Land use and land cover, 1972, Cleveland, Ohio; Pennsylvania, U.S.; Ontario, Canada (U.S. portion only)
No abstract available.
Ohio, Pennsylvania↗
SEARCH · USGS Science
Results for “Land Use and Land Cover”
Search indexed USGS publications on groundwater, aquifers, geologic maps, mineral resources and earthquakes. Explore source records by subject and place.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
Unravelling long-term vegetation change patterns in a binational watershed using multitemporal land cover data and historical photography
A significant amount of research conducted in the Sonoran Desert of North America has documented, both anecdotally and empirically, major vegetation changes over the past century due to human land use activities. However, many studies lack coincidental landscape-scale data characterizing the spatial and temporal manifestation of these changes. Vegetation changes in a binational (USA and Mexico) watershed were documented using a series of four land cover maps (1979-2009) derived from multispectral satellite imagery. Cover changes are compared to georeferenced, repeat oblique photographs dating from the late 19 th century to present. Results indicate the expansion of grassland over the past 20 years following nearly a century of decline. Historical repeat photography documents early-mid 20 th century mesquite invasions, but recent land cover data and rephotography demonstrate declines in xeroriparian/riparian mesquite communities in recent decades. These vegetation changes are variable over the landscape and influenced by topography and land management.
Arizona, California↗
Unravelling long-term vegetation change patterns in a binational watershed using multitemporal land cover data and historical photography
A significant amount of research conducted in the Sonoran Desert of North America has documented, both anecdotally and empirically, major vegetation changes over the past century due to human land use activities. However, many studies lack coincidental landscape-scale data characterizing the spatial and temporal manifestation of these changes. Vegetation changes in a binational (USA and Mexico) watershed were documented using a series of four land cover maps (1979-2009) derived from multispectral satellite imagery. Cover changes are compared to georeferenced, repeat oblique photographs dating from the late 19th century to present. Results indicate the expansion of grassland over the past 20 years following nearly a century of decline. Historical repeat photography documents early-mid 20th century mesquite invasions, but recent land cover data and rephotography demonstrate declines in xeroriparian/riparian mesquite communities in recent decades. These vegetation changes are variable over the landscape and influenced by topography and land management.
Conference Paper↗
Temporal and spatial trends in nutrient and sediment loading to Lake Tahoe, California-Nevada, USA
Since 1980, the Lake Tahoe Interagency Monitoring Program (LTIMP) has provided stream-discharge and water quality data—nitrogen (N), phosphorus (P), and suspended sediment—at more than 20 stations in Lake Tahoe Basin streams. To characterize the temporal and spatial patterns in nutrient and sediment loading to the lake, and improve the usefulness of the program and the existing database, we have (1) identified and corrected for sources of bias in the water quality database; (2) generated synthetic datasets for sediments and nutrients, and resampled to compare the accuracy and precision of different load calculation models; (3) using the best models, recalculated total annual loads over the period of record; (4) regressed total loads against total annual and annual maximum daily discharge, and tested for time trends in the residuals; (5) compared loads for different forms of N and P; and (6) tested constituent loads against land use-land cover (LULC) variables using multiple regression. The results show (1) N and P loads are dominated by organic N and particulate P; (2) there are significant long-term downward trends in some constituent loads of some streams; and (3) anthropogenic impervious surface is the most important LULC variable influencing water quality in basin streams. Many of our recommendations for changes in water quality monitoring and load calculation methods have been adopted by the LTIMP.
