details of Remote Sensing inpections undertaken

This course explores the theory, technology, and applications of remote sensing. It is designed for individuals with an interest in GIS and geospatial science who have no prior experience working with remotely sensed data. Lab exercises make use of the web and the ArcGIS Pro software. You will work with and explore a wide variety of data types including aerial imagery, satellite imagery, multispectral imagery, digital terrain data, light detection and ranging (LiDAR), thermal data, and synthetic aperture RaDAR (SAR). Remote sensing is a rapidly changing field influenced by big data, machine learning, deep learning, and cloud computing. In this course you will gain an overview of the subject of remote sensing, with a special emphasis on principles, limitations, and possibilities. In addition, this course emphasizes information literacy, and will develop your skills in finding, evaluating, and using scholarly information. You will be asked to work through a series of modules that present information relating to a specific topic. You will also complete a series of lab exercises to reinforce the material. Lastly, you will complete paper reviews and a term project. We have also provided additional bonus material and links associated with surface hydrologic analysis with TauDEM, geographic object-based image analysis (GEOBIA), Google Earth Engine (GEE), and the geemap Python library for Google Earth Engine. Please see the sequencing document for our suggested order in which to work through the material. We have also provided PDF versions of the lectures with the notes included.

GEOSatDB is a semantic representation of Earth observation satellites and sensors that can be used to easily discover available Earth observation resources for specific research objectives.BackgroundThe widespread availability of coordinated and publicly accessible Earth observation (EO) data empowers decision-makers worldwide to comprehend global challenges and develop more effective policies. Space-based satellite remote sensing, which serves as the primary tool for EO, provides essential information about the Earth and its environment by measuring various geophysical variables. This contributes significantly to our understanding of the fundamental Earth system and the impact of human activities.Over the past few decades, many countries and organizations have markedly improved their regional and global EO capabilities by deploying a variety of advanced remote sensing satellites. The rapid growth of EO satellites and advances in on-board sensors have significantly enhanced remote sensing data quality by expanding spectral bands and increasing spatio-temporal resolutions. However, users face challenges in accessing available EO resources, which are often maintained independently by various nations, organizations, or companies. As a result, a substantial portion of archived EO satellite resources remains underutilized. Enhancing the discoverability of EO satellites and sensors can effectively utilize the vast amount of EO resources that continue to accumulate at a rapid pace, thereby better supporting data for global change research.MethodologyThis study introduces GEOSatDB, a comprehensive semantic database specifically tailored for civil Earth observation satellites. The foundation of the database is an ontology model conforming to standards set by the International Organization for Standardization (ISO) and the World Wide Web Consortium (W3C). This conformity enables data integration and promotes the reuse of accumulated knowledge. Our approach advocates a novel method for integrating Earth observation satellite information from diverse sources. It notably incorporates a structured prompt strategy utilizing a large language model to derive detailed sensor information from vast volumes of unstructured text.Dataset InformationThe GEOSatDB portal(https://www.geosatdb.cn) has been developed to provide an interactive interface that facilitates the efficient retrieval of information on Earth observation satellites and sensors.The downloadable files in RDF Turtle format are located in the data directory and contain a total of 132,681 statements:- GEOSatDB_ontology.ttl: Ontology modeling of concepts, relations, and properties.- satellite.ttl: 2,453 Earth observation satellites and their associated entities.- sensor.ttl: 1,035 Earth observation sensors and their associated entities.- sensor2satellite.ttl: relations between Earth observation satellites and sensors.GEOSatDB undergoes quarterly updates, involving the addition of new satellites and sensors, revisions based on expert feedback, and the implementation of additional enhancements.

LimnoSat-US is an analysis-ready remote sensing database that includes reflectance values spanning 36 years for 56,792 lakes across > 328,000 Landsat scenes. The database comes pre-processed with cross-sensor standardization and the effects of clouds, cloud shadows, snow, ice, and macrophytes removed. In total, it contains over 22 million individual lake observations with an average of 393 +/- 233 (mean +/- standard deviation) observations per lake over the 36 year period. The data and code contained within this repository are as follows:

HydroLakes_DP.shp: A shapefile containing the deepest points for all U.S. lakes within HydroLakes. For more information on the deepest point see https://doi.org/10.5281/zenodo.4136754 and Shen et al (2015).

