AVHRR channel selection for land cover classification

Mapping land cover of large regions often requires processing of satellite images collected from several time periods at many spectral wavelength channels. However, manipulating and processing large amounts of image data increases the complexity and time, and hence the cost, that it takes to produce a land cover map. Very few studies have evaluated the importance of individual Advanced Very High Resolution Radiometer (AVHRR) channels for discriminating cover types, especially the thermal channels (channels 3, 4 and 5). Studies rarely perform a multi-year analysis to determine the impact of inter-annual variability on the classification results. We evaluated 5 years of AVHRR data using combinations of the original AVHRR spectral channels (1-5) to determine which channels are most important for cover type discrimination, yet stabilize inter-annual variability. Particular attention was placed on the channels in the thermal portion of the spectrum. Fourteen cover types over the entire state of Colorado were evaluated using a supervised classification approach on all two-, three-, four- and five-channel combinations for seven AVHRR biweekly composite datasets covering the entire growing season for each of 5 years. Results show that all three of the major portions of the electromagnetic spectrum represented by the AVHRR sensor are required to discriminate cover types effectively and stabilize inter-annual variability. Of the two-channel combinations, channels 1 (red visible) and 2 (near-infrared) had, by far, the highest average overall accuracy (72.2%), yet the inter-annual classification accuracies were highly variable. Including a thermal channel (channel 4) significantly increased the average overall classification accuracy by 5.5% and stabilized interannual variability. Each of the thermal channels gave similar classification accuracies; however, because of the problems in consistently interpreting channel 3 data, either channel 4 or 5 was found to be a more appropriate choice. Substituting the thermal channel with a single elevation layer resulted in equivalent classification accuracies and inter-annual variability.     

Land cover changes around a major east African wildlife reserve: The Mara Ecosystem (Kenya)

The dynamics of savannah vegetation are still poorly understood. This study aims at analysing land cover changes over the past 20 years in the rangelands area of Narok District, Kenya. To analyse the impact of inter-annual climate variability and human activities on land cover modifications in the area, change detection techniques based on remote sensing data at different spatial and temporal resolutions were used. Coarse spatial, high temporal resolution NOAA (National Oceanic and Atmospheric Administration) data were analysed to investigate the role of inter-annual climate variations on the ecosystem. A combination of time contextual and spatial contextual change detection approaches was used on a set of three high spatial resolution Landsat images to map land cover modifications over the past 20 years. Both datasets are highly complementary in the detection of land cover dynamics. On the one hand, the coarse spatial resolution data detected areas that are sensitive to inter-annual climate fluctuations, but are not subjected to land cover conversion. On the other hand, the high spatial resolution data allowed the detection of land cover conversions or modifications between two consecutive dates that have a more permanent character and are independent of climate-induced fluctuations in surface attributes.     

Monitoring land cover change and ecological integrity in Canada's national parks

Canada's national parks system includes 43 terrestrial parks covering 3% (276,275 km²) of the country's landmass and representing its full range of natural regions. Considering the vast and often remote areas under protection, Parks Canada Agency envisions Earth Observation technology to be the basis for a Park Ecological Integrity Observing System (Park-EIOS), and integral component of a larger national parks ecological integrity (EI) monitoring program. Park-EIOS is planned for operational use beginning in 2008 and includes coarse filter EI indicators corresponding to landscape pattern, succession and retrogression, net primary productivity, and focal species distributions within parks and their surrounding greater park ecosystems. A primary input to produce all four indicators is a time series of land cover information derived from medium (~ 30 m) resolution, Landsat-class sensors. This paper describes a generic, end-to-end change detection framework developed for Park-EIOS, labelled Automated Multi-temporal Updating through Signature Extension (AMUSE). AMUSE involves radiometric normalization steps, production of a baseline land cover, change vector analysis to identify changed pixels, and a new constrained signature extension approach to update the land cover of changed areas. We present the method and results applied to six pilot parks using time series of Landsat TM/ETM+ imagery from 1985-2005.     