California, Nevada↗
Effects of roads, topography, and land use on forest cover dynamics in the Brazilian Atlantic Forest
Roads and topography can determine patterns of land use and distribution of forest cover, particularly in tropical regions. We evaluated how road density, land use, and topography affected forest fragmentation, deforestation and forest regrowth in a Brazilian Atlantic Forest region near the city of São Paulo. We mapped roads and land use/land cover for three years (1962, 1981 and 2000) from historical aerial photographs, and summarized the distribution of roads, land use/land cover and topography within a grid of 94 non-overlapping 100 ha squares. We used generalized least squares regression models for data analysis. Our models showed that forest fragmentation and deforestation depended on topography, land use and road density, whereas forest regrowth depended primarily on land use. However, the relationships between these variables and forest dynamics changed in the two studied periods; land use and slope were the strongest predictors from 1962 to 1981, and past (1962) road density and land use were the strongest predictors for the following period (1981–2000). Roads had the strongest relationship with deforestation and forest fragmentation when the expansions of agriculture and buildings were limited to already deforested areas, and when there was a rapid expansion of development, under influence of São Paulo city. Furthermore, the past (1962) road network was more important than the recent road network (1981) when explaining forest dynamics between 1981 and 2000, suggesting a long-term effect of roads. Roads are permanent scars on the landscape and facilitate deforestation and forest fragmentation due to increased accessibility and land valorization, which control land-use and land-cover dynamics. Topography directly affected deforestation, agriculture and road expansion, mainly between 1962 and 1981. Forest are thus in peril where there are more roads, and long-term conservation strategies should consider ways to mitigate roads as permanent landscape features and drivers facilitators of deforestation and forest fragmentation.
São Paulo↗
Spatially distributed groundwater recharge for 2010 land cover estimated using a water-budget model for the Island of O‘ahu, Hawai‘i
Owing mainly to projected population growth, demand for freshwater on the Island of Oʻahu is expected to increase by about 26 percent between 2010 and 2030, according to the City and County of Honolulu. Estimates of groundwater recharge are needed to evaluate the availability of fresh groundwater. For this study, a water-budget model with a daily computation interval was developed and used to estimate the spatial distribution of recharge on Oʻahu for average climate conditions (1978–2007 rainfall and 2010 land cover) and for drought conditions (1998–2002 rainfall and 2010 land cover). For average climate conditions, mean annual recharge for Oʻahu is about 660 million gallons per day, or about 36 percent of precipitation (rainfall and fog interception). Recharge for average climate conditions is about 34 percent of total water inflow, which consists of precipitation, irrigation, septic leachate, water-main leakage, and seepage from reservoirs and cesspools. Recharge is high along the crest of the Koʻolau Range, reaching as much as about 180 inches per year in the north-central part of the range. Recharge is much lower outside of the mountainous areas of the island, commonly less than 5 inches per year in unirrigated areas. The island-wide estimate of groundwater recharge for average climate conditions from this study is within 1 percent of the recharge estimate used in the 2008 State of Hawaiʻi Water Resource Protection Plan, which divides the Island of Oʻahu into 23 aquifer systems for groundwater management purposes. To facilitate direct comparisons with this study, these 23 aquifer systems were consolidated into 21 aquifer systems. Recharge estimates from this study are higher for 12 of the aquifer-system areas and lower for 9. Differences in mean rainfall distribution and the inclusion of irrigation in this study are the primary reasons for discrepancies in recharge estimates between this study and the 2008 Hawaiʻi Water Resources Protection Plan. For drought conditions, mean annual recharge for Oʻahu is about 417 million gallons per day, which is about 37 percent less than recharge for average climate conditions. For individual aquifer-system areas, recharge for drought conditions is about 25 to 70 percent less than recharge for average climate conditions.
Hawai'i↗
Using an ecoregion framework to analyze land-cover and land-use dynamics.
The United States has a highly varied landscape because of wide-ranging differences in combinations of climatic, geologic, edaphic, hydrologic, vegetative, and human management (land use) factors. Land uses are dynamic, with the types and rates of change dependent on a host of variables, including land accessibility, economic considerations, and the internal increase and movement of the human population. There is a convergence of evidence that ecoregions are very useful for organizing, interpreting, and reporting information about land-use dynamics. Ecoregion boundaries correspond well with patterns of land cover, urban settlement, agricultural variables, and resource-based industries. We implemented an ecoregion framework to document trends in contemporary land-cover and land-use dynamics over the conterminous United States from 1973 to 2000. Examples of results from six eastern ecoregions show that the relative abundance, grain of pattern, and human alteration of land-cover types organize well by ecoregion and that these characteristics of change, themselves, change through time.