LakeExport.py: Python code to extract reflectance values for U.S. lakes from Google Earth Engine.

GEE_pull_functions.py: Functions called within LakeExport.py

01_LakeExtractor.Rmd: An R Markdown file that takes the raw data from LakeExport.py and processes it for the final database.

SceneMetadata.csv: A file containing additional information such as scene cloud cover and sun angle for all Landsat scenes within the database. Can be joined to the final database using LandsatID.

srCorrected_us_hydrolakes_dp_20200628: The final LimnoSat-US database containing all cloud free observations of U.S. lakes from 1984-2020. Missing values for bands not shared between sensors (Aerosol and TIR2) are denoted by -99. dWL is the dominant wavelength calculated following Wang et al. (2015). pCount_dswe1 represents the number of high confidence water pixels within 120 meters of the deepest point. pCount_dswe3 represents the number of vegetated water pixels within 120 meters and can be used as a flag for potential reflectance noise. All reflectance values represent the median value of high confidence water pixels within 120 meters. The final database is provided in both as a .csv and .feather formats. It can be linked to SceneMetadata.cvs using LandsatID. All reflectance values are derived from USGS T1-SR Landsat scenes.

The Remote Sensing Division is responsible for providing data to support the Coastal Mapping Program, Emergency Response efforts, and the Aeronautical Survey Program through the use of remotely sensed data. NOAA Coastal Mapping Remote Sensing Data includes metric-quality aerial photographs from film and digital cameras, orthomosaics, and Light Detection and Ranging (lidar). The predecessors to...

The need to monitor change in sagebrush steppe is urgent due to the increasing impacts of climate change, shifting fire regimes, and management practices on ecosystem health. Remote sensing provides a cost-effective and reliable method for monitoring change through time and attributing changes to drivers. We report an automated method of mapping rangeland fractional component cover over a large portion of the Northern Great Basin, USA, from 1986 to 2016 using a dense Landsat imagery time series. 2012 was excluded from the time-series due to a lack of quality imagery. Our method improved upon the traditional change vector method by considering the legacy of change at each pixel. We evaluate cover trends stratified by climate bin and assess spatial and temporal relationships with climate variables. Finally, we statistically evaluate the minimum time density needed to accurately characterize temporal patterns and relationships with climate drivers. Over the 30-yr period, shrub cover decli ...

The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.

1Moderate Resolution Imaging Spectroradiometer (MODIS). Available at: https://lpdaac.usgs.gov/lpdaac/products/modis_products_table (accessed: 5 January 2009).2African Data Dissemination Service (ADDS). Available at: http://earlywarning.usgs.gov/adds/ (accessed: 5 January 2009).3Digital elevation model (DEM). Available at: http://eros.usgs.gov/ (accessed: 4 January 2009].4HealthMapper database. Available at: http://www.who.int/health_mapping/tools/healthmapper/en/index.html (accessed: 4 March 2009).5LandScan™ Global Population Database. Available at: http://www.ornl.gov/landscan/ (accessed: 20 January 2011).

This data set encompasses all of the remote sensing data collected in relation to sea ice during the BaySys 2018 expedition (radiometric and UAV data).

NAAMES_Ocean_AircraftRemoteSensing_Data are remotely sensed ocean measurements collected onboard the C-130 aircraft during the North Atlantic Aerosols and Marine Ecosystems Study (NAAMES). These measurements were collected from November 4, 2015 – November 29, 2015, May 11, 2016 – June 5, 2016 and August 30, 2017-September 22, 2017 over the North Atlantic Ocean. The primary objective of NAAMES was to resolve key processes controlling ocean system function, their influences on atmospheric aerosols and clouds and their implications for climate. The airborne products link local-scale processes and properties to the larger scale continuous satellite record. Related ocean property measurements are available in the NAAMES_AerosolCloud_AircraftRemoteSensing_Data_1. Data collection for this product is complete.The NASA North Atlantic Aerosols and Marine Ecosystems Study (NAAMES) project was the first NASA Earth Venture – Suborbital mission focused on studying the coupled ocean ecosystem and atmosphere. NAAMES utilizes a combination of ship-based, airborne, autonomous sensor, and remote sensing measurements that directly link ocean ecosystem processes, emissions of ocean-generated aerosols and precursor gases, and subsequent atmospheric evolution and processing. Four deployments coincide with the seasonal cycle of phytoplankton in the North Atlantic Ocean: the Winter Transition (November 5 – December 2, 2015), the Bloom Climax (May 11 – June 5, 2016), the Deceleration Phase (August 30 – September 24, 2017), and the Acceleration Phase (March 20 – April 13, 2018). Ship-based measurements were conducted from the Woods Hole Oceanographic Institution Research Vessel Atlantis in the middle of the North Atlantic Ocean, while airborne measurements were conducted on a NASA Wallops Flight Facility C-130 Hercules that was based at St. John's International Airport, Newfoundland, Canada. Data products in the ASDC archive focus on the NAAMES atmospheric aerosol, cloud, and trace gas data from the ship and aircraft, as well as related satellite and model data subsets. While a few ocean-remote sensing data products (e.g., from the high-spectral resolution lidar) are also included in the ASDC archive, most ocean data products reside in a companion archive at SeaBass.

