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Integrating habitat status, human population pressure, and protection status into biodiversity conservation priority setting

Priority setting is an essential component of biodiversity conservation. Existing methods to identify priority areas for conservation have focused almost entirely on biological factors. We suggest a new relative ranking method for identifying priority conservation areas that integrates both biological and social aspects. It is based on the following criteria: the habitat's status, human population pressure, human efforts to protect habitat, and number of endemic plant and vertebrate species. We used this method to rank 25 hotspots, 17 megadiverse countries, and the hotspots within each megadiverse country. We used consistent, comprehensive, georeferenced, and multiband data sets and analytical remote sensing and geographic information system tools to quantify habitat status, human population pressure, and protection status. The ranking suggests that the Philippines, Atlantic Forest, Mediterranean Basin, Caribbean Islands, Caucasus, and Indo-Burma are the hottest hotspots and that China, the Philippines, and India are the hottest megadiverse countries. The great variation in terms of habitat, protected areas, and population pressure among the hotspots, the megadiverse countries, and the hotspots within the same country suggests the need for hotspot- and country-specific conservation policies.

Conservation Biology↗

The U.S. Geological Survey Land Remote Sensing Program

The fundamental goals of the U.S. Geological Survey's Land Remote Sens-ing (LRS) Program are to provide the Federal Government and the public with a primary source of remotely sensed data and applications and to be a leader in defining the future of land remote sensing, nationally and internationally. Remotely sensed data provide information that enhance the understand-ing of ecosystems and the capabilities for predicting ecosystem change. The data promote an understanding of the role of the environment and wildlife in human health issues, the requirements for disaster response, the effects of climate variability, and the availability of energy and mineral resources. Also, as land satellite systems acquire global coverage, the program coordinates a network of international receiving stations and users of the data. It is the responsibility of the program to assure that data from land imaging satellites, airborne photography, radar, and other technologies are available to the national and global science communities.

Fact Sheet↗

A Landscape Indicator Approach to the Identification and Articulation of the Ecological Consequences of Land Cover Change in the Chesapeake Bay Watershed, 1970-2000

The advancement of geographic science in the area of land surface status and trends and land cover change is at the core of the current geographic scientific research of the U.S. Geological Survey (USGS) (McMahon and others, 2005). Perhaps the least developed or articulated aspects of USGS land change science have been the identification and analysis of the ecological consequences of land cover change. Changes in land use and land cover significantly affect the ability of ecosystems to provide essential ecological goods and services, which, in turn, affect the economic, public health, and social benefits that these ecosystems provide. One of the great scientific challenges for geographic science is to understand and calibrate the effects of land use and land cover change and the complex interaction between human and biotic systems at a variety of natural, geographic, and political scales. Understanding the dynamics of land surface change requires an increased understanding of the complex nature of human-environmental systems and will require a suite of scientific tools that include traditional geographic data and analysis methods, such as remote sensing and geographic information systems (GIS), as well as innovative approaches to understanding the dynamics of complex systems. One such approach that has gained much recent scientific attention is the landscape indicator, or landscape assessment, approach, which has been developed with the emergence of the science of landscape ecology.

Fact Sheet↗

South Florida Everglades: satellite image map

These satellite image maps are one product of the USGS Land Characteristics from Remote Sensing project, funded through the USGS Place-Based Studies Program (http://access.usgs.gov/) with support from the Everglades National Park (http://www.nps.gov/ever/). The objective of this project is to develop and apply innovative remote sensing and geographic information system techniques to map the distribution of vegetation, vegetation characteristics, and related hydrologic variables through space and over time. The mapping and description of vegetation characteristics and their variations are necessary to accurately simulate surface hydrology and other surface processes in South Florida and to monitor land surface changes. As part of this research, data from many airborne and satellite imaging systems have been georeferenced and processed to facilitate data fusion and analysis. These image maps were created using image fusion techniques developed as part of this project.

