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USGS2003

Monitoring vegetation phenology using MODIS

Accurate measurements of regional to global scale vegetation dynamics (phenology) are required to improve models and understanding of inter-annual variability in terrestrial ecosystem carbon exchange and climate–biosphere interactions. Since the mid-1980s, satellite data have been used to study these processes. In this paper, a new methodology to monitor global vegetation phenology from time series of satellite data is presented. The method uses series of piecewise logistic functions, which are fit to remotely sensed vegetation index (VI) data, to represent intra-annual vegetation dynamics. Using this approach, transition dates for vegetation activity within annual time series of VI data can be determined from satellite data. The method allows vegetation dynamics to be monitored at large scales in a fashion that it is ecologically meaningful and does not require pre-smoothing of data or the use of user-defined thresholds. Preliminary results based on an annual time series of Moderate Resolution Imaging Spectroradiometer (MODIS) data for the northeastern United States demonstrate that the method is able to monitor vegetation phenology with good success.

Remote Sensing of Environment↗

USGS2003

Calibration of remotely sensed, coarse resolution NDVI to CO2 fluxes in a sagebrush–steppe ecosystem

The net ecosystem exchange (NEE) of carbon flux can be partitioned into gross primary productivity (GPP) and respiration ( R ). The contribution of remote sensing and modeling holds the potential to predict these components and map them spatially and temporally. This has obvious utility to quantify carbon sink and source relationships and to identify improved land management strategies for optimizing carbon sequestration. The objective of our study was to evaluate prediction of 14-day average daytime CO 2 fluxes ( F day ) and nighttime CO 2 fluxes ( R n ) using remote sensing and other data. F day and R n were measured with a Bowen ratio–energy balance (BREB) technique in a sagebrush ( Artemisia spp.)–steppe ecosystem in northeast Idaho, USA, during 1996–1999. Micrometeorological variables aggregated across 14-day periods and time-integrated Advanced Very High Resolution Radiometer (AVHRR) Normalized Difference Vegetation Index (iNDVI) were determined during four growing seasons (1996–1999) and used to predict F day and R n . We found that iNDVI was a strong predictor of F day ( R 2 =0.79, n =66, P <0.0001). Inclusion of evapotranspiration in the predictive equation led to improved predictions of F day ( R 2 =0.82, n =66, P <0.0001). Crossvalidation indicated that regression tree predictions of F day were prone to overfitting and that linear regression models were more robust. Multiple regression and regression tree models predicted R n quite well ( R 2 =0.75–0.77, n =66) with the regression tree model being slightly more robust in crossvalidation. Temporal mapping of F day and R n is possible with these techniques and would allow the assessment of NEE in sagebrush–steppe ecosystems. Simulations of periodic F day measurements, as might be provided by a mobile flux tower, indicated that such measurements could be used in combination with iNDVI to accurately predict F day . These periodic measurements could maximize the utility of expensive flux towers for evaluating various carbon management strategies, carbon certification, and validation and calibration of carbon flux models.

Idaho↗

USGS2003

Thematic accuracy of the 1992 National Land-Cover Data for the eastern United States: Statistical methodology and regional results

The accuracy of the 1992 National Land-Cover Data (NLCD) map is assessed via a probability sampling design incorporating three levels of stratification and two stages of selection. Agreement between the map and reference land-cover labels is defined as a match between the primary or alternate reference label determined for a sample pixel and a mode class of the mapped 3×3 block of pixels centered on the sample pixel. Results are reported for each of the four regions comprising the eastern United States for both Anderson Level I and II classifications. Overall accuracies for Levels I and II are 80% and 46% for New England, 82% and 62% for New York/New Jersey (NY/NJ), 70% and 43% for the Mid-Atlantic, and 83% and 66% for the Southeast.

