Abstract

Central America and Southern Mexico (Meso America) contain high biodiversity related to the existence of a variety of tropical forest life zones ranging from lowland dry and wet forest too Montane rain forests. Regional deforestation rates are among the highest in the world and the land cover/land use changes have important links to global climate change. Baseline land cover/land use data and compilation of ecological data bases are needed to initiate a comprehensive scientific research program across the Meso American biological corridor.

In an effort to develop the foundation for cooperative research between North American and Meso American earth scientists, we propose to use a variety of medium to high resolution satellite sensors to map land cover, forest and land cover conversion and forest biomass throughout the region. The primary data set to support regionwide land cover and biomass mapping is the JERS-1 synthetic aperture radar imagery collected during the 1996 Global Rain Forest mapping project. Optical imagery (MODIS, Landsat and ASTER) will be acquired for selected intensive study sites to monitor land cover/land use change and provide calibration and validation data to support the regionwide SAR mapping. Because carbon accumlates at high rates in young second-growth forests, we propose to test the capability of JERS-1 SAR and SAR merged with optical data to detect second growth age classes and estimate biomass levels. Land cover/land use history and biomass regrowth rates will vary among life zones therefore the intensive sites will be selected to represent the range of environment conditions in the region.

The proposed research will be performed in collaboration with scientists and resource managers from all eight Meso American countries who will be involved in the selection of study sites, ground and GIS data collection, data analysis, validation studies, and training activities. The physical and biological data bases including the first complete (cloud-free) land cover map will facilitate the initiation of a regionwide monitoring program for the Meso American biological corridor perhaps modeled after the National GAP Analysis program in the United States.

Introduction

The effects of tropical forest clearing and biomass burning on rising atmospheric CO2 levels have been the subject of great concern and debate in recent years (Houghton et al., 1983; Houghton et al., 1991; Lugo and Brown, 1992; Brown et al., 1993). Houghton et al. (1983) reported that the range of global carbon flux estimates could be reduced by 60 percent with more reliable data on the rates and permanence of tropical deforestation. Indeed, any attempt to model the global carbon budget will depend on quantifying all carbon sinks and sources. The debate about tropical forests as a source or sink of carbon has remained a central issue in part because the uptake of CO2 from regenerating vegetation has not been studied extensively (Houghton et al., 1983; Hall and Uhlig, 1991; Brown and Lugo, 1990; Brown et al, 1993).

Forest regeneration accumulates carbon quickly in the first 10-20 years following disturbance (Lugo and Brown, 1992; Brown et al., 1993) and this is a critical period for accurate estimates of biomass and carbon storage. The percentage of the tropical region occupied by second growth forest and the crop/fallow ratios of shifting cultivation zones are not well known and therefore are not included in most carbon models. For example, Schwartz (1990) estimated the crop to fallow ratio in the northern Peten district of Guatemala to be 2:4-7, whereby two years of cropping is followed by four to seven years of regrowth before the cycle is repeated. He also reported that the ratio dropped to 1-2:3-5 in southern Peten where deforestation was extensive and land use pressures were high. The area of forest regeneration as well as the history and intensity of land use activities prior to land abandonment can have a significant effect on the rates of carbon uptake and biomass accumulation (Uhl et al., 1988; Brown et al., 1993).

Most remote sensing investigations of forest change conducted over the past two decades have emphasized only the forest clearing aspects. Estimates of vegetation regrowth and land-use conversion are rarely reported (Sader et al., 1990; Brondizio et al., 1996). However, recent investigations have included vegetation regrowth as a component of the forest change scenario using satellite observations (Sader et al., 1994; Skole et al., 1994; Sader, 1995; Rignot et al., 1997).