IMAP↗

Northern Everglades, Florida, satellite image map

These satellite image maps are one product of the USGS Land Characteristics from Remote Sensing project, funded through the USGS Place-Based Studies Program with support from the Everglades National Park. The objective of this project is to develop and apply innovative remote sensing and geographic information system techniques to map the distribution of vegetation, vegetation characteristics, and related hydrologic variables through space and over time. The mapping and description of vegetation characteristics and their variations are necessary to accurately simulate surface hydrology and other surface processes in South Florida and to monitor land surface changes. As part of this research, data from many airborne and satellite imaging systems have been georeferenced and processed to facilitate data fusion and analysis. These image maps were created using image fusion techniques developed as part of this project.

Florida↗

Using a remote sensing/GIS model to predict southwestern Willow Flycatcher breeding habitat along the Rio Grande, New Mexico

Introduction The Southwestern Willow Flycatcher (Empidonax traillii extimus; hereafter SWFL) is a federally endangered bird (USFWS 1995) that breeds in riparian areas in portions of New Mexico, Arizona, southwestern Colorado, extreme southern Utah and Nevada, and southern California (USFWS 2002). Across this range, it uses a variety of plant species as nesting/breeding habitat, but in all cases prefers sites with dense vegetation, high canopy, and proximity to surface water or saturated soils (Sogge and Marshall 2000). As of 2005, the known rangewide breeding population of SWFLs was roughly 1,214 territories, with approximately 393 territories distributed among 36 sites in New Mexico (Durst et al. 2006), primarily along the Rio Grande. One of the key challenges facing the management and conservation of the Southwestern Willow Flycatcher is that riparian areas are dynamic, with individual habitat patches subject to cycles of creation, growth, and loss due to drought, flooding, fire, and other disturbances. Former breeding patches can lose suitability, and new habitat can develop within a matter of only a few years, especially in reservoir drawdown zones. Therefore, measuring and predicting flycatcher habitat - either to discover areas that might support SWFLs, or to identify areas that may develop into appropriate habitat - requires knowledge of recent/current habitat conditions and an understanding of the factors that determine flycatcher use of riparian breeding sites. In the past, much of the determination of whether a riparian site is likely to support breeding flycatchers has been based on qualitative criteria (for example, 'dense vegetation' or 'large patches'). These determinations often require on-the-ground field evaluations by local or regional SWFL experts. While this has proven valuable in locating many of the currently known breeding sites, it is difficult or impossible to apply this approach effectively over large geographic areas (for example, the middle Rio Grande). The SWFL Recovery Plan (USFWS 2002) recognizes the importance of developing new approaches to habitat identification, and recommends the development of drainage-scale, quantitative habitat models. In particular, the plan suggests using models based on remote sensing and Geographic Information System (GIS) technology that can capture the relatively dynamic habitat changes that occur in southwestern riparian systems. In 1999, Arizona Game and Fish Department (AGFD) developed a GIS-based model (Hatten and Paradzick 2003) to identify SWFL breeding habitat from Landsat Thematic Mapper imagery and 30-m resolution digital elevation models (DEMs). The model was developed with presence/absence survey data acquired along the San Pedro and Gila rivers, and from the Salt River and Tonto Creek inlets to Roosevelt Lake in southern Arizona (collectively called the project area). The GIS-based model used a logistic regression equation to divide riparian vegetation into 5 probability classes based upon characteristics of riparian vegetation and floodplain size. This model was tested by predicting SWFL breeding habitat at Alamo Lake, Arizona, located 200 km from the project area (Hatten and Paradzick 2003). The GIS-based model performed as expected by identifying riparian areas with the highest SWFL nest densities, located in the higher probability classes. In 2002, AGFD applied the GIS-based model throughout Arizona, for riparian areas below 1,524 m (5,000 ft) elevation and within 1.6 km of perennial or intermittent waters (Dockens et al. 2004). Overall model accuracy (using probability classes 1-5, with class 5 having the greatest probability of nesting activity) for predicting the location of 2001 nest sites was 96.5 percent; accuracy decreased when fewer probability classes were defined as suitable. Map accuracy, determined from errors of commission, increased in higher probability classes in a fashion similar to errors of omission. Map accuracy, li