Remote Sensing of Environment↗

USGS2003

Statistical sampling to characterize recent United States land-cover change

The U.S. Geological Survey, in conjunction with the U.S. Environmental Protection Agency, is conducting a study focused on developing methods for estimating changes in land-cover and landscape pattern for the conterminous United States from 1973 to 2000. Eleven land-cover and land-use classes are interpreted from Landsat imagery for five sampling dates. Because of the high cost and potential effect of classification error associated with developing change estimates from wall-to-wall land-cover maps, a probability sampling approach is employed. The basic sampling unit is a 20 x 20 km area, and land cover is obtained for each 60 x 60 m pixel within the sampling unit. The sampling design is stratified based on ecoregions, and land-cover change estimates are constructed for each stratum. The sampling design and analyses are documented, and estimates of change accompanied by standard errors are presented to demonstrate the methodology. Analyses of the completed strata suggest that the sampling unit should be reduced to a 10 x 10 km block, and poststratified estimation and regression estimation are viable options to improve precision of estimated change. ?? 2003 Elsevier Inc. All rights reserved.

Remote Sensing of Environment↗

USGS2003

Assessing vegetation response to drought in the northern Great Plains using vegetation and drought indices

The Normalized Difference Vegetation Index (NDVI) derived from the Advanced Very High Resolution Radiometer (AVHRR) has been widely used to monitor moisture-related vegetation condition. The relationship between vegetation vigor and moisture availability, however, is complex and has not been adequately studied with satellite sensor data. To better understand this relationship, an analysis was conducted on time series of monthly NDVI (1989–2000) during the growing season in the north and central U.S. Great Plains. The NDVI was correlated to the Standardized Precipitation Index (SPI), a multiple-time scale meteorological-drought index based on precipitation. The 3-month SPI was found to have the best correlation with the NDVI, indicating lag and cumulative effects of precipitation on vegetation, but the correlation between NDVI and SPI varies significantly between months. The highest correlations occurred during the middle of the growing season, and lower correlations were noted at the beginning and end of the growing season in most of the area. A regression model with seasonal dummy variables reveals that the relationship between the NDVI and SPI is significant in both grasslands and croplands, if this seasonal effect is taken into account. Spatially, the best NDVI–SPI relationship occurred in areas with low soil water-holding capacity. Our most important finding is that NDVI is an effective indicator of vegetation-moisture condition, but seasonal timing should be taken into consideration when monitoring drought with the NDVI.

Remote Sensing of Environment↗

USGS1969

Equipment and techniques for low-altitude aerial sensing of water-vapor concentration and movement

Progress in the development of equipment and techniques for making rapid measurements of moisture movement through the atmosphere over a large area is described. Airborne sensing elements measure relative humidity, temperature, and air currents. These data are telemetered to a ground-based station and recorded. A radar unit tracks the aircraft and electronically plots its position on a base map of the area being studied. Thus the distribution of atmospheric conditions can be directly related to the underlying terrain and vegetation features.

Remote Sensing of Environment↗

USGS1970

Using radar imagery for crop discrimination: a statistical and conditional probability study

A number of the constraints with which remote sensing must contend in crop studies are outlined. They include sensor, identification accuracy, and congruencing constraints; the nature of the answers demanded of the sensor system; and the complex temporal variances of crops in large areas. Attention is then focused on several methods which may be used in the statistical analysis of multidimensional remote sensing data. Crop discrimination for radar K-band imagery is investigated by three methods. The first one uses a Bayes decision rule, the second a nearest-neighbor spatial conditional probability approach, and the third the standard statistical techniques of cluster analysis and principal axes representation. Results indicate that crop type and percent of cover significantly affect the strength of the radar return signal. Sugar beets, corn, and very bare ground are easily distinguishable, sorghum, alfalfa, and young wheat are harder to distinguish. Distinguishability will be improved if the imagery is examined in time sequence so that changes between times of planning, maturation, and harvest provide additional discriminant tools. A comparison between radar and photography indicates that radar performed surprisingly well in crop discrimination in western Kansas and warrants further study.

Remote Sensing of Environment↗

USGS1970

Color infrared film as a negative material

Original problems encountered in endeavors to use color infraredfilm as a negative material have been overcome by a simple modification in processing. This makes more feasible the production of infrared color prints for field use and yields an infrared counterpart to Aero-Neg.

