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USGS2018

Modeling intrinsic potential for beaver (Castor canadensis) habitat to inform restoration and climate change adaptation

Through their dam-building activities and subsequent water storage, beaver have the potential to restore riparian ecosystems and offset some of the predicted effects of climate change by modulating streamflow. Thus, it is not surprising that reintroducing beaver to watersheds from which they have been extirpated is an often-used restoration and climate-adaptation strategy. Identifying sites for reintroduction, however, requires detailed information about habitat factors—information that is not often available at broad spatial scales. Here we explore the potential for beaver relocation throughout the Snohomish River Basin in Washington, USA with a model that identifies some of the basic building blocks of beaver habitat suitability and does so by relying solely on remotely sensed data. More specifically, we developed a generalized intrinsic potential model that draws on remotely sensed measures of stream gradient, stream width, and valley width to identify where beaver could become established if suitable vegetation were to be present. Thus, the model serves as a preliminary screening tool that can be applied over relatively large extents. We applied the model to 5,019 stream km and assessed the ability of the model to correctly predict beaver habitat by surveying for beavers in 352 stream reaches. To further assess the potential for relocation, we assessed land ownership, use, and land cover in the landscape surrounding stream reaches with varying levels of intrinsic potential. Model results showed that 33% of streams had moderate or high intrinsic potential for beaver habitat. We found that no site that was classified as having low intrinsic potential had any sign of beavers and that beaver were absent from nearly three quarters of potentially suitable sites, indicating that there are factors preventing the local population from occupying these areas. Of the riparian areas around streams with high intrinsic potential for beaver, 38% are on public lands and 17% are on large tracts of privately-owned timber land. Thus, although there are a large number of areas that could be suitable for relocation and restoration using beavers, current land use patterns may substantially limit feasibility in these areas.

Washington↗

USGS1976

Availability of Earth resources data

The purpose of this booklet is to help the reader to become aware of the volume of Earth resources data that have been collected by agencies of the United States Government and to find out where and how these data may be obtained. Earth resources data as discussed here are those obtained by remote-sensing instruments, including aerial cameras, multispectral cameras, multispectral scanners, and radar. They are obtained by spacecraft and high-altitude aircraft. Also, various agencies have been acquiring vast quantities of conventional aerial mapping photography from low and medium altitudes for more than 50 years. There is also brief information to assist the reader in locating published references and identifying research projects relating to application of Earth resources data to a variety of purposes. In all cases, it is not the intent to say what data are needed or how the data are to be used, but to describe where an interested person can begin to locate Earth resources data.

General Information Product↗

USGS1993

SPECtrum Processing Routines User's Manual Version 3 (program SPECPR)

Introduction The SPECtrum Processing Routines (SPECPR) is a large-scale interactive program for general one-dimensional array processing and optimized for reflectance spectroscopy data and analysis. The program processes one-dimensional arrays up to 4852 data points and the operations include addition, subtraction, multiplication, division, trigonometric functions, logarithmic and exponential functions, and many more specialized routines. The Specpr user interface is a menu driven, character command system with all user input entered as ascii characters to the program. User commands are thoroughly checked for the context in which they will be used. This provides for essentially all user input mistakes to be caught and appropriate error messages to be issued. The user interface also allows for command aliasing, variable substitutions, command history and batch command processing to occur at any point in the program. The user can also control his or her own variables, increment and decrement them, and monitor certain internal variables. This flexibility allows for sophisticated programming, e.g. from specific computations, to management of a database. All arrays are treated as one dimensional lists of numbers, each with an appropriate header for identification and history. Any array can be plotted versus another array, and each axis labeled appropriately. For example, you may plot a reflectance spectrum versus wavelength or wavelength versus reflectance. The array type is simply a label, so data like temperature versus time can be treated just as easily as a reflectance spectrum. Some routines are specific to certain purposes, however. For example, the Planck black body generator is specific to intensity versus wavelength, whereas a smoothing routine could operate equally on reflectance versus wavelength or temperature versus time. Each array element can have an error bar associated with it and errors are propagated through all appropriate routines. Data points can be marked deleted and deleted points are tracked appropriately. These features allow the program to be effectively used on real world data. This philosophy has resulted in a flexible system for which a user can manipulate data arrays efficiently. The program was designed for analysis of laboratory, field, telescopic, and spacecraft spectroscopic data, and although general in nature, there are biases built in. For example, horizontal axis labels default to wavelength in micrometers, but the defaults can be changed. Because of the orientation in Specpr to process planetary and terrestrial data, and the need to access such data, Specpr has been extended to have access to non-Specpr default file types. In particular, Specpr can "skewer" an 3-dimensional (3D) data file along any of the three axes. Further, block skewers can be done with the standard deviation of the mean computed for each channel in the block. Currently, Specpr can access any of the standard file types common in the terrestrial and planetary remote sensing communities. Details on 3D file I/O are given in Chapter 6. Specpr has multiple record types available within a single Specpr data file. Currently defined record types are data and text. In a data record, a standard one-dimensional array is held, along with its header information. The header information includes a title, history, dates and time of data acquisition and when the data were last processed, the user who processed the data, information typical of a spectrum like temperature and viewing geometry, as well as pointers to wavelengths, resolution, and text. If a spectrum is more than 256 channels in length, then the data gets put in succeeding records in the file, and the following records are continuation records. The second record type is the text, where a title and a block of text (up to 19 kbytes) can be stored. This record type is typically used for a description of samples, experiments, instruments, or data processing for a particular data set. It could also store the actual commands used to create a spectrum. The commands for a complicated plot for a publication might be stored in a text record. That way, you can easily regenerate the plot, or even write the commands to a non-Specpr file, modify them with any editor and re-execute them. The file types, text and command processing, combined with the math and special functions, provide for very powerful and general analysis tool. These facilities also provide the framework for database management.

