Geology topics

Volker C. Radeloff

Publications and source records attributed to Volker C. Radeloff.

At least 19 recordsLinked to original sources

USGS2025

Rising rates of wildfire building destruction in the conterminous United States

Many regions of the world have seen an increase in highly destructive wildfires, driven by well-documented increases in burned area and growth of housing in the wildland–urban interface (WUI), which exposes more homes to fire. However, it is unclear whether wildfires are also becoming more destructive due to changes in wildfire behavior or in the development patterns of exposed communities. Here, we assessed trends in wildfire building exposure and destruction rates in the conterminous United States from 2002 to 2022. We mapped destroyed and surviving buildings within 100 m of all wildfires that destroyed 10 or more buildings (n = 362) and assessed trends relative to major ecoregions and vegetation types. We used logistic regression to assess relationships between destruction rates and landscape factors. We found that 10% of exposed buildings were destroyed in 2002–2012, but this percentage increased to 32% in 2013–2022. This increase was largely due to greater building exposure in evergreen forests in the northwestern United States, where exposed buildings were more than 3.4 times as likely to be destroyed as those in grass and shrublands. However, annual destruction rates also significantly increased in all other vegetation types and were correlated with development type, weather, and burn severity. These results indicate that increasing wildfire destruction in the United States has resulted not only from increased exposure but from rising rates of building destruction, potentially indicating more extreme wildfire behavior. This finding underscores the need to better understand how fuel management, community planning, and hardening buildings can reduce vulnerability.

conterminous United States↗

USGS2024

Need and vision for global medium-resolution Landsat and Sentinel-2 data products

Global changes in climate and land use are threatening natural ecosystems, biodiversity, and the ecosystem services people rely on. This is why it is necessary to track and monitor spatiotemporal change at a level of detail that can inform science, management, and policy development. The current constellation of multiple Landsat and Sentinel-2 satellites collecting imagery at predominantly ≤ "> ≤ 30-m spatial resolution affords an opportunity for the generation of global medium- resolution products every few days. Our goal is to both identify the information needs and provide direction towards the generation of a suite of global, high-level, systematically-generated, medium-resolution products designed for both management and science. Our vision builds on the success of the NASA MODIS/VIIRS product suite, while recognizing the unique strengths of medium-resolution satellite data given their higher spatial resolution and longer time series. We propose a suite of 13 essential products that enable the characterization of the current state and changes in the biosphere, cryosphere, and hydrosphere, and would fill information needs identified by the Committee on Earth Observation Satellites for the Global Climate Observing System and the Global Terrestrial Observing System, by the National Research Council of the US National Academies in the decadal survey, and by others. These products are: land cover, land cover change, burned area, forest loss, vegetation indices, phenology, dynamic habitat indices, albedo, land surface temperature, snow cover, ice extent, surface water extent, and evapotranspiration. Furthermore, we provide a list of desirable products poised for addition to the essential products (e.g., crop type, emissivity, and ice sheet velocity). Lastly, we suggest aspirational products requiring further algorithm development (e.g., forest structure and crop yield). For the identified essential products, algorithms are in place, making it feasible to begin generating products systematically. These products should be accompanied by quality and accuracy assessments undertaken following consensus protocols. Five decades after the first Landsat satellite, and two decades after the MODIS products were first produced, it is time now for readily available, standardized, and consistent high-level products built upon medium-resolution imagery, thereby fulfilling the promise and the vision that inspired the Landsat program since its inception.

Remote Sensing of Environment↗

USGS2023

The global wildland-urban interface

The wildland–urban interface (WUI) is where buildings and wildland vegetation meet or intermingle . It is where human–environmental conflicts and risks can be concentrated, including the loss of houses and lives to wildfire, habitat loss and fragmentation and the spread of zoonotic diseases . However, a global analysis of the WUI has been lacking. Here, we present a global map of the 2020 WUI at 10 m resolution using a globally consistent and validated approach based on remote sensing-derived datasets of building area and wildland vegetation . We show that the WUI is a global phenomenon, identify many previously undocumented WUI hotspots and highlight the wide range of population density, land cover types and biomass levels in different parts of the global WUI. The WUI covers only 4.7% of the land surface but is home to nearly half its population (3.5 billion). The WUI is especially widespread in Europe (15% of the land area) and the temperate broadleaf and mixed forests biome (18%). Of all people living near 2003–2020 wildfires (0.4 billion), two thirds have their home in the WUI, most of them in Africa (150 million). Given that wildfire activity is predicted to increase because of climate change in many regions , there is a need to understand housing growth and vegetation patterns as drivers of WUI change.