Colorado, New Mexico↗

2020 Four-band aerial imagery testing and acquisition for 2020 land cover/land use mission

The aerial camera testing project lays the groundwork for the collection of aerial imagery that will be used in the creation of the next iteration of systemic land cover/land use data for the Upper Mississippi River System. Prior to acquisition in the summer of 2020, the new 4-band aerial camera will be assessed for image quality at various resolutions and be compared to the camera used for the 2010/2011 collection. Systemic aerial imagery has been acquired, and vegetation datasets derived from that imagery, by the Upper Mississippi River Restoration Program’s Long Term Resource Monitoring element on a decadal basis beginning in 1989 and follow-up imagery missions in 2000 and 2010/2011. Remote sensing and geographic information system technology has changed dramatically during this time, transitioning from a workflow based on 9-inch by 9-inch aerial film-based cameras to today’s 80-megapixel four-band digital camera. In addition to the camera testing, this report also provides historical information of previous aerial imagery acquisition efforts and how that process has continued to advance in the 30 years since the Program’s genesis.

Long Term Resource Monitoring Technical Report↗

Mapping winter cover crops using remote sensing of crop phenology

Winter cover crops (WCC) are an effective agricultural conservation practice for improving soil health and water quality. Several U.S. states have established incentive programs to promote planting WCC between cash crop rotations. In recent years, the area planted with WCC has steadily increased due to these efforts, although the planting area varies among states. Since these programs are largely led by individual counties or state programs, there is no centralized data repository that quantifies the total WCC planted annually or their locations. Therefore, there is a practical need to identify fields that have been planted with WCC, as well as to quantify the total planted area both during and after the growing season. Furthermore, incentive programs often require verification that WCC were planted to release payments to participating growers in a timely manner. Satellite remote sensing time series can provide information on WCC growth status. However, it remains unclear what kind of WCC the remote sensing data detects. For example, separating WCC from weeds and winter commodity crops remains a challenge. Additionally, a binary classification of WCC presence and absence cannot accurately describe the category and performance of the WCC. This paper presents a new remote sensing phenology-based approach to map WCC likelihood. We assessed model predictions of WCC likelihood using WCC planting records and ground observations over fields at the Beltsville Agricultural Research Center (BARC) from 2018 to 2024. Regional WCC maps were assessed using the Maryland Department of Agriculture's records of WCC fields enrolled in the 2019 and 2020 incentive programs. Our results show that the balanced overall accuracy for WCC and non-WCC ranges from 75% to 85% but could exceed 90%, with limited training samples. However, the high accuracy of WCC corresponds with low accuracy for non-WCC due to confusion with weeds and perennial grasses. Early-season detection yields promising results, with overall accuracies comparable to late-season detection. The after-season detection can differentiate between winter cereal grain cash crops and incentive WCC based on the ending date of the growing season. With a small set of WCC samples and management information, the phenology-based mapping approach offers a rapid and scalable solution for mapping WCC over a large region during or after each WCC growing season.

Maryland↗

Department of the Interior: DOI

Remotely sensed data and derived information contribute substantially to mission-critical work across the Department of the Interior (DOI). 1 This DOI section highlights a sample of DOI remote sensing applications and illustrates a range of technology, platforms, and specialized sensors employed.

conterminous United States↗

Transitioning from change detection to monitoring with remote sensing: A paradigm shift

The use of time series analysis with moderate resolution satellite imagery is increasingly common, particularly since the advent of freely available Landsat data. Dense time series analysis is providing new information on the timing of landscape changes, as well as improving the quality and accuracy of information being derived from remote sensing. Perhaps most importantly, time series analysis is expanding the kinds of land surface change that can be monitored using remote sensing. In particular, more subtle changes in ecosystem health and condition and related to land use dynamics are being monitored. The result is a paradigm shift away from change detection, typically using two points in time, to monitoring, or an attempt to track change continuously in time. This trend holds many benefits, including the promise of near real-time monitoring. Anticipated future trends include more use of multiple sensors in monitoring activities, increased focus on the temporal accuracy of results, applications over larger areas and operational usage of time series analysis.