Remote Sensing of Environment↗

USGS1998

Assessing map accuracy in a remotely sensed, ecoregion-scale cover map

Landscape- and ecoregion-based conservation efforts increasingly use a spatial component to organize data for analysis and interpretation. A challenge particular to remotely sensed cover maps generated from these efforts is how best to assess the accuracy of the cover maps, especially when they can exceed 1000 s/km2 in size. Here we develop and describe a methodological approach for assessing the accuracy of large-area cover maps, using as a test case the 21.9 million ha cover map developed for Utah Gap Analysis. As part of our design process, we first reviewed the effect of intracluster correlation and a simple cost function on the relative efficiency of cluster sample designs to simple random designs. Our design ultimately combined clustered and subsampled field data stratified by ecological modeling unit and accessibility (hereafter a mixed design). We next outline estimation formulas for simple map accuracy measures under our mixed design and report results for eight major cover types and the three ecoregions mapped as part of the Utah Gap Analysis. Overall accuracy of the map was 83.2% (SE=1.4). Within ecoregions, accuracy ranged from 78.9% to 85.0%. Accuracy by cover type varied, ranging from a low of 50.4% for barren to a high of 90.6% for man modified. In addition, we examined gains in efficiency of our mixed design compared with a simple random sample approach. In regard to precision, our mixed design was more precise than a simple random design, given fixed sample costs. We close with a discussion of the logistical constraints facing attempts to assess the accuracy of large-area, remotely sensed cover maps.

Remote Sensing of Environment↗

USGS1998

Infrared measurements of pristine and disturbed soils 1. Spectral contrast differences between field and laboratory data

Comparison of emissivity spectra (8-13 ??m) of pristine soils in the field with laboratory reflectance spectra of the same soils showed that laboratory spectra tend to have less spectral contrast than field spectra (see following article). We investigated this the phenomenon by measuring emission spectra of both undisturbed (in situ) and disturbed soils (prepared as if for transport to the laboratory). The disturbed soils had much less spectral contrast than the undisturbed soils in the reststrahlen region near 9 ??m. While the increased porosity of a disturbed soil can decrease spectral contrast due to multiple scattering, we hypothesize that the effect is dominantly the result of a difference in grain-size distribution of the optically active layer (i.e., fine particle coatings). This concept was proposed by Salisbury et al. (1994) to explain their observations that soils washed free of small particles adhering the larger grains exhibited greater spectral contrast than unwashed soils. Our laboratory reflectance spectra of wet- and dry-sieved soils returned from field sites also show greater spectral contrast for wet-sieved (washed) soils. We therefore propose that undisturbed soils in the field can be characterized as 'clean' soils (washed free of fine particles at the surface due to rain and wind action) and that disturbed soils represent 'dirty' soils (contaminated with fine particle coatings). The effect of packing soils in the field and laboratory also increases spectral contrast but not to the magnitude of that observed for undisturbed and wet-sieved soils. Since it is a common practice to use laboratory spectra of field samples to interpret spectra obtained remotely, we suggest that the influence of fine particle coatings on disturbed soils, if unrecognized, could influence interpretations of remote sensing data.Comparison of emissivity spectra (8-13 ??m) of pristine soils in the field with laboratory reflectance spectra of the same soils showed that laboratory spectra tend to have less spectral contrast than field spectra (see following article). We investigated this phenomenon by measuring emission spectra of both undisturbed (in situ) and disturbed soils (prepared as if for transport to the laboratory). The disturbed soils had much less spectral contrast than the undisturbed soils in the reststrahlen region near 9 ??m. While the increased porosity of a disturbed soil can decrease spectral contrast due to multiple scattering, we hypothesize that the effect is dominantly the result of a difference in grain-size distribution of the optically active layer (i.e., fine particle coatings). This concept was proposed by Salisbury et al. (1994) to explain their observations that soils washed free of small particles adhering to larger grains exhibited greater spectral contrast than unwashed soils. Our laboratory reflectance spectra of wet- and dry-sieved soils returned from field sites also show greater spectral contrast for wet-sieved (washed) soils. We therefore propose that undisturbed soils in the field can be characterized as `clean' soils (washed free of fine particles at the surface due to rain and wind action) and that disturbed soils represent `dirty' soils (contaminated with fine particle coatings). The effect of packing soils in the field and laboratory also increases spectral contrast but not to the magnitude of that observed for undisturbed and wet-sieved soils. Since it is a common practice to use laboratory spectra of field samples to interpret spectra obtained remotely, we suggest that the influence of fine particle coatings on disturbed soils, if unrecognized, could influence interpretations of remote sensing data.