Nature↗

USGS2023

Changes in wildfire occurrence and risk to homes from 1990 through 2019 in the Southern Rocky Mountains, USA

Wildfires and housing development have increased since the 1990s, presenting unique challenges for wildfire management. However, it is unclear how the relative influences of housing growth and changing wildfire occurrence have altered risk to homes, or the potential for wildfire to threaten homes. We used a random forests model to predict burn probability in relation to weather variables at 1-km resolution and monthly intervals from 1990 through 2019 in the Southern Rocky Mountains ecoregion. We quantified risk by combining the predicted burn probabilities with decadal housing density. We then compared the predicted burn probabilities and risk across the study area with observed values and quantified trends. Finally, we evaluated how housing growth and changes in burn probability influenced risk individually and combined. Fires burned 9055 km 2 and exposed more than 8500 homes from 1990 to 2019. Observed burned area increased 632% from the 1990s to the 2000s, which combined with housing growth, resulted in a 1342% increase in homes exposed. Increases continued in the 2010s but at lower rates; burned area by 65% and exposure by 32%. The random forests model had excellent fit and high correlation with observations (AUC = 0.88 and r = 0.9). Observed values were within the 95% uncertainty interval for all years except 2016 (burned area) and 2000 (exposure). However, our model overpredicted in years with low observed burned area and underpredicted in years with high observed burned area. Overpredictions in risk resulted in lower rates of change in predicted risk compared with change in observed exposure. Increases in risk between the 1990s and 2000s were primarily due to warmer and drier weather conditions and secondarily because of housing growth. However, increases between the 2000s and 2010s were primarily due to housing growth. Our modeling approach identifies spatial and temporal patterns of wildfire potential and risk, which is critical information to guide decision-making. Because the drivers behind risk shift over time, strategies to mitigate risk may need to account for multiple drivers simultaneously.

Colorado, New Mexico, Wyoming↗

USGS2023

Identifying building locations in the wildland–urban interface before and after fires with convolutional neural networks

Background: Wildland–urban interface (WUI) maps identify areas with wildfire risk, but they are often outdated owing to the lack of building data. Convolutional neural networks (CNNs) can extract building locations from remote sensing data, but their accuracy in WUI areas is unknown. Additionally, CNNs are computationally intensive and technically complex, making them challenging for end-users, such as those who use or create WUI maps, to apply. Aims: We identified buildings pre- and post-wildfire and estimated building destruction for three California wildfires: Camp, Tubbs and Woolsey. Methods: We evaluated a CNN-based building dataset and a CNN model from a separate commercial vendor to detect buildings from high-resolution imagery. This dataset and model represent to end-users the state of the art of what is readily available for potential WUI mapping. Key results: We found moderate accuracies for the building dataset and the CNN model and a severe underestimation of buildings and their destruction rates where trees occluded buildings. The CNN model performed best post-fire with accuracies ≥73%. Conclusions: Existing CNNs may be used with moderate accuracy for identifying individual buildings post-fire and mapping the extent of the WUI. The implications are, however, that CNNs are too inaccurate for post-fire damage assessments or building counts in the WUI.

International Journal of Wildland Fire↗

USGS2023

The extent of buildings in wildland vegetation of the conterminous U.S. and the potential for conservation in and near National Forest private inholdings

Development in natural areas is a leading threat to biodiversity. Global conservationists have called for the expansion of protected areas to preserve wildlands that are free from buildings, and in the U.S., the ‘America the Beautiful’ initiative aims to protect 30% of land and water areas by 2030 (known as the ‘30x30’ target). Here, we determined opportunities and limitations for conservation in the conterminous U.S. by assessing the extent of buildings in wildland vegetation. We focused specifically on National Forest lands, as these contain numerous private inholdings where development may occur. Using a newly available building footprint dataset, we determined 1) whether buildings were present and 2) numbers of buildings within three distances of wildland vegetation (100, 250, and 500 m), representing varying magnitudes of ecological impact. Our findings revealed that 29% of wildland vegetation nationwide was within 500 m of a building, 15% was within 250 m, and 5% was within 100 m. National Forest lands were less affected by building disturbance, but a substantial proportion (12%) of wildland vegetation area was within 500 m of a building. Of National Forest lands that were within 500 m of an inholding, 76% was not yet in proximity to a building; consequently, ∼10% of National Forest lands (143,474 km 2 ) are susceptible to impacts from future development on inholdings. We conclude that National Forest inholdings are therefore important opportunity areas for 30x30 conservation goals. Our assessments can inform where conservation efforts can limit impacts from present and future development on biodiversity.