Environmental Management↗
Digital data used to relate nutrient input to water quality in the Chesapeake Bay watershed
Digital data sets compiled by the U.S. Geological Survey were used as input for a collection of Spatially Referenced Regressions On Watershed (SPARROW) attributes for the Chesapeake Bay region including parts of Delaware, Maryland, New York, Pennsylvania, Virginia, West Virginia, and the District of Columbia. These regressions use a nonlinear statistical approach to relate nutrient sources and land-surface characteristics to nutrient loads of streams throughout the Chesapeake Bay watershed. A digital segmented-watershed network serves as the primary framework for spatially referencing nutrient-source and land-surface characteristic data within a geographic information system. Flow direction and flow accumulation generated from a 30-meter cell-size Digital Elevation Model and attributes from 1:500,000-scale stream data were used to generate stream and watershed networks. Spatial data sets representing nutrient inputs of total nitrogen and total phosphorus from the early 1990's were created and compiled from numerous sources. Data include atmospheric deposition, septic systems, point-source locations, land use, land cover, and agricultural sources such as commercial fertilizer and manure. Some land-surface characteristic data sets representing factors that affect the transport of nutrients also were compiled. Data sets include land use, land cover, average-annual precipitation and temperature, slope, hydrogeomorphic regions, and soil permeability. Nutrient-input and land-surface characteristic data sets merged with the segmented-watershed network provide the spatial detail by watershed segment required by SPARROW. Stream-nutrient load estimates for 132 sampling sites representing the early 1990's (103 for total nitrogen and 121 for total phosphorus) serve as the dependent variables for the regressions. These estimates were used to calibrate models of total nitrogen and total phosphorus depicting 1992 land-surface conditions. Examples of model predictions consist of stream-nutrient load and source percentages contributed locally to each stream reach, as well as percentages of the load that reach Chesapeake Bay.
Open-File Report↗
Impact of land use and land cover change on groundwater recharge and quality in the southwestern US
Humans have exerted large‐scale changes on the terrestrial biosphere, primarily through agriculture; however, the impacts of such changes on the hydrologic cycle are poorly understood. The purpose of this study was to test the hypothesis that the conversion of natural rangeland ecosystems to agricultural ecosystems impacts the subsurface portion of the hydrologic cycle by changing groundwater recharge and flushing salts to underlying aquifers. The hypothesis was examined through point and areal studies investigating the effects of land use/land cover (LU/LC) changes on groundwater recharge and solute transport in the Amargosa Desert (AD) in Nevada and in the High Plains (HP) in Texas, US. Studies use the fact that matric (pore‐water‐pressure) potential and environmental‐tracer profiles in thick unsaturated zones archive past changes in recharging fluxes. Results show that recharge is related to LU/LC as follows: discharge through evapotranspiration (i.e., no recharge; upward fluxes <0.1 mm yr −1 ) in natural rangeland ecosystems (low matric potentials; high chloride and nitrate concentrations); moderate‐to‐high recharge in irrigated agricultural ecosystems (high matric potentials; low‐to‐moderate chloride and nitrate concentrations) (AD recharge: ∼130–640 mm yr −1 ); and moderate recharge in nonirrigated (dryland) agricultural ecosystems (high matric potentials; low chloride and nitrate concentrations, and increasing groundwater levels) (HP recharge: ∼9–32 mm yr −1 ). Replacement of rangeland with agriculture changed flow directions from upward (discharge) to downward (recharge). Recent replacement of rangeland with irrigated ecosystems was documented through downward displacement of chloride and nitrate fronts. Thick unsaturated zones contain a reservoir of salts that are readily mobilized under increased recharge related to LU/LC changes, potentially degrading groundwater quality. Sustainable land use requires quantitative knowledge of the linkages between ecosystem change, recharge, and groundwater quality.
