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At least 235 records · Page 13Linked to original sources
USGS2020

Substantially greater carbon emissions estimated based on annual land-use transition data

Quantifying land-use and land-cover change (LULCC) effects on carbon sources and sinks has been very challenging because of the availability and quality of LULCC data. As the largest estuary in the United States, Chesapeake Bay is a rapidly changing region and is affected by human activities. A new annual land-use and land-cover (LULC) data product developed by the U.S. Geological Survey Land Change Monitoring and Analysis Program (LCMAP) from 2001 to 2011 was analyzed for transitions between agricultural land, developed land, grassland, forest land and wetland. The Land Use and Carbon Scenario Simulator was used to simulate effects of LULCC and ecosystem disturbance in the south of the Chesapeake Bay Watershed (CBW) on carbon storage and fluxes, with carbon parameters derived from the Integrated Biosphere Simulator. We found that during the study period: (1) areas of forest land, disturbed land, agricultural land and wetland decreased by 90, 82, 57, and 65 km 2 , respectively, but developed lands gained 293 km 2 (29 km 2 annually); (2) total ecosystem carbon stock in the CBW increased by 13 Tg C from 2001 to 2011, mainly due to carbon sequestration of the forest ecosystem; (3) carbon loss was primarily attributed to urbanization (0.224 Tg C·yr −1 ) and agricultural expansion (0.046 Tg C·yr −1 ); and (4) estimated carbon emissions and harvest wood products were greater when estimated with the annual LULC input. We conclude that a dense time series of LULCC, such as that of the LCMAP program, may provide a more accurate accounting of the effects of land use change on ecosystem carbon, which is critical to understanding long-term ecosystem carbon dynamics.

Delaware, Maryland, Virginia↗
USGS2016

LANDFIRE 2010—Updates to the national dataset to support improved fire and natural resource management

The Landscape Fire and Resource Management Planning Tools (LANDFIRE) 2010 data release provides updated and enhanced vegetation, fuel, and fire regime layers consistently across the United States. The data represent landscape conditions from approximately 2010 and are the latest release in a series of planned updates to maintain currency of LANDFIRE data products. Enhancements to the data products included refinement of urban areas by incorporating the National Land Cover Database 2006 land cover product, refinement of agricultural lands by integrating the National Agriculture Statistics Service 2011 cropland data layer, and improved wetlands delineations using the National Land Cover Database 2006 land cover and the U.S. Fish and Wildlife Service National Wetlands Inventory data. Disturbance layers were generated for years 2008 through 2010 using remotely sensed imagery, polygons representing disturbance events submitted by local organizations, and fire mapping program data such as the Monitoring Trends in Burn Severity perimeters produced by the U.S. Geological Survey and the U.S. Forest Service. Existing vegetation data were updated to account for transitions in disturbed areas and to account for vegetation growth and succession in undisturbed areas. Surface and canopy fuel data were computed from the updated vegetation type, cover, and height and occasionally from potential vegetation. Historical fire frequency and succession classes were also updated. Revised topographic layers were created based on updated elevation data from the National Elevation Dataset. The LANDFIRE program also released a new Web site offering updated content, enhanced usability, and more efficient navigation.

Open-File Report↗
USGS2024

Remote sensing large-wood storage downstream of reservoirs during and after dam removal: Elwha River, Washington, USA

