Soil Surveys, Vegetation Indices, and Topographic Analysis for Conservation Planning

• Autor(a) principal: Mueller, Tom
• Data de Publicação: 2015
• Outros Autores: Zourarakis, Demetrio, Sassenrath, Gretchen F., Mijatovic, Blazan, Dillon, Carl R., Gianello, Eduardo M., Barbieri, Rafael Mazza, Rodrigues, Marcos, Rienzi, Eduardo A., Faleiros, Gabriel D. [UNESP]
• Tipo de documento: Capítulo de livro
• Idioma: eng
• Título da fonte: Repositório Institucional da UNESP
• Texto Completo: http://dx.doi.org/10.1201/b18173-8; http://hdl.handle.net/11449/245841

Powerful computers and geographic information systems (GISs) allow analysts to more effectively utilize digital data resources such as soil survey maps, remotely sensed imagery, and terrain data for conservation planning. The objectives of this chapter are to (1) demonstrate analyses of digital data for conservation assessment and (2) provide an assignment and detailed instructions for readers to analyze publically available geospatial data using GIS and automated modeling techniques. Research studies demonstrate that (1) eroded concentrated water flow pathways in agricultural fields requiring grassed waterways can be identified with early-spring high-resolution (i.e., 0.3-m) imagery and light detection and ranging (LiDAR)-derived terrain maps (i.e., hillshade and terrain attribute maps); (2) vegetation indices calculated from the National Agricultural Imagery Program (NAIP) imagery are effective tools for detecting poorly vegetated grassed waterways requiring replanting; and (3) terrain and economic modeling techniques can be used for slope and yield map analysis for determining the profitability of cropping steeper slopes. Four appendices are provided with step-by-step instructions for (1) obtaining publically available USDA NAIP imagery and US Geological Service (USGS) digital elevation models (DEMs); (2) calculating vegetation indices with USDA NAIP imagery; (3) creating terrain attribute, contour, and hillshade maps from USGS DEM data; and (4) conducting elevation models from public point cloud LiDAR elevation data.