Remote Sensing of Environment↗

USGS1998

An analysis of relationships among climate forcing and time-integrated NDVI of grasslands over the U.S. northern and central Great Plains

Time-integrated normalized difference vegetation index (TI NDVI) derived from the multitemporal satellite imagery (1989–1993) was used as a surrogate for primary production to investigate climate impacts on grassland performance for central and northern Great Plains grasslands. Results suggest that spatial and temporal variability in growing season precipitation, potential evapotranspiration, and growing degree days are the most important controls on grassland performance and productivity. When TI NDVI and climate data of all grassland land cover classes were examined as a whole, a statistical model showed significant positive correlation between the TI NDVI and accumulated spring and summer precipitation, and a negative correlation between TI NDVI and spring potential evapotranspiration. The coefficient of determination (R 2 ) of the general model was 0.45. When the TI NDVI-climate relationship was examined by individual land cover type, the relationship was generally better defined in terms of the variance accounted for by class-specific models . The photosynthetic pathway is an important determinant of grassland performance with northern mixed prairie (mixture of C 3 and C 4 grassland) TI NDVI affected by both thermal and moisture conditions during the growing season while southern plains grasslands (primarily C 4 grassland) were predominantly influenced by spring and summer precipitation. Grassland land cover classes associated with sandy soils also demonstrated a strong relationship between TI NDVI and growing season rainfall. Significant impact of interannual climate variability on the TI NDVI–climate relationship was also observed. The study suggests an integrated approach involving numerical models, satellite remote sensing, and field observations to monitor grassland ecosystem dynamics on a regional scale.

Remote Sensing of Environment↗

USGS1998

AIRSAR studies of woody shrub density in semiarid rangeland: Jornada del Muerto, New Mexico

This study evaluates the use of polarimetric Airborne Synthetic Aperture Radar (AIRSAR) data to assess woody shrub density in a semiarid site where the vegetation consists primarily of varied mixtures of herbaceous vegetation and shrubs. AIRSAR data and field observations of vegetation cover and growth form-composition were obtained for 59 sites in the Jornada del Muerto plain in southern New Mexico. Radar signature measures examined were C-, L- and P-band backscattering coefficients (??0) for HH, HV and W polarizations, ratios of ??0HH and ??0HV to ??0VV, and the HH-VV polarization phase difference and correlation coefficient. The most effective measure for estimation of shrub density was L-band ??0HV, which distinguished among shrub density classes with no misclassification. Sensitivity of this measure to small amounts of shrub cover was indicated by successful separation of sites with <1% shrub cover from sites with 1-5% cover. Separability of shrub density classes was generally least for C-band signature measures. A distinctive radar signature was exhibited by dense stands of Yucca elata, a semitreelike plant with uniformly thick ???10 cm diameter) fibrous stems. Yucca sites were distinguished from others by their high P-band ??0HV relative to L-band ??0HV. The results are largely explained by the greater sensitivity of lounger wavelengths to larger canopy structural elements. L-band ??0HV and other measures responsive to canopy volume scattering were more strongly related to shrub than to herbaceous plant cover because woody shrub canopies have numerous stems of the intermediate size to which L-band is most sensitive, whereas stems of this size are mostly lacking in herbaceous canopies. The uniform-diameter stems of yucca have larger dimensions to which P-band is more sensitive than L-band.This study evaluates the use of polarimetric Airborne Synthetic Aperture Radar (AIRSAR) data to assess woody shrub density in a semiarid site where the vegetation consists primarily of varied mixtures of herbaceous vegetation and shrubs. AIRSAR data and field observations of vegetation cover and growth form-composition were obtained for 59 sites in the Jornada del Muerto plain in southern New Mexico. Radar signature measures examined were C-, L- and P-band backscattering coefficients (??0) for HH, HV and VV polarizations, ratios of ??0HH and ??0HV to ??0VV, and the HH-VV polarization phase difference and correlation coefficient. The most effective measure for estimation of shrub density was L-band ??0HV, which distinguished among shrub density classes with no misclassification. Sensitivity of this measure to small amounts of shrub cover was indicated by successful separation of sites with <1% shrub cover from sites with 1-5% cover. Separability of shrub density classes was generally least for C-band signature measures. A distinctive radar signature was exhibited by dense stands of Yucca elata, a semitreelike plant with uniformly thick (???10 cm diameter) fibrous stems. Yucca sites were distinguished from others by their high P-band ??0HV relative to L-band ??0HV. The results are largely explained by the greater sensitivity of longer wavelengths to larger canopy structural elements. L-band ??0HV and other measures responsive to canopy volume scattering were more strongly related to shrub than to herbaceous plant cover because woody shrub canopies have numerous stems of the intermediate size to which L-band is most sensitive, whereas stems of this size are mostly lacking in herbaceous canopies. The uniform-diameter stems of yucca have larger dimensions to which P-band is more sensitive than L-band.

