Explore the vast range of titles available.

Forest cover is an important input variable for assessing changes to carbon stocks, climate and hydrological systems, biodiversity richness, and other sustainability science disciplines. Despite incremental improvements in our ability to quantify rates of forest clearing, there is still no definitive understanding on global trends. Without timely and accurate forest monitoring methods, policy responses will be uninformed concerning the most basic facts of forest cover change. Results of a feasible and cost-effective monitoring strategy are presented that enable timely, precise, and internally consistent estimates of forest clearing within the humid tropics. A probability-based sampling approach that synergistically employs low and high spatial resolution satellite datasets was used to quantify humid tropical forest clearing from 2000 to 2005. Forest clearing is estimated to be 1.39% (SE 0.084%) of the total biome area. This translates to an estimated forest area cleared of 27.2 million hectares (SE 2.28 million hectares), and represents a 2.36% reduction in area of humid tropical forest. Fifty-five percent of total biome clearing occurs within only 6% of the biome area, emphasizing the presence of forest clearing \"hotspots.\" Forest loss in Brazil accounts for 47.8% of total biome clearing, nearly four times that of the next highest country, Indonesia, which accounts for 12.8%. Over three-fifths of clearing occurs in Latin America and over one-third in Asia. Africa contributes 5.4% to the estimated loss of humid tropical forest cover, reflecting the absence of current agro-industrial scale clearing in humid tropical Africa.

This study examines the suitability of 250 m MODIS (MODerate Resolution Imaging Spectroradiometer) data for mapping global cropland extent. A set of 39 multi-year MODIS metrics incorporating four MODIS land bands, NDVI (Normalized Difference Vegetation Index) and thermal data was employed to depict cropland phenology over the study period. Sub-pixel training datasets were used to generate a set of global classification tree models using a bagging methodology, resulting in a global per-pixel cropland probability layer. This product was subsequently thresholded to create a discrete cropland/non-cropland indicator map using data from the USDA-FAS (Foreign Agricultural Service) Production, Supply and Distribution (PSD) database describing per-country acreage of production field crops. Five global land cover products, four of which attempted to map croplands in the context of multiclass land cover classifications, were subsequently used to perform regional evaluations of the global MODIS cropland extent map. The global probability layer was further examined with reference to four principle global food crops: corn, soybeans, wheat and rice. Overall results indicate that the MODIS layer best depicts regions of intensive broadleaf crop production (corn and soybean), both in correspondence with existing maps and in associated high probability matching thresholds. Probability thresholds for wheat-growing regions were lower, while areas of rice production had the lowest associated confidence. Regions absent of agricultural intensification, such as Africa, are poorly characterized regardless of crop type. The results reflect the value of MODIS as a generic global cropland indicator for intensive agriculture production regions, but with little sensitivity in areas of low agricultural intensification. Variability in mapping accuracies between areas dominated by different crop types also points to the desirability of a crop-specific approach rather than attempting to map croplands in aggregate.

In recent years there has been a dramatic increase in the demand for timely, comprehensive global agricultural intelligence. Timely information on global crop production is indispensable for combating the growing stress on the world’s crop production and for securing both short-term and long-term stable and reliable supply of food. Global agriculture monitoring systems are critical to providing this kind of intelligence and global earth observations are an essential component of an effective global agricultural monitoring system as they offer timely, objective, global information on croplands distribution, crop development and conditions as the growing season progresses. The Global Agriculture Monitoring Project (GLAM), a joint NASA, USDA, UMD and SDSU initiative, has built a global agricultural monitoring system that provides the USDA Foreign Agricultural Service (FAS) with timely, easily accessible, scientifically-validated remotely-sensed data and derived products as well as data analysis tools, for crop-condition monitoring and production assessment. This system is an integral component of the USDA’s FAS Decision Support System (DSS) for agriculture. It has significantly improved the FAS crop analysts’ ability to monitor crop conditions, and to quantitatively forecast crop yields through the provision of timely, high-quality global earth observations data in a format customized for FAS alongside a suite of data analysis tools. FAS crop analysts use these satellite data in a ‘convergence of evidence’ approach with meteorological data, field reports, crop models, attaché reports and local reports. The USDA FAS is currently the only operational provider of timely, objective crop production forecasts at the global scale. These forecasts are routinely used by the other US Federal government agencies as well as by commodity trading companies, farmers, relief agencies and foreign governments. This paper discusses the operational components and new developments of the GLAM monitoring system as well as the future role of earth observations in global agricultural monitoring.