Global Change Biology↗
Estimates of ground-water recharge to the Yakima River Basin aquifer system, Washington, for predevelopment and current land-use and land-cover conditions
Two models were used to estimate ground-water recharge to the Yakima River Basin aquifer system, Washington for predevelopment (estimate of natural conditions) and current (a multi-year, 1995-2004, composite) land-use and land-cover conditions. The models were the Precipitation-Runoff Modeling System (PRMS) and the Deep Percolation Model (DPM) that are contained in the U.S. Geological Survey's Modular Modeling System. Daily values of recharge were estimated for water years 1950-98 using previously developed PRMS-watershed models for four mainly forested upland areas, and for water years 1950-2003 using DPM applied to 17 semiarid to arid areas in the basin. The mean annual recharge under predevelopment conditions was estimated to be about 11.9 in. or 5,450 ft 3 /s (about 3.9 million acre-ft) for the 6,207 mi 2 in the modeled area. In the modeled areas, recharge ranged from 0.08 in. (1.2 ft 3 /s) to 34 in. (2,825 ft 3 /s). About 97 percent of the recharge occurred in the 3,667 mi 2 area included in the upland-area models, but much of this quantity is not available to recharge the bedrock hydrogeologic units. Only about 1.0 in., or 187 ft 3 /s (about 0.14 million acre-ft), was estimated to occur in the 2,540 mi 2 area included in the semiarid to arid lowland modeled areas. The mean annual recharge to the aquifer system under current conditions was estimated to be about 15.6 in., or 7,149 ft 3 /s (about 5.2 million acre-ft). The increase in recharge is due to the application of irrigation water to croplands. The annual quantity of irrigation was more than five times the annual precipitation for some of the modeled areas. Mean annual actual evapotranspiration was estimated to have increased from predevelopment conditions by more than 1,700 ft 3 /s (about 1.2 million acre-ft) due to irrigation.
Washington↗
Global cropland-extent product at 30-m resolution (GCEP30) derived from Landsat satellite time-series data for the year 2015 using multiple machine-learning algorithms on Google Earth Engine cloud
Executive Summary Global food and water security analysis and management require precise and accurate global cropland-extent maps. Existing maps have limitations, in that they are (1) mapped using coarse-resolution remote-sensing data, resulting in the lack of precise mapping location of croplands and their accuracies; (2) derived by collecting and collating national statistical data that are often subjective, leading to substantial uncertainties in cropland-area estimates, as well as their locations; and (3) extracted from one or more classes of a land use–land cover product in which cropland classes are not the focus of mapping, leading to their mixing with other classes and creating significant errors of omission and commission. These limitations can be overcome by producing high-resolution cropland-extent maps using satellite-sensor data, such as Landsat 30-m resolution or higher. The most fundamental cropland product is the high-resolution cropland-extent map because all higher level cropland products, such as crop-watering method (that is, whether crops are irrigated or rainfed), crop types, cropping intensities, cropland fallows, crop productivity, and crop-water productivity, are dependent on a precise and accurate cropland-extent product. Given these realities, the overarching goal of this study was to produce a Landsat satellite-derived global cropland-extent product at 30-m resolution. The work, which involved a paradigm shift in how global cropland-extent maps are produced, involved the following five key steps: (1) petabyte-scale computing that involved multiyear, 8- to 16-day, time-series Landsat 30-m resolution data for the global land surface; (2) composition of analysis-ready data (ARD) cubes; (3) creation of a large global-reference data hub for machine learning; (4) use of multiple machine-learning algorithms (MLAs) by writing software and computing in the cloud; and (5) Google Earth Engine (GEE) cloud computing. The five key steps involved nine distinct phases. First, the world was segmented into 74 agroecological zones (AEZs). Second, Landsat 8- to 16-day data were used to time-composite 10-band (blue, green, red, near-infrared, short-wave infrared band 1, short-wave infrared band 2, thermal infrared, enhanced vegetation index, normalized difference water index, and normalized difference vegetation index) Landsat 30-m resolution data cubes for every 2- to 4-month time period during 3- to 4-year periods (stated as nominal-year 2015 or, simply, 2015), along with two additional 30-m resolution bands (Shuttle Radar Topography Mission elevation, and slope) in each of the 74 AEZs. Third, more than 100,000 reference-training data samples were collected using ground data (some of which were collected using a mobile application), as well as submeter- to 5-m-resolution, very high-resolution imagery sourced from other reliable sources. Fourth, reference-training data were used to create a knowledge base for separating cropland