User Tools

Site Tools


remote_sensing_methods:rsac_riparian_mapping_tool

Differences

This shows you the differences between two versions of the page.

Link to this comparison view

Both sides previous revision Previous revision
remote_sensing_methods:rsac_riparian_mapping_tool [2012/03/26 08:43]
calbury Removed HGVC
remote_sensing_methods:rsac_riparian_mapping_tool [2012/07/23 14:01] (current)
calbury
Line 31: Line 31:
 ===== Workflow ===== ===== Workflow =====
 ==== Data Preparation ==== ==== Data Preparation ====
-The toolbar performs a number of data preparation steps for valley bottom modeling, and also for vegetation mapping. ​ The procedures include: at-sensor reflectance correction for SPOT 5 and Landsat TM/ETM+ data; calculation of various spectral indices from Landsat TM/ETM+, SPOT 5, and NAIP datamosaic images; and various topographic derivatives such as slope and height above stream channel. ​ These steps eliminate the timely manual data preparation tasks inherent with most remote sensing-based modeling procedures.+The toolbar performs a number of data preparation steps for valley bottom modeling, and also for vegetation mapping. ​ The procedures include: at-sensor reflectance correction for SPOT 5 and Landsat TM/ETM+ data; calculation of various spectral indices from Landsat TM/ETM+, SPOT 5, and NAIP datamosaic images; and various topographic derivatives such as slope and height above stream channel. ​ These steps eliminate the timely manual data preparation tasks inherent with most remote sensing-based modeling procedures.
 ==== Valley Bottom Modeling ==== ==== Valley Bottom Modeling ====
 The logistic regression-based valley bottom tool provides a highly adaptable fuzzy classification or probability that an area is a valley bottom. ​ This tool relies on user-provided training points that are classified as either valley bottom (1) or not valley bottom (0).  These training data and the associated values of user-selected independent topographic variables, such as height above the stream channel and slope for each point, are fitted to the logit function. The logit function is a special case of the generalized linear model used in logistic regression. The logistic regression-based valley bottom tool provides a highly adaptable fuzzy classification or probability that an area is a valley bottom. ​ This tool relies on user-provided training points that are classified as either valley bottom (1) or not valley bottom (0).  These training data and the associated values of user-selected independent topographic variables, such as height above the stream channel and slope for each point, are fitted to the logit function. The logit function is a special case of the generalized linear model used in logistic regression.
Line 42: Line 42:
  
 ==== Streamlined Vegetation Mapping ==== ==== Streamlined Vegetation Mapping ====
-The RSAC Riparian Mapping Toolbar provides the use of object-oriented vegetation mapping capabilities using a random forests classifier. ​ This is a streamlined version of the classification process described in the Object-based Classification: ​[[remote_sensing_methods:​classification_and_regression_tree_cart]] method. ​ To use these capabilities,​ the user provides image segments for the study area and representative training data for the different classes of vegetation. ​ The image segments must be generated independent of the toolbar using eCognition or another segmentation software package. ​ Forest Service users have access to eCognition through their regional remote sensing coordinators. Training data can be field-verified or photo-interpreted. Based on the training data, random forests creates a model that maps the vegetation throughout the study area.  The streamlined vegetation methods have proven effective at adapting to a variety of study areas featuring different challenges to modeling riparian extent and composition.+The RSAC Riparian Mapping Toolbar provides the use of object-oriented vegetation mapping capabilities using a random forests classifier. ​ This is a streamlined version of the classification process described in the [[remote_sensing_methods:​classification_and_regression_tree_cart]] method. ​ To use these capabilities,​ the user provides image segments for the study area and representative training data for the different classes of vegetation. ​ The image segments must be generated independent of the toolbar using eCognition or another segmentation software package. ​ Forest Service users have access to eCognition through their regional remote sensing coordinators. Training data can be field-verified or photo-interpreted. Based on the training data, random forests creates a model that maps the vegetation throughout the study area.  The streamlined vegetation methods have proven effective at adapting to a variety of study areas featuring different challenges to modeling riparian extent and composition.
    
 {{remote_sensing_methods:​RSAC_VB_Figure_3.png|}} {{remote_sensing_methods:​RSAC_VB_Figure_3.png|}}
remote_sensing_methods/rsac_riparian_mapping_tool.txt ยท Last modified: 2012/07/23 14:01 by calbury