Remote Sensing of Environment↗

The potential of remote sensing for improved infectious disease ecology research and practice

Outbreaks of Covid-19 in humans, Dutch elm disease in forests, and highly pathogenic avian influenza in wild birds and poultry highlight the disruptive impacts of emerging infectious diseases on public health, ecosystems, and economies. Infectious disease dynamics often depend on environmental conditions that drive occurrence, transmission, and outbreaks. Remote sensing can contribute to infectious disease research and management by providing standardized environmental data across broad spatial and temporal extents, often at no cost to the user. Here, we 1) conduct a systematic review of primary literature to quantify current uses of remote sensing in disease ecology and 2) synthesize qualitative information to identify opportunities for further integration of remote sensing into disease ecology. We identify that modern advances in airborne remote sensing are promoting early detection of forest pathogens and that satellite data is contributing to the study of geographically widespread human diseases. We discuss opportunities for increased use of data products that characterize vegetation, surface water, and soil; provide data at high spatio-temporal and spectral resolutions; and quantify uncertainty in measurements. Additionally, combining remote sensing with animal movement telemetry can provide novel insights into wildlife disease. Integrating these opportunities will advance research and management of infectious diseases.

Proceedings of the Royal Society B: Biological Sci↗

Tsunami exposure estimation with land-cover data: Oregon and the Cascadia subduction zone

A Cascadia subduction-zone earthquake has the potential to generate tsunami waves which would impact more than 1000 km of coastline on the west coast of the United States and Canada. Although the predictable extent of tsunami inundation is similar for low-lying land throughout the region, human use of tsunami-prone land varies, creating variations in community exposure and potential impacts. To better understand such variations, land-cover information derived from midresolution remotely-sensed imagery (e.g., 30-m-resolution Landsat Thematic Mapper imagery) was coupled with tsunami-hazard information to describe tsunami-prone land along the Oregon coast. Land-cover data suggest that 95% of the tsunami-prone land in Oregon is undeveloped and is primarily wetlands and unconsolidated shores. Based on Spearman rank correlation coefficients (r s ), correlative relationships are strong and statistically significant (p < 0.05) between city-level estimates of the amount of land-cover pixels classified as developed (impervious cover greater than 20%) and the amount of various societal assets, including residential and employee populations, homes, businesses, and tax-parcel values. Community exposure to tsunami hazards, described here by the amount and relative percentage of developed land in tsunami-prone areas, varies considerably among the 26 communities of the study area, and these variations relate to city size. Correlative relationships are strong and significant (p < 0.05) for community exposure rankings based on land-cover data and those based on aggregated socioeconomic data. In the absence of socioeconomic data or community-based knowledge, the integration of hazards information and land-cover information derived from midresolution remotely-sensed imagery to estimate community exposure may be a useful first step in understanding variations in community vulnerability to regional hazards.

Applied Geography↗

Monitoring grasshopper and locust habitats in Sahelian Africa using GIS and remote sensing technology

Development programmes in Sahelian Africa are beginning to use geographic information system (GIS) technology. One of the GIS and remote sensing programmes introduced to the region in the late 1980s was the use of seasonal vegetation maps made from satellite data to support grasshopper and locust control. Following serious outbreaks of these pests in 1987, the programme addressed a critical need, by national and international crop protection organizations, to monitor site-specific dynamic vegetation conditions associated with grasshopper and locust breeding. The primary products used in assessing vegetation conditions were vegetation index (greenness) image maps derived from National Oceanic and Atmospheric Administration satellite imagery. Vegetation index data were integrated in a GIS with digital cartographic data of individual Sahelian countries. These near-real-time image maps were used regularly in 10 countries for locating potential grasshopper and locust habitats. The programme to monitor vegetation conditions is currently being institutionalized in the Sahel.