Large wood is an integral part of many rivers, often defining river-corridor morphology and habitat, but its occurrence, magnitude, and evolution in a river system are much less well understood than the sedimentary and hydraulic components, and due to methodological limitations, have seldom previously been mapped in substantial detail. We present a new method for this, representing a substantial advance in automated deep-learning-based image segmentation. From these maps, we measured large wood and sediment deposits from high-resolution orthoimages to explore the dynamics of large wood in two reaches of the Elwha River, Washington, USA, between 2012 and 2017 as it adjusted to upstream dam removals. The data set consists of a time series of orthoimages (12.5-cm resolution) constructed using Structure-from-Motion photogrammetry on imagery from 14 aerial surveys. Model training was optimized to yield maximum accuracy for estimated wood areas, compared to manually digitized wood, therefore model development and intended application were coupled. These fully reproducible methods and model resulted in a maximum of 15% error between observed and estimated total wood areas and wood deposit size-distributions over the full spatio-temporal extent of the data. Areal extent of wood in the channel margin approximately doubled in the years following dam removal, with greatest increases in large wood in wider, lower-gradient sections. Large-wood deposition increased between the start of dam removal (2011) and winter 2013, then plateaued. Sediment bars continued to grow up until 2016/17, assisted by a partially static wood framework deposited predominantly during the period up to winter 2013.

Washington↗
USGS2021

Estimating actual evapotranspiration over croplands using vegetation index methods and dynamic harvested area

Advances in estimating actual evapotranspiration (ETa) with remote sensing (RS) have contributed to improving hydrological, agricultural, and climatological studies. In this study, we evaluated the applicability of Vegetation-Index (VI) -based ETa (ET-VI) for mapping and monitoring drought in arid agricultural systems in a region where a lack of ground data hampers ETa work. To map ETa (2000–2019), ET-VIs were translated and localized using Landsat-derived 3- and 2-band Enhanced Vegetation Indices (EVI and EVI2) over croplands in the Zayandehrud River Basin (ZRB) in Iran. Since EVI and EVI2 were optimized for the MODerate Imaging Spectroradiometer (MODIS), using these VIs with Landsat sensors required a cross-sensor transformation to allow for their use in the ET-VI algorithm. The before- and after- impact of applying these empirical translation methods on the ETa estimations was examined. We also compared the effect of cropping patterns’ interannual change on the annual ETa rate using the maximum Normalized Difference Vegetation Index (NDVI) time series. The performance of the different ET-VIs products was then evaluated. Our results show that ETa estimates agreed well with each other and are all suitable to monitor ETa in the ZRB. Compared to ETc values, ETa estimations from MODIS-based continuity corrected Landsat-EVI (EVI2) (EVI MccL and EVI2 MccL ) performed slightly better across croplands than those of Landsat-EVI (EVI2) without transformation. The analysis of harvested areas and ET-VIs anomalies revealed a decline in the extent of cultivated areas and a loss of corresponding water resources downstream. The findings show the importance of continuity correction across sensors when using empirical algorithms designed and optimized for specific sensors. Our comprehensive ETa estimation of agricultural water use at 30 m spatial resolution provides an inexpensive monitoring tool for cropping areas and their water consumption.

Remote Sensing↗
USGS2009

Using LiDAR and quickbird data to model plant production and quantify uncertainties associated with wetland detection and land cover generalizations

Spatiotemporal data from satellite remote sensing and surface meteorology networks have made it possible to continuously monitor global plant production, and to identify global trends associated with land cover/use and climate change. Gross primary production (GPP) and net primary production (NPP) are routinely derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard satellites Terra and Aqua, and estimates generally agree with independent measurements at validation sites across the globe. However, the accuracy of GPP and NPP estimates in some regions may be limited by the quality of model input variables and heterogeneity at fine spatial scales. We developed new methods for deriving model inputs (i.e., land cover, leaf area, and photosynthetically active radiation absorbed by plant canopies) from airborne laser altimetry (LiDAR) and Quickbird multispectral data at resolutions ranging from about 30??m to 1??km. In addition, LiDAR-derived biomass was used as a means for computing carbon-use efficiency. Spatial variables were used with temporal data from ground-based monitoring stations to compute a six-year GPP and NPP time series for a 3600??ha study site in the Great Lakes region of North America. Model results compared favorably with independent observations from a 400??m flux tower and a process-based ecosystem model (BIOME-BGC), but only after removing vapor pressure deficit as a constraint on photosynthesis from the MODIS global algorithm. Fine-resolution inputs captured more of the spatial variability, but estimates were similar to coarse-resolution data when integrated across the entire landscape. Failure to account for wetlands had little impact on landscape-scale estimates, because vegetation structure, composition, and conversion efficiencies were similar to upland plant communities. Plant productivity estimates were noticeably improved using LiDAR-derived variables, while uncertainties associated with land cover generalizations and wetlands in this largely forested landscape were considered less important.

