Land Cover Data: The Foundation for Conservation Planning

1. Land Cover Data: The Foundation for Conservation Planning

2. Introduction • Definition - “the vegetational and artificial constructions covering the land surface" (Burley 1961). • Advent of remote sensing and needs of resource managers, etc., has led to increased development and use of land cover data. • Persistent myth that land cover data is accurate and up-to-date. • All the different land cover classifications have been likened to the mythical Tower of Babel where everyone is working hard but all speaking different languages – “progress that has been made so far is despite the large number of schemes, not because of them” (Adams 1999). • Land cover data typically forms the basis for conservation planning such as habitat suitability for focal species. • Typically use “available” data rather than data that is really needed.

3. The Basics of Remote Sensing A working knowledge of remote sensing is needed since it forms the basis for most land cover data • Remote sensing is “the science and art of obtaining information about an object, area, or phenomenon through the analysis of data acquired by a device that is not in contact with the object, area, or phenomenon under investigation” (Lillesand and Kiefer 1994). • Brief history, beginning with the use of balloons and pigeons to carry cameras to current use of satellites carrying active and passive sensors.

4. The Basics of Remote Sensing (continued) Figure 1 indicates what sensors with different spatial resolutions “see” on the ground within a 1km extent. • Spatial Resolution • 1 km AVHRR imagery • 30m Landsat • 4m IKONOS • 1m orthophoto A B D C

5. The Basics of Remote Sensing (continued) • Spectral Resolution All surfaces reflect, absorb, or transmit energy. Figure 2 provides reflectance curves for bare soil, green vegetation, and water. Measuring reflectance within discrete intervals corresponding to these peaks and valleys forms the basis for classification with optical-based remote sensing data. Figure from Lillesand and Kiefer (1994).

6. The Basics of Remote Sensing (continued) • Temporal Resolution - the amount of time between repeat coverage for a sensor. Important to collect data over a large area, to provide multiple dates for better classification, and for intra-annual change detection. • Scale - often misused term which states that one unit of distance on a map, aerial photograph, etc. represents a specific unit of distance on the ground. Often presented as a fraction (1/25,000) or ratio (1:25,000) where 1 inch on the map represents 25,000 feet on the ground. Subsequently, 1:25,000 defines a smaller scale than 1:10,000 and provides less detail than 1:10,000 (larger scale).

7. The Basics of Remote Sensing (continued) • Classification Accuracy and Precision - accuracy defines “correctness”, the agreement between an assumed standard and the predicted class of data, precision defines “detail” of land cover classification. Precision determines the usefulness for a specific application. Figure 3 indicates the general relationship between accuracy and precision of land cover data. Producer’s accuracy - percent of features classified correctly amongst those that are of that type on the ground; classification of 400 features as forest from 500 features that are known to contain forest results in a producers accuracy of 80.0%. Consumer’s accuracy - reliability of the classification as a predictive device. In this situation, the correct classification of the 400 features as forest amongst 800 features that were classified as forest in the predictive map results in a consumer’s accuracy of 50%.

8. Linking Remote Sensing with Vegetation on the Ground - What exactly are we mapping? This section describes the difficulties in matching what we see on the ground with remote sensing data and the classification process • There is a disconnect between the natural science community for defining vegetation in the field and the remote sensing community for providing useful classifications of land cover data. • e.g. common taxonomic classification such as Idaho fescue/bluebunch wheatgrass and Idaho fescue/tufted hairgrasscannot be differentiated with remote sensing data.

9. Linking Remote Sensing with Vegetation on the Ground (continued) • The interaction of grain and extent with heterogeneity of the landscape from an ecological perspective is analogous to the influences of pixel size and extent from a remote sensing perspective. “As the extent of the study is increased (large squares), landscape elements that were not present in the original study area are encountered. As the grain of samples is correspondingly increased (small squares), small patches that initially could be differentiated are now included within samples and the differences among them are averaged out.” Figure and quotation from Wiens 1989

10. Linking Remote Sensing with Vegetation on the Ground (continued) • Pixel Size (grain) in Relation to Land Cover Classification: All other things being equal, a smaller pixel size will encounter less mixing of vegetation types within each pixel and be able to detect smaller patches on the ground. Smaller pixel size can therefore increase the precision of land cover classification Other factors influencing the precision are spectral resolution, issues associated with image registration, topographic shadowing, vegetative structure, etc.