International Journal of Geographical Information ↗

Multiscale framework for assessing land cover change on barrier islands from extreme storms and restoration

Often found along the estuarine-marine interface, barrier islands and mainland coastal zones are shaped by tides, currents, extreme storms, and relative sea-level rise. These systems provide ecosystem services such as storm surge and wave attenuation, erosion protection to inland areas, habitat for fish and wildlife, recreation, and tourism. Given the importance of these ecosystems coupled with their dynamic nature, information on how these coastal systems are changing can help to inform natural resource management. Remote sensing advancements have led to an abundance of data for monitoring change in coastal settings. This study developed a multiscale framework that can provide trajectory information from screening-level analyses by using existing or custom moderate spatial resolution land cover maps. Using the north-central Gulf Coast as a case study, the trajectory of land cover area for barrier islands and mainland coastal zones was assessed using several geospatial data sets, including: (1) long-term moderate-resolution remote sensing products with an annual (or more frequent) temporal frequency; (2) a restoration database ( e.g. , beach/dune restoration, sediment placement, and dune enhancement); and (3) a tropical storm database. Due to the coarser spatial resolution of data sets used for screening-level analyses, detailed or application-specific analyses are often needed to reduce uncertainty in smaller changes that may not be captured. These may include land cover change analyses ( i.e. this study), periodic land cover maps with higher spatial resolution and more detailed land cover classes, or elevation-related analyses ( e.g. , dune change or inundation change). Using this framework, abrupt changes in land cover on Dauphin Island, Alabama, resulting from extreme storms were detected using moderate spatial resolution screening-level data, while restoration impact analyses may require higher resolution data. Further, land cover change analyses that incorporate change allocation provide robust information for understanding land cover change in dynamic coastal settings.

Alabama, Florida↗

Advances in hyperspectral remote sensing of vegetation and agricultural croplands

Recent advances in hyperspectral remote sensing (or imaging spectroscopy) demonstrate a great utility for a variety of land monitoring applications. It is now possible to be diagnostic in sensing species and plant communities using remotely sensed data and to do so in a direct and informed manner using modern tools and analyses. Hyperspectral data analyses are superior to traditional broadband analyses in spectral information. Many investigations explore and document remote sensing of vegetation and agricultural croplands. Some examples include (a) detecting plant stress [1], (b) measuring chlorophyll content of plants [2], (c) identifying small differences in percent of green vegetation cover [3], (d) extracting biochemical variables such as nitrogen and lignin [2,4–6], (e) discriminating land-cover types [7], (f) detecting crop moisture variations [8], (g) sensing subtle variations in leaf pigment concentrations [2,9,10], (h) modeling biophysical and yield characteristics of agricultural crops [6,11,12], (i) improving the detection of changes in sparse vegetation [13], and (j) assessing absolute water content in plant leaves [14]. This is a fairly detailed list but not exhaustive, meant to provide the reader with a measure of the current, proven experimental capabilities, and operational applications, and stimulate investigations of new, ambitious applications.

Book↗

Spectroscopic remote sensing for material identification, vegetation characterization, and mapping

Identifying materials by measuring and analyzing their reflectance spectra has been an important procedure in analytical chemistry for decades. Airborne and space-based imaging spectrometers allow materials to be mapped across the landscape. With many existing airborne sensors and new satellite-borne sensors planned for the future, robust methods are needed to fully exploit the information content of hyperspectral remote sensing data. A method of identifying and mapping materials using spectral feature analyses of reflectance data in an expert-system framework called MICA (Material Identification and Characterization Algorithm) is described. MICA is a module of the PRISM (Processing Routines in IDL for Spectroscopic Measurements) software, available to the public from the U.S. Geological Survey (USGS) at http://pubs.usgs.gov/of/2011/1155/. The core concepts of MICA include continuum removal and linear regression to compare key diagnostic absorption features in reference laboratory/field spectra and the spectra being analyzed. The reference spectra, diagnostic features, and threshold constraints are defined within a user-developed MICA command file (MCF). Building on several decades of experience in mineral mapping, a broadly-applicable MCF was developed to detect a set of minerals frequently occurring on the Earth's surface and applied to map minerals in the country-wide coverage of the 2007 Afghanistan HyMap data set. MICA has also been applied to detect sub-pixel oil contamination in marshes impacted by the Deepwater Horizon incident by discriminating the C-H absorption features in oil residues from background vegetation. These two recent examples demonstrate the utility of a spectroscopic approach to remote sensing for identifying and mapping the distributions of materials in imaging spectrometer data.