Remote Sensing of Environment↗
USGS2026

The impacts of cover crop biomass on satellite-based detectability of cover crops in Maryland

Cover crop adoption in the U.S. has increased over the past decades, increasing the need to quantify their performance and environmental benefits. While remote sensing (RS)-based approaches for detecting cover crop presence have been developed, there has been limited research on how cover crops with varied biomass and management practices influence detectability. Using unique field-level cover crop presence and biomass datasets in Maryland, U.S., we investigated how RS-based detectability changes for cover crops with varied aboveground biomass, planting, and termination dates. Specifically, we proposed a time-integrated satellite-based greenness feature from Harmonized Landsat-8 and Sentinel-2 (HLS) time series from 2017 to 2021 to estimate biomass of cover crops and evaluated their detectability using a phenology-based cover crop detection framework. The impacts of cover crop planting and termination dates on cover crop biomass were also analyzed. Our results demonstrated that Normalized Difference Vegetation Index (NDVI) estimated biomass with higher accuracy compared to other vegetation indices, and the time-integrated model estimated biomass with higher accuracy than the single-date “snapshot” linear model (R2 from 0.53 to 0.66 and RMSE from 922 kg/ha to 747 kg/ha). While the snapshot models were species sensitive, the time-integrated models showed strong robustness across different species. Detectability increased with cover crop biomass, as detected cover crops averaged 963.3 ±719.5 kg/ha compared to 297.2 ± 209.0 kg/ha for non-detected cover crops. Detection accuracy reached 96.1% for fields exceeding 500 kg/ha, compared with an overall accuracy of 62.7%. Earlier planting and later termination increased biomass and detectability, with biomass rising by 4.14 kg/ha/day (p < 0.01). This study demonstrates how management practices affect cover crop biomass and detectability via satellite time series and provides insights that can inform management of cover crops and monitoring of their effects on agroecosystems.

Maryland↗
USGS2022

Revealing active Mars with HiRISE digital terrain models

Many discoveries of active surface processes on Mars have been made due to the availability of repeat high-resolution images from the High Resolution Imaging Science Experiment (HiRISE) onboard the Mars Reconnaissance Orbiter. HiRISE stereo images are used to make digital terrain models (DTMs) and orthorectified images (orthoimages). HiRISE DTMs and orthoimage time series have been crucial for advancing the study of active processes such as recurring slope lineae, dune migration, gully activity, and polar processes. We describe the process of making HiRISE DTMs, orthoimage time series, DTM mosaics, and the difference of DTMs, specifically using the ISIS/SOCET Set workflow. HiRISE DTMs are produced at a 1 and 2 m ground sample distance, with a corresponding estimated vertical precision of tens of cm and ∼1 m, respectively. To date, more than 6000 stereo pairs have been acquired by HiRISE and, of these, more than 800 DTMs and 2700 orthoimages have been produced and made available to the public via the Planetary Data System. The intended audiences of this paper are producers, as well as users, of HiRISE DTMs and orthoimages. We discuss the factors that determine the effective resolution, as well as the quality, precision, and accuracy of HiRISE DTMs, and provide examples of their use in time series analyses of active surface processes on Mars.