The effects of aggregated land cover data on estimating NPP in northern Wisconsin

Ecosystem models are routinely used to estimate net primary production (NPP) from the stand to global scales. Complex ecosystem models, implemented at small scales (< 10 km²), are impractical at global scales and, therefore, require simplifying logic based on key ecological first principles and model drivers derived from remotely sensed data. There is a need for an improved understanding of the factors that influence the variability of NPP model estimates at different scales so we can improve the accuracy of NPP estimates at the global scale. The objective of this study was to examine the effects of using leaf area index (LAI) and three different aggregated land cover classification products-two factors derived from remotely sensed data and strongly affect NPP estimates-in a light use efficiency (LUE) model to estimate NPP in a heterogeneous temperate forest landscape in northern Wisconsin, USA. Three separate land cover classifications were derived from three different remote sensors with spatial resolutions of 15, 30, and 1000 m. Average modeled net primary production (NPP) ranged from 402 gC m^− 2 year^− 1 (15 m data) to 431 gC m^− 2 year^− 1 (1000 m data), for a maximum difference of 7%. Almost 50% of the difference was attributed each to LAI estimates and land cover classifications between the fine and coarse scale NPP estimate. Results from this study suggest that ecosystem models that use biome-level land cover classifications with associated LUE coefficients may be used to model NPP in heterogeneous land cover areas dominated by cover types with similar NPP. However, more research is needed to examine scaling errors in other heterogeneous areas and NPP errors associated with deriving LAI estimates.     

AVHRR composite period selection for land cover classification

Multitemporal satellite image datasets provide valuable information on the phenological characteristics of vegetation, thereby significantly increasing the accuracy of cover type classifications compared to single date classifications. However, the processing of these datasets can become very complex when dealing with multitemporal data combined with multispectral data. Advanced Very High Resolution Radiometer (AVHRR) biweekly composite data are commonly used to classify land cover over large regions. Selecting a subset of these biweekly composite periods may be required to reduce the complexity and cost of land cover mapping. The objective of our research was to evaluate the effect of reducing the number of composite periods and altering the spacing of those composite periods on classification accuracy. Because inter-annual variability can have a major impact on classification results, 5 years of AVHRR data were evaluated. AVHRR biweekly composite images for spectral channels 1-4 (visible, nearinfrared and two thermal bands) covering the entire growing season were used to classify 14 cover types over the entire state of Colorado for each of five different years. A supervised classification method was applied to maintain consistent procedures for each case tested. Results indicate that the number of composite periods can be halved-reduced from 14 composite dates to seven composite dates-without significantly reducing overall classification accuracy (80.4% Kappa accuracy for the 14-composite dataset as compared to 80.0% for a seven-composite dataset). At least seven composite periods were required to ensure the classification accuracy was not affected by inter-annual variability due to climate fluctuations. Concentrating more composites near the beginning and end of the growing season, as compared to using evenly spaced time periods, consistently produced slightly higher classification values over the 5 years tested (average Kappa of 80.3% for the heavy early/late case as compared to 79.0% for the alternate dataset case).     

Application of knowledge for automated land cover change monitoring

This paper outlines an approach for updating baseline land cover datasets. Knowledge about land cover, as used during manual mapping, is combined with simple remote sensing analyses to determine land cover change direction. The philosophy is to treat reflectance data as one source of information about land cover features. Applying expert knowledge with reflectance and biogeographical data allows generic solutions to the problem. The approach is demonstrated in areas of semi-natural vegetation and shown to differentiate ecologically subtle but spectrally similar land cover classes. Further, the advantages of manual mapping techniques and of high resolution remotely sensed imagery are combined. This approach is suitable for incorporation into automated approaches: it makes no assumption about the distribution of land cover features, can be applied to different remotely sensed data and is not classification specific. It has been incorporated into SYMOLAC, an expert system for monitoring land cover change.     

Dasymetric modelling of small-area population distribution using land cover and light emissions data

Despite the improvements made in census procedures over recent decades, the availability of detailed population data is limited. For many applications, including environmental and health analyses, methods are therefore needed to model population distribution at the small-area level. With the development of GIS and remote sensing techniques, the ability to develop such models has greatly improved. This paper describes a GIS-based approach using remotely sensed land cover and nighttime light emissions data to model population distribution at the land parcel level across the European Union. Light emission data from the DMSP satellites were first resampled and modelled using kriging and inverse distance weighting methods to provide a 200-m resolution light emissions map. This was then matched to CORINE land cover classes across the EU. Regression methods were used to derive models of relationships between census population counts (at NUTS 5 level) and land cover area and light emissions. Models were developed at both national and EU scale, using a range of different modelling strategies. Model performance, as indicated by the regression statistics, was seen to be good, with R² typically in the order of 0.8-0.9 and SEE ca. 4000 people. In southern countries, especially, incorporation of light emissions data was found to improve model performance considerably compared to models based only on land cover data. More detailed post hoc validation in Great Britain, using independent data on population at census tract (enumeration district and output area) and postcode level, for 1991 and 2001, showed that models gave good predictions of population at the 1 km level (R² > 0.9), but were less reliable at resolutions below ca. 500 m. Impending enhancements in the available land cover and light emissions data are expected to improve the capability of this modelling approach in the future.     

