Ross S. Lunetta

Regional Scale Land Cover Characterization Using MODIS-NDVI 250 m Multi-Temporal Imagery: A Phenology-Based Approach

Currently available land cover data sets for large geographic regions are produced on an intermittent basis and are often dated. Ideally, annually updated data would be available to support environmental status and trends assessments and ecosystem process modeling. This research examined the potential for vegetation phenology-based land cover classification over the 52,000 km2 Albemarle-Pamlico estuarine system (APES) that could be performed annually. Traditional hyperspectral image classification techniques were applied using MODIS-NDVI 250 m 16-day composite data over calendar year 2001 to support the multi-temporal image analysis approach. A reference database was developed using archival aerial photography that provided detailed mixed pixel cover-type data for 31,322 sampling sites corresponding to MODIS 250 m pixels. Accuracy estimates for the classification indicated that the overall accuracy of the classification ranged from 73% for very heterogeneous pixels to 89% when only homogeneous pixels were examined. These accuracies are comparable to similar classifications using much higher spatial resolution data, which indicates that there is significant value added to relatively coarse resolution data though the addition of multi-temporal observations.

Remote sensing and GIS accuracy assessment

Putting the Map Back in Map Accuracy Assessment Russell G. Congalton Sampling Design for Accuracy Assessment of Large-Area, Land-Cover Maps: Challenges and Future Directions Stephen V. Stehman Validation of Global Land-Cover Products by the Committee on Earth Observing Satellites Jeffrey T. Morisette, Jeffrey L. Privette, Alan Strahler, Philippe Mayaux, and Christopher O. Justice In Situ Estimates of Forest LAI for MODIS Data Validation John S. Iiames, Jr., Andrew N. Pilant, and Timothy E. Lewis Light Attenuation Profiling as an Indicator of Structural Changes in Coastal Marshes Elijah Ramsey III, Gene Nelson, Frank Baarnes, and Ruth Spell Participatory Reference Data Collection Methods for Accuracy Assessment of Land-Cover Change Maps John Sydenstricker-Neto, Andrea Wright Parmenter, and Stephen D. DeGloria Thematic Accuracy Assessment of Regional Scale Land-Cover Data Siamak Khorram, Joseph F. Knight, and Halil I. Cakir An Independent Reliability Assessment for the Australian Agricultural Land-Cover Change Project 1990/91-1995 Michele Barson, Vivienne Bordas, Kim Lowell, and Kim Malafant Assessing the Accuracy of Satellite-Derived Land-Cover Classification Using Historical Aerial Photography, Digital Orthophoto Quadrangles, and Airborne Video Data Susan M. Skirvin, William G. Kepner, Stuart E. Marsh, Samuel E. Drake, John K. Maingi, Curtis M. Edmonds, Christopher J. Watts, and David R. Williams Using Classification Consistency in Interscene Overlap Areas to Model Spatial Variations in Land-Cover Accuracy over Large Geographic Regions Bert Guindon and Curtis M. Edmonds Geostatistical Mapping of Thematic Classification Uncertainty Phaedon C. Kyriakidis, Xiaohang Liu, and Michael F. Goodchild An Error Matrix Approach to Fuzzy Accuracy Assessment: The NIMA Geocover Project Kass Green and Russell G. Congalton Mapping Spatial Accuracy and Estimating Landscape Indicators from Thematic Land-Cover Maps Using Fuzzy Set Theory Liem T. Tran, S. Taylor Jarnagin, C. Gregory Knight, and Latha Baskaran Fuzzy Set and Spatial Analysis Techniques for Evaluating Thematic Accuracy of a Land-Cover Map Sarah R. Falzarano and Kathryn A. Thomas The Effects of Classification Accuracy on Landscape Indices Guofan Shao and Wenchun Wu Assessing Uncertainty in Spatial Landscape Metrics Derived from Remote Sensing Data Daniel G. Brown, Elisabeth A. Addink, Jiunn-Der Duh, and Mark A. Bowersox Components of Agreement Between Categorical Maps at Multiple Resolutions R. Gil Pontius, Jr. and Beth Suedmeyer Accuracy Assessments of Airborne Hyperspectral Data for Mapping Opportunistic Plant Species in Freshwater Coastal Wetlands Ricardo D. Lopez, Curtis M. Edmonds, Anne C. Neale, Terrence Slonecker, K. Bruce Jones, Daniel T. Heggem, John G. Lyon, Eugene Jaworski, Donald Garofalo, and David Williams A Technique for Assessing the Accuracy of Subpixel Impervious Surface Estimates Derived from Landsat TM Imagery S. Taylor Jarnagin, David B. Jennings, and Donald W. Ebert Area and Positional Accuracy of DMSP Nighttime Lights Data Christopher D. Elvidge, Jeffrey Safran, Ingrid L. Nelson, Benjamin T. Tuttle, Vinita Ruth Hobson, Kimberly E. Baugh, John B. Dietz, and Edward H. Erwin