Landscape and Urban Planning↗

USGS2022

The wildland-urban interface in the United States based on 125 million building locations

The wildland-urban interface (WUI) is the focus of many important land management issues, such as wildfire, habitat fragmentation, invasive species, and human-wildlife conflicts. Wildfire is an especially critical issue, because housing growth in the WUI increases wildfire ignitions and the number of homes at risk. Identifying the WUI is important for assessing and mitigating impacts of development on wildlands and for protecting homes from natural hazards, but data on housing development for large areas are often coarse. We created new WUI maps for the conterminous U.S. based on 125 million individual building locations, offering higher spatial precision compared to existing maps based on U.S. census housing data. Building point locations were based on a building footprint dataset from Microsoft®. We classified WUI across the conterminous U.S. at 30-m resolution using a circular neighborhood mapping algorithm with a variable radius to determine thresholds of housing density and vegetation cover. We used our maps to (1) determine the total area of the WUI and number of buildings included, (2) assess the sensitivity of WUI area included and spatial pattern of WUI maps to choice of neighborhood size, (3) assess regional differences between building-based WUI maps and census-based WUI maps, and (4) determine how building location accuracy affected WUI map accuracy. Our building-based WUI maps identified 5.6% – 18.8% of the conterminous U.S. as being in the WUI, with larger neighborhoods increasing WUI area but excluding isolated building clusters. Building-based maps identified more WUI area relative to census-based maps for all but the smallest neighborhoods, particularly in the north-central states, and large differences were attributable to high numbers of non-housing structures in rural areas. Overall WUI classification accuracy was 98.0%. For wildfire risk mapping and for general purposes, WUI maps based on the 500-m neighborhood represent the original Federal Register definition of the WUI; these maps include clusters of buildings in and adjacent to wildlands and exclude remote, isolated buildings. Our approach for mapping the WUI offers flexibility and high spatial detail, and can be widely applied to take advantage of the growing availability of high-resolution building footprint datasets and classification methods.

Conterminous United States↗

USGS2022

Fifty years of Landsat science and impacts

Since 1972, the Landsat program has been continually monitoring the Earth, to now provide 50 years of digital, multispectral, medium spatial resolution observations. Over this time, Landsat data were crucial for many scientific and technical advances. Prior to the Landsat program, detailed, synoptic depictions of the Earth's surface were rare, and the ability to acquire and work with large datasets was limited. The early years of the Landsat program delivered a series of technological breakthroughs, pioneering new methods, and demonstrating the ability and capacity of digital satellite imagery, creating a template for other global Earth observation missions and programs. Innovations driven by the Landsat program have paved the way for subsequent science, application, and policy support activities. The economic and scientific value of the knowledge gained through the Landsat program has been long recognized, and despite periods of funding uncertainty, has resulted in the program's 50 years of continuity, as well as substantive and ongoing improvements to payload and mission performance. Free and open access to Landsat data, enacted in 2008, was unprecedented for medium spatial resolution Earth observation data and substantially increased usage and led to a proliferation of science and application opportunities. Here, we highlight key developments over the past 50 years of the Landsat program that have influenced and changed our scientific understanding of the Earth system. Major scientific and programmatic impacts have been realized in the areas of agricultural crop mapping and water use, climate change drivers and impacts, ecosystems and land cover monitoring, and mapping the changing human footprint. The introduction of Landsat collection processing, coupled with the free and open data policy, facilitated a transition in Landsat data usage away from single images and towards time series analyses over large areas and has fostered the widespread use of science-grade data. The launch of Landsat-9 on September 27, 2021, and the advanced planning of its successor mission, Landsat-Next, underscore the sustained institutional support for the program. Such support and commitment to continuity is recognition of both the historic impact the program, and the future potential to build upon Landsat's remarkable 50-year legacy.