Open-File Report↗

USGS1995

Effects of empirical versus model-based reflectance calibration on automated analysis of imaging spectrometer data: a case study from the Drum Mountains, Utah

Data collected by the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) have been calibrated to surface reflectance using an empirical method and an atmospheric model-based method. Single spectra extracted from both calibrated data sets for locations with known mineralogy compared favorably with laboratory and field spectral measurements of samples from the same locations. Generally, spectral features were somewhat subdued in data calibrated using the model-based method when compared with those calibrated using the empirical method. Automated feature extraction and expert system analysis techniques have been successfully applied to both data sets to produce similar endmember probability images and spectral endmember libraries. Linear spectral unmixing procedures applied to both calibrated data sets produced similar image maps. These comparisons demonstrated the utility of the model-based approach for atmospherically correcting imaging spectrometer data prior to extraction of scientific information. The results indicated that imaging spectrometer data can be calibrated and analyzed without a priori knowledge of the remote target.

Photogrammetric Engineering and Remote Sensing↗

USGS2017

Linking occupancy surveys with habitat characteristics to estimate abundance and distribution in an endangered cryptic bird

Accurate estimates of the distribution and abundance of endangered species are crucial to determine their status and plan recovery options, but such estimates are often difficult to obtain for species with low detection probabilities or that occur in inaccessible habitats. The Puaiohi ( Myadestes palmeri ) is a cryptic species endemic to Kauaʻi, Hawai‘i, and restricted to high elevation ravines that are largely inaccessible. To improve current population estimates, we developed an approach to model distribution and abundance of Puaiohi across their range by linking occupancy surveys to habitat characteristics, territory density, and landscape attributes. Occupancy per station ranged from 0.17 to 0.82, and was best predicted by the number and vertical extent of cliffs, cliff slope, stream width, and elevation. To link occupancy estimates with abundance, we used territory mapping data to estimate the average number of territories per survey station (0.44 and 0.66 territories per station in low and high occupancy streams, respectively), and the average number of individuals per territory (1.9). We then modeled Puaiohi occupancy as a function of two remote-sensed measures of habitat (stream sinuosity and elevation) to predict occupancy across its entire range. We combined predicted occupancy with estimates of birds per station to produce a global population estimate of 494 (95% CI 414–580) individuals. Our approach is a model for using multiple independent sources of information to accurately track population trends, and we discuss future directions for modeling abundance of this, and other, rare species.