Remote Sensing↗
USGS2017

LANDFIRE 2015 Remap – Utilization of Remotely Sensed Data to Classify Existing Vegetation Type and Structure to Support Strategic Planning and Tactical Response

The LANDFIRE Program produces national scale vegetation, fuels, fire regimes, and landscape disturbance data for the entire U.S. These data products have been used to model the potential impacts of fire on the landscape [1], the wildfire risks associated with land and resource management [2, 3], and those near population centers and accompanying Wildland Urban Interface zones [4], as well as many other applications . The initial LANDFIRE National Existing Vegetation Type (EVT ) and vegetation structure layers, including vegetation percent cover and height, were mapped circa 2001 and released in 2009 [5]. Each EVT is representative of the dominant plant community within a given area. The EVT layer has since been updated by identifying areas of landscape change and modifying the vegetation types utilizing a series of rules that consider the disturbance type, severity of disturbance, and time since disturbance [6, 7]. Non-disturbed areas were adjusted for vegetation growth and succession. LANDFIRE vegetation structure layers also have been updated by using data modeling techniques [see 6 for a full description]. The subsequent updated versions of LANDFIRE include LANDFIRE 2008, 2010, 2012 , and LANDFIRE 2014 is being incrementally released, with all data being released in early 2017. Additionally, a comprehensive remap of the baseline data, LANDFIRE 2015 Remap , is being prototyped, and production is tentatively planned to begin in early 2017 to provide a more current baseline for future updates.

Earthzine↗
USGS2006

Identification and extraction of the seaward edge of terrestrial vegetation using digital aerial photography

This report is created as part of the Aerial Data Collection and Creation of Products for Park Vital Signs Monitoring within the Northeast Region Coastal and Barrier Network project, which is a joint project between the National Park Service Inventory and Monitoring Program (NPS-IM), the National Aeronautics and Space Administration (NASA) Observational Sciences Branch, and the U.S. Geological Survey (USGS) Center for Coastal and Watershed Studies (CCWS). This report is one of a series that discusses methods for extracting topographic features from aerial survey data. It details step-by-step methods used to extract a spatially referenced digital line from aerial photography that represents the seaward edge of terrestrial vegetation along the coast of Assateague Island National Seashore (ASIS). One component of the NPS-IM/USGS/NASA project includes the collection of NASA aerial surveys over various NPS barrier islands and coastal parks throughout the National Park Service's Northeast Region. These aerial surveys consist of collecting optical remote sensing data from a variety of sensors, including the NASA Airborne Topographic Mapper (ATM), the NASA Experimental Advanced Airborne Research Lidar (EAARL), and down-looking digital mapping cameras.

Open-File Report↗
USGS2012

Producing fractional rangeland component predictions in a sagebrush ecosystem, a Wyoming sensitivity analysis

Remote sensing information has been widely used to monitor vegetation condition and variations in a variety of ecosystems, including shrublands. Careful application of remotely sensed imagery can provide additional spatially explicit, continuous, and extensive data on the composition and condition of shrubland ecosystems. Historically, the most widely available remote sensing information has been collected by Landsat, which has offered large spatial coverage and moderate spatial resolution data globally for nearly three decades. Such medium-resolution satellite remote sensing information can quantify the distribution and variation of terrestrial ecosystems. Landsat imagery has been frequently used with other high-resolution remote sensing data to classify sagebrush components and quantify their spatial distributions (Ramsey and others, 2004; Seefeldt and Booth, 2004; Stow and others, 2008; Underwood and others, 2007). Modeling algorithms have been developed to use field measurements and satellite remote sensing data to quantify the extent and evaluate the quality of shrub ecosystem components in large geographic areas (Homer and others, 2009). The percent cover of sagebrush ecosystem components, including bare-ground, herbaceous, litter, sagebrush, and shrub, have been quantified for entire western states (Homer and others, 2012). Furthermore, research has demonstrated the use of current measurements with historical archives of Landsat imagery to quantify the variations of these components for the last two decades (Xian and others, 2012). The modeling method used to quantify the extent and spatial distribution of sagebrush components over a large area also has required considerable amounts of training data to meet targeted accuracy requirements. These training data have maintained product accuracy by ensuring that they are derived from good quality field measurements collected during appropriate ecosystem phenology and subsequently maximized by extrapolation on high-resolution remote sensing data (Homer and others, 2012). This method has proven its utility; however, to develop these products across even larger areas will require additional cost efficiencies to ensure that an adequate product can be developed for the lowest cost possible. Given the vast geographic extent of shrubland ecosystems in the western United States, identifying cost efficiencies with optimal training data development and subsequent application to medium resolution satellite imagery provide the most likely areas for methodological efficiency gains. The primary objective of this research was to conduct a series of sensitivity tests to evaluate the most optimal and practical way to develop Landsat scale information for estimating the extent and distribution of sagebrush ecosystem components over large areas in the conterminous United States. An existing dataset of sagebrush components developed from extensive field measurements, high-resolution satellite imagery, and medium resolution Landsat imagery in Wyoming was used as the reference database (Homer and others, 2012). Statistical analysis was performed to analyze the relation between the accuracy of sagebrush components and the amount and distribution of training data on Landsat scenes needed to obtain accurate predictions.