from noncropland. Fifth, MLAs such as the pixel-based supervised random forest and support-vector machines were written on the GEE using Python and JavaScript. Sixth, object-based recursive hierarchical segmentation algorithm was used, in addition to MLAs, to overcome uncertainties. Seventh, MLAs used the knowledge base to classify and separate cropland from noncropland. Eighth, accuracy assessment was conducted by generating error matrices for each of the 74 AEZs using 19,171 independent validation-data samples. Ninth, cropland areas were computed for all countries of the world and compared with United Nation’s (UN’s) Food and Agricultural Organization (FAO) and other national statistics. The outcome was a Landsat-derived global cropland-extent product at 30-m resolution (GCEP30), which has an overall accuracy of 91.7 percent. For the cropland class, producer’s accuracy was 83.4 percent, and user’s accuracy was 78.3 percent. GCEP30 calculated (using direct pixel count) the global net-cropland area (GNCA) for the year 2015 as 1.873 billion hectares (~12.6 percent of the Earth’s terrestrial area). The continental cropland distribution as a percentage of GNCA was Asia, 33 percent; Europe, 25.5 percent; Africa, 16.7 percent; North America, 14.4 percent; South America, 8.1 percent; and Australia and Oceania, 2.4 percent. The worldwide cropland areas in GCEP30 for 2015 were higher by 236 to 299 million hectares (Mha) compared to national statistics reported elsewhere for the same year (for example, in Food and Agriculture Organization’s corporate statistical database [FAOSTAT] and in the monthly irrigated and rainfed crop areas [MIRCA] database). The global cropland area reported for 2015 increased by 344 Mha (22.5 percent), compared to the year 2000. During the same period (2000–2015), the world’s population increased by 20 percent. Whereas some of these areal increases are real increases in cropland areas, others are due to the types of data, methods, and approaches used. Using the highest known resolution (compared to previous coarse-resolution global products) enabled this study to capture fragmented croplands. Coarse-resolution data compute areas on the basis of subpixels, which, for a large proportion of certain land use–land cover classes, will show only a certain percentage of the total pixel area as actual area. Subpixel areas can lead to substantial uncertainties in area computation, as determining the exact fraction of cropland areas within a coarse-resolution pixel is resource intensive and subject to errors. Other innovations in GCEP30 include reference-data hubs, machine learning, and cloud computing. Cropland areas in 214 countries, territories, departments, and regions were calculated for the year 2015 using GCEP30, on the basis of UN’s global administrative unit layers (GAUL) boundaries. The 10 leading countries in terms of cropland area (as a percentage of the GNCA) were India (9.6 percent), United States (8.95 percent), China (8.82 percent), Russia (8.32 percent), Brazil (3.42 percent), Ukraine (2.32 percent), Canada (2.29 percent), Argentina (2.05 percent), Indonesia (2 percent), and Nigeria (1.91 percent). Together, these 10 countries occupy 50 percent of the global cropland, and they have 52 percent of the global population. Their combined cropland area increased by 2 percent between 2000 and 2015, compared to the substantial increase in population of 517 million (15.5 percent). Together, India, United States, China, and Russia encompass 36 percent of the total area. In the United States and Canada, from 2000 to 2015, cropland decreased by about 2 percent, whereas their populations increased by 14 and 13 percent, respectively. The additional food requirements in these 10 countries, which are caused by increased populations, as well as increasing nutritional demands, are met by production increases in existing cropland or through virtual food trade, or both. More than 18 countries, territories, departments, or regions had 60 percent or more of their geographic area as cropland: Republic of Moldova, San Marino, and Hungary had more than 80 percent of the country’s area as cropland; Denmark, Ukraine, Ireland, and Bangladesh, 70 to 80 percent; and Uruguay, Netherlands, United Kingdom, Spain, Lithuania, Poland, Gaza Strip, Czechia, Italy, India, and Azerbaijan, 60 to 70 percent. Europe and South Asia can be considered agricultural capitals of the world, on the basis of their percentages of geographic area as cropland. United States, China, and Russia, which all have high cropland areas, are ranked second, third, and fourth in the world; India is ranked first. However, the amount of cropland as a percentage of the country’s geographic area is relatively very low for United States (18.3 percent), China (17.7 percent), and Russia (9.5 percent), whereas it is 60.5 percent for India. Most African and South American countries, territories, departments, or regions have less than 15 percent of their geographic area as cropland. China and India together house 36 percent of the world’s population; however, between 2000 and 2015, the amount of China’s cropland area fell by 18.9 percent, owing to