Jeffrey Richardson

Cost-effective targeting of conservation investments to reduce the northern Gulf of Mexico hypoxic zone

Significance Hypoxic (low-oxygen) zones threaten an increasing number of marine ecosystems. Hypoxia in the Gulf of Mexico is the second largest in the world. The United States has a policy goal of reducing the average zone to 5,000 km2. Reductions in nutrients from cropland in the Mississippi-Atchafalaya River Basin are needed to achieve this goal. We use an integrated assessment model coupled with optimization to identify the cost-effective locations to target cropland conservation investments across the Basin’s 550 agricultural subwatersheds and to identify the nature of tradeoffs between hypoxia and costs of conservation investments. Targeted conservation practice investments are estimated to achieve the hypoxia reduction goal at the cost of

Bioenergy that supports ecological restoration.

2.7 billion annually. A seasonally occurring summer hypoxic (low oxygen) zone in the northern Gulf of Mexico is the second largest in the world. Reductions in nutrients from agricultural cropland in its watershed are needed to reduce the hypoxic zone size to the national policy goal of 5,000 km2 (as a 5-y running average) set by the national Gulf of Mexico Task Force’s Action Plan. We develop an integrated assessment model linking the water quality effects of cropland conservation investment decisions on the more than 550 agricultural subwatersheds that deliver nutrients into the Gulf with a hypoxic zone model. We use this integrated assessment model to identify the most cost-effective subwatersheds to target for cropland conservation investments. We consider targeting of the location (which subwatersheds to treat) and the extent of conservation investment to undertake (how much cropland within a subwatershed to treat). We use process models to simulate the dynamics of the effects of cropland conservation investments on nutrient delivery to the Gulf and use an evolutionary algorithm to solve the optimization problem. Model results suggest that by targeting cropland conservation investments to the most cost-effective location and extent of coverage, the Action Plan goal of 5,000 km2 can be achieved at a cost of

Terrestrial Laser Scanning for Vegetation Sampling

2.7 billion annually. A large set of cost-hypoxia tradeoffs is developed, ranging from the baseline to the nontargeted adoption of the most aggressive cropland conservation investments in all subwatersheds (estimated to reduce the hypoxic zone to less than 3,000 km2 at a cost of

Assessing the utility of green LiDAR for characterizing bathymetry of heavily forested narrow streams

5.6 billion annually).

Evaluation of the contribution of LiDAR data and postclassification procedures to object-based classification accuracy

Bioenergy development can offer beneficial ecological and economic synergies through the expansion of ecological restoration projects. Such synergies are demonstrated by means of a case study conducted in central Washington State, where a 52.4-ha ecological restoration site on the Yakama Reservation generated 34 mega-grams (Mg) of invasive tree biomass per hectare, costing

An Integrated Approach for Monitoring Contemporary and Recruitable Large Woody Debris

988 ha−1. A geospatial model of transportation costs estimated that extracted invasive tree biomass can generate revenues throughout 1103 803 ha when delivered to a proposed bioenergy facility in White Swan, Washington, providing 53 000–180 000 Mg of biomass per year for several decades. Thermochemical analyses revealed that the elevated nitrogen, sulfur, and ash content in two prolific invasive trees – Russian olive (Elaeagnus angustifolia) and salt cedar (Tamarix spp) – will limit demand for either of these invasive species. We compare our regional data to national estimates, and show the broader potential for expanding ecological restoration activities and biomass supplies through the revenues generated by the sale of invasive tree wood-waste into bioenergy markets.

Modeling approaches to estimate effective leaf area index from aerial discrete-return LIDAR.

We developed new vegetation indices utilizing terrestrial laser scanning (TLS) to quantify the three-dimensional spatial configuration of plant communities. These indices leverage the novelty of TLS data and rely on the spatially biased arrangement of a TLS point cloud. We calculated these indices from TLS data acquired within an existing long term manipulation of forest structure in Central Oregon, USA, and used these data to test for differences in vegetation structure. Results provided quantitative evidence of a significant difference in vegetation density due to thinning and burning, and a marginally significant difference in vegetation patchiness due to grazing. A comparison to traditional field sampling highlighted the novelty of the TLS based method. By creating a linkage between traditional field sampling and landscape ecology, these indices enable field investigations of fine-scale spatial patterns. Applications include experimental assessment, long-term monitoring, and habitat characterization.

Strengths and limitations of assessing forest density and spatial configuration with aerial LiDAR

Stream depth and bathymetry in forested environments is difficult and costly to measure in the field, but critically important for stream-dwelling organisms. Green (bathymetric) LiDAR can be used to characterize stream bathymetry, but little is known of its ability to accurately characterize stream bathymetry in narrow (width less than 5 m), heavily forested streams. We compared digital elevation models (DEMs) derived from green and near-infrared (NIR) LiDAR to field measurements in a narrow, forested stream in Oregon, USA, as well as comparing the two DEMs to each other along the length of the stream and to estimates of leaf area index. Our results suggest that green LiDAR may be limited in accurately characterizing the bathymetry of narrow streams in heavily forested environments due to difficulty penetrating canopy and interactions with complex topography.

Uncertainty in urban forest canopy assessment: Lessons from Seattle, WA, USA

Abstract Object-based image analysis (OBIA) is becoming an increasingly common method for producing land use/land cover (LULC) classifications in urban areas. In order to produce the most accurate LULC map, LiDAR data and postclassification procedures are often employed, but their relative contributions to accuracy are unclear. We examined the contribution of LiDAR data and postclassification procedures to increase classification accuracies over using imagery alone and assessed sources of error along an ecologically complex urban-to-rural gradient in Olympia, Washington. Overall classification accuracy and user’s and producer’s accuracies for individual classes were evaluated. The addition of LiDAR data to the OBIA classification resulted in an 8.34% increase in overall accuracy, while manual postclassification to the imagery + LiDAR classification improved accuracy only an additional 1%. Sources of error in this classification were largely due to edge effects, from which multiple different types of errors result.

Uncertainty in biomass supply estimates: Lessons from a Yakama Nation case study

Large woody debris (LWD) plays a critical structural role in riparian ecosystems, but it can be difficult and time-consuming to quantify and survey in the field. We demonstrate an automated method for quantifying LWD using aerial LiDAR and object-based image analysis techniques, as well as a manual method for quantifying LWD using image interpretation derived from LiDAR rasters and aerial four-band imagery. In addition, we employ an established method for estimating the number of individual trees within the riparian forest. These methods are compared to field data showing high accuracies for the LWD method and moderate accuracy for the individual tree method. These methods can be integrated to quantify the contemporary and recruitable LWD in a river system.