Wyoming↗
USGS2013

Land surface phenology

Certain vegetation types (e.g., deciduous shrubs, deciduous trees, grasslands) have distinct life cycles marked by the growth and senescence of leaves and periods of enhanced photosynthetic activity. Where these types exist, recurring changes in foliage alter the reflectance of electromagnetic radiation from the land surface, which can be measured using remote sensors. The timing of these recurring changes in reflectance is called land surface phenology (LSP). During recent decades, a variety of methods have been used to derive LSP metrics from time series of reflectance measurements acquired by satellite-borne sensors. In contrast to conventional phenology observations, LSP metrics represent the timing of reflectance changes that are driven by the aggregate activity of vegetation within the areal unit measured by the satellite sensor and do not directly provide information about the phenology of individual plants, species, or their phenophases. Despite the generalized nature of satellite sensor-derived measurements, they have proven useful for studying changes in LSP associated with various phenomena. This chapter provides a detailed overview of the use of satellite remote sensing to monitor LSP. First, the theoretical basis for the application of satellite remote sensing to the study of vegetation phenology is presented. After establishing a theoretical foundation for LSP, methods of deriving and validating LSP metrics are discussed. This chapter concludes with a discussion of major research findings and current and future research directions.

Book chapter↗
USGS2022

Long-term ice phenology records spanning up to 578 years for 78 lakes around the Northern Hemisphere

In recent decades, lakes have experienced unprecedented ice loss with widespread ramifications for winter ecological processes. The rapid loss of ice, resurgence of winter biology, and proliferation of remote sensing technologies, presents a unique opportunity to integrate disciplines to further understand the broad spatial and temporal patterns in ice loss and its consequences. Here, we summarize ice phenology records for 78 lakes in 12 countries across North America, Europe, and Asia to permit the inclusion and harmonization of in situ ice phenology observations in future interdisciplinary studies. These ice records represent some of the longest climate observations directly collected by people. We highlight the importance of applying the same definition of ice-on and ice-off within a lake across the time-series, regardless of how the ice is observed, to broaden our understanding of ice loss across vast spatial and temporal scales.

Scientific Data↗
USGS2009

EAARL Coastal Topography - St. John, U.S. Virgin Islands 2003: First Surface

These remotely sensed, geographically referenced elevation measurements of Lidar-derived first surface (FS) topography were produced as a collaborative effort between the U.S. Geological Survey (USGS), Florida Integrated Science Center (FISC), St. Petersburg, FL; the National Park Service (NPS), South Florida-Caribbean Network, Miami, FL; and the National Aeronautics and Space Administration (NASA), Wallops Flight Facility, VA.