Spatial and temporal variability of macrophyte cover and productivity in the eastern Amazon floodplain: A remote sensing approach

Herbaceous aquatic macrophytes cover extensive areas on the floodplains of the Amazon basin and are an important habitat and input of organic carbon. These communities have large intra- and inter-annual variability, and characterization of this variability is necessary to quantify the role of macrophytes in the ecology and biogeochemistry of the floodplain. A novel approach for mapping the temporal evolution of aquatic vegetation in the Amazon floodplain, which could be adapted to other spatially and temporally changing environments, is presented. Macrophyte cover varied seasonally and inter-annually, ranging between 104 and 198 km² for the floodplain examined (total area, 984 km²). The observed evolution of plant distribution indicated a spatial and temporal partition of macrophyte communities into short-lived and annual groups. A simulation of macrophyte net primary production (NPP) based on the mapping results indicated that at least 3% of NPP could be attributed to the short-lived communities. The present results suggest that significant changes in the macrophyte's contribution to carbon cycling in the Amazon floodplain could occur as a result of the predicted increase in frequency of drought years for the Amazon system due to climate change.     

Updating the 2001 National Land Cover Database land cover classification to 2006 by using Landsat imagery change detection methods

The recent release of the U.S. Geological Survey (USGS) National Land Cover Database (NLCD) 2001, which represents the nation's land cover status based on a nominal date of 2001, is widely used as a baseline for national land cover conditions. To enable the updating of this land cover information in a consistent and continuous manner, a prototype method was developed to update land cover by an individual Landsat path and row. This method updates NLCD 2001 to a nominal date of 2006 by using both Landsat imagery and data from NLCD 2001 as the baseline. Pairs of Landsat scenes in the same season in 2001 and 2006 were acquired according to satellite paths and rows and normalized to allow calculation of change vectors between the two dates. Conservative thresholds based on Anderson Level I land cover classes were used to segregate the change vectors and determine areas of change and no-change. Once change areas had been identified, land cover classifications at the full NLCD resolution for 2006 areas of change were completed by sampling from NLCD 2001 in unchanged areas. Methods were developed and tested across five Landsat path/row study sites that contain several metropolitan areas including Seattle, Washington; San Diego, California; Sioux Falls, South Dakota; Jackson, Mississippi; and Manchester, New Hampshire. Results from the five study areas show that the vast majority of land cover change was captured and updated with overall land cover classification accuracies of 78.32%, 87.5%, 88.57%, 78.36%, and 83.33% for these areas. The method optimizes mapping efficiency and has the potential to provide users a flexible method to generate updated land cover at national and regional scales by using NLCD 2001 as the baseline.     

Comparison and assessment of coarse resolution land cover maps for Northern Eurasia

