Continuous In-woods Production of Biochar Using a Trailer-mounted Air Curtain Burner

Fuel treatments and other forest restoration thinning practices aim to reduce wildfire risk while building forest resilience to drought, insects, and diseases and increasing aboveground carbon (C) sequestration. However, fuel treatments generate large amounts of unmerchantable woody biomass residues that are often burned in open piles, releasing significant quantities of greenhouse gases and particulates, and potentially damaging the soil beneath the pile. Air curtain burners offer a solution to mitigate these issues, helping to reduce smoke and particulates from burning operations, more fully burn biomass residues compared to pile burning, and eliminate the direct and intense fire contact that can harm soil beneath the slash pile. In an air curtain burner, burning takes place in a controlled environment. Smoke is contained and recirculated by the air curtain, and therefore burning can be conducted under a variety of climatic conditions (e.g., wind, rain, snow), lengthening the burning season for disposal of slash material. The mobile pyrolysis unit that continuously creates biochar was specifically designed to dispose of residual woody biomass at log landings, green wood at landfills, or salvaged logged materials and create biochar in the process. This high-carbon biochar output can be used to enhance soil resilience by improving its chemical, physical, and biological properties and has potential applications in remediating contaminated soils, including those at abandoned mine sites. Here, we describe the general use of this equipment, appropriate siting, loading methods, quenching requirements, and lessons learned about operating this new technology.

MODIS Imagery Improves Pest Risk Assessment: A Case Study of Wheat Stem Sawfly (Cephus cinctus, Hymenoptera: Cephidae) in Colorado, USA

Wheat stem sawfly (Cephus cinctus Norton, Hymenoptera: Cephidae) has long been a significant insect pest of spring, and more recently, winter wheat in the northern Great Plains. Wheat stem sawfly was first observed infesting winter wheat in Colorado in 2010 and, subsequently, has spread rapidly throughout wheat production regions of the state. Here, we used maximum entropy modeling (MaxEnt) to generate habitat suitability maps in order to predict the risk of crop damage as this species spreads throughout the winter wheat-growing regions of Colorado. We identified environmental variables that influence the current distribution of wheat stem sawfly in the state and evaluated whether remotely sensed variables improved model performance. We used presence localities of C. cinctus and climatic, topographic, soils, and normalized difference vegetation index and enhanced vegetation index data derived from Moderate Resolution Imaging Spectroradiometer (MODIS) imagery as environmental variables. All models had high performance in that they were successful in predicting suitable habitat for C. cinctus in its current distribution in eastern Colorado. The enhanced vegetation index for the month of April improved model performance and was identified as a top contributor to MaxEnt model. Soil clay percent at 0-5 cm, temperature seasonality, and precipitation seasonality were also associated with C. cinctus distribution in Colorado. The improved model performance resulting from integrating vegetation indices in our study demonstrates the ability of remote sensing technologies to enhance species distribution modeling. These risk maps generated can assist managers in planning control measures for current infestations and assess the future risk of C. cinctus establishment in currently uninfested regions.

Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling (SAHM)

Early detection of invasive plant species is vital for the management of natural resources and protection of ecosystem processes. The use of satellite remote sensing for mapping the distribution of invasive plants is becoming more common, however conventional imaging software and classification methods have been shown to be unreliable. In this study, we test and evaluate the use of five species distribution model techniques fit with satellite remote sensing data to map invasive tamarisk (Tamarix spp.) along the Arkansas River in Southeastern Colorado. The models tested included boosted regression trees (BRT), Random Forest (RF), multivariate adaptive regression splines (MARS), generalized linear model (GLM), and Maxent. These analyses were conducted using a newly developed software package called the Software for Assisted Habitat Modeling (SAHM). All models were trained with 499 presence points, 10,000 pseudo-absence points, and predictor variables acquired from the Landsat 5 Thematic Mapper (TM) sensor over an eight-month period to distinguish tamarisk from native riparian vegetation using detection of phenological differences. From the Landsat scenes, we used individual bands and calculated Normalized Difference Vegetation Index (NDVI), Soil-Adjusted Vegetation Index (SAVI), and tasseled capped transformations. All five models identified current tamarisk distribution on the landscape successfully based on threshold independent and threshold dependent evaluation metrics with independent location data. To account for model specific differences, we produced an ensemble of all five models with map output highlighting areas of agreement and areas of uncertainty. Our results demonstrate the usefulness of species distribution models in analyzing remotely sensed data and the utility of ensemble mapping, and showcase the capability of SAHM in pre-processing and executing multiple complex models.

