Adjusting Lidar-Derived Digital Terrain Models in Coastal Marshes Based on Estimated Aboveground Biomass Density

Digital elevation models (DEMs) derived from airborne lidar are traditionally unreliable in coastal salt marshes due to the inability of the laser to penetrate the dense grasses and reach the underlying soil. To that end, we present a novel processing methodology that uses ASTER Band 2 (visible red), an interferometric SAR (IfSAR) digital surface model, and lidar-derived canopy height to classify biomass density using both a three- class scheme (high, medium and low) and a two-class scheme (high and low). Elevation adjustments associated with these classes using both median and quartile approaches were applied to adjust lidar-derived elevation values closer to true bare earth elevation. The performance of the method was tested on 229 elevation points in the lower Apalachicola River Marsh. The two-class quartile-based adjusted DEM produced the best results, reducing the RMS error in elevation from 0.65 m to 0.40 m, a 38% improvement. The raw mean errors for the lidar DEM and the adjusted DEM were 0.61 +/- 0.24 m and 0.32 +/- 0.24 m, respectively, thereby reducing the high bias by approximately 49%

Surface Roughness Parameterization Using Land Use / Land Cover Enhanced by Lidar Point Cloud Data

Source: ICHE Conference Archive - https://mdi-de.baw.de/icheArchiv

Using lidar remote sensing and support vector machines to classify fire disturbance legacies in a Florida oak scrub landscape

Background/Question/Methods

Ecologists have long emphasized the reciprocal interactions between spatial pattern and ecological processes in the creation of landscape mosaics. While an enormous amount of recent research has focused on the quantification of spatial patterns, efforts to infer process from pattern have been hindered by the presence of multi-scale, often confounding, drivers of pattern in many landscapes. At the mesoscale, Holling’s extended keystone hypothesis posits that spatially contagious disturbances such as fire are the dominant pattern-generating processes. To test this hypothesis, we used fire history data and discrete, small-footprint lidar remote sensing acquired over a 22 sq. km landscape of oak scrub in the Kennedy Space Center/Merritt Island National Wildlife Refuge area on the east-central coast of Florida. We binned the lidar return data into 1 m vertical height intervals for each 5 m x 5 m horizontal cell. Since community structure tends to recover by 7 years post-fire, we tested for significant differences between recently-burned (< 7 years) and unburned (≥ 7 years) patches with multivariate analysis of variance. To predict the burn status of each cell, we then used distribution-free, nonlinear support vector machine (SVM) classifiers, which have proven to be highly accurate for complex pattern recognition problems.

Results/Conclusions 

We detected statistically significant differences in vegetation structure between burned and unburned patches for all of the dominant land cover types (upland non-forested, upland forested, wetland hardwood forest, and non-forested wetlands) in the study area. Initially, we obtained a predicted error rate of approximately 34% from the SVM classifier; by averaging the binned lidar data over a moving window of increasing size, however, we achieved substantial reductions in the predicted error rate for the SVM classifier. The optimal window size of 100 m x 100 m yielded a predicted misclassification rate of approximately 3%, an order of magnitude lower than the error rate obtained on the same data using a logistic regression classifier. These results suggest that, as predicted by the extended keystone hypothesis, fire disturbance is a dominant pattern-generating process at the patch scale in this oak scrub landscape. Furthermore, these results indicate that it is possible to use vertical vegetation structure, as represented by the binned lidar data, to predict burn status with a high level of accuracy. While our study employed a simple binary classification scheme, future research will focus on using SVM regression techniques to predict burn status with finer-grained classes of time since fire

Dune vegetation fertilization by nesting sea turtles

Sea turtle nesting presents a potential pathway to subsidize nutrient-poor dune ecosystems, which provide the nesting habitat for sea turtles. To assess whether this positive feedback between dune plants and turtle nests exists, we measured N concentration and delta N-15 values in dune soils, leaves from a common dune plant ( sea oats [Uniola paniculata]), and addled eggs of loggerhead (Caretta caretta) and green turtles ( Chelonia mydas) across a nesting gradient ( 200 - 1050 nests/km) along a 40.5-km stretch of beach in east central Florida, USA. The delta N-15 levels were higher in loggerhead than green turtle eggs, denoting the higher trophic level of loggerhead turtles. Soil N concentration and delta N-15 values were both positively correlated to turtle nest density. Sea oat leaf tissue delta N-15 was also positively correlated to nest density, indicating an increased use of augmented marine-based nutrient sources. Foliar N concentration was correlated with delta N-15, suggesting that increased nutrient availability from this biogenic vector may enhance the vigor of dune vegetation, promoting dune stabilization and preserving sea turtle nesting habitat

Use of Airborne LiDAR to Delineate Canopy Degradation and Encroachment along the Guatemala-Belize Border

Tropical rainforest clearing and degradation significantly reduces carbon sequestration and increases the rate of biodiversity loss. There has been a concerted international effort to develop remote sensing techniques to monitor broad-scale patterns of forest canopy disturbance. In addition to loss of natural resources, recent deforestation in Mesoamerica threatens historic cultural resources that for centuries lay hidden below the protective canopy. Here, we compare satellite-derived measures of canopy disturbance that occurred over a three decade period since 1980 to those derived from a 2009 airborne LiDAR campaign over the Caracol Archaeological Reserve in western Belize. Scaling up fine-resolution canopy height measures to the 30 m resolution of Landsat Thematic Mapper, we found LiDAR revealed a \u3e58% increase in the extent of canopy disturbance where there was an overlap of the remotely sensed data sources. For the entire archaeological reserve, with the addition of LiDAR, there was a 2.5% increase of degraded canopy than estimated with Landsat alone, indicating that 11.3% of the reserve has been subjected to illegal selective logging and deforestation. Incursions into the reserve from the Guatemala border, represented by LiDAR-detected canopy disturbance, extended 1 km deeper (to 3.5 km) into Belize than were derived with Landsat. Thus, while LiDAR enables a synoptic, never-seen-before, below-canopy view of the Maya city of Caracol, it also reveals the degree of canopy disturbance and potential looting of areas yet to be documented by archaeologists on the ground

Investigation of the Effects of Sea Level Rise on Sea Turtle, Shorebird, Seabird, and Beach Mouse Nesting Distributions Within the South Atlantic Landscape Conservation Cooperative Region

Source: ICHE Conference Archive - https://mdi-de.baw.de/icheArchiv