Information on land cover at global and continental scales is critical for addressing a range of ecological, socioeconomic and policy questions. Global land cover maps have evolved rapidly in the last decade, but efforts to evaluate map uncertainties have been limited, especially in remote areas like Northern Eurasia. Northern Eurasia comprises a particularly diverse region covering a wide range of climate zones and ecosystems: from arctic deserts, tundra, boreal forest, and wetlands, to semi-arid steppes and the deserts of Central Asia. In this study, we assessed four of the most recent global land cover datasets: GLC-2000, GLOBCOVER, and the MODIS Collection 4 and Collection 5 Land Cover Product using cross-comparison analyses and Landsat-based reference maps distributed throughout the region. A consistent comparison of these maps was challenging because of disparities in class definitions, thematic detail, and spatial resolution. We found that the choice of sampling unit significantly influenced accuracy estimates, which indicates that comparisons of reported global map accuracies might be misleading. To minimize classification ambiguities, we devised a generalized legend based on dominant life form types (LFT) (tree, shrub, and herbaceous vegetation, barren land and water). LFT served as a necessary common denominator in the analyzed map legends, but significantly decreased the thematic detail. We found significant differences in the spatial representation of LFT's between global maps with high spatial agreement (above 0.8) concentrated in the forest belt of Northern Eurasia and low agreement (below 0.5) concentrated in the northern taiga-tundra zone, and the southern dry lands. Total pixel-level agreement between global maps and six test sites was moderate to fair (overall agreement: 0.67-0.74, Kappa: 0.41-0.52) and increased by 0.09-0.45 when only homogenous land cover types were analyzed. Low map accuracies at our tundra test site confirmed regional disagreements and difficulties of current global maps in accurately mapping shrub and herbaceous vegetation types at the biome borders of Northern Eurasia. In comparison, tree dominated vegetation classes in the forest belt of the region were accurately mapped, but were slightly overestimated (10%-20%), in all maps. Low agreement of global maps in the northern and southern vegetation transition zones of Northern Eurasia is likely to have important implications for global change research, as those areas are vulnerable to both climate and socio-economic changes.     

Monitoring land use and cover around parks: A conceptual approach

Many of the habitats and resources which influence ecological functioning within National Parks, and protected areas in general, are located outside of their borders in unprotected areas. Hence, land use and land cover changes in surrounding areas may substantially influence the natural resources within parks. The US National Park Service has recognized these threats and incorporated land use and land cover monitoring into its Inventory and Monitoring Program. The purpose of this paper is to provide a framework based on a conceptual approach for planning and implementing monitoring within this Program. We present a conceptual model, based on ecological theory, which illustrates how land use and land cover change impact park resources, and helps to identify monitoring indicators that will measure relevant attributes of land use and land cover change. We also discuss potential sources of data for quantifying indicators of land use and land cover change over time, including remote sensing data and ancillary spatial datasets. Finally, we describe steps for analyzing monitoring data so that the intensity and direction of changes in land use and land cover over time are quantified, as well as trends in the status of important park resources impacted by these changes. Integration of land use and land cover monitoring data and park resource data will allow for analysis of change from past to present, and can be used to project trends into the future to provide knowledge about potential land use and land cover change scenarios and ecological impacts. We illustrate our monitoring approach with an example from the Inventory and Monitoring Program's Greater Yellowstone Network.     

Land cover classification of tundra environments in the Arctic Lena Delta based on Landsat 7 ETM+ data and its application for upscaling of methane emissions

The Lena River Delta, situated in Northern Siberia (72.0-73.8° N, 122.0-129.5° E), is the largest Arctic delta and covers 29,000 km². Since natural deltas are characterised by complex geomorphological patterns and various types of ecosystems, high spatial resolution information on the distribution and extent of the delta environments is necessary for a spatial assessment and accurate quantification of biogeochemical processes as drivers for the emission of greenhouse gases from tundra soils. In this study, the first land cover classification for the entire Lena Delta based on Landsat 7 Enhanced Thematic Mapper (ETM+) images was conducted and used for the quantification of methane emissions from the delta ecosystems on the regional scale. Nine land cover classes of aquatic and terrestrial ecosystems in the wetland dominated (72%) Lena Delta could be defined by this classification approach. The mean daily methane emission of the entire Lena Delta was calculated with 10.35 mg CH₄ m^− 2 d^− 1. Taking our multi-scale approach into account we find that the methane source strength of certain tundra wetland types is lower than calculated previously on coarser scales.     