Impacts of vegetation dynamics on the identification of land-cover change in a biologically complex community in North Carolina, USA

Abstract A land-cover (LC) change detection experiment was performed in the biologically complex landscape of the Neuse River Basin (NRB), North Carolina using Landsat 5 and 7 imagery collected in May of 1993 and 2000. Methods included pixel-wise Normalized Difference Vegetation Index (NDVI) and Multiband Image Difference (MID) techniques. The NDVI method utilized non-normalized (raw) imagery data, while the MID method required normalized imagery. Image normalization techniques included both automatic scattergram-controlled regression (ASCR) and localized relative radiometric normalization (LRRN) techniques. Change/no-change thresholds for each method were optimized using calibration curves developed from reference data and a series of method-specific binary change masks. Cover class-specific thresholds were derived for each of the four methods using a previously developed NRB-LC classification (1998–1999) to support data stratification. An independent set of accuracy assessment points was selected using a disproportionate stratified sampling strategy to support the development of error matrices. Area-weighted conditional probability accuracy statistics were calculated based on the areal extent of change and no change for each cover class. All methods tested exhibited acceptable accuracies, ranging between 80% and 91%. However, change omission errors for woody cover types were unacceptably high, with values ranging between 60% and 79%. Overall commission errors in the change category were also high (42–51%) and strongly affected by the agriculture class. There were no significant differences in the Kappa coefficient between the NDVI, MID ASCR, and LRRN normalization methods. The MID non-normalized method was inferior to both the NDVI and MID ASCR methods. Stratification by major LC type had no effect on overall accuracies, regardless of method.

Mapping Cropland and Major Crop Types across the Great Lakes Basin using MODIS-NDVI Data

This research evaluated the potential for using the MODIS Normalized Difference Vegetation Index (NDVI) 16-day composite (MOD13Q) 250 m time-series data to develop an annual crop type mapping capability throughout the 480,000 km 2 Great Lakes Basin (GLB). An ecoregion-stratified approach was developed using a two-step processing approach that included an initial differentiation of cropland versus non-cropland and subsequent identification of individual crop types. Major crop types were mapped for the calendar years of 2002 and 2007. National Agricultural Statistics Service (NASS) census data were used to assess county level accuracies on a unit area basis (2002), and the NASS Crop Data Layer (C DL ) was used to generate 231,616 reference data points to support a pixel-wise assessment of the MODIS crop type classification (2007) accuracy across the US portion of the CLB. County level comparisons for 2002 indicated 2.2, ―6.8, ―6.0, and ―5.8 percent of area bias errors for corn, soybeans, wheat, and hay, respectively. Detailed pixel-wise accuracy assessments resulted in an overall crop type classification accuracy of 84 percent (Kappa = 0.73) for 2007. Kappa coefficients ranged from 0.74 to 0.69 for individual ecoregions. The users accuracies for corn, soybean, wheat, and hay were 87, 82, 81, and 70 percent, respectively. There were spatial variations of classification performances across ecoregions, especially for soybean and hay. Field sizes had a direct impact on the variable classification performances across the GLB.