Remote Sensing of Environment↗

USGS1999

SAR studies in the Yuma Desert, Arizona: Sand penetration, geology, and the detection of military ordnance debris

Synthetic Aperture Radar (SAR) images acquired over part of the Yuma Desert in southwestern Arizona demonstrate the ability of C-band (5.7-cm wavelength), L-band (24.5 cm), and P-band (68 cm) AIRSAR signals to backscatter from increasingly greater depths reaching several meters in blow sand and sandy alluvium. AIRSAR images obtained within the Barry M. Goldwater Bombing and Gunnery Range near Yuma, Arizona, show a total reversal of C- and P-band backscatter contrast (image tone) for three distinct geologic units. This phenomenon results from an increasingly greater depth of radar imaging with increasing radar wavelength. In the case of sandy- and small pebble-alluvium surfaces mantled by up to several meters of blow sand, backscatter increases directly with SAR wavelength as a result of volume scattering from a calcic soil horizon at shallow depth and by volume scattering from the root mounds of healthy desert vegetation that locally stabilize blow sand. AIRSAR images obtained within the military range are also shown to be useful for detecting metallic military ordnance debris that is located either at the surface or covered by tens of centimeters to several meters of blow sand. The degree of detectability of this ordnance increases with SAR wavelength and is clearly maximized on P-band images that are processed in the cross-polarized mode (HV). This effect is attributed to maximum signal penetration at P-band and the enhanced PHV image contrast between the radar-bright ordnance debris and the radar-dark sandy desert. This article focuses on the interpretation of high resolution AIRSAR images but also Compares these airborne SAR images with those acquired from spacecraft sensors such as ERS-SAR and Space Radar Laboratory (SIR-C/X-SAR).Synthetic Aperture Radar (SAR) images acquired over part of the Yuma Desert in southwestern Arizona demonstrate the ability of C-band (5.7-cm wavelength), L-band (24.5 cm), and P-band (68 cm) AIRSAR signals to backscatter from increasingly greater depths reaching several meters in blow sand and sandy alluvium. AIRSAR images obtained within the Barry M. Goldwater Bombing and Gunnery Range near Yuma, Arizona, show a total reversal of C- and P-band backscatter contrast (image tone) for three distinct geologic units. This phenomenon results from an increasingly greater depth of radar imaging with increasing radar wavelength. In the case of sandy- and small pebble-alluvium surfaces mantled by up to several meters of blow sand, backscatter increases directly with SAR wavelength as a result of volume scattering from a calcic soil horizon at shallow depth and by volume scattering from the root mounds of healthy desert vegetation that locally stabilize blow sand. AIRSAR images obtained within the military range are also shown to be useful for detecting metallic military ordnance debris that is located either at the surface or covered by tens of centimeters to several meters of blow sand. The degree of detectability of this ordnance increases with SAR wavelength and is clearly maximized on P-band images that are processed in the cross-polarized mode (HV). This effect is attributed to maximum signal penetration at P-band and the enhanced PHV image contrast between the radar-bright ordnance debris and the radar-dark sandy desert. This article focuses on the interpretation of high resolution AIRSAR images but also compares these airborne SAR images with those acquired from spacecraft sensors such as ERS-SAR and Space Radar Laboratory (SIR-C/X-SAR).