MODIS tree cover validation for the circumpolar taiga-tundra transition zone

A validation of the 2005 500 m MODIS vegetation continuous fields (VCF) tree cover product in the circumpolar taiga-tundra ecotone was performed using high resolution Quickbird imagery. Assessing the VCF's performance near the northern limits of the boreal forest can help quantify the accuracy of the product within this vegetation transition area. The circumpolar region was divided into 7 longitudinal zones and validation sites were selected in areas of varying tree cover where Quickbird imagery is available in Google Earth. Each site was linked to the corresponding VCF pixel and overlaid with a regular dot grid within the VCF pixel's boundary to estimate percent tree crown cover in the area. Percent tree crown cover was estimated using Quickbird imagery for 396 sites throughout the circumpolar region and related to the VCF's estimates of canopy cover for 2000-2005. Regression results of VCF inter-annual comparisons (2000-2005) and VCF-Quickbird image-interpreted estimates indicate that: (1) Pixel-level, inter-annual comparisons of VCF estimates of percent canopy cover were linearly related (mean R² = 0.77) and exhibited an average root mean square error (RMSE) of 10.1% and an average root mean square difference (RMSD) of 7.3%. (2) A comparison of image-interpreted percent tree crown cover estimates based on dot counts on Quickbird color images by two different interpreters were more variable (R² = 0.73, RMSE = 14.8%, RMSD = 18.7%) than VCF inter-annual comparisons. (3) Across the circumpolar boreal region, 2005 VCF-Quickbird comparisons were linearly related, with an R² = 0.57, a RMSE = 13.4% and a RMSD = 21.3%, with a tendency to over-estimate areas of low percent tree cover and anomalous VCF results in Scandinavia. The relationship of the VCF estimates and ground reference indicate to potential users that the VCF's tree cover values for individual pixels, particularly those below 20% tree cover, may not be precise enough to monitor 500 m pixel-level tree cover in the taiga-tundra transition zone.     

Effect of inter-image spectral variation on land cover separability in heterogeneous areas

Supervised classification is a popular approach for deriving land cover data from satellite imagery, but the collection of suitable training data of large areas is expensive. Signature extension has been proposed as a method of limiting the number of training areas. Signature extension is particularly difficult in large, heterogeneous areas where the spectral characteristics of land cover classes are highly variable.

The quantification of spectral separability can be used to determine to what extent a set of training areas collected in a small area can be extended to classify a larger area. This article investigates the changes in spectral separability of land cover classes in an increasing geographical area. A highly heterogeneous study area, containing nine different vegetation biomes, was chosen. Separability analyses were carried out on four Landsat-8 scenes that were sequentially mosaicked. The effect of multi-seasonal imagery on separability was also investigated. The results show that the mean spectral separability did not change when the geographical area was increased. We conclude that supervised classification with a small subset of training data should be possible in the chosen study area, since there is high separability between the classes. Some classes, however, require multi-temporal imagery as input.     

An evaluation of sampling strategies to improve precision of estimates of gross change in land use and land cover

Statistical sampling offers a cost-effective, practical alternative to complete-coverage mapping for the objective of estimating gross change in land cover over large areas. Because land cover change is typically rare, the sampling strategy must take advantage of design and analysis tools that enhance precision. Using two populations of land cover change in the eastern United States, we demonstrate that the choice of sampling unit size and use of a survey sampling regression estimator can significantly improve precision with only a minor increase in cost.     

A curve fitting procedure to derive inter-annual phenologies from time series of noisy satellite NDVI data

Annual, inter-annual and long-term trends in time series derived from remote sensing can be used to distinguish between natural land cover variability and land cover change. However, the utility of using NDVI-derived phenology to detect change is often limited by poor quality data resulting from atmospheric and other effects. Here, we present a curve fitting methodology useful for time series of remotely sensed data that is minimally affected by atmospheric and sensor effects and requires neither spatial nor temporal averaging. A two-step technique is employed: first, a harmonic approach models the average annual phenology; second, a spline-based approach models inter-annual phenology. The principal attributes of the time series (e.g., amplitude, timing of onset of greenness, intrinsic smoothness or roughness) are captured while the effects of data drop-outs and gaps are minimized. A recursive, least squares approach captures the upper envelope of NDVI values by upweighting data values above an average annual curve. We test this methodology on several land cover types in the western U.S., and find that onset of greenness in an average year varied by less than 8 days within land cover types, indicating that the curve fit is consistent within similar systems. Between 1990 and 2002, temporal variability in onset of greenness was between 17 and 35 days depending on the land cover type, indicating that the inter-annual curve fit captures substantial inter-annual variability. Employing this curve fitting procedure enhances our ability to measure inter-annual phenology and could lead to better understanding of local and regional land cover trends.     