Timely and accurate data on forest change within Indonesia is required to provide government, private and civil society interests with the information needed to improve forest management. The forest clearing rate in Indonesia is among the highest reported by the United Nations Food and Agriculture Organization (FAO), behind only Brazil in terms of forest area lost. While the rate of forest loss reported by FAO was constant from 1990 through 2005 (1.87 Mhayr−1), the political, economic, social and environmental drivers of forest clearing changed at the close of the last century. We employed a consistent methodology and data source to quantify forest clearing from 1990 to 2000 and from 2000 to 2005. Results show a dramatic reduction in clearing from a 1990s average of 1.78 Mhayr−1 to an average of 0.71Mhayr−1 from 2000 to 2005. However, annual forest cover loss indicator maps reveal a near-monotonic increase in clearing from a low in 2000 to a high in 2005. Results illustrate a dramatic downturn in forest clearing at the turn of the century followed by a steady resurgence thereafter to levels estimated to exceed 1 Mhayr−1 by 2005. The lowlands of Sumatra and Kalimantan were the site of more than 70% of total forest clearing within Indonesia for both epochs; over 40% of the lowland forests of these island groups were cleared from 1990 to 2005. The method employed enables the derivation of internally consistent, national-scale changes in the rates of forest clearing, results that can inform carbon accounting programs such as the Reducing Emissions from Deforestation and Forest Degradation in Developing Countries (REDD) initiative.

Monitoring and mapping of U.S. croplands has long been a primary goal of many users of earth observation satellite data. The advantages of using low spatial and high temporal resolution data are (i) increased ability to monitor the phenological change of crop plants, and (ii) the possibility of generating consistent large area crop cover maps. This study investigates the potential of 500-m MODIS (MODerate Resolution Imaging Spectroradiometer) data in estimating corn (Zea mays L.) and soybean [Glycine max (L.) Merr.] area for the dominant production areas of the USA. To avoid cloud cover, MODIS 32-day composites for all land bands, normalized difference vegetation index (NDVI), and land surface temperature (LST), were used covering March 2002 to February 2003. These time-sequential images were further composited to produce 279 annual time-integrated metrics. Using USDA-NASS Cropland Data Layers (CDL) as subpixel training data, percentage soybean and corn cover per 500-m pixel was calculated and accuracy was assessed at national, state, and county scales using data from the 2002 NASS Census of Agriculture. When these estimates were compared with the NASS Census, r2 values for corn, soybean, and combined corn and soybean areas were 0.957, 0.949, and 0.984 at the state level, respectively. At the national scale, MODIS estimates of corn and soybean cover differed by 6 and 4%, respectively. Results indicate a robust potential for using MODIS in crop type monitoring applications.

The increase of wildfire size and behavior in many western U.S. forests is due to increased fuel loads resulting from the past century’s fire suppression, logging, and grazing policies of the 20th century, along with compounding climactic changes including increased drought and temperatures. Fuel hazard treatments are the key land management tool used to reduce fire intensity and severity however these treatments are often not possible on steep terrain of over 30% slope. Cable tethered cut to length machinery opens new avenues for managers to treat forests in steep slopes, but there is limited data on how effective the treatments will be. I conducted a numerical experiment using the wildfire model, FIRETEC, coupled with the atmospheric dynamics model, HIGRAD, to understand the complex interactions of wind, topography, and fire behaviors of two cut to length forest treatments on slopes of up to 60%. Results show that treatments can effectively reduce some fire behaviors such as heat release and canopy consumption when compared to untreated forests on slopes. However, increased sub canopy wind penetration along the slopes following treatments results in marginal fire severity reduction regarding biomass consumption and variable results on rates of spread. The results of these numerical experiments indicate that CTL treatment can effectively reduce some fire behavior and severity, however the effects were marginal and additional research is needed to better understand treatment’s effects.

