Browsing by Subject "remote sensing"

Now showing 1 - 5 of 5
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    Changes in evapotranspiration and phenology as consequences of shrub removal in dry forests of central Argentina
    (Ecohydrology, 2015-10-01) Marchesini, VA; Fernández, RJ; Reynolds, JF; Sobrino, JA; Di Bella, CM
    More than half of the dry woodlands (forests and shrublands) of the world are in South America, mainly in Brazil and Argentina, where in the last years intense land use changes have occurred. This study evaluated how the transition from woody-dominated to grass-dominated system affected key ecohydrological variables and biophysical processes over 20000ha of dry forest in central Argentina. We used a simplified surface energy balance model together with moderate-resolution imaging spectroradiometer-normalized difference vegetation index data to analyse changes in above primary productivity, phenology, actual evapotranspiration, albedo and land surface temperature for four complete growing seasons (2004-2009). The removal of woody vegetation decreased aboveground primary productivity by 15-21%, with an effect that lasted at least 4years, shortened the growing season between 1 and 3months and reduced evapotranspiration by as much as 30%. Albedo and land surface temperature increased significantly after the woody to grassland conversion. Our findings highlight the role of woody vegetation in regulating water dynamics and ecosystem phenology and show how changes in vegetative cover can influence regional climatic change. © 2015 John Wiley
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    Community-scale changes to landfast ice along the coast of Alaska over 2000-2022
    (Environmental Research Letters, 2024-02-01) Cooley, SW; Ryan, JC
    Landfast sea ice that forms along the Arctic coastline is of great importance to coastal Alaskan communities. It provides a stable platform for transportation and traditional activities, protects the coastline from erosion, and serves as a critical habitat for marine mammals. Here we present a full assessment of landfast ice conditions across a continuous 7885 km length of the Alaska coastline over 2000-2022 using satellite imagery. We find that the maximum landfast ice extent, usually occurring in March, averaged 67 002 km2 during our study period: equivalent to 4% of the state’s land area. The maximum extent of landfast ice, however, exhibits considerable interannual variability, from a minimum of 29 871 km2 in 2019 to a maximum of 87 571 km2 in 2010. Likewise, the landfast ice edge position averages 22.9 km from the coastline but, at the community-scale, can range from 2.8 km (in Gambell) to 71.1 km (in Deering). Landfast ice breakup date averages 2 June but also varies considerably both between communities (3 May in Quinhagak to 24 July in Nuiqsut) and interannually. We identify a strong control of air temperature on breakup timing and use this relationship to project future losses of ice associated with Paris Climate Agreement targets. Under 2 °C of global air temperature warming, we estimate the average Alaskan coastal community will lose 19 days of ice, with the northernmost communities projected to lose 50 days or more. Overall, our results emphasize the highly localized nature of landfast ice processes and the vulnerability of coastal Arctic communities in a warming climate.
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    Identifying Malaria Transmission Risk in the Peruvian Amazon: A Geospatial Approach
    (2014-04-25) Yin, Elizabeth
    Peru has endured a long history with malaria, an infectious disease caused by the mosquito-borne transmission of the Plasmodium parasite. Throughout the 20th century, disease prevalence has varied tremendously with a number of factors including Peru’s growth and development, variable support for malaria control measures, and the migration of immunologically naïve populations. However, many researchers believe that anthropogenic deforestation is at the root of a recent resurgence of malaria in the Peruvian Amazon. Deforestation creates favorable conditions for disease transmission by increasing mosquito habitat and placing humans in close proximity to more abundant disease vectors. In addition, rural communities often lack the resources to combat malaria due to the prohibitive cost of conventional technologies and lack of access to health care. Using data derived from field collections and remotely sensed images in the Loreto department of Peru, this study proposes a new method for characterizing malaria risk in the Peruvian Amazon. A variety of novel geospatial and remote sensing techniques were used to develop environmental layers from satellite imagery and produce the species distribution model. A geospatial risk model synthesized the predicted mosquito habitat and associated community risk factors into an assessment of malaria exposure risk. The threat model developed from this study can be used to create maps that will help local communities manage their malaria risk. Management efforts, such as the reduction of available mosquito breeding habitat, can be concentrated in areas identified as high-risk for malaria exposure.