The change of land cover and land use and its impact factors in upriver key regions of the Yellow River

Owing to the influence of global change, land cover and land use have changed significantly over the last decade in the cold and arid regions of China, such as Madoi County which is located in the source area of the Yellow River. In this paper, land‐use/cover change and landscape dynamics are investigated using satellite remote sensing (RS) and a geographical information system (GIS). The objectives of this paper are to determine land‐use/cover transition rates between different cover types in the Madoi County over 10 years e.g., from 1990 to 2000. Second, the changes of landscape metrics using various indices and models are quantified. The impact factors of LUCC (Land‐Use land cover Change) are systematically identified by integrating remote sensing as well as statistical data, including climate, frozen soil, hydrological data and the socio‐economic data. Using 30 m×30 m spatial resolution Landsat (Enhanced) Thematic Mapper (TM/ETM+) data in our study area, nine land cover classes can be discriminated. Our results show that Grassland, Marshes and Water Bodies decrease notably, while oppositely, Sands ‐ Gobi and Barren land increase significantly. The number of lakes with an acreage larger than six hectares decreased from 405 in 1990 to 261 in 2000. Numerous small lakes dried out. The area of grassland with a high cover fraction decreased as well, while the surface area of grassland with a medium level of cover fraction increased. The medium cover fraction grassland mainly originates from high cover fraction grassland. The desertification of land is a serious issue. (ii) The inter‐transformations between Grasslands, Barren Land, Sands, Gobi, Water Bodies and Marshes are remarkable. The Shannon–Weaver Diversity Index (SWDI), the Evenness Index (EI) and the extent of Landscape Heterogeneity (LH) has improved. Marshes have become more fragmented hence, with less connected patches. (iii) In the recent 30 years, average annual temperature, the power of evaporation and the index of dryness did increase significantly. Moreover, soil moisture content (SMC) decreased and the drought trend accelerated. The degradation of frozen soil has impacted on the decrease of surface water area and induced a drop in groundwater levels. Monitoring LUCC in sensitive regions would not only benefit from a study of vulnerable ecosystems in cold and high altitude regions, but would provide scientifically based decision‐making tools for local governments as well.     

Validation of a large area land cover product using purpose-acquired airborne video

Large area land cover products generated from remotely sensed data are difficult to validate in a timely and cost effective manner. As a result, pre-existing data are often used for validation. Temporal, spatial, and attribute differences between the land cover product and pre-existing validation data can result in inconclusive depictions of map accuracy. This approach may therefore misrepresent the true accuracy of the land cover product, as well as the accuracy of the validation data, which is not assumed to be without error. Hence, purpose-acquired validation data is preferred; however, logistical constraints often preclude its use — especially for large area land cover products. Airborne digital video provides a cost-effective tool for collecting purpose-acquired validation data over large areas. An operational trial was conducted, involving the collection of airborne video for the validation of a 31,000 km² sub-sample of the Canadian large area Earth Observation for Sustainable Development of Forests (EOSD) land cover map (Vancouver Island, British Columbia, Canada). In this trial, one form of agreement between the EOSD product and the airborne video data was defined as a match between the mode land cover class of a 3 by 3 pixel neighbourhood surrounding the sample pixel and the primary or secondary choice of land cover for the interpreted video. This scenario produced the highest level of overall accuracy at 77% for level 4 of classification hierarchy (13 classes). The coniferous treed class, which represented 71% of Vancouver Island, had an estimated user's accuracy of 86%. Purpose acquired video was found to be a useful and cost-effective data source for validation of the EOSD land cover product. The impact of using multiple interpreters was also tested and documented. Improvements to the sampling and response designs that emerged from this trial will benefit a full-scale accuracy assessment of the EOSD product and also provides insights for other regional and global land cover mapping programs.     

Accuracy assessment of mixed land cover using a GIS‐designed sampling scheme

Land cover maps, based on remotely sensed data, are widely developed and used for studying global ecosystems and land use/land cover change. However, accuracy assessment of mixed land cover classes, including varying dominance of invasive species, is complicated by uncertainty about where to define a threshold of presence/absence. Geographic Information Science (GIS) can be used to target sampling locations that encompass a range of mixed pixels, but are also easily accessible for an efficient accuracy assessment. Here, an accuracy assessment of a Landsat‐derived map of the invasive species cheatgrass (Bromus tectorum) in the state of Nevada, USA is presented. The stratified random design used GIS to increase efficiency by limiting the target area while still sampling the distribution of mixed pixels present in the larger study area, and a receiver operating characteristic (ROC) curve was used to assess overall map accuracy with different thresholds of cheatgrass presence/absence. This approach is useful for validating map accuracy in the presence of mixed pixels.