Remote Sensing of Environment↗

USGS2021

Growth of the wildland-urban interface within and around U.S. National Forests and Grasslands, 1990-2010

The wildland-urban interface (WUI), where housing is in close proximity to or intermingled with wildland vegetation, is widespread throughout the United States, but it is unclear how this type of housing development affects public lands. We used a national dataset to examine WUI distribution and growth (1990–2010) in proximity to National Forests and created a typology to characterize each National Forest’s combination of WUI area and housing growth. We found that National Forests are hotspots for WUI growth, with a 38% increase in WUI area and 46% growth in WUI houses from 1990 to 2010, in excess of WUI growth for the conterminous U.S. Growth within National Forests was higher than the surrounding area. Diffuse intermix WUI, where houses are intermingled with wildland vegetation, is common within National Forests, but WUI houses around National Forests were primarily in denser interface WUI areas, which lack substantial wildland vegetation. WUI was more prevalent within and around National Forests in the East, while National Forests in the West experienced higher rates of WUI growth. National Forests with the most challenging WUI issues—extensive WUI area and rapid growth in intermix and interface—were found primarily in the South and interior West. Given the diversity of WUI landscapes, effectively responding to current and future WUI challenges will require both engagement with individual homeowners dispersed throughout National Forests, as well as increased emphasis on mitigating denser interface development around National Forests. At a time when wildfire risks are expected to intensify due to climate change, and 75% of privately owned land within and around National Forests is not yet WUI, understanding WUI growth patterns in proximity to public lands is vital for land management and human well-being.

Landscape and Urban Planning↗

USGS2019

Assessing vulnerability and threat from housing development to Conservation Opportunity Areas in State Wildlife Action Plans across the United States

Targeting conservation actions efficiently requires information on vulnerability of and threats to conservation targets, but such information is rarely included in conservation plans. In the U.S., recently updated State Wildlife Action Plans identify Conservation Opportunity Areas (COAs) selected by each state as priority areas for future action to conserve wildlife and habitats. The question is how threatened these COAs are by habitat loss and degradation, major threats to wildlife in the U.S. that are often caused by housing development. We compiled spatial data on COAs across the conterminous U.S. We estimated COA vulnerability using current land protection status and COA threat using projected housing growth derived from U.S. census data. COAs comprise 1–46% of each region. Across regions, 28–82% of the area within COAs is vulnerable to future housing development, and 5–55% and 7–23% of that vulnerable COA area is threatened by projected dense housing and rapid housing growth, respectively. COA vulnerability is greatest in the East. Threat from dense housing and rapid housing growth is highest in the Northeast and Pacific Southwest, respectively. Results highlight that many areas identified as important for reducing wildlife listings under the U.S. Endangered Species Act may need further protection to fulfill their conservation goals because they are both vulnerable to and threatened by future housing development. Our analyses can help practitioners target local government outreach, land protection efforts, and landscape-scale mitigation programs to decrease future COA loss from housing development, and could be expanded to address additional COA threats (e.g., wildfire, invasive species).

Landscape and Urban Planning↗

USGS2018

Rapid growth of the US wildland-urban interface raises wildfire risk

The wildland-urban interface (WUI) is the area where houses and wildland vegetation meet or intermingle, and where wildfire problems are most pronounced. Here we report that the WUI in the United States grew rapidly from 1990 to 2010 in terms of both number of new houses (from 30.8 to 43.4 million; 41% growth) and land area (from 581,000 to 770,000 km 2 ; 33% growth), making it the fastest-growing land use type in the conterminous United States. The vast majority of new WUI areas were the result of new housing (97%), not related to an increase in wildland vegetation. Within the perimeter of recent wildfires (1990–2015), there were 286,000 houses in 2010, compared with 177,000 in 1990. Furthermore, WUI growth often results in more wildfire ignitions, putting more lives and houses at risk. Wildfire problems will not abate if recent housing growth trends continue.