Remote Sensing of Environment↗

USGS1999

The importance of SAR wavelength in penetrating blow sand in Northern Arizona

Multifrequency and polarimetric Airborne Synthetic Aperture Radar (AIRSAR) images of a sand streak in northern Arizona demonstrate the ability of C- (6-cm wavelength), L- (24 cm), and P-band (68 cm) radar signals to backscatter, respectively, from increasingly greater depths reaching 2 m or more in active blow sand. The studied sand streak, located on a salient of Ward Terrace at lat 35??48.4'N and long 111??15.0'W, is sourced in the normally dry Little Colorado River channel and migrates northeastward toward the Moenkopi Plateau Scarp. The sand streak is 'radar dark' compared to the 'radar-bright' surface of Ward Terrace on all C-band images. It is, however, penetrated completely at P-band and is invisible on these images. The sand streak is recognizable on the LHH image but is poorly defined on the LHV image. High resolution AIRSAR images constitute the primary data set analyzed; in addition, a C-band radar image acquired by the European Remote Sensing (ERS) satellite is also presented. The spaceborne image data confirms the results of the aircraft data at C-band.Multifrequency and polarimetric Airborne Synthetic Aperture Radar (AIRSAR) images of a sand streak in northern Arizona demonstrate the ability of C- (6-cm wavelength), L- (24 cm), and P-band (68 cm) radar signals to backscatter, respectively, from increasingly greater depths reaching 2 m or more in active blow sand. The studied sand streak, located on a salient of Ward Terrace at lat 35??48.4???N and long 111??15.0???W, is sourced in the normally dry Little Colorado River channel and migrates northeastward toward the Moenkopi Plateau Scarp. The sand streak is `radar dark' compared to the `radar-bright' surface of Ward Terrace on all C-band images. It is, however, penetrated completely at P-band and is invisible on these images. The sand streak is recognizable on the LHH image but is poorly defined on the LHV image. High resolution AIRSAR images constitute the primary data set analyzed; in addition, a C-band radar image acquired by the European Remote Sensing (ERS) satellite is also presented. The spaceborne image data confirms the results of the aircraft data at C-band.

Remote Sensing of Environment↗

USGS2000

Orbital radar studies of paleodrainages in the central Namib Desert

Orbital radar images of the central Namib Desert show clearly the extent of relict fluvial deposits associated with former courses of the Tsondab and Kuiseb rivers. South of the Kuiseb River, radar data show the existence of a drainage network developed in calcrete-cemented late Tertiary fluvial deposits. The sand-filled paleovalleys are imaged as radar-dark tones in contrast to the radar-bright interfluves where the calcreted gravels occur. The drainage network developed as a result of local runoff from indurated gravels and channeled surface and subsurface flow to the sites of the many interdune lacustrine deposits found in the area. (C) Elsevier Science Inc., 2000.Orbital radar images of the central Namib Desert show clearly the extent of relict fluvial deposits associated with former courses of the Tsondab and Kuiseb rivers. South of the Kuiseb River, radar data show the existence of a drainage network developed in calcrete-cemented late Tertiary fluvial deposits. The sand-filled paleovalleys are imaged as radar-dark tones in contrast to the radar-bright interfluves where the calcreted gravels occur. The drainage network developed as a result of local runoff from indurated gravels and channeled surface and subsurface flow to the sites of the many interdune lacustrine deposits found in the area.