Biodiversity and Conservation↗

USGS2019

Historical background and current developments for mapping burned area from satellite Earth observation

Fire has a diverse range of impacts on Earth's physical and social systems. Accurate and up to date information on areas affected by fire is critical to better understand drivers of fire activity, as well as its relevance for biogeochemical cycles , climate, air quality, and to aid fire management. Mapping burned areas was traditionally done from field sketches. With the launch of the first Earth observation satellites, remote sensing quickly became a more practical alternative to detect burned areas, as they provide timely regional and global coverage of fire occurrence. This review paper explores the physical basis to detect burned area from satellite observations, describes the historical trends of using satellite sensors to monitor burned areas, summarizes the most recent approaches to map burned areas and evaluates the existing burned area products (both at global and regional scales). Finally, it identifies potential future opportunities to further improve burned area detection from Earth observation satellites.

Remote Sensing of Environment↗

USGS2021

Monitoring multi-decadal variations of urban heat island intensity

Urban development and associated land cover transitions alter the thermal and physical properties of the land surface, resulting the temperature in urban area higher than in rural area or urban heat island (UHI). Remote sensing and land cover data is usually used to assess UHI intensity and temporal change trends. In this study, we implemented a prototype approach to characterize the UHI intensity and its spatiotemporal variation using the recently available time series of Landsat land surface temperature products and annual land change information. We analyzed land surface temperature change in urban and surrounding non-urban lands to quantify the UHI intensity and change in the Atlanta and Sioux Falls metropolitan areas of the United States. Our results suggested that the land cover type in rural areas and urban imperviousness cover determine UHI intensity and the urban land cover dynamics plays a major role in controlling temporal trend of UHI.

Conference Paper↗

USGS2006

Coalbed Methane Extraction and Soil Suitability Concerns in the Powder River Basin, Montana and Wyoming

The Powder River Basin is located in northeastern Wyoming and southeastern Montana. It is an area of approximately 55,000 square kilometers. Extraction of methane gas from the coal seams that underlie the Powder River Basin began in Wyoming in the late 1980s and in Montana in the late 1990s. About 100-200 barrels of co-produced water per day are being extracted from each active well in the Powder River Basin, which comes to over 1.5 million barrels of water per day for all the active coalbed methane wells in the Basin. Lab testing indicates that Powder River Basin co-produced water is potable but is high in sodium and other salts, especially in the western and northern parts of the Powder River Basin. Common water management strategies include discharge of co-produced water into drainages, stock ponds, evaporation ponds, or infiltration ponds; treatment to remove sodium; or application of the water directly on the land surface via irrigation equipment or atomizers. Problems may arise because much of the Powder River Basin contains soils with high amounts of swelling clays. As part of the USGS Rocky Mountain Geographic Science Center's hyperspectral research program, researchers are investigating whether hyperspectral remote sensing data can be beneficial in locating areas of swelling clays. Using detailed hyperspectral data collected over parts of the Powder River Basin and applying our knowledge of how the clays of interest reflect energy, we will attempt to identify and map areas of swelling clays. If successful, such information will be useful to resource and land managers.

Fact Sheet↗

USGS2018

Analysis ready data: Enabling analysis of the Landsat archive

Data that have been processed to allow analysis with a minimum of additional user effort are often referred to as Analysis Ready Data (ARD). The ability to perform large scale Landsat analysis relies on the ability to access observations that are geometrically and radiometrically consistent, and have had non-target features (clouds) and poor quality observations flagged so that they can be excluded. The United States Geological Survey (USGS) has processed all of the Landsat 4 and 5 Thematic Mapper (TM), Landsat 7 Enhanced Thematic Mapper Plus (ETM+), Landsat 8 Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) archive over the conterminous United States (CONUS), Alaska, and Hawaii, into Landsat ARD. The ARD are available to significantly reduce the burden of pre-processing on users of Landsat data. Provision of pre-prepared ARD is intended to make it easier for users to produce Landsat-based maps of land cover and land-cover change and other derived geophysical and biophysical products. The ARD are provided as tiled, georegistered, top of atmosphere and atmospherically corrected products defined in a common equal area projection, accompanied by spatially explicit quality assessment information, and appropriate metadata to enable further processing while retaining traceability of data provenance.

