Richard L. Farnsworth

Cost-Effective Targeting of Land Retirement to Improve Water Quality with Endogenous Sediment Deposition Coefficients

An integrated framework that combines spatial and biophysical attributes of land with a hydrological model and an economic model is developed to identify cropland for enrollment in the Conservation Reserve Enhancement Program. Sediment deposition coefficients are determined endogenously depending on the land-use decisions on other land parcels. Application of this framework to a watershed in Illinois demonstrates that highly sloping land adjacent to water bodies should be selected for retirement. A marginal value rental payment scheme can achieve program goals of 20% sediment abatement at 39% lower cost than a productivity-based rental scheme.

Integrating economic, environmental and GIS modeling to target cost effective land retirement in multiple watersheds

Abstract An integrated framework of economic, environmental and GIS modeling is developed to study cost-effective retirement of cropland within and across multiple watersheds to achieve environmental goals. This framework is applied to 12 contiguous agricultural watersheds in the Illinois Conservation Reserve Enhancement Program region of the United States. A key goal of this program is to reduce sediment loadings in the Illinois River by 20% by retiring land from crop production. The characteristics of land parcels to be targeted for retirement within each watershed and the criteria for cost-effective allocation of abatement responsibility across watersheds are analyzed. Our analysis suggests that program costs are minimized when the abatement standard is set for the region rather than uniformly for each watershed. For both policy scenarios, the land parcels targeted for retirement should be those that are highly sloping and adjacent to a water body.

Optimizing unsupervised classifications of remotely sensed imagery with a data-assisted labeling approach

The quality of remotely sensed land use and land cover (LULC) maps is affected by the accuracy of image data classifications. Various efforts have been made in advancing supervised or unsupervised classification methods to increase the repeatability and accuracy of LULC mapping. This study incorporates a data-assisted labeling approach (DALA) into the unsupervised classification of remotely sensed imagery. The DALA-unsupervised classification algorithm consists of three steps: (1) creation of N spectral-class maps using Iterative Self-Organizing Data Analysis Technique Algorithm (ISODATA); (2) development of LULC maps with assistance of reference data; and (3) accuracy assessments of all the LULC maps using independent reference data and selection of one LULC map with the highest accuracy. Classification experiments with a composite image of a Landsat Thematic Mapper (TM) image and an Enhanced Thematic Mapper Plus (ETM+) image suggest that DALA was effective in making unsupervised classification process more objective, automatic, and accurate. A comparison between the DALA-unsupervised classifications and some conventional classifications suggests that the DALA-unsupervised classification algorithm yielded better classification accuracies compared to these conventional approaches. Such a simple, effective approach has not been systematically examined before but has great potential for many applications in the geosciences.