urban expansion and the abandonment of farmlands caused by demographic changes (that is, the movement of population from villages to cities). In contrast, China’s population grew by 10 percent. The amount of India’s cropland increased by 8.5 percent, whereas its population grew by 20 percent. This study showed that, out of the 10 leading cropland countries, Ukraine, Nigeria, Russia, and Indonesia showed an 18 to 31 percent increase in cropland areas, on the basis of GCEP30 by the year 2015, compared to 2000. Nigeria’s cropland area increased by 25 percent, and its population increased by 31 percent in the same period. In these countries, food security is maintained by cropland expansion, productivity increases, and virtual food trade. Nevertheless, this trend of increasing net-cropland area and productivity will likely become difficult to maintain, owing to diminishing arable lands and plateauing of 50 years of continual yield increases, requiring policymakers to explore novel and data-supported approaches to solving future food security issues. The GCEP30 product, which can be browsed at full resolution at www.croplands.org , has been released for public download and use through U.S. Geological Survey (USGS)–National Aeronautics and Space Administration (NASA) Land Processes Distributed Active Archive Center (see https://lpdaac.usgs.gov/news/release-of-gfsad-30-meter-cropland-extent-products/ ).
Professional Paper↗
Preliminary comparison of landscape pattern-normalized difference vegetation index (NDVI) relationships to central plains stream conditions
We explored relationships of water quality parameters with landscape pattern metrics (LPMs), land use–land cover (LULC) proportions, and the advanced very high resolution radiometer (AVHRR) normalized difference vegetation index (NDVI) or NDVI-derived metrics. Stream sites (271) in Nebraska, Kansas, and Missouri were sampled for water quality parameters, the index of biotic integrity, and a habitat index in either 1994 or 1995. Although a combination of LPMs (interspersion and juxtaposition index, patch density, and percent forest) within Ozark Highlands watersheds explained >60% of the variation in levels of nitrite–nitrate nitrogen and conductivity, in most cases the LPMs were not significantly correlated with the stream data. Several problems using landscape pattern metrics were noted: small watersheds having only one or two patches, collinearity with LULC data, and counterintuitive or inconsistent results that resulted from basic differences in land use–land cover patterns among ecoregions or from other factors determining water quality. The amount of variation explained in water quality parameters using multiple regression models that combined LULC and LPMs was generally lower than that from NDVI or vegetation phenology metrics derived from time-series NDVI data. A comparison of LPMs and NDVI indicated that NDVI had greater promise for monitoring landscapes for stream conditions within the study area.
Kansas, Missouri, Nebraska↗
Pragmatically mapping Phragmites with unoccupied aerial systems: A comparison of invasive species land cover classification using RGB and multispectral imagery
Unoccupied aerial systems (UASs) are increasingly being deployed in coastal environments to rapidly map and monitor changes to geomorphology, vegetation, and infrastructure, particularly in difficult to access areas. UAS data, relative to airplane or satellite data, typically have higher spatial resolution, sensor customization, and increased flexibility in temporal resolution, which benefits monitoring applications. UAS data have been used to map and monitor invasive species occurrence and expansion, such as Phragmites australis , a reed species in wetlands throughout the eastern United States. To date, the work on this species has been largely opportunistic or ad hoc. Here, we statistically and qualitatively compare results from several sensors and classification workflows to develop baseline understanding of the accuracy of different approaches used to map Phragmites . Two types of UAS imagery were collected in a Phragmites -invaded salt marsh setting—natural color red-green-blue (RGB) imagery and multispectral imagery spanning visible and near infrared wavelengths. We evaluated whether one imagery type provided significantly better classification results for mapping land cover than the other, also considering trade-offs like overall accuracy, financial costs, and effort. We tested the transferability of classification workflows that provided the highest thematic accuracy to another barrier island environment with known Phragmites stands. We showed that both UAS sensor types were effective in classifying Phragmites cover, with neither resulting in significantly better classification results than the other for Phragmites detection (overall accuracy up to 0.95, Phragmites recall up to 0.86 at the pilot study site). We also found the highest accuracy workflows were transferrable to sites in a barrier island setting, although the quality of results varied across these sites (overall accuracy up to 0.97, Phragmites recall up to 0.90 at the additional study sites).