11. Linking Remote Sensing with Vegetation on the Ground (continued) • Extent in Relation to Land Cover Classification and Conservation Planning Similar to ecological aspects, heterogeneity of the landscape increases at larger spatial extents which decreases the precision of classification. Additionally, the use of high resolution data is currently limited to small areas due to cost and size of data sets. These factors generally contribute to a decline in precision as spatial extents increase. It is unlikely classification using high resolution data is available for large areas commonly used in conservation planning or encompassing entire areas of interest.

12. Matching the Needs of Wildlife with Land Cover Data This section describes the requirements of landcover data for developing accurate habitat models • Vegetative requirements of wildlife can be floristic, structural, or a combination of the 2. E.g. fisher generally avoid open areas and utilize a range of plant communities containing high amounts of vegetative structure, while sage grouse are strongly associated with sagebrush habitat but require specific structural components of sagebrush and amounts of herbaceous cover within sagebrush habitat. Habitat Specialists Precision of landcover must match the requirements of species modeled. Habitat Generalists

13. Matching the Needs of Wildlife with Land Cover Data (continued) • Every species observes the environment through its own unique suite of scales for space and time (Levin 1992). E.g. hierarchical orders of selection by herbivores (Senft et al. 1987, Bailey et al. 1996). e.g. third order for grizzly bears but second order for sage grouse Requirements for precision of land cover data varies depending on the level of selection within species as well as amongst species.

14. Matching the Needs of Wildlife with Land Cover Data (continued) • Subsequent Problems • Existing land cover classifications do not meet requirements for all species or different selection levels within species. • Project-specific classifications are often developed, but they seldom cover desired areas of interest or all requirements of the suite of focal species selected. • Important parameters such as “early seral lodgepole forest” may need to be extrapolated onto broader classes of “lodgepole forest” or “coniferous forest” which will in itself overestimate the amount of suitable habitat. • Classification errors can potentially compound the lack of precision in many applications.

15. Selection of Land Cover Data • There is not one ideal classification of land cover to meet all applications and it is unlikely that one could ever be developed (Anderson et al. 1976). Due to the advancement in available sensors and techniques, this statement is probably truer today than when it was first made. • In addition to issues concerning precision and accuracy, it can also be difficult to find consistent land cover data across jurisdictional boundaries that are common in conservation planning. The following are some sources of commonly available land cover data and examples of other options for classification. Some of the benefits and associated problems of each are discusses.

16. Selection of Land Cover Data (continued) • Global land cover data treating all parts of the world equally and representing actual land cover of our planet have been available since the 1990’s. Advantages are availability and consistency worldwide but coarse resolution and very general categories. Table from Strand et al. 2007

17. Selection of Land Cover Data (continued) • National Landcover Database (NLCD) Advantages: Available for the conterminous United States, Alaska, and Puerto Rico. Use of 30m Landsat data provides good spatial heterogeneity of cover types. Estimates of canopy coverage for the 2001 version. Disadvantages: Broad classes not well suited for delineating wildlife habitat Poor accuracy of some classes and locations

18. Selection of Land Cover Data (continued) • NLCD Categories

19. Selection of Land Cover Data (continued) • NLCD Region 8 (MT, WY, ND, SD, UT, CO) Accuracy

20. Selection of Land Cover Data (continued) • GAP Land Cover Advantages: Available for the U S and Puerto Rico. Generally higher precision than NLCD due to smaller extents. Disadvantages: Although the intent of GAP was to maintain animal and plant species, the broad classes that are typically used often are not of sufficient precision to define habitat for many species. Inconsistency in classification amongst states.

21. Selection of Land Cover Data (continued) • Montana GAP Categories Better suited for habitat delineation than NLCD classes

22. Selection of Land Cover Data (continued) • Comparison of Montana vs. Wyoming GAP Classification 8,028 acres where Montana and Wyoming GAP data overlap in Yellowstone National Park Wyoming: 80% lodgepole pine, 20% subalpine meadow Montana: 6 distinct types, spatially arranged within the polygon

23. Selection of Land Cover Data (continued) • Crosswalking vegetation types is often used when the area of interest crosses jurisdictional boundaries. However, detailed information on floristic composition and canopy cover are required to correctly crosswalk between vegetation types (Brohman and Bryant 2005, FGDC 1997, FGDC 2008, Jennings et al. 2004). Example of crosswalked vegetation types for a portion of the Inland Temperate Rainforest encompassing parts of MT, ID, WA, and British Columbia. The new regional classifications being produced by the GAP program will resolve differences across state boundaries but at the cost of more generalized classes than many of the state projects produced.