This dataset provides a presentation that highlights the role NASA research and researchers played in developing a wide range of significant, quantitative ecological applications of satellite data. The presentation by Dr Diane E. Wickland, former NASA Terrestrial Ecology Program Manager and Lead for NASA Carbon Cycle and Ecosystems Focus Area, provides a top-level overview from her perspective of the development and evolution of the program. Dr Wickland joined NASA in 1985 to manage a newly formed Terrestrial Ecosystems Program. Along with other NASA program managers, she was charged with reorienting the program to be less empirical and have a greater focus on first principles, and to prepare for a next generation of earth-observing satellites. As an ecologist, she thought that focusing on important ecological questions and recruiting practicing ecologists to the program would facilitate such a change in directions. The presentation emphasizes the early years of U.S. satellite remote sensing and covers a few highlights after 2005.

Multi-temporal landslide inventories are important information for the understanding of landslide dynamics and related predisposing and triggering factors, and thus a crucial prerequisite for probabilistic hazard and risk assessment. Despite the great importance of these inventories, they do not exist for many landslide prone regions in the world. In this context, the recently evolving global-scale availability of high temporal and spatial resolution optical satellite imagery (RapidEye, Sentinel-2A/B, planet) has opened up new opportunities for the creation of these multi-temporal inventories. To derive such multi-temporal landslide inventories, a semi-automated spatiotemporal landslide mapper was developed at the Remote Sensing Section of the GFZ Potsdam being capable of deriving post-failure landslide objects (polygons) from multi-sensor optical satellite time series data (Behling et al., 2016). The developed approach represents an extension of the original methodology (Behling et al., 2014, Behling and Roessner, 2020) and facilitates the integration of optical time series data acquired by different satellite systems. The goal of combining satellite data originating from variable sensor systems has been the establishment of longest possible time series for retrospective systematic assessment of multi-temporal landslide activity at highest possible temporal and spatial resolution. We applied the developed approach to a 2500 km² study area in Southern Kyrgyzstan using an optical satellite database acquired by the Landsat TM/ETM+, SPOT 1/5, IRS1-C LISSIII, ASTER, and RapidEye sensor systems covering a time period between 1986 and 2013. A multi-temporal landslide inventory from 2009-2013 derived from RapidEye satellite time series data is available as separate publications (Behling et al., 2014; Behling and Roessner, 2020). The resulting systematic multi-temporal landslide inventory being subject of this data publication is supplementary to the article of Behling et al. (2016), which describes the extended spatiotemporal landslide mapper in detail. This multi-sensor approach prioritizes most suitable images within the available multi-sensor satellite time series using parameters, such as spatial resolution, cloud coverage, similarity of sensor characteristics and seasonality related to vegetation characteristics with the goal of establishing a robust back-bone time series for initial detection of possible landslide objects. In a second step, this initial analysis gets more refined in order to achieve the best possible approximation of the date of landslide occurrence. For a more detailed description of the methodology of the extended spatiotemporal landslide mapper, please see Behling et al. (2016). In general, this landslide mapper detects landslide objects by analyzing temporal NDVI-based vegetation cover changes and relief-oriented parameters in a rule-based approach combining pixel- and object-based analysis. Typical landslide-related vegetation changes comprise abrupt disturbances of vegetation cover in the result of the actual failure as well as post-failure revegetation which usually happens at a slower pace compared to vegetation growth in the surrounding undisturbed areas, since the displaced landslide masses are susceptible to subsequent erosion and reactivation processes. The resulting landslide-specific temporal surface cover dynamics in form of temporal trajectories is used as input information to identify freshly occurred landslides and to separate them from other temporal variations in the surrounding vegetation cover (e.g., seasonal vegetation changes or changes due to agricultural activities) and from permanently non-vegetated areas (e.g., urban non-vegetated areas, water bodies, rock outcrops). The data are provided in vector format (polygons) in form of a standard shapefile contained in the zip-file 2020-002_Behling_et-al_2016_landslide_inventory_SouthernKyrgyzstan_1986_2013.zip and are described in more detail in the associated data description.