Remote Sensing↗

USGS2018

Mapping forest change using stacked generalization: An ensemble approach

The ever-increasing volume and accessibility of remote sensing data has spawned many alternative approaches for mapping important environmental features and processes. For example, there are several viable but highly varied strategies for using time series of Landsat imagery to detect changes in forest cover. Performance among algorithms varies across complex natural systems, and it is reasonable to ask if aggregating the strengths of an ensemble of classifiers might result in increased overall accuracy. Relatively simple rules have been used in the past to aggregate classifications among remotely sensed maps (e.g. using majority predictions), and in other fields, empirical models have been used to create situationally specific algorithm weights. The latter process, called “stacked generalization” (or “stacking”), typically uses a parametric model for the fusion of algorithm outputs. We tested the performance of several leading forest disturbance detection algorithms against ensembles of the outputs of those same algorithms based upon stacking using both parametric and Random Forests-based fusion rules. Stacking using a Random Forests model cut omission and commission error rates in half in many cases in relation to individual change detection algorithms, and cut error rates by one quarter compared to more conventional parametric stacking. Stacking also offers two auxiliary benefits: alignment of outputs to the precise definitions built into a particular set of empirical calibration data; and, outputs which may be adjusted such that map class totals match independent estimates of change in each year. In general, ensemble predictions improve when new inputs are added that are both informative and uncorrelated with existing ensemble components. As increased use of cloud-based computing makes ensemble mapping methods more accessible, the most useful new algorithms may be those that specialize in providing spectral, temporal, or thematic information not already available through members of existing ensembles.

Remote Sensing of Environment↗

USGS2007

Consequences of land-cover misclassification in models of impervious surface

Model estimates of impervious area as a function of landcover area may be biased and imprecise because of errors in the land-cover classification. This investigation of the effects of land-cover misclassification on impervious surface models that use National Land Cover Data (NLCD) evaluates the consequences of adjusting land-cover within a watershed to reflect uncertainty assessment information. Model validation results indicate that using error-matrix information to adjust land-cover values used in impervious surface models does not substantially improve impervious surface predictions. Validation results indicate that the resolution of the landcover data (Level I and Level II) is more important in predicting impervious surface accurately than whether the land-cover data have been adjusted using information in the error matrix. Level I NLCD, adjusted for land-cover misclassification, is preferable to the other land-cover options for use in models of impervious surface. This result is tied to the lower classification error rates for the Level I NLCD. ?? 2007 American Society for Photogrammetry and Remote Sensing.

Photogrammetric Engineering and Remote Sensing↗

USGS2019

Near-field remote sensing of Alaskan Rivers

The U.S. Geological Survey (USGS) Geomorphology and Sediment Transport Laboratory (GSTL), in collaboration with the U.S. Army Corps of Engineers Cold Regions Research and Engineering Laboratory (CRREL), acquired remotely sensed data from several Alaskan rivers in 2017 and 2018 with the goal of developing a methodology for measuring streamflow from a helicopter. CRREL operates a custom airborne lidar system that can be deployed in a helicopter-based pod (HeliPod). Data were collected with the HeliPod near existing USGS streamflow information stations on the Knik, Matanuska, Chena, and Salcha Rivers in both 2017 and 2018. Sites on the Tanana and Snow Rivers were added in 2018. In 2018, the HeliPod was modified to accommodate both a thermal infrared and a visible camera. The cameras were integrated with the flight management software to simultaneously acquire imagery with lidar. The Global Navigation Satellite System (GNSS) and inertial measurement unit (IMU) in the HeliPod were used to compute trajectories with precise position and orientation information needed for image orthorectification. The HeliPod sensors provide data for measuring river channel characteristics. Lidar can map the elevation of the water surface and thus be used to measure water-surface slopes and return intensity can be used to delineate the extent of the wetted river channel. Various approaches are currently being evaluated to estimate surface flow velocity from visible and thermal image time series. In this paper, we examine and compare water-surface elevation returns and slopes derived from the HeliPod lidar and found good agreement with measurements made using conventional field-based techniques.