Remote Sensing of Environment↗

USGS2000

A diurnal animation of thermal images from a day-night pair

Interpretation of thermal images is often complicated because the physical property information is contained in both the spatial and temporal variations of the data and thermal models are necessary to extract and display this information. A linearized radiative transfer solution to the surface flux has been used to derive a function that is invariant with respect to thermal inertia. This relationship makes it possible to predict the temperature variation at any time in the diurnal cycle using only two distinct measurements (e.g., noon and midnight). An animation can then be constructed from a pair of day-night images to view both the spatial and temporal temperature changes throughout the diurnal cycle. A more complete solution for the invariant function, using the method of Laplace transforms and based on the linearized solution, was introduced. These results indicate that the linear model does not provide a sufficiently accurate estimate. Using standard conditions (latitude 30??, solar declination 0??, acquisition times at noon and midnight), this new relationship was used to predict temperature throughout the diurnal cycle to an rms error of 0.2??C, which is close to the system noise of most thermal scanners. The method was further extended to include the primary effects of topographic slope with similar accuracy. The temperature was computed at 48 equally spaced times in the diurnal cycle with this algorithm using a co-registered day and night TIMS (Thermal Infrared Multispectral Scanner) data pair (330 pixels, 450 lilies) acquired of the Carlin, Nevada, area and a co-registered DEM (Digital Elevation Model). (Any reader can view the results by downloading the animation file from an identified tip site). The results illustrate the power of animation to display subtle temporal and spatial temperature changes, which can provide clues to structural controls and material property differences. This 'visual change' approach could significantly increase the use of thermal data for environmental, hazard, and resource studies. Published by Elsevier Science Inc., 2000.A linearized radiative transfer solution of determining the surface flux is proposed to predict the temperature variation at any time in the diurnal cycle using only two distinct measurements. An animation is constructed from a pair of day-night images to view the spatial and temporal temperature changes throughout the diurnal cycle. The results illustrate the effectiveness of animation to display subtle temporal and spatial temperature changes, which can provide clues to structural controls and material property differences.

Remote Sensing of Environment↗

USGS2014

Establishing a baseline for regional scale monitoring of eelgrass ( Zostera marina ) habitat on the lower Alaska Peninsula

Seagrass meadows, one of the world’s most widespread and productive ecosystems, provide a wide range of services with real economic value. Worldwide declines in the distribution and abundance of seagrasses and increased threats to coastal ecosystems from climate change have prompted a need to acquire baseline data for monitoring and protecting these important habitats. We assessed the distribution and abundance of eelgrass ( Zostera marina ) along nearly 1200 km of shoreline on the lower Alaska Peninsula, a region of expansive eelgrass meadows whose status and trends are poorly understood. We demonstrate the effectiveness of a multi-scale approach by using Landsat satellite imagery to map the total areal extent of eelgrass while integrating field survey data to improve map accuracy and describe the physical and biological condition of the meadows. Innovative use of proven methods and processing tools was used to address challenges inherent to remote sensing in high latitude, coastal environments. Eelgrass was estimated to cover ~31,000 ha, 91% of submerged aquatic vegetation on the lower Alaska Peninsula, nearly doubling the known spatial extent of eelgrass in the region. Mapping accuracy was 80%–90% for eelgrass distribution at locations containing adequate field survey data for error analysis.

Alaska↗

USGS2022

Animal tracing with sulfur isotopes: Spatial segregation and climate variability in Africa likely contribute to population trends of a migratory songbird

Climatic conditions affect animals but range-wide impacts at the population level remain largely unknown, especially in migratory species. However, studying climate–population relationships is still challenging in small migrants due to a lack of efficient and cost-effective geographic tracking method. Spatial distribution patterns of environmental stable isotopes (so called ‘isoscapes’) generally overcome these limitations but none of the currently available isoscapes provide a substantial longitudinal gradient in species-rich sub-Saharan Africa. In this region, sulphur ( δ 34 S) has not been sufficiently explored on a larger scale. We developed a δ 34 S isoscape to trace animal origins in sub-Saharan Africa by coupling known-origin samples from tracked migratory birds with continental remotely sensed environmental data building on environment– δ 34 S relationships using a flexible machine learning technique. Furthermore, we link population-specific nonbreeding grounds with interannual climatic variation that might translate to breeding population trends. The predicted δ 34 S isotopic map featured east–west and coast-to-inland isotopic gradients and was applied to predict nonbreeding grounds of three breeding populations of Eurasian Reed Warblers Acrocephalus scirpaceus with two distinct migratory phenotypes. Breeding populations as well as migratory phenotypes exhibited large-scale segregation within the African nonbreeding range. These regions also differed substantially in the interannual climatic variation, with higher interannual variability in the eastern part of the range during 2001–2012. Over the same period, the eastern European breeding population seemed to have experienced a more steep decline in population size. The link between migratory patterns and large-scale climatic variability appears important to better understand population trajectories in many declining migratory animals. We believe animal tracing using sulphur isotopes will facilitate these efforts and offers manifold ecological and forensic applications in the biodiversity hotspot of sub-Saharan Africa.

Journal of Animal Ecology↗