Lian P. Rampi

The effects of data selection and thematic detail on the accuracy of high spatial resolution wetland classifications

Accurate wetland maps are of critical importance for preserving the ecosystem functions provided by these valuable landscape elements. Though extensive research into wetland mapping methods using remotely sensed data exists, questions remain as to the effects of data type and classifi cation scheme on classifi cation accuracy when high spatial resolution data are used. The goal of this research was to examine the effects on wetland mapping accuracy of varying input datasets and thematic detail in two physiographically different study areas using a decision tree classifi er. The results indicate that: topographic data and derivatives signifi cantly increase mapping accuracy over optical imagery alone, the source of the elevation data and the type of topographic derivatives used were not major factors, the inclusion of radar and leaf-off imagery did not improve mapping accuracy, and increasing thematic detail resulted in signifi cantly lower mapping accuracies i.e., particularly in more diverse wetland areas.

Wetland mapping in the Upper Midwest United States: an object-based approach integrating lidar and imagery data: An object-based approach integrating lidar and imagery data

This study investigated the effectiveness of using high resolution data to map wetlands in three ecoregions in Minnesota. High resolution data included multispectral leaf-off aerial imagery and lidar elevation data. These data were integrated using an Object-Based Image Analysis (OBIA) approach. Results for each study area were compared against field and image interpreted reference data using error matrices, accuracy estimates, and the kappa statistic. Producer’s and user’s accuracies were in the range of 92 to 96 percent and 91 to 96 percent, respectively, and overall accuracies ranged from 96-98 percent for wetlands larger than 0.20 ha (0.5 acres). The results of this study may allow for increased accuracy of mapping wetlands efforts over traditional remote sensing methods.

The Effects of Point or Polygon Based Training Data on RandomForest Classification Accuracy of Wetlands

Wetlands are dynamic in space and time, providing varying ecosystem services. Field reference data for both training and assessment of wetland inventories in the State of Minnesota are typically collected as GPS points over wide geographical areas and at infrequent intervals. This status-quo makes it difficult to keep updated maps of wetlands with adequate accuracy, efficiency, and consistency to monitor change. Furthermore, point reference data may not be representative of the prevailing land cover type for an area, due to point location or heterogeneity within the ecosystem of interest. In this research, we present techniques for training a land cover classification for two study sites in different ecoregions by implementing the RandomForest classifier in three ways: (1) field and photo interpreted points; (2) fixed window surrounding the points; and (3) image objects that intersect the points. Additional assessments are made to identify the key input variables. We conclude that the image object area training method is the most accurate and the most important variables include: compound topographic index, summer season green and blue bands, and grid statistics from LiDAR point cloud data, especially those that relate to the height of the return.

Minnesota Land Cover Classification and Impervious Surface Area by Landsat and Lidar: 2013 Update

Funding provided by the Minnesota ENTRF (Environment and Natural Resources Trust) as recommended by the Legislative-Citizens Commission on Minnesota Resources

Spatial Ensemble Learning for Heterogeneous Geographic Data with Class Ambiguity: A Summary of Results

Class ambiguity refers to the phenomenon whereby samples with similar features belong to different classes at different locations. Given heterogeneous geographic data with class ambiguity, the spatial ensemble learning (SEL) problem aims to find a decomposition of the geographic area into disjoint zones such that class ambiguity is minimized and a local classifier can be learned in each zone. SEL problem is important for applications such as land cover mapping from heterogeneous earth observation data with spectral confusion. However, the problem is challenging due to its high computational cost (finding an optimal zone partition is NP-hard). Related work in ensemble learning either assumes an identical sample distribution (e.g., bagging, boosting, random forest) or decomposes multi-modular input data in the feature vector space (e.g., mixture of experts, multimodal ensemble), and thus cannot effectively minimize class ambiguity. In contrast, our spatial ensemble framework explicitly partitions input data in geographic space. Our approach first preprocesses data into homogeneous spatial patches and uses a greedy heuristic to allocate pairs of patches with high class ambiguity into different zones. Both theoretical analysis and experimental evaluations on two real world wetland mapping datasets show the feasibility of the proposed approach.

Mapping wetland change of prairie pothole region in bigstone county from 1938 year to 2011 year

Combining historical white and black aerial photo with more recent LiDAR and high resolution imagery, this research mapped wetland with high accuracy from 1930s to 2010s in object-based image analysis approach (OBIA). This research shows good potential in combining grey level information with OBIA method to map accurate historical wetland. We found that there were more small wetlands in 1938, but more large wetlands in 1978. In 2011, there were similar amount of small wetlands with 1938. Though 2011 had fewer large wetlands than 1978, 2011 years individual large wetland had much larger area than those in 1978 and thus contributed to much larger total wetland area in 2011 than in 1978. We found significantly increasing precipitation and decreasing temperature over the time series, also drought in 1938, and this may explain wetland change well.