Alaska↗

USGS2014

Evaluation of aerial thermal infrared remote sensing to identify groundwater-discharge zones in the Meduxnekeag River, Houlton, Maine

Residents of the area near Houlton, Maine, have observed seasonal episodic blooms of algae and documented elevated concentrations of fecal-coliform bacteria and inorganic nutrients and low dissolved oxygen concentrations in the Meduxnekeag River. Although point and nonpoint sources of urban and agricultural runoff likely contribute to water-quality impairment, the role of shallow groundwater inflows in delivering such contaminants to the Meduxnekeag River has not been well understood. To provide information about possible groundwater inflows to the river, airborne thermal infrared videography was evaluated as a means to identify and classify thermal anomalies in a 25-mile reach of the mainstem and tributaries of the Meduxnekeag River near Houlton, Maine. The U.S. Geological Survey, in cooperation with the Houlton Band of Maliseet Indians, collected thermal infrared images from a single-engine, fixed-wing aircraft during flights on December 3–4, 2003, and November 26, 2004. Eleven thermal anomalies were identified on the basis of data from the December 2003 flight and 17 from the November 2004 flight, which covered the same reaches of stream. Following image analysis, characterization, and prioritization, the georeferenced infrared images of the thermal anomalies were compared to features on topographic maps of the study area. The mapped anomalies were used to direct observations on the ground to confirm discharge locations and types of inflow. The variations in grayscale patterns on the images were thus confirmed as representing shallow groundwater-discharge zones (seeps), outfalls of treated wastewater, or ditches draining runoff from impervious surfaces.

Maine↗

USGS2018

Full-range, solar-reflected hyperspectral microscopy to support earth remote sensing research

Over the past 20 years, hyperspectral microscopy has grown into a robust field of analysis for a number of applications. The visible to near-infrared (VNIR; 400 to 1000 nm) region of the spectrum has demonstrated utility for the characterization of healthy and diseased tissue and of biomolecular indicators at the cellular level. Here, we describe the development of a hyperspectral imaging (HSI) microscope that is aimed at material characterization to complement traditional stand-off, earth remote sensing with hyperspectral sensors. We combine commercial off the shelf technology to build an HSI microscope to collect spectral data with illumination provided by a tunable laser. Hyperspectral imaging microscopy (HIM) facilitates detailed examination of target materials at the subcentimeter spatial scale. The custom-built, laser illumination HSI microscope covers the NIR to shortwave infrared (NIR/SWIR; 900 to 2500 nm) solar-reflected spectral range. It is combined with a separate VNIR sensor (400 to 900 nm) that utilizes quartz–tungsten–halogen lamps for illumination. The combined sensors provide a means to collect <10,000 s of spectra in the full VNIR/SWIR spectral range from both pure substances and precisely engineered linear and nonlinear mixtures. The large abundance of spectra allows for a more detailed understanding of the variability and multivariate probability distributions of spectral signatures. This additional information aids in understanding the variability observed in ground truth spectra collected from portable spectrometers, and it greatly enhances sample description and metadata content. In addition, HIM data cubes can serve as proxies, as “microscenes,” for systems engineering applications such as trade studies for HSI acquired by air- and space-borne sensors.

Journal of Applied Remote Sensing↗

USGS2015

Global land cover mapping using Earth observation satellite data: Recent progresses and challenges

Land cover is an important variable for many studies involving the Earth surface, such as climate, food security, hydrology, soil erosion, atmospheric quality, conservation biology, and plant functioning. Land cover not only changes with human caused land use changes, but also changes with nature. Therefore, the state of land cover is highly dynamic. In winter snow shields underneath various other land cover types in higher latitudes. Floods may persist for a long period in a year over low land areas in the tropical and subtropical regions. Forest maybe burnt or clear cut in a few days and changes to bare land. Within several months, the coverage of crops may vary from bare land to nearly 100% crops and then back to bare land following harvest. The highly dynamic nature of land cover creates a challenge in mapping and monitoring which remains to be adequately addressed. As economic globalization continues to intensify, there is an increasing trend of land cover/land use change, environmental pollution, land degradation, biodiversity loss at the global scale, timely and reliable information on global land cover and its changes is urgently needed to mitigate the negative impact of global environment change.