Proceedings of the National Academy of Sciences↗

USGS2018

Slow and steady wins the race? Future climate and land use change leaves the imperiled Blanding's turtle (Emydoidea blandingii) behind

Climate change is accompanied by shifts in species distributions, as portions of current ranges become less suitable. Maintaining or improving landscape connectivity to facilitate species movements is a primary approach to mitigate the effects of climate change on biodiversity. However, it is not clear how ongoing changes in land use and climate may affect the existing connectivity of landscapes. We evaluated shifts in habitat suitability and connectivity for the imperiled Blanding's turtle ( Emydoidea blandingii ) in Wisconsin using species distribution modeling in combination with different future scenarios of both land use change and climate change for the 2050s. We found that climate change had significant effects on both habitat suitability and connectivity, however, there was little difference in the magnitude of effects among different economic scenarios. Under both our low- and high-CO 2 emissions scenarios, suitable habitat for the Blanding's turtle shifted northward. In the high-emissions scenario, almost no suitable habitat remained for Blanding's turtle in Wisconsin by the 2050s and there was up to a 100,000-fold increase in landscape resistance to turtle movement, suggesting the landscape essentially becomes impassable. Habitat loss and landscape resistance were exponentially greater in southern versus northern Wisconsin, indicating a strong trailing edge effect. Thus, populations at the southern edge of the range are likely to “fall behind” shifts in suitable habitat faster than northern populations. Given its limited dispersal capability, loss of suitable habitat may occur at a rate far faster than the Blanding's turtle can adjust to changing conditions via shifts in range.

Wisconsin↗

USGS2016

Evaluation of downscaled, gridded climate data for the conterminous United States

Weather and climate affect many ecological processes, making spatially continuous yet fine-resolution weather data desirable for ecological research and predictions. Numerous downscaled weather data sets exist, but little attempt has been made to evaluate them systematically. Here we address this shortcoming by focusing on four major questions: (1) How accurate are downscaled, gridded climate data sets in terms of temperature and precipitation estimates?, (2) Are there significant regional differences in accuracy among data sets?, (3) How accurate are their mean values compared with extremes?, and (4) Does their accuracy depend on spatial resolution? We compared eight widely used downscaled data sets that provide gridded daily weather data for recent decades across the United States. We found considerable differences among data sets and between downscaled and weather station data. Temperature is represented more accurately than precipitation, and climate averages are more accurate than weather extremes. The data set exhibiting the best agreement with station data varies among ecoregions. Surprisingly, the accuracy of the data sets does not depend on spatial resolution. Although some inherent differences among data sets and weather station data are to be expected, our findings highlight how much different interpolation methods affect downscaled weather data, even for local comparisons with nearby weather stations located inside a grid cell. More broadly, our results highlight the need for careful consideration among different available data sets in terms of which variables they describe best, where they perform best, and their resolution, when selecting a downscaled weather data set for a given ecological application.

Ecological Applications↗

USGS2016

Future frequencies of extreme weather events in the National Wildlife Refuges of the conterminous U.S.

Climate change is a major challenge for managers of protected areas world-wide, and managers need information about future climate conditions within protected areas. Prior studies of climate change effects in protected areas have largely focused on average climatic conditions. However, extreme weather may have stronger effects on wildlife populations and habitats than changes in averages. Our goal was to quantify future changes in the frequency of extreme heat, drought, and false springs, during the avian breeding season, in 415 National Wildlife Refuges in the conterminous United States. We analyzed spatially detailed data on extreme weather frequencies during the historical period (1950–2005) and under different scenarios of future climate change by mid- and late-21st century. We found that all wildlife refuges will likely experience substantial changes in the frequencies of extreme weather, but the types of projected changes differed among refuges. Extreme heat is projected to increase dramatically in all wildlife refuges, whereas changes in droughts and false springs are projected to increase or decrease on a regional basis. Half of all wildlife refuges are projected to see increases in frequency (> 20% higher than the current rate) in at least two types of weather extremes by mid-century. Wildlife refuges in the Southwest and Pacific Southwest are projected to exhibit the fastest rates of change, and may deserve extra attention. Climate change adaptation strategies in protected areas, such as the U.S. wildlife refuges, may need to seriously consider future changes in extreme weather, including the considerable spatial variation of these changes.