Remote Sensing Technology Market valued at $19.47 Billion in 2023, and is projected to $USD 50.96 Billion by 2032, at a CAGR of 5.29% from 2023 to 2032.

Remote Sensing Technology Market Outlook

According to our latest research, the global remote sensing technology market size reached USD 20.7 billion in 2024, reflecting robust expansion driven by technological advancements and increasing demand across various sectors. The market is currently growing at a CAGR of 11.2%, and is forecasted to attain a value of USD 54.6 billion by 2033. This remarkable growth is primarily attributed to the rising adoption of remote sensing solutions in environmental monitoring, agriculture, urban planning, and defense applications, as well as the proliferation of satellite and UAV-based sensing platforms.

One of the most significant growth factors for the remote sensing technology market is the increasing need for precise and timely data collection to support critical decision-making in sectors such as agriculture, environmental monitoring, and disaster management. The integration of advanced sensors and imaging technologies, including hyperspectral and multispectral imaging, has enabled the capture of high-resolution data, which is essential for resource management and environmental sustainability. Furthermore, the advent of cloud computing and big data analytics has enhanced the ability to process and analyze vast amounts of remote sensing data efficiently, thereby expanding the market's potential applications and driving widespread adoption across both developed and emerging economies.

Another key driver for market growth is the expanding use of remote sensing in defense and intelligence operations. Governments and military organizations worldwide are investing heavily in satellite-based surveillance and reconnaissance systems to enhance national security and border monitoring capabilities. The increasing frequency of natural disasters and the need for rapid response and mitigation strategies have also fueled demand for remote sensing technology in disaster management. Additionally, the commercial sector is leveraging remote sensing for infrastructure development, urban planning, and natural resource exploration, further broadening the market’s scope. The collaboration between public and private entities in developing innovative remote sensing platforms and solutions is expected to accelerate market growth over the forecast period.

The regional outlook for the remote sensing technology market reveals significant growth opportunities across all major regions, with North America and Asia Pacific leading in adoption and innovation. North America, particularly the United States, dominates the market due to its advanced technological infrastructure, significant investments in defense and space exploration, and the presence of major market players. Asia Pacific is witnessing rapid growth, driven by increasing investments in satellite technology, expanding agricultural applications, and government initiatives to enhance disaster management and environmental monitoring. Europe also holds a substantial market share, supported by robust research and development activities and strong regulatory frameworks promoting sustainable development. Meanwhile, Latin America and the Middle East & Africa are emerging as promising markets, propelled by growing awareness and investment in remote sensing for resource management and urban planning.

Technology Analysis

The technology segment of the remote sensing technology market is broadly categorized into active and passive remote sensing. Active remote sensing technologies, such as LiDAR and radar, have gained significant traction due to their ability to operate under various environmental conditions, including night and cloudy weather. These systems emit their own signals and measure the reflected response, making them highly effective for applications requiring high precision and reliability, such as topographic mapping, forestry management, and military surveillance. The continuous advancements in laser and radar technologies have not only improved the accuracy and resolution of active remote sensing systems but have also reduced their operational costs, m