ISPRS Journal of Photogrammetry and Remote Sensing↗

USGS2004

Land cover mapping of North and Central America—Global Land Cover 2000

The Land Cover Map of North and Central America for the year 2000 (GLC 2000-NCA), prepared by NRCan/CCRS and USGS/EROS Data Centre (EDC) as a regional component of the Global Land Cover 2000 project, is the subject of this paper. A new mapping approach for transforming satellite observations acquired by the SPOT4/VGTETATION (VGT) sensor into land cover information is outlined. The procedure includes: (1) conversion of daily data into 10-day composite; (2) post-seasonal correction and refinement of apparent surface reflectance in 10-day composite images; and (3) extraction of land cover information from the composite images. The pre-processing and mosaicking techniques developed and used in this study proved to be very effective in removing cloud contamination, BRDF effects, and noise in Short Wave Infra-Red (SWIR). The GLC 2000-NCA land cover map is provided as a regional product with 28 land cover classes based on modified Federal Geographic Data Committee/Vegetation Classification Standard (FGDC NVCS) classification system, and as part of a global product with 22 land cover classes based on Land Cover Classification System (LCCS) of the Food and Agriculture Organisation. The map was compared on both areal and per-pixel bases over North and Central America to the International Geosphere–Biosphere Programme (IGBP) global land cover classification, the University of Maryland global land cover classification (UMd) and the Moderate Resolution Imaging Spectroradiometer (MODIS) Global land cover classification produced by Boston University (BU). There was good agreement (79%) on the spatial distribution and areal extent of forest between GLC 2000-NCA and the other maps, however, GLC 2000-NCA provides additional information on the spatial distribution of forest types. The GLC 2000-NCA map was produced at the continental level incorporating specific needs of the region.

Remote Sensing of Environment↗

USGS2021

Validation of the U.S. Geological Survey’s Land Change Monitoring, Assessment and Projection (LCMAP) collection 1.0 annual land cover products 1985–2017

The U.S. Geological Survey Land Change Monitoring, Assessment and Projection (USGS LCMAP) has released a suite of annual land cover and land cover change products for the conterminous United States (CONUS). The accuracy of these products was assessed using an independently collected land cover reference sample dataset produced by analysts interpreting Landsat data, high-resolution aerial photographs, and other ancillary data. The reference sample of nearly 25,000 pixels and the accompanying 33-year time series of annual land cover reference labels allowed for a comprehensive assessment of accuracy of the LCMAP land cover and land cover change products. Overall accuracy (± standard error) for the per-pixel assessment across all years for the eight land cover classes was 82.5% (±0.2%). Overall accuracy was consistent year-to-year within a range of 1.5% but varied regionally with lower accuracy in the eastern United States. User’s accuracy (UA) and producer’s accuracy (PA) for CONUS ranged from the higher accuracies of Water (UA=96%, PA=93%) and Tree Cover (UA=90%, PA=83%) to the lower accuracies of Wetland (UA=69%, PA=74%) and Barren (UA=43%, PA=57%). For a binary change / no change classification, UA of change was 13% (±0.5%) and PA was 16% (±0.6%) for CONUS when agreement was defined as a match by the exact year of change. UA and PA improved to 28% and 34% when agreement was defined as the change being detected by the map and reference data within a ±2-year window. Change accuracy was higher in the eastern United States compared to the western US. UA was 49% (±0.3) and PA was 54% (±0.3) for the footprint of change (defined as the area experiencing at least one land cover change from 1985–2017). For class-specific loss and gain when agreement was defined as an exact year match, UA and PA were generally below 30%, with Tree Cover loss being the most accurately mapped change (UA=25%, PA=31%). These accuracy results provide users with information to assess the suitability of LCMAP data and information to guide future research for improving LCMAP products, particularly focusing on the challenges of accurately mapping annual land cover change.

Remote Sensing of Environment↗

USGS2019

Characterizing crop water use dynamics in the Central Valley of California using landsat-derived evapotranspiration

Understanding how different crops use water over time is essential for planning and managing water allocation, water rights, and agricultural production. The main objective of this paper is to characterize the spatiotemporal dynamics of crop water use in the Central Valley of California using Landsat-based annual actual evapotranspiration (ETa) from 2008 to 2018 derived from the Operational Simplified Surface Energy Balance (SSEBop) model. Crop water use for 10 crops is characterized at multiple scales. The Mann–Kendall trend analysis revealed a significant increase in area cultivated with almonds and their water use, with an annual rate of change of 16,327 ha in area and 13,488 ha-m in water use. Conversely, alfalfa showed a significant decline with 12,429 ha in area and 13,901 ha-m in water use per year during the same period. A pixel-based Mann–Kendall trend analysis showed the changing crop type and water use at the level of individual fields for all of Kern County in the Central Valley. This study demonstrates the useful application of historical Landsat ET to produce relevant water management information. Similar studies can be conducted at regional and global scales to understand and quantify the relationships between land cover change and its impact on water use.

California↗