Massachusetts↗
Exploring landscape and geologic controls on spatial patterning of streambank groundwater discharge in a mixed land use watershed
Preferential groundwater discharge features along stream corridors are ecologically important at local and stream network scales, yet we lack quantification of the multiscale controls on the spatial patterning of groundwater discharge. Here we identify physical attributes that best explain variation in the presence and lateral extent of preferential groundwater discharges along two 5th order streams, the Housatonic and Farmington Rivers, and 32 1st to 4th order reaches across the Farmington River network. We mapped locations of preferential groundwater discharge exposed along streambanks using handheld thermal infrared cameras paired with high-resolution topographic and land use land cover datasets, surficial soil characteristic maps, and depth-to-bedrock geophysical measurements. The unconfined Housatonic River, MA, USA (12 km) had fewer discharge locations and less lateral extent (41 discharge locations with 38 m of active discharge/km of river) compared to the partially confined Farmington River, CT, USA (26 km; 169 discharge locations with 129 m of active discharge/km of river). Using a moving window analysis, we found along both rivers that discharge was more likely to occur where bank slopes were steeper, floodplain extent was narrower, and degree of confinement was higher. Along the Farmington River, groundwater discharge was more likely to occur where saturated hydraulic conductivity was higher and depth-to-bedrock was shallower. Among the 32 stream reaches surveyed (33.2 km of total stream length) within the Farmington River watershed, preferential discharge was observed in all but two stream reaches, varied from 0 to 25% of lateral extent along stream banks (mean = 6%), and was more likely to occur where stream reach slopes were steep, saturated hydraulic conductivity was high, and watershed urbanization was low. Our results show that, though both surface (e.g., topographic, land use land cover) and subsurface (e.g., soil characteristics, bedrock depth) factors control the prevalence of streambank preferential groundwater discharge, the dominant controls vary across valley settings and stream sizes.
Connecticut, Massachusetts↗
Regional forest land cover characterisation using medium spatial resolution satellite data
Increasing demands on forest resources require comprehensive, consistent and up-to-date information on those resources at spatial scales appropriate for management decision-making and for scientific analysis. While such information can be derived using coarse spatial resolution satellite data (e.g. Tucker et al. 1984; Zhu and Evans 1994; Cihlar et al. 1996; Cihlar et al., Chapter 12), many regional applications require more spatial and thematic details than can be derived by using coarse resolution imagery. High spatial resolution satellite data such as IKONOS and Quick Bird images (Aplin et al. 1997), though usable for deriving detailed forest information (Culvenor, Chapter 9), are currently not feasible for wall-to-wall regional applications because of extremely high data cost, huge data volume, and lack of contiguous coverage over large areas. Forest studies over large areas have often been accomplished using data acquired by intermediate spatial resolution sensor systems, including the Multi-Spectral Scanner (MSS), Thematic Mapper (TM) and the Enhanced Thematic Mapper Plus (ETM+) of Landsat, the High Resolution Visible (HRV) of the Systeme Pour l'Observation de la Terre (SPOT), and the Linear Image Self-Scanner (LISS) of the Indian Remote Sensing satellite. These sensor systems are more appropriate for regional applications because they can routinely produce spatially contiguous data over large areas at relatively low cost, and can be used to derive a host of forest attributes (e.g. Cohen et al. 1995; Kimes et al. 1999; Cohen et al. 2001; Huang et al. 2001; Sugumaran 2001). Of the above intermediate spatial resolution satellites, Landsat is perhaps the most widely used in various types of land remote sensing applications, in part because it has provided more extensive spatial and temporal coverage of the globe than any other intermediate resolution satellite. Spatially contiguous Landsat data have been developed for many regions of the globe (e.g. Lunetta and Sturdevant 1993; Fuller et al. 1994b; Skole et al. 1997), and a circa 1990 Landsat image data set covering the entire land area of the globe has also been developed recently (Jones and Smith 2001). An acquisition strategy aimed at acquiring at least one cloud free image per year for the entire land area of the globe has been initiated for Landsat-7 (Arvidson et al. 2001). This will probably ensure the continued dominance of Landsat in the near future.