This project serves as a focal point of capability and expertise for integrating remote sensing, satellite telemetry and GIS. Working collaboratively with other principal investigators, this project applies satellite and software technologies to study spatial and temporal interactions between wildlife populations and their environment. There are three primary objectives: 1) develop optimal structures for wildlife distribution databases with emphasis on satellite tracking data; 2) develop environmental thematic databases with emphasis on Arctic regions; and 3) develop GIS algorithms for integrated data analyses. Commensurate with accelerating advances in remote sensing, satellite telemetry, and geographic information system (GIS) technology, the primary objective of this task is to evaluate and apply these state-of-the-art tools for developing or improving the methodologies used in wildlife and ecosystem research. The need for cost-effective techniques to systematically acquire environmental data for remote or inaccessible areas, and locational data for highly mobile or migratory species, crosses bureau, program and issue boundaries. This is especially true in arctic regions, where numerous fish and wildlife populations often range internationally, across extensive landscapes of tundra, boreal forest, polar sea-ice, and aquatic ecosystems. Remote sensing technologies provide alternatives to traditional sampling methods, which are typically too expensive to implement across large spatial scales or severely compromised by hazardous weather conditions and extended winter darkness. Publications: Douglas, D.C., 2010, Arctic sea ice decline: Projected changes in timing and extent of sea ice in the Bering and Chukchi Seas: U.S. Geological Survey Open-File Report 2010-1176, 32 p. Belchansky, G. I., D. C. Douglas, and N. G. Platonov (2005), Spatial and temporal variations in the age structure of Arctic sea ice, Geophys. Res. Lett.,32, L18504, doi:10.1029/2005GL023976 Belchanksy, G. I., D. C. Douglas, I. N. Mordvintsev, and N. G. Platonov (2004), Estimating the time of melt onset and freeze onset over Arctic sea-ice area using active and passive microwave data. Remote Sens. Environ., 92 , 21-39. Belchansky, G. I., D. C. Douglas, and N. G. Platonov (2004), Duration of the Arctic sea ice melt season: Regional and interannual variability, 1979-2001, J. Climate, 17 , 67-80. Belchansky, G. I., D. C. Douglas, I. V. Alpatsky, and N. G. Platonov (2004) , Spatial and temporal multiyear sea ice distributions in the Arctic : A neural network analysis of SSM/I data, 1988-2001, J. Geophys. Res. , 109 (C12), doi:10.1029/2004JC002388. Stone, R. S., D. C. Douglas, G. I. Belchansky, S. D. Drobot, and J. Harris (2005), Cause and effect of variations in western Arctic snow and sea ice cover. 8.3, Proc. Am. Meteorol. Soc. 8 th Conf. on Polar Oceanogr. and Meteorol. , San Diego , CA , 9-13 January. Belchansky, G. I., D. C. Douglas, V. A. Eremeev, and N. G. Platonov (2005), Variations in the Arctic's multiyear sea ice cover: A neural network analysis of SMMR-SSM/I data, 1979-2004. Geophys. Res. Lett. Vol. 32, No. 9, L09605, doi:10.1029/2005GL022395. Stone, R. S., D. C. Douglas, G. I. Belchansky, and S. D. Drobot (2005), Polar climate: Arctic sea ice, Pages 39-41 in D. H. Levinson (ed.), State of the Climate in 2004, Bull. Amer. Meterol. Soc., Vol. 86, No. 6, 86 pp. Stone, R. S., D. C. Douglas, G. I. Belchansky, and S. D. Drobot (2005), Correlated declines in western Arctic snow and sea ice cover. Arctic Res. United States, 19:18-25.

Remotely sensed imagery is increasingly used by emergency managers to monitor and map the impact of flood events to support preparedness, response, and critical decision making throughout the flood event lifecycle. To reduce latency in delivery of imagery-derived information, ensure consistent and reliably derived map products, and facilitate processing of an increasing volume of remote sensing data-streams, automated flood mapping workflows are needed. The U.S. Geological Survey is facilitating the development and integration of machine-learning algorithms in collaboration with NASA, National Geospatial Intelligence Agency (NGA), University of Alabama, and University of Illinois to create a workflow for rapidly generating improved flood-map products. A major bottleneck to the training of robust, generalizable machine learning algorithms for pattern recognition is a lack of training data that is representative across the landscape. To overcome this limitation for the training of algorithms capable of detection of surface inundation in diverse contexts, this publication includes the data developed from MAXAR Worldview sensors that is input as training data for machine learning. This data release consists of 100 thematic rasters, in GeoTiff format, with image labels representing five discrete categories: water, not water, maybe water, clouds and background/no data. Specifically, these training data were created by labeling 8-band, multispectral scenes from the MAXAR-Digital Globe, Worldview-2 and 3 satellite-based sensors. Scenes were selected to be spatially and spectrally diverse and geographically representative of different water features within the continental U.S. The labeling procedures used a hybrid approach of unsupervised classification for the initial spectral clustering, followed by expert-level manual interpretation and QA/QC peer review to finalize each labeled image. Updated versions of the data may be issued along with version update documentation. The 100 raster files that make up the training data are available to download here (https://doi.org/10.5066/P9C7HYRV).

