Tree Species Classification Using Hyperspectral Imagery: A Comparison of Two Classifiers

The identification of tree species can provide a useful and efficient tool for forest managers for planning and monitoring purposes. Hyperspectral data provide sufficient spectral information to classify individual tree species. Two non-parametric classifiers, support vector machines (SVM) and random forest (RF), have resulted in high accuracies in previous classification studies. This research takes a comparative classification approach to examine the SVM and RF classifiers in the complex and heterogeneous forests of Muir Woods National Monument and Kent Creek Canyon in Marin County, California. The influence of object- or pixel-based training samples and segmentation size on the object-oriented classification is also explored. To reduce the data dimensionality, a minimum noise fraction transform was applied to the mosaicked hyperspectral image, resulting in the selection of 27 bands for the final classification. Each classifier was also assessed individually to identify any advantage related to an increase in training sample size or an increase in object segmentation size. All classifications resulted in overall accuracies above 90%. No difference was found between classifiers when using object-based training samples. SVM outperformed RF when additional training samples were used. An increase in training samples was also found to improve the individual performance of the SVM classifier

Tree Species Classification Using Hyperspectral Imagery: A Comparison of Two Classifiers

The identification of tree species can provide a useful and efficient tool for forest managers for planning and monitoring purposes. Hyperspectral data provide sufficient spectral information to classify individual tree species. Two non-parametric classifiers, support vector machines (SVM) and random forest (RF), have resulted in high accuracies in previous classification studies. This research takes a comparative classification approach to examine the SVM and RF classifiers in the complex and heterogeneous forests of Muir Woods National Monument and Kent Creek Canyon in Marin County, California. The influence of object- or pixel-based training samples and segmentation size on the object-oriented classification is also explored. To reduce the data dimensionality, a minimum noise fraction transform was applied to the mosaicked hyperspectral image, resulting in the selection of 27 bands for the final classification. Each classifier was also assessed individually to identify any advantage related to an increase in training sample size or an increase in object segmentation size. All classifications resulted in overall accuracies above 90%. No difference was found between classifiers when using object-based training samples. SVM outperformed RF when additional training samples were used. An increase in training samples was also found to improve the individual performance of the SVM classifier

Remote Sensing for Wetland Mapping and Historical Change Detection at the Nisqually River Delta

Coastal wetlands are important ecosystems for carbon storage and coastal resilience to climate change and sea-level rise. As such, changes in wetland habitat types can also impact ecosystem functions. Our goal was to quantify historical vegetation change within the Nisqually River watershed relevant to carbon storage, wildlife habitat, and wetland sustainability, and identify watershed-scale anthropogenic and hydrodynamic drivers of these changes. To achieve this, we produced time-series classifications of habitat, photosynthetic pathway functional types and species in the Nisqually River Delta for the years 1957, 1980, and 2015. Using an object-oriented approach, we performed a hierarchical classification on historical and current imagery to identify change within the watershed and wetland ecosystems. We found a 188.4 ha (79%) increase in emergent marsh wetland within the Nisqually River Delta between 1957 and 2015 as a result of restoration efforts that occurred in several phases through 2009. Despite these wetland gains, a total of 83.1 ha (35%) of marsh was lost between 1957 and 2015, particularly in areas near the Nisqually River mouth due to erosion and shifting river channels, resulting in a net wetland gain of 105.4 ha (44%). We found the trajectory of wetland recovery coincided with previous studies, demonstrating the role of remote sensing for historical wetland change detection as well as future coastal wetland monitoring

Corrigendum to “A remote sensing-based model of tidal marsh aboveground carbon stocks for the conterminous United States”[ISPRS J. Photogram. Rem. Sens. 139 (2018) 255--271]

The authors regret that two thirds of the San Francisco Bay biomass data included in the Landsat random forest models were not scaled to the proper units of grams per square meter. This error affects the Landsat-only models in the article, which are models #1-4 shown in Table 6. The authors have thoroughly investigated the error and found that the final random forest model, including the selected dependent and independent variables, is still the most appropriate model for representing CONUS-wide tidal marsh aboveground biomass and carbon (C). Using the properly scaled biomass data we have corrected remote sensing-based estimates of tidal marsh aboveground biomass and C stocks, and we have corrected Tables 4, 6, 7 and 8 and Figures 5, 6, and 9 of the original article

A remote sensing-based model of tidal marsh aboveground carbon stocks for the conterminous United States

Remote sensing based maps of tidal marshes, both of their extents and carbon stocks, have the potential to play a key role in conducting greenhouse gas inventories and implementing climate mitigation policies. Our objective was to generate a single remote sensing model of tidal marsh aboveground biomass and carbon that represents nationally diverse tidal marshes within the conterminous United States (CONUS). We developed the first calibration-grade, national-scale dataset of aboveground tidal marsh biomass, species composition, and aboveground plant carbon content (%C) from six CONUS regions: Cape Cod, MA, Chesapeake Bay, MD, Everglades, FL, Mississippi Delta, LA, San Francisco Bay, CA, and Puget Sound, WA. Using the random forest machine learning algorithm, we tested whether imagery from multiple sensors, Sentinel-1 C-band synthetic aperture radar, Landsat, and the National Agriculture Imagery Program (NAIP), can improve model performance. The final model, driven by six Landsat vegetation indices and with the soil adjusted vegetation index as the most important (n = 409, RMSE = 310 g/m 2, 10.3% normalized RMSE), successfully predicted biomass for a range of marsh plant functional types defined by height, leaf angle and growth form. Model results were improved by scaling field-measured biomass calibration data by NAIP-derived 30 m fraction green vegetation. With a mean plant carbon content of 44.1% (n = 1384, 95% C.I. = 43.99%–44.37%), we generated regional 30 m aboveground carbon density maps for estuarine and palustrine emergent tidal marshes as indicated by a modified NOAA Coastal Change Analysis Program map. We applied a multivariate delta method to calculate uncertainties in regional carbon densities and stocks that considered standard error in map area, mean biomass and mean %C. Louisiana palustrine emergent marshes had the highest C density (2.67 ± 0.004 Mg/ha) of all regions, while San Francisco Bay brackish/saline marshes had the highest C density of all estuarine emergent marshes (2.03 ± 0.004 Mg/ha). Estimated C stocks for predefined jurisdictional areas ranged from 1023 ± 39 Mg in the Nisqually National Wildlife Refuge in Washington to 507,761 ± 14,822 Mg in the Terrebonne and St. Mary Parishes in Louisiana. This modeling and data synthesis effort will allow for aboveground C stocks in tidal marshes to be included in the coastal wetland section of the U.S. National Greenhouse Gas Inventory. With the increased availability of free post-processed satellite data, we provide a tractable means of modeling tidal marsh aboveground biomass and carbon at the global extent as well.</p