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    Improving population estimates of difficult-to-observe species: A dung decay model for forest elephants with remotely sensed imagery
    (Animal Conservation, 2021-01-01) Meier, AC; Shirley, MH; Beirne, C; Breuer, T; Lewis, M; Masseloux, J; Jasperse-Sjolander, L; Todd, A; Poulsen, JR
    Accurate and ecologically relevant wildlife population estimates are critical for species management. One of the most common survey methods for forest mammals – line transects for animal sign with distance sampling – has assumptions regarding conversion factors that, if violated, can induce substantial bias in abundance estimates. Specifically, for sign (e.g. nests, dung) surveys, a single number representing total time for decay is used as a multiplier to convert estimated sign density into animal density. This multiplier is likely inaccurate if not derived from a study reflecting the spatiotemporal variation in decay times. Using dung decay observations from three protected areas in Gabon, and a previous study in Nouabalé-Ndoki National Park (Congo), we developed Weibull survival models to adaptively predict forest elephant (Loxodonta cyclotis) dung decay based on environmental variables from field collected and remotely sensed data. Seasonal decay models based on remotely sensed covariates explained 86% of the variation for the wet season and 79% for the dry season. These models included canopy cover, cloud cover, humidity, vegetation complexity and slope as factors influencing dung decay. With these models, we assessed sensitivity of elephant density estimates to spatiotemporal environmental heterogeneity, showing that our methods work best for large-scale studies >50 km2. We simulated decay studies with and without these variables in four Gabonese national parks and reanalyzed two previous surveys of elephants in Minkébé National Park, Gabon. Disregarding spatial and temporal variation in decay rate biased population estimates up to 1.6 and 6.9 times. Our reassessment of surveys in Minkébé National Park showed an expected loss of 78% of forest elephants over ten years, but the elephant abundance was 222% higher than previously estimated. Our models incorporate field or remotely sensed variables to provide an ecological context essential for accurate population estimates while reducing need for expensive decay field studies.
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    Towards a global drylands observing system: Observational requirements and institutional solutions
    (Land Degradation & Development, 2011) Verstraete; MM; Hutchinson, CF; Grainger, A; Smith, M Stafford; Scholes, RJ; REYNOLDS, JF; Barbosa, P; Léon, A; Mbow, C
    Quantitative data on dryland changes and their effects on the people living there are required to support policymaking and environmental management at all scales. Data are regularly acquired by international, national or local entities, but presently exhibit specific gaps. Promoting sustainable development in drylands necessitates a much stronger integration, coordination and synthesis of available information. Space-based remote sensing systems continue to play an important role but do not fulfill all needs. Dedicated networks and observing systems, operating over a wide range of scales and resolutions, are needed to address the key issues that concern decision-makers at the scale of local communities, countries and the international community. This requires a mixture of 'bottom-up' and 'top-down' design principles, and multiple ownership of the resultant system. This paper reviews the limitations of current observing systems and suggests establishing a Global Drylands Observing System, which would capitalize on the achievements of systems already established to support the other Rio Conventions. This Global Drylands Observing System would provide an integrated, coherent entry point and user interface to a range of underlying information systems, identify and help generate missing information, propose a set of standards for the acquisition, archiving and distribution of data where these are lacking, evaluate the quality and reliability of these data and promote scientific research in these fields by improving access to data. The paper outlines the principles and main objectives of a Global Drylands Observing System and calls for renewed efforts to invigorate cooperation mechanisms between the many global environmental conventions. Copyright © 2010 John Wiley & Sons, Ltd.