Social computing scholars have long known that people do not interact with knowledge in straightforward ways, especially in digital environments. While policies around knowledge are essential for targeting misinformation, they are value-laden; in choosing how to present information, we undermine non-traditional -- often non-Western -- ways of knowing. Epistemic injustice is the systemic exclusion of certain people and methods from the knowledge canon. Epistemic injustice chips away at one's testimony and vocabulary until they are stripped of their due right to know and understand. In this paper, we articulate how epistemic injustice in sociotechnical applications leads to material harm. Inspired by a hybrid collaborative autoethnography of 14 CSCW practitioners, we present three cases of epistemic injustice in sociotechnical applications: online transgender healthcare, identity sensemaking on r/bisexual, and Indigenous ways of knowing on r/AskHistorians. We further explore signature tensions across our autoethnographic materials and relate them to previous CSCW research areas and personal non-technological experiences. We argue that epistemic injustice can serve as a unifying and intersectional lens for CSCW research by surfacing dimensions of epistemic community and power. Finally, we present a call to action of three changes the CSCW community should make to move toward its own goals of research justice. We call for CSCW researchers to center individual experiences, bolster communities, and remediate issues of epistemic power as a means towards epistemic justice. In sum, we recount, synthesize, and propose solutions for the various forms of epistemic injustice that CSCW sites of study -- including CSCW itself -- propagate.

Together they highlight the challenges related to articulating expressions of American Indian rhetoric, as contemporary rhetorical theory continues to be steeply rooted in Western theory and culture, and how the key to recognizing historical and ongoing examples of this articulation rests with the acknowledgment of Indigenous inherent sovereignty, as explained in the introduction. [...]the book dons a lens of decolonization to structure the discourse so readers may begin to privilege Indigenous narratives while informing themselves on pertinent issues related to our place(s) in the world. In Chapter 4, Matthew Brigham and Paul Mabrey explain the metaphorical implications of \"Homeland Security\" rhetorics produced by American Indians that reinforced notions of sovereignty and defense of the land in the wake of the September 11th terrorist attacks. By using \"chrono-logics,\" rhetoric that articulates temporal relationships and how these inform our actions and ethical systems (p. 106), American Indians successfully criticized American warmongering in light of the dispossession we've suffered in the name of defending our homelands to reaffirm our agency in both the existing historical narratives and the current dominant chrono-logics, poignantly indicating that for American Indians, \"the homeland has been under attack since the 1492 colonial-terrorist invasion\" (p. 115). Morris and Schmitt use personal interviews with the Powwow Comedy Jam (PCJ) and Ladies of Native Comedy (LNC) to demonstrate Native standup comic engagement in contact zones as a space to foster cross-cultural connection.

Clearance of intracellular pathogens, such as ( .) , depends on an immune response with well-regulated cytokine signaling. Here we describe a pathogen-mediated mechanism of evading CXCL10, a chemokine with diverse antimicrobial functions, including T cell recruitment. Infection with in a human monocyte cell line induced robust transcription without increasing extracellular CXCL10 protein concentrations. We found that this transcriptionally independent suppression of CXCL10 is mediated by the virulence factor and protease, glycoprotein-63 ( . Specifically, GP63 cleaves CXCL10 after amino acid A81 at the base of a C-terminal alpha-helix. Cytokine cleavage by GP63 demonstrated specificity, as GP63 cleaved CXCL10 and its homologs, which all bind the CXCR3 receptor, but not distantly related chemokines, such as CXCL8 and CCL22. Further characterization demonstrated that CXCL10 cleavage activity by GP63 was produced by both extracellular promastigotes and intracellular amastigotes. Crucially, CXCL10 cleavage impaired T cell chemotaxis , indicating that cleaved CXCL10 cannot signal through CXCR3. Ultimately, we propose CXCL10 suppression is a convergent mechanism of immune evasion, as and also suppress CXCL10. This commonality suggests that counteracting CXCL10 suppression may provide a generalizable therapeutic strategy against intracellular pathogens. Leishmaniasis, an infectious disease that annually affects over one million people, is caused by intracellular parasites that have evolved to evade the host's attempts to eliminate the parasite. Cutaneous leishmaniasis results in disfiguring skin lesions if the host immune system does not appropriately respond to infection. A family of molecules called chemokines coordinate recruitment of the immune cells required to eliminate infection. Here, we demonstrate a novel mechanism that spp. employ to suppress host chemokines: a encoded protease cleaves chemokines known to recruit T cells that fight off infection. We observe that other common human intracellular pathogens, including and , reduce levels of the same chemokines, suggesting a strong selective pressure to avoid this component of the immune response. Our study provides new insights into how intracellular pathogens interact with the host immune response to enhance pathogen survival.