24. Selection of Land Cover Data (continued) • Classification Using High Resolution Data such as IKONOS or QuickBird. Potential for greater precision: IKONOS imagery has shown the ability to identify tree canopy (Snyder et al. 2005), individual trees (Read et al. 2003), and to differentiate between vegetation types in dry shrub/grassland (Depew 2004). However, there currently are not any widespread classifications using these types of imagery. The extent of each scene is generally small, data are fairly costly, and the spatial resolution provided by these sensors results in very large data sets

25. Selection of Land Cover Data (continued) • Vegetation Resource Inventory of British Columbia A hierarchical classification based on physiognomy of vegetation Many species are more concerned with vegetation structure than plant species. Land cover data providing taxonomic classes often require assumptions about structural or density classes for habitat models (e.g. denser conifers on north slopes). This classification provides the life form and density. It is easier to use this classification and a DEM to make assumptions about plant species (when required) than vice versa. Major drawbacks to this classification system is that it is based on air photo interpretation.

26. Selection of Land Cover Data (continued) • Classification Using Digital Orthophotos A combination of air photo interpretation and machine classification can be used to classify land cover. Similar to satellite imagery, digital color air photos provide distinct values of red, green, and blue for each pixel that allows classification with remote sensing software. Advantages: Better able to determine landscape heterogeneity. Should provide greater accuracy associated with air photo interpretation but with the greater speed of machine classification. Disadvantages: Digital values do not correspond to peaks and valleys of objects. Limited to small areas. Potential need for classification of individual photos depending on required precision. Example of method: A = color orthophoto, B = corresponding GAP classification, C = classification using digital orthophoto. A B C

27. Recommendations for Future Research • Conservation planning often uses “available” land cover data and seldom acknowledges different seasonal requirements or the orders of selection that habitat delineation encompasses. Similar to the rigorous selection process for focal species, methods could be developed to define what species and selection orders can be defined using available data as well as land cover requirements for desired species and selection orders. • LiDAR, hyperspectral, and high resolution multispectral such as IKONOS and QuickBird will become more common and improve classification abilities. • As sensor technology increases, so will the ability to fuse active and passive sensors to obtain the benefits of each. • But we cannot wait for these technologies to advance and must strive to make improvements with the tools we have. Therefore, I have proposed the following land cover classification using readily available satellite imagery to better meet the needs of conservation planning.

28. Recommendations for Future Research(continued) Proposed Classification • Based on the use of readily available Landsat imagery or other optical-based data and other available GIS data. • Hierarchical classification – nested levels of classification to improve accuracy, better adapt to combining different types of remote sensing data, provides the ability to define habitat for specific species or different levels of selection within subsets of each level. • A combination of traditional remote sensing techniques and GIS modeling applications. Different methods and hierarchical levels are intended to match the desired output at each level. • Separate classification by Ecological Sections (McNab et al. 2007) rather than just a desired area of interest to decrease consequences of errors.

29. Recommendations for Future Research(continued) Proposed Classification • Provides both structural and taxonomic information. The interaction between these variables should improve the accuracy of both. • Applicable over large areas, increased precision for smaller areas. Provides compatibility when different locations are classified at different times. Different sub-models can be incorporated at different times to account for different species, orders of selection, or broad-scale changes in underlying factors (e.g. fire, influences of climate change). • Although it has not actually been used, data are available and methods (GIS models, etc.) have been developed to implement the classification process in a pilot study or actual project.

30. Proposed Classification

31. General Recommendations for Conservation Applications • Land cover data is often the limiting factor in what we can and cannot do in conservation planning. It should therefore be a major consideration early on when defining the goals and objectives of any project. • Limitations of results due to the accuracy and precision of land cover data should be detailed in results. Accuracy of land cover data can be variable across an AOI. Accuracy assessments should be conducted for the AOI using air photo interpretation or field data. A brief discussion of confusion between classes and limitations of the data may explain reasons for discrepancies in results. • FGDC standards for collection of field data should be followed for accuracy assessments and use in other classifications.