The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.

Testing the use of Morpho to store image data. Visit https://dataone.org/datasets/doi%3A10.5063%2FAA%2Freeves.18.1 for complete metadata about this dataset.

Remote Sensing Data Analysis Market size is growing with a CAGR of 11.8% in the prediction period & it crosses USD 47.24 Bn by 2032 from USD 21.64 Bn in 2025

This product is part of the Landscape Change Monitoring System (LCMS) data suite. It shows LCMS change attribution classes for each year. See additional information about change in the Entity_and_Attribute_Information or Fields section below.LCMS is a remote sensing-based system for mapping and monitoring landscape change across the United States. Its objective is to develop a consistent approach using the latest technology and advancements in change detection to produce a "best available" map of landscape change. Because no algorithm performs best in all situations, LCMS uses an ensemble of models as predictors, which improves map accuracy across a range of ecosystems and change processes (Healey et al., 2018). The resulting suite of LCMS change, land cover, and land use maps offer a holistic depiction of landscape change across the United States over the past four decades.Predictor layers for the LCMS model include outputs from the LandTrendr and CCDC change detection algorithms and terrain information. These components are all accessed and processed using Google Earth Engine (Gorelick et al., 2017). To produce annual composites, the cFmask (Zhu and Woodcock, 2012), cloudScore, and TDOM (Chastain et al., 2019) cloud and cloud shadow masking methods are applied to Landsat Tier 1 and Sentinel 2a and 2b Level-1C top of atmosphere reflectance data. The annual medoid is then computed to summarize each year into a single composite. The composite time series is temporally segmented using LandTrendr (Kennedy et al., 2010; Kennedy et al., 2018; Cohen et al., 2018). All cloud and cloud shadow free values are also temporally segmented using the CCDC algorithm (Zhu and Woodcock, 2014). LandTrendr, CCDC and terrain predictors can be used as independent predictor variables in a Random Forest (Breiman, 2001) model. LandTrendr predictor variables include fitted values, pair-wise differences, segment duration, change magnitude, and slope. CCDC predictor variables include CCDC sine and cosine coefficients (first 3 harmonics), fitted values, and pairwise differences from the Julian Day of each pixel used in the annual composites and LandTrendr. Terrain predictor variables include elevation, slope, sine of aspect, cosine of aspect, and topographic position indices (Weiss, 2001) from the USGS 3D Elevation Program (3DEP) (U.S. Geological Survey, 2019). Reference data are collected using TimeSync, a web-based tool that helps analysts visualize and interpret the Landsat data record from 1984-present (Cohen et al., 2010).Outputs fall into three categories: change, land cover, and land use. Change relates specifically to vegetation cover and includes slow loss (not included for PRUSVI), fast loss (which also includes hydrologic changes such as inundation or desiccation), and gain. These values are predicted for each year of the time series and serve as the foundational products for LCMS. References: Breiman, L. (2001). Random Forests. In Machine Learning (Vol. 45, pp. 5-32). https://doi.org/10.1023/A:1010933404324Chastain, R., Housman, I., Goldstein, J., Finco, M., and Tenneson, K. (2019). Empirical cross sensor comparison of Sentinel-2A and 2B MSI, Landsat-8 OLI, and Landsat-7 ETM top of atmosphere spectral characteristics over the conterminous United States. In Remote Sensing of Environment (Vol. 221, pp. 274-285). https://doi.org/10.1016/j.rse.2018.11.012Cohen, W. B., Yang, Z., and Kennedy, R. (2010). Detecting trends in forest disturbance and