index) is an indicator that describes the greenness of vegetative covers, and it is sensitive to percentage of biomass, leaf size and healthiness of vegetation.

March 2016

Metadata creation continued for an additional eight baseline conservation practices and riparian vegetation geodatabases. A private company was contracted to fly five watersheds in early April collecting four-band data. Comparing the baseline data to this current imagery and updating the database with practice changes will be the next step. Project staff members are investigating a semi-automated method to extract vegetated areas from color infrared imagery and then use ancillary data to remove areas not relevant to this project, such as road ditches, fence lines, and farm grounds. Ancillary datasets also are being identified to help locate grassed waterways from vegetated polygons by the use of different soil attributes.

 

December 2015

Additional work was undertaken this quarter to further investigate sources of error in residue cover and cover crop estimation procedures. The automated script developed last quarter enabled further investigations into both residue cover and cover crops. For residue cover, investigations determined that defining crop residue types is not straightforward from the majority land cover in the Ag Conservation Planning Framework (ACPF) field boundary database. Work is underway to refine the ACPF crop cover types to split fields with multiple residue types. For cover crops, the year-to-year variability complicates setting a consistent vegetation index threshold across dates, as does the difference in spectral response from fall- and spring-planted cover crops. Late fall detection is particularly challenging, as variable seeding dates and weather result in highly variable cover crop establishment. This makes it difficult to discriminate fields with cover crops from those with bare soil or some residue cover. Spring detection still shows promise, but will be complicated by variability of cover crop planting dates and/or how they over-winter. 

September 2015

During this quarter a source for ground truth data on cover crops in the Middle Cedar River basin was found and is proving useful in developing cover crop identification thresholds. Residue cover data from 1,000 fields in the South Fork of the Iowa River watershed collected by Agren Inc. in 2013 was converted into a usable form. Also residue cover data on approximately 150 fields across central, east central and southeast Iowa was collected and is being analyzed. The team has begun developing a program to automatically calculate field average vegetation and tillage indexes for classifying residue cover and cover crop status. This will work on data previously added to a GIS facility file server. 

June 2015

Iowa LandSAT imagery from 2000-2014 that are 50% cloud cover or less has been downloaded and processed. These data are available on a GIS facility file server. Several viable sources for ground truth data on cover crops have been identified within the Middle Cedar basin, and are expected to be available this spring. The team is beginning to analyze 2013 ground truth data on residue cover in the South Fork watershed. Collection of additional ground truth residue cover measurements in the Water Quality Initiative watersheds will be done this summer.  

March 2015

This project is developing remote sensing protocols to inventory cover crops and residue cover. Two students are gathering all Iowa LandSAT imagery from 2000-2014 that are 50% cloud cover or less. A data management system to archive and organize this data on local servers is being built. This will allow the data to be accessible and sorted by team members. Several viable sources for ground truth data on cover crops have been identified within the Middle Cedar basin, which will be the first study area for mapping adoption of cover crops. The team also is working with partners to get ground truth data on residue cover in the South Fork watershed, which will be the first study area for the residue/tillage mapping.

December 2014

An accurate inventory of in-field and off-site nutrient management practices is essential to establish baseline conditions and document implementation of the Iowa Nutrient Reduction Strategy. For some practices, such as crop rotation, available satellite sensor data already has been processed into crop rotation data sets. For most practices, more research is needed to refine methodologies and calibrate results. This project will develop standard remote sensing protocols to inventory cover crops and residue cover. A data structure to store the thousands of satellite images required to map cover crops, residue cover and vegetative buffers has been developed. Work has begun to download required imagery from the U.S. Geological Survey.