U.S. Virgin Islands, St. John↗
USGS2025

System characterization report on Resourcesat-2A Linear Imaging Self Scanning-3 sensor

Executive Summary This report addresses system characterization of the Indian Space Research Organisation Resourcesat-2A Linear Imaging Self Scanning-3 sensor 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 since 2021. These reports present and detail the methodology and procedures for characterization, present technical and operational information about the specific sensing system being evaluated, and provide a summary of test measurements, data retention practices, data analysis results, and conclusions. Resourcesat-2A is identical to Resourcesat-2 and was launched in 2016 on the Polar Satellite Launch Vehicle-C36 for continuity of data and improved temporal resolution. The Resourcesat-2 platform (which includes Resourcesat-2A) is of Indian Remote Sensing Satellites-1C/1D–P3 heritage and was built by the Indian Space Research Organisation. Resourcesat-2 and Resourcesat-2A carry the Linear Imaging Self Scanning-3 and Linear Imaging Self Scanning-4 sensors for medium-resolution imaging. More information on Indian Space Research Organisation satellites and sensors is available in the “2022 Joint Agency Commercial Imagery Evaluation—Remote Sensing Satellite Compendium” and from the manufacturer at https://www.isro.gov.in/ . 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. To summarize the results, we have determined that this sensor provides an interior geometric performance with mean offsets in the range of 1.75 meters (m; 0.06 pixel) to 6.83 m (0.23 pixel) in easting and −1.83 m (−0.06 pixel) to 1.81 m (0.06 pixel) in northing in band-to-band registration and a root mean square error in the range of 3.81 m (0.13 pixel) to 8.19 m (0.27 pixel) in easting and 2.21 m (0.09 pixel) to 4.72 m (0.16 pixel) in northing. We have measured an exterior geometric error offset in the range of −21.29 to 6.88 m in easting and −7.35 to −2.63 m in northing, and the root mean square error is in the range of 7.19 to 21.43 m in easting and 3.64 to 8.19 m in northing in comparison to the Landsat 8 Operational Land Imager. The measured radiometric performance was in the range of −0.002 to 0.031 in offset and 0.701 to 0.940 in slope, and the spatial performance was in the range of 1.204 to 1.265 pixels for full width at half maximum with a modulation transfer function at a Nyquist frequency in the range of 0.251 to 0.277.

Open-File Report↗
USGS2018

Landscape-scale variation in canopy water content of giant sequoias during drought

Recent drought (2012–2016) caused unprecedented foliage dieback in giant sequoias ( Sequoiadendron giganteum ), a species endemic to the western slope of the southern Sierra Nevada in central California. As part of an effort to understand and map sequoia response to droughts, we studied the patterns of remotely sensed canopy water content (CWC), both within and among sequoia groves in two successive years during the drought period (2015 and 2016). Our aims were: (1) to quantify giant sequoia responses to severe drought stress at a landscape scale using CWC as an indicator of crown foliage status, and (2) to estimate the effect of environmental correlates that mediate CWC change within and among giant sequoia groves. We utilized airborne high fidelity imaging spectroscopy (HiFIS) and light detection and ranging (LiDAR) data from the Carnegie Airborne Observatory to assess giant sequoia foliage status during 2015 and 2016 of the 2012–2016 droughts. A series of statistical models were generated to classify giant sequoias and to map their location in Sequoia and Kings Canyon National Parks (SEKI) and vicinity. We explored the environmental correlates and the spatial patterns of CWC change at the landscape scale. The mapped CWC was highly variable throughout the landscape during the two observation years, and proved to be most closely related to geological substrates, topography, and site-specific water balance. While there was an overall net gain in sequoia CWC between 2015 and 2016, certain locations (lower elevations, steeper slopes, areas more distant from surface water sources, and areas with greater climate water deficit) showed CWC losses. In addition, we found greater CWC loss in shorter sequoias and those growing in areas with lower sequoia stem densities. Our results suggest that CWC change indicates sequoia response to droughts across landscapes. Long-term monitoring of giant sequoia CWC will likely be useful for modeling and predicting their population-level response to future climate change.