recovery using yearly Landsat time series: 2. TimeSync - Tools for calibration and validation. In Remote Sensing of Environment (Vol. 114, Issue 12, pp. 2911-2924). https://doi.org/10.1016/j.rse.2010.07.010Cohen, W. B., Yang, Z., Healey, S. P., Kennedy, R. E., and Gorelick, N. (2018). A LandTrendr multispectral ensemble for forest disturbance detection. In Remote Sensing of Environment (Vol. 205, pp. 131-140). https://doi.org/10.1016/j.rse.2017.11.015Foga, S., Scaramuzza, P.L., Guo, S., Zhu, Z., Dilley, R.D., Beckmann, T., Schmidt, G.L., Dwyer, J.L., Hughes, M.J., Laue, B. (2017). Cloud detection algorithm comparison and validation for operational Landsat data products. Remote Sensing of Environment, 194, 379-390. http://doi.org/10.1016/j.rse.2017.03.026Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., and Moore, R. (2017). Google Earth Engine: Planetary-scale geospatial analysis for everyone. In Remote Sensing of Environment (Vol. 202, pp. 18-27). https://doi.org/10.1016/j.rse.2017.06.031Healey, S. P., Cohen, W. B., Yang, Z., Kenneth Brewer, C., Brooks, E. B., Gorelick, N., Hernandez, A. J., Huang, C., Joseph Hughes, M., Kennedy, R. E., Loveland, T. R., Moisen, G. G., Schroeder, T. A., Stehman, S. V., Vogelmann, J. E., Woodcock, C. E., Yang, L., and Zhu, Z. (2018). Mapping forest change using stacked generalization: An ensemble approach. In Remote Sensing of Environment (Vol. 204, pp. 717-728). https://doi.org/10.1016/j.rse.2017.09.029Kennedy, R. E., Yang, Z., and Cohen, W. B. (2010). Detecting trends in forest disturbance and recovery using yearly Landsat time series: 1. LandTrendr - Temporal segmentation algorithms. In Remote Sensing of Environment (Vol. 114, Issue 12, pp. 2897-2910). https://doi.org/10.1016/j.rse.2010.07.008Kennedy, R., Yang, Z., Gorelick, N., Braaten, J., Cavalcante, L., Cohen, W., and Healey, S. (2018). Implementation of the LandTrendr Algorithm on Google Earth Engine. In Remote Sensing (Vol. 10, Issue 5, p. 691). https://doi.org/10.3390/rs10050691Olofsson, P., Foody, G. M., Herold, M., Stehman, S. V., Woodcock, C. E., and Wulder, M. A. (2014). Good practices for estimating area and assessing accuracy of land change. In Remote Sensing of Environment (Vol. 148, pp. 42-57). https://doi.org/10.1016/j.rse.2014.02.015Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M. and Duchesnay, E. (2011). Scikit-learn: Machine Learning in Python. In Journal of Machine Learning Research (Vol. 12, pp. 2825-2830).Pengra, B. W., Stehman, S. V., Horton, J. A., Dockter, D. J., Schroeder, T. A., Yang, Z., Cohen, W. B., Healey, S. P., and Loveland, T. R. (2020). Quality control and assessment of interpreter consistency of annual land cover reference data in an operational national monitoring program. In Remote Sensing of Environment (Vol. 238, p. 111261). https://doi.org/10.1016/j.rse.2019.111261U.S. Geological Survey. (2019). USGS 3D Elevation Program Digital Elevation Model, accessed August 2022 at https://developers.google.com/earth-engine/datasets/catalog/USGS_3DEP_10mWeiss, A.D. (2001). Topographic position and landforms analysis Poster Presentation, ESRI Users Conference, San Diego, CAZhu, Z., and Woodcock, C. E. (2012). Object-based cloud and cloud shadow detection in Landsat imagery. In Remote Sensing of Environment (Vol. 118, pp. 83-94). https://doi.org/10.1016/j.rse.2011.10.028Zhu, Z., and Woodcock, C. E. (2014). Continuous change detection and classification of land cover using all available Landsat data. In Remote Sensing of Environment (Vol. 144, pp. 152-171). https://doi.org/10.1016/j.rse.2014.01.011This record was taken from the USDA Enterprise Data Inventory that feeds into the https://data.gov catalog. Data for this record includes the following resources: ISO-19139 metadata ArcGIS Hub Dataset ArcGIS GeoService For complete information, please visit https://data.gov.