Ten ways remote sensing can contribute to conservation

In an effort to increase conservation effectiveness through the use of Earth observation technologies, a group of remote sensing scientists affiliated with government and academic institutions and conservation organizations identified 10 questions in conservation for which the potential to be answered would be greatly increased by use of remotely sensed data and analyses of those data. Our goals were to increase conservation practitioners' use of remote sensing to support their work, increase collaboration between the conservation science and remote sensing communities, identify and develop new and innovative uses of remote sensing for advancing conservation science, provide guidance to space agencies on how future satellite missions can support conservation science, and generate support from the public and private sector in the use of remote sensing data to address the 10 conservation questions. We identified a broad initial list of questions on the basis of an email chain-referral survey. We then used a workshop-based iterative and collaborative approach to whittle the list down to these final questions (which represent 10 major themes in conservation): How can global Earth observation data be used to model species distributions and abundances? How can remote sensing improve the understanding of animal movements? How can remotely sensed ecosystem variables be used to understand, monitor, and predict ecosystem response and resilience to multiple stressors? How can remote sensing be used to monitor the effects of climate on ecosystems? How can near real-time ecosystem monitoring catalyze threat reduction, governance and regulation compliance, and resource management decisions? How can remote sensing inform configuration of protected area networks at spatial extents relevant to populations of target species and ecosystem services? How can remote sensing-derived products be used to value and monitor changes in ecosystem services? How can remote sensing be used to monitor and evaluate the effectiveness of conservation efforts? How does the expansion and intensification of agriculture and aquaculture alter ecosystems and the services they provide? How can remote sensing be used to determine the degree to which ecosystems are being disturbed or degraded and the effects of these changes on species and ecosystem functions?

Using habitat suitability models to target invasive plant species surveys

Managers need new tools for detecting the movement and spread of nonnative, invasive species. Habitat suitability models are a popular tool for mapping the potential distribution of current invaders, but the ability of these models to prioritize monitoring efforts has not been tested in the field. We tested the utility of an iterative sampling design (i.e., models based on field observations used to guide subsequent field data collection to improve the model), hypothesizing that model performance would increase when new data were gathered from targeted sampling using criteria based on the initial model results. We also tested the ability of habitat suitability models to predict the spread of invasive species, hypothesizing that models would accurately predict occurrences in the field, and that the use of targeted sampling would detect more species with less sampling effort than a nontargeted approach. We tested these hypotheses on two species at the state scale (Centaurea stoebe and Pastinaca sativa) in Wisconsin (USA), and one genus at the regional scale (Tamarix) in the western United States. These initial data were merged with environmental data at 30-m2 resolution for Wisconsin and 1-km2 resolution for the western United States to produce our first iteration models. We stratified these initial models to target field sampling and compared our models and success at detecting our species of interest to other surveys being conducted during the same field season (i.e., nontargeted sampling). Although more data did not always improve our models based on correct classification rate (CCR), sensitivity, specificity, kappa, or area under the curve (AUC), our models generated from targeted sampling data always performed better than models generated from nontargeted data. For Wisconsin species, the model described actual locations in the field fairly well (kappa = 0.51, 0.19, P < 0.01), and targeted sampling did detect more species than nontargeted sampling with less sampling effort (chi2 = 47.42, P < 0.01). From these findings, we conclude that habitat suitability models can be highly useful tools for guiding invasive species monitoring, and we support the use of an iterative sampling design for guiding such efforts.

Characterization and distribution of Hxt1, a Na(+)/Cl(-)-dependent orphan transporter, in the human brain

Rxt1, a transporter-like protein structurally related to the large family of Na(+)/Cl(-)-dependent carriers, was isolated from the rat brain. In the present study, Hxt1, the homologue of Rxt1, was isolated from human cortex cDNA. Comparison of their respective nucleotidic sequences revealed a 96% conservation between Hxt1 and Rxt1. Genetic mapping with human genome radiation hybrids allowed the location of the gene coding for Hxt1 between 323ya5 and 084xb3 AFM markers, on a portion of chromosome 1p which spans over 7 cM or 118 cRay. Northern blot analyses demonstrated that Hxt1 mRNA ( approximately 7.5 Kb) is expressed in the human brain but not in peripheral tissues. The immunodistribution of Hxt1 was determined with antibodies raised against the C-terminus of Rxt1. Hxt1 is concentrated in the cerebral cortex, caudate-putamen, substantia nigra, hippocampus, and cerebellum, appearing as a diffuse or a punctate labeling at the light microscope level. This regional and cellular distribution suggests that Hxt1, as its rat homologue, could be present in axon terminals of glutamatergic neurons. The high pressure of selection exerted upon this protein, its strategic anatomical and subcellular distributions suggest that this orphan transporter could be involved in critical functions in the central nervous system.