Research Justification

With much media attention focused on the Amazon Basin, it is a little known fact that in the 1980's Meso American (Central America and Mexico) had the second highest deforestation rate of any region in the world (1.5% per year, following Southeast Asia at 1.6%) according to FAO (1993). Costa Rica had one of the highest rates of any country (Sader and Joyce, 1988; WRI, 1990; FAO, 1993). Recent studies in No. Guatemala report forest clearing rates of > 3% per year and nearly 5% per year in the Maya Biosphere buffer zone and agricultural zone south of the reserve, respectively (Sader et al., 1997, Sader et al., manuscript in review). The deforestation rates present some urgency in development of conservation management strategies by establishing a foundation and scientific data base for continuous monitoring of the Meso American biological corridor. Comision Centroamerica de Ambiente y Desarrollo (CCAD - Central American Commission on Environment and Development) was established in 1989 and endorsed by the Presidents of the Central American countries to initiate a mission to establish a Mesoamerican biological corridor and monitor the ecological conditions and function into the future. The Meso American countries are lacking baseline land cover/land use (LCLU) data and ecological data bases to initiate a comprehensive scientific research program at the regional level.

Nearly perpetual cloud cover over much of the region hinders the acquisition of acceptable quality optical satellite imagery to form the basis for ecological studies that rely on vegetation and LCLU mapping. We are aware of at least two regional land cover maps developed from 1992-93 NOAA, 1 km AVHRR NDVI composites. These maps are now out-dated and too coarse in ground resolution to support comprehensive ecological monitoring. The AVHRR maps contain significant clouds that result in lack of coverage for several areas. Recent radar satellites launched by several countries now provide regional radar imagery that is not hindered by clouds. Two synthetic aperture radar (SAR) satellites (Radarsat and JERS-1) provide regionwide coverage within 1-2 month temporal windows in 1995 and 1996. The L-band SAR of the JERS- 1 satellite (hh polarization) is particularly suitable for developing a region-wide SAR mosiac to support LCLU mapping. The JERS mosaic and LCLU mapping efforts are well underway in other regions (e.g., Amazon basin). Investigations of SAR backscatter relationships to forest age classes, structure and biomass suggest that radar data and radar merged with optical sensor data may become important tools for terrestrial carbon modeling to support regional and global climate change studies (Sader, 1987; Saatchi et al., 1997; Rignot et al., 1997; Saatchi 1996).

Meso America contains a wide range of Life Zones (Holdridge, 1971) from dry Pacific and wet Atlantic forest up to montane rain forest along the Central Cordillera from Mexico to Panama. The ecological zones of the region have experienced historical deforestation and periods of agricultural conversion, and land abandonment. The successional pathways of these life zones provide a range of test environments to investigate LCLU change and regrowth / biomass relations utilizing optical and radar sensors.

Project Objectives:

• Develop a regionwide JERS-1 SAR mosaic and a land cover/land use map to support scientific research along the Meso American biological corridor.
• Validate the LCLU map using ancillary GIS data and ground data collected on validation test sites selected throughout the region.
• Develop a LCLU change monitoring approach based upon current and future satellite remote sensing systems and validate the techniques in a range of life zones undergoing moderate to intensive land cover change.
• Map the primary and secondary regrowth forest patches and estimate aboveground biomass in the entire region using JERS-1 SAR and SAR combined with optical remotely sensed data for selected study sites.
• Collect, compile and organize national and regional level GIS data and develop an information system at CCAD to support an ecological research program (e.g. GAP Analysis) for the Meso American region.
• Conduct training and capacity development for Meso American scientists in remote sensing/image processing, GIS/spatial analysis and ecological monitoring.
• Initiate cooperative research with Meso American scientists to validate the LCLU map, estimate woody biomass and carbon stocks, and develop a LCLU change monitoring technique for the Meso American biological corridor.

Approach

Land Cover Mapping

Our primary data sets to achieve the objectives of the proposal will be the JERS-1 high-resolution SAR (synthetic aperture radar) data, high-resolution DEM (3 arc sec.), available optical imagery such as AVHRR, MODIS, ASTER and Landsat. JERS-1 data are available through the Alaska SAR facility free of charge to the project. Archival Landsat MSS and TM data are available free or for nominal charges through EOSDIS DAAC archives. MODIS and ASTER data will