Book chapter↗
Landscape characteristics affecting streams in urbanizing regions of the Delaware River Basin (New Jersey, New York, and Pennsylvania, U.S.)
Widespread and increasing urbanization has resulted in the need to assess, monitor, and understand its effects on stream water quality. Identifying relations between stream ecological condition and urban intensity indicators such as impervious surface provides important, but insufficient information to effectively address planning and management needs in such areas. In this study we investigate those specific landscape metrics which are functionally linked to indicators of stream ecological condition, and in particular, identify those characteristics that exacerbate or mitigate changes in ecological condition over and above impervious surface. The approach used addresses challenges associated with redundancy of landscape metrics, and links landscape pattern and composition to an indicator of stream ecological condition across a broad area of the eastern United States. Macroinvertebrate samples were collected during 2000-2001 from forty-two sites in the Delaware River Basin, and landscape data of high spatial and thematic resolution were obtained from photointerpretation of 1999 imagery. An ordination-derived 'biotic score' was positively correlated with assemblage tolerance, and with urban-related chemical characteristics such as chloride concentration and an index of potential pesticide toxicity. Impervious surface explained 56% of the variation in biotic score, but the variation explained increased to as high as 83% with the incorporation of a second land use, cover, or configuration metric at catchment or riparian scales. These include land use class-specific cover metrics such as percent of urban land with tree cover, forest fragmentation metrics such as aggregation index, riparian metrics such as percent tree cover, and metrics related to urban aggregation. Study results indicate that these metrics will be important to monitor in urbanizing areas in addition to impervious surface. ?? 2010 US Government.
Landscape Ecology↗
A land-use and land-cover modeling strategy to support a national assessment of carbon stocks and fluxes
Changes in land use, land cover, disturbance regimes, and land management have considerable influence on carbon and greenhouse gas (GHG) fluxes within ecosystems. Through targeted land-use and land-management activities, ecosystems can be managed to enhance carbon sequestration and mitigate fluxes of other GHGs. National-scale, comprehensive analyses of carbon sequestration potential by ecosystem are needed, with a consistent, nationally applicable land-use and land-cover (LULC) modeling framework a key component of such analyses. The U.S. Geological Survey has initiated a project to analyze current and projected future GHG fluxes by ecosystem and quantify potential mitigation strategies. We have developed a unique LULC modeling framework to support this work. Downscaled scenarios consistent with IPCC Special Report on Emissions Scenarios (SRES) were constructed for U.S. ecoregions, and the FORE-SCE model was used to spatially map the scenarios. Results for a prototype demonstrate our ability to model LULC change and inform a biogeochemical modeling framework for analysis of subsequent GHG fluxes. The methodology was then successfully used to model LULC change for four IPCC SRES scenarios for an ecoregion in the Great Plains. The scenario-based LULC projections are now being used to analyze potential GHG impacts of LULC change across the U.S.
Applied Geography↗
Urban effects on regional climate: a case study in the Phoenix and Tucson ‘sun’ corridor
Land use and land cover change (LULCC) due to urban expansion alter the surface albedo, heat capacity, and thermal conductivity of the surface. Consequently, the energy balance in urban regions is different from that of natural surfaces. To evaluate the changes in regional climate that could arise due to projected urbanization in the Phoenix-Tucson corridor, Arizona, we applied the coupled WRF-NOAH-UCM (which includes a detailed urban radiation scheme) to this region. Land cover changes were represented using land cover data for 2005 and projections to 2050, and historical North American Regional Reanalysis (NARR) data were used to specify the lateral boundary conditions. Results suggest that temperature changes will be well defined, reflecting the urban heat island (UHI) effect within areas experiencing LULCC. Changes in precipitation are less robust, but seem to indicate reductions in precipitation over the mountainous regions northeast of Phoenix and decreased evening precipitation over the newly-urbanized area.
Earth Interactions↗