California↗
USGS1992

A geographic information systems technique for monitoring contaminants in Galveston Bay, Texas

A geographic information system (GIS) was used to compile a series of databases containing details concerning metal contamination and pollution sources to study their impacts on the Galveston Bay (TX) ecosystem. Using these databases, a series of maps with various overlays was created with ARC/INFO software. These maps allowed patterns of spatial distribution of numerous variables to be easily visualized so that the impacts of urbanization and industrialization on the natural resources could be examined. Resource managers can use these capabilities in their comprehensive plans for managing individual activities within an overall regulatory framework. The visual displays and cartographic output of the system can be used to locate and identify ecologically sensitive areas and to study trends in these areas over time. These vector GIS techniques have the potential to be more integrated with aerial photography and other remotely sensed data for a more extensive overview of the environment and patterns of interaction

Texas↗
USGS2009

Remote monitoring of tamarisk defoliation and evapotranspiration following saltcedar leaf beetle attack

Tamarisk (Tamarix spp.) has invaded riparian ecosystems throughout the Western United States, including significant portions of riparian ecosystems within U.S. National Parks and Monuments. Recently, the saltcedar leaf beetle (Diorhabda elongata) was released as a tamarisk biocontrol agent. Although initial releases have been monitored, no comprehensive program is currently in place to monitor the rapid spread of Diorhabda that has resulted from numerous subsequent releases by county and state agencies. Long term monitoring of tamarisk defoliation and its impacts on habitat and water resources is needed. This study examines the potential for using higher spatial resolution Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data and lower spatial resolution Moderate Resolution Imaging Spectroradiometer (MODIS) data for monitoring defoliation caused by Diorhabda and subsequent changes in evapotranspiration (ET). Widespread tamarisk defoliation was observed in an eastern Utah study area during summer 2007. ASTER normalized difference vegetation index (NDVI) showed only minor changes between 2005 and 2006, but a significant drop in NDVI was found within riparian areas between 2006 and 2007. The decrease in NDVI caused by defoliation was apparent despite partial refoliation within the study area. MODIS time series data revealed that absolute decline in EVI varied by site, but that the timing of EVI decline during summer 2007 was early with respect to phenological patterns from 2001 through 2006. Defoliation caused decreases in ET values estimated from both ASTER and MODIS data. MODIS estimated ET declined earlier than in previous years, although annual ET was not significantly different than ET in previous years due to high year-to-year variability. Challenges to detection and monitoring of tamarisk defoliation include spectral mixing of tamarisk and other cover types at subpixel spatial resolution, spatial coregistration of time series images, the timing of image acquisition, and changes unrelated to defoliation in non-tamarisk land cover over time. Continued development of the techniques presented in this paper may allow monitoring the spread of Diorhabda and assessment of potential water salvage resulting from biocontrol of tamarisk. ?? 2009 Elsevier Inc.

Remote Sensing of Environment↗
USGS2009

An automated imagery orthorectification pilot

Automated orthorectification of raw image products is now possible based on the comprehensive metadata collected by Global Positioning Systems and Inertial Measurement Unit technology aboard aircraft and satellite digital imaging systems, and based on emerging pattern-matching and automated image-to-image and control point selection capabilities in many advanced image processing systems. Automated orthorectification of standard aerial photography is also possible if a camera calibration report and sufficient metadata is available. Orthorectification of historical imagery, for which only limited metadata was available, was also attempted and found to require some user input, creating a semi-automated process that still has significant potential to reduce processing time and expense for the conversion of archival historical imagery into geospatially enabled, digital formats, facilitating preservation and utilization of a vast archive of historical imagery. Over 90 percent of the frames of historical aerial photos used in this experiment were successfully orthorectified to the accuracy of the USGS 100K base map series utilized for the geospatial reference of the archive. The accuracy standard for the 100K series maps is approximately 167 feet (51 meters). The main problems associated with orthorectification failure were cloud cover, shadow and historical landscape change which confused automated image-to-image matching processes. Further research is recommended to optimize automated orthorectification methods and enable broad operational use, especially as related to historical imagery archives.

Journal of Remote Sensing↗