Sub-Pixel Mapping of Tree Canopy, Impervious Surfaces, and Cropland in the Laurentian Great Lakes Basin Using MODIS Time-Series Data

This research examined sub-pixel land-cover classification performance for tree canopy, impervious surface, and cropland in the Laurentian Great Lakes Basin (GLB) using both time-series MODIS (Moderate Resolution Imaging Spectro radiometer) NDVI (Normalized Difference Vegetation Index) and surface reflectance data. Classification training strategies included both an entire-region approach and an ecoregion-stratified approach, using multi-layer perceptron neural network classifiers. Although large variations in classification performances were observed for different ecoregions, the ecoregion-stratified approach did not significantly improve classification accuracies. Sub-pixel classification performances were largely dependent on different types of MODIS input datasets. Overall, the combination of MODIS surface reflectance bands 1-7 generated the best sub-pixel estimations of tree canopy (R2 = 0.57), impervious surface (R2 = 0.63) and cropland (R2 = 0.30), which are considerable higher than those derived using only MODIS-NDVI data (tree canopy R2 = 0.50, impervious surface R2 = 0.51, and cropland R2 = 0.24). Also, sub-pixel classification accuracies were much improved when the results were aggregated from 250 m to 500 m spatial resolution. The use of individual date MODIS images were also examined with the best results being achieved for Julian days 185 (early July), 217 (early August), and 113 (late April) for tree canopy, impervious surface, and cropland, respectively. The results suggested the relative importance of the image data input selection, spatial resolution, and acquisition dates for the sub-pixel mapping of major cover types in the GLB.

An experimental assessment of minimum mapping unit size

Land-cover (LC) maps derived from remotely sensed data are often presented using a minimum mapping unit (MMU) to characterize a particular landscape theme of interest. The choice of an MMU that is appropriate for the projected use of a classification is an important consideration. The objective of this experiment was to determine the effect of MMU on a LC classification of the Neuse River Basin (NRB) in North Carolina. The results of this work indicate that MMU size had a significant effect on accuracy estimates only when the MMU was changed by relatively large amounts. Typically, an MMU is selected as close as possible to the original data resolution so as to reduce the loss of specificity introduced in the resampling process. Since only large MMU changes resulted in significant differences in the accuracy estimates, an analyst may have the flexibility to select from a range of MMUs that are appropriate for a given application.

Barriers to adopting satellite remote sensing for water quality management

Sustainable practices require a long-term commitment to creating solutions to environmental, social, and economic issues. The most direct way to ensure that management practices achieve sustainability is to monitor the environment. Remote sensing technology has the potential to accelerate the engagement of communities and managers in the implementation and performance of best management practices. Over the last few decades, satellite technology has allowed measurements on a global scale over long time periods, and is now proving useful in coastal waters, estuaries, lakes, and reservoirs, which are relevant to water quality managers. Comprehensive water quality climate data records have the potential to provide rapid water quality assessments, thus providing new and enhanced decision analysis methodologies and improved temporal/spatial diagnostics. To best realize the full application potential of these emerging technologies an open and effective dialogue is needed between scientists, policy makers, environmental managers, and stakeholders at the federal, state, and local levels. Results from an internal US Environmental Protection Agency qualitative survey were used to determine perceptions regarding the use of satellite remote sensing for monitoring water quality. The goal of the survey was to begin understanding why management decisions do not typically rely on satellite-derived water quality products.