Biological Conservation↗

USGS2016

Divergent projections of future land use in the United States arising from different models and scenarios

A variety of land-use and land-cover (LULC) models operating at scales from local to global have been developed in recent years, including a number of models that provide spatially explicit, multi-class LULC projections for the conterminous United States. This diversity of modeling approaches raises the question: how consistent are their projections of future land use? We compared projections from six LULC modeling applications for the United States and assessed quantitative, spatial, and conceptual inconsistencies. Each set of projections provided multiple scenarios covering a period from roughly 2000 to 2050. Given the unique spatial, thematic, and temporal characteristics of each set of projections, individual projections were aggregated to a common set of basic, generalized LULC classes (i.e., cropland, pasture, forest, range, and urban) and summarized at the county level across the conterminous United States. We found very little agreement in projected future LULC trends and patterns among the different models. Variability among scenarios for a given model was generally lower than variability among different models, in terms of both trends in the amounts of basic LULC classes and their projected spatial patterns. Even when different models assessed the same purported scenario, model projections varied substantially. Projections of agricultural trends were often far above the maximum historical amounts, raising concerns about the realism of the projections. Comparisons among models were hindered by major discrepancies in categorical definitions, and suggest a need for standardization of historical LULC data sources. To capture a broader range of uncertainties, ensemble modeling approaches are also recommended. However, the vast inconsistencies among LULC models raise questions about the theoretical and conceptual underpinnings of current modeling approaches. Given the substantial effects that land-use change can have on ecological and societal processes, there is a need for improvement in LULC theory and modeling capabilities to improve acceptance and use of regional- to national-scale LULC projections for the United States and elsewhere.

Ecological Modelling↗

USGS2015

The pace of past climate change vs. potential bird distributions and land use in the United States

Climate change may drastically alter patterns of species distributions and richness, but predicting future species patterns in occurrence is challenging. Significant shifts in distributions have already been observed, and understanding these recent changes can improve our understanding of potential future changes. We assessed how past climate change affected potential breeding distributions for landbird species in the conterminous United States. We quantified the bioclimatic velocity of potential breeding distributions, that is, the pace and direction of change for each species’ suitable climate space over the past 60 years. We found that potential breeding distributions for landbirds have shifted substantially with an average velocity of 1.27 km yr −1 , about double the pace of prior distribution shift estimates across terrestrial systems globally (0.61 km yr −1 ). The direction of shifts was not uniform. The majority of species’ distributions shifted west, northwest, and north. Multidirectional shifts suggest that changes in climate conditions beyond mean temperature were influencing distributional changes. Indeed, precipitation variables that were proxies for extreme conditions were important variables across all models. There were winners and losers in terms of the area of distributions; many species experienced contractions along west and east distribution edges, and expansions along northern distribution edges. Changes were also reflected in the potential species richness, with some regions potentially gaining species (Midwest, East) and other areas potentially losing species (Southwest). However, the degree to which changes in potential breeding distributions are manifested in actual species richness depends on landcover. Areas that have become increasingly suitable for breeding birds due to changing climate are often those attractive to humans for agriculture and development. This suggests that many areas might have supported more breeding bird species had the landscape not been altered. Our study illustrates that climate change is not only a future threat, but something birds are already experiencing.

Global Change Biology↗

USGS2015

The importance of range edges for an irruptive species during extreme weather events

Context Threats to wildlife species from extreme events, such as droughts, are predicted to increase in frequency and magnitude with climate change. Extreme events can cause mortality and community-level changes, but for some mobile species, movement away from areas affected may be a viable option. Objectives We examined the effect of extreme weather on spatial patterns of abundance for an irruptive grassland bird species, the Dickcissel ( Spiza americana ). Methods We calculated route-level annual abundances and abundance anomalies from 1980 to 2012 from North American Breeding Bird Survey data, and classified the Dickcissel’s range into core and edge regions using these abundances. We then compared abundances in the core and edge regions to the standardized precipitation evapotranspiration index, a measure of drought, in linear regressions. Results We found that Dickcissel irruptions in the northern range edges were related to drought conditions in the range core, potentially a consequence of birds being ‘pushed’ to the range edge when weather was unsuitable. Specifically, Dickcissels moved into refuge sites containing a high proportion of cultivated crops, with higher vegetation greenness, than those areas they leave during drought years. Conclusions In a changing climate where more frequent extreme weather may be more common, conservation strategies for weather-sensitive species may require consideration of habitat in the edges of species’ ranges, even though non-core areas may be unoccupied in ‘normal’ years. Our results highlight the conservation importance of range edges in providing refuge from extreme events, such as drought, and climate change.

Landscape Ecology↗

USGS2014

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?

Conservation Biology↗