Conference Paper↗

Classification of oil spill by thicknesses using multiple remote sensors

Satellite Synthetic Aperture Radar (SAR) is an operational tool for monitoring and assessment of oil spills. Satellite SAR has primarily been used to detect the presence/absence of oil, yet its ability to discriminate oil emulsions within a detected oil slick has not been fully exploited. Additionally, one of the challenges in the past has been the ability to deliver strategic information derived from satellite remote sensing in a timely fashion to responders in the field. This study presents methods for the rapid classification of oil types and estimated thicknesses, from which information about thick oil and oil emulsions (i.e., “actionable” oil) can be delivered in an operational timeframe to responders in the field. Experiments carried out at the OHMSETT test facility in New Jersey demonstrate that under specific viewing conditions, a single polarization satellite SAR image can record a signal variance between thick stable emulsions and non-emulsified oil. During a series of field campaigns in the Gulf of Mexico with in situ measurements of oil thickness, multiple satellite data were obtained including fully polarimetric C-band SAR imagery from RADARSAT-2 and multispectral imagery from ASTER and WorldView-2. One campaign included the airborne polarimetric UAVSAR L-band sensor. An oil/emulsion thickness classification product was generated based on RADARSAT-2 polarimetric imagery using entropy and the damping ratio derivations. Herein, we present the classification methods to generate oil thickness products from SAR, validated by sea-truth observations, the multispectral imagery, and the UAVSAR data. We tested the ability to deliver these products with minimum latency to responding vessels via NOAA. During field operations in the Gulf of Mexico, a satellite SAR-based product of oil delineation by relative thickness was delivered to a responding vessel 42 min after the RADARSAT-2 data acquisition. This proof-of-concept test using satellite SAR and multispectral imagery to detect emulsions and deliver a derived information product to a vessel in near-real-time points directly to methods for satellite-based assets to be used in the near future for oil spill tactical response operations.

Louisiana↗

System characterization report on the German Aerospace Center (DLR) Earth Sensing Imaging Spectrometer (DESIS)

Executive Summary This report addresses system characterization of the German Aerospace Center (DLR) Earth Sensing Imaging Spectrometer (DESIS) and is part of a series of system characterization reports produced and delivered by the U.S. Geological Survey Earth Resources Observation and Science Cal/Val Center of Excellence. These reports present the methodology and procedures for characterization and the technical and operational information about the specific sensing system being evaluated. These reports also provide a description of data measurements, data retention practices, and data analysis results and provide system characterization conclusions. In partnership with Teledyne Brown Engineering, DLR built the DESIS hyperspectral instrument, which Teledyne Brown Engineering then integrated onto its International Space Station-based imaging platform, the Multi-User System for Earth Sensing. DLR developed the processing software and, together with Innovative Imaging and Research, completes the validation and calibration of the data products. DESIS was launched in 2018, and the data are used for scientific research in atmospheric physics and Earth sciences. The DESIS sensor contributes to the scientific and commercial utilization of the International Space Station and helps to further hyperspectral remote sensing technologies for future satellites. More information on DLR satellites and sensors is included within the “2020 Joint Agency Commercial Imagery Evaluation—Remote Sensing Satellite Compendium” and at https://www.dlr.de/DE/Home/home_node.html . The Earth Resources Observation and Science Cal/Val Center of Excellence system characterization team completed data analyses to characterize the geometric (interior and exterior), radiometric, and spatial performances. Results of these analyses indicate that DESIS has an interior geometric performance of less than a 3.30-meter (less than 0.11 pixel) root mean square error in band-to-band registration, an exterior geometric performance in the range of a 2.40- (0.08 pixel) to 17.40-meter (0.58 pixel) offset in comparison to the Landsat 8 Operational Land Imager, a radiometric performance in the range of −0.013 to 1.011 (offset and slope), and a spatial performance for band 130 of 1.5 pixels at full width at half maximum, with a modulation transfer function at a Nyquist frequency of 0.167.

Open-File Report↗