Remote sensing of selected water-quality indicators with the hyperspectral imager for the coastal ocean HICO sensor

The Hyperspectral Imager for the Coastal Ocean (HICO) offers the coastal environmental monitoring community an unprecedented opportunity to observe changes in coastal and estuarine water quality across a range of spatial scales not feasible with traditional field-based monitoring or existing ocean colour satellites. HICO, an Office of Naval Research-sponsored programme, is the first space-based maritime hyperspectral imaging instrument designed specifically for the coastal ocean. HICO has been operating since September 2009 from the Japanese Experiment Module – Exposed Facility on the International Space Station (ISS). The high pixel resolution (approximately 95 m at nadir) and hyperspectral imaging capability offer a unique opportunity for characterizing a wide range of water colour constituents that could be used to assess environmental condition. In this study, we transform atmospherically corrected ISS/HICO hyperspectral imagery and derive environmental response variables routinely used for evaluating the environmental condition of coastal ecosystem resources. Using atmospherically corrected HICO imagery and a comprehensive field validation programme, three regionally specific algorithms were developed to estimate basic water-quality properties traditionally measured by monitoring agencies. Results indicated that a three-band chlorophyll a algorithm performed best (R2 = 0.62) when compared with in situ measurement data collected 2–4 hours of HICO acquisitions. Coloured dissolved organic matter (CDOM) (R2 = 0.93) and turbidity (R2 = 0.67) were also highly correlated. The distributions of these water-quality indicators were mapped for four estuaries along the northwest coast of Florida from April 2010 to May 2012. However, before the HICO sensor can be transitioned from proof-of-concept to operational status and its data applied to benefit decisions made by coastal managers, problems with vicarious calibration of the sensor need to be resolved and standardized protocols are required for atmospheric correction. Ideally, the sensor should be placed on a polar orbiting platform for greater spatial and temporal coverage as well as for image synchronization with field validation efforts.

Measurement of water colour using AVIRIS imagery to assess the potential for an operational monitoring capability in the Pamlico Sound Estuary, USA

The monitoring of water colour parameters can provide an important diagnostic tool for the assessment of aquatic ecosystem condition. Remote sensing has long been used to effectively monitor chlorophyll concentrations in open ocean systems; however, operational monitoring in coastal and estuarine areas has been limited because of the inherent complexities of coastal systems, and the coarse spectral and spatial resolutions of available satellite systems. Data were collected using the National Aeronautics and Space Administration (NASA) Advanced Visible–Infrared Imaging Spectrometer (AVIRIS) flown at an altitude of approximately 20 000 m to provide hyperspectral imagery and simulate both MEdium Resolution Imaging Spectrometer (MERIS) and Moderate Resolution Imaging Spectrometer (MODIS) data. AVIRIS data were atmospherically corrected using a radiative transfer modelling approach and analysed using band ratio and linear regression models. Regression analysis was performed with simultaneous field measurements data in the Neuse River Estuary (NRE) and Pamlico Sound on 15 May 2002. Chlorophyll a (Chl a) concentrations were optimally estimated using AVIRIS bands (9.5 nm) centred at 673.6 and 692.7 nm, resulting in a coefficient of determination (R 2) of 0.98. Concentrations of Chromophoric Dissolved Organic Matter (CDOM), Total Suspended Solids (TSS) and Fixed Suspended Solids (FSS) were also estimated, resulting in coefficients of determination of R 2 = 0.90, 0.59 and 0.64, respectively. Ratios of AVIRIS bands centred at or near those corresponding to the MERIS and MODIS sensors indicated that relatively good satellite‐based estimates could potentially be derived for water colour constituents at a spatial resolution of 300 and 500 m, respectively. **Current address: University of Minnesota, Department of Forest Resources, St Paul, MN 55108, USA.The monitoring of water colour parameters can provide an important diagnostic tool for the assessment of aquatic ecosystem condition. Remote sensing has long been used to effectively monitor chlorophyll concentrations in open ocean systems; however, operational monitoring in coastal and estuarine areas has been limited because of the inherent complexities of coastal systems, and the coarse spectral and spatial resolutions of available satellite systems. Data were collected using the National Aeronautics and Space Administration (NASA) Advanced Visible-Infrared Imaging Spectrometer (AVIRIS) flown at an altitude of approximately 20000 m to provide hyperspectral imagery and simulate both MEdium Resolution Imaging Spectrometer (MERIS) and Moderate Resolution Imaging Spectrometer (MODIS) data. AVIRIS data were atmospherically corrected using a radiative transfer modelling approach and analysed using band ratio and linear regression models. Regression analysis was performed with simultaneous field measurements data in the Neuse River Estuary (NRE) and Pamlico Sound on 15 May 2002. Chlorophyll a (Chl a) concentrations were optimally estimated using AVIRIS bands (9.5 nm) centred at 673.6 and 692.7 nm, resulting in a coefficient of determination (R2) of 0.98. Concentrations of Chromophoric Dissolved Organic Matter (CDOM), Total Suspended Solids (TSS) and Fixed Suspended Solids (FSS) were also estimated, resulting in coefficients of determination of R2=0.90, 0.59 and 0.64, respectively. Ratios of AVIRIS bands centred at or near those corresponding to the MERIS and MODIS sensors indicated that relatively good satellite-based estimates could potentially be derived for water colour constituents at a spatial resolution of 300 and 500 m, respectively.

A Quantitative Assessment of a Combined Spectral and GIS Rule-Based Land-Cover Classification in the Neuse River Basin of North Carolina

mentation processes. Of particular importance, is the applicaThe 14,582 km 2 Neuse River Basin in North Carolina was tion of LC data for the generation of landscape-based assesscharacterized based on a user-defined land-cover (LC) classi- ment metrics to evaluate relative ecosystem condition over a fication system developed specifically to support spatially wide range of analysis scales (i.e., watershed to national) to asexplicit, non-point source nitrogen allocation modeling studies. sess impacts attributable to human land-use activities (WickData processing incorporated both spectral and GIS rule-based ham and Norton, 1994; Jones et al., 1997; Riitters et al., 1997). analytical techniques using multiple date SPOT 4 (XS), Landsat Currently, high priority non-point-source (NPS) issues are 7( ETM + ), and ancillary data sources. Unique LC classification focused on nutrient and sediment transport from the landscape elements included the identification of urban classes based to receiving streams. These NPS loadings are used to support the on impervious surfaces and specific row crop type identifi- development of total maximum daily loads (TMDL) determinacations. Individual pixels were aggregated to produce variable tions of streams and rivers (USEPA, 1999). These dynamic, ecominimum mapping units or landscape “patches” correspond- system NPS processes function at multiple analytical scales ing to both riparian buffer zones (0.1 ha), and general watershed and require relatively high-resolution geospatial data to supareas (0.4 ha). An accuracy assessment was performed using port watershed-scale modeling efforts. Landscape parameters reference data derived from in situ field measurements and required to support these spatially explicit modeling approaches, imagery (camera) data. Multiple data interpretations were used include the identification and delineation of individual LC eleto develop a reference database with known data variability ments or “patches.” Landscape “patches” typically represent to support a quantitative accuracy assessment of LC classi- the primary modeling unit of a spatially explicit landscape fication results. Confusion matrices were constructed to incor- model. They are defined in this study as contiguous and relaporate the variability of the reference data directly in the tively homogeneous LC types that can be repetitively mapped accuracy assessment process. Accuracies were reported for using remote sensor data. hierarchal classification levels with overall Level 1 classiThe characterization of riparian buffer zones is required to fication accuracy of 82 percent (n 825) for general watershed evaluate their functional capacity and ecosystem value. Typiareas, and 73 percent (n 391) for riparian buffer zone cally, riparian buffer zones are defined as areas directly adjacent locations. A Kappa Test Z statistic of 3.3 indicated a significant to the top-of-the-stream bank and extending outward in a perdifference between the two results. Classes that performed pendicular direction for a distance of approximately 20 to 30 m. poorly were largely associated with the confusion of herba- Riparian buffer zones play important functional roles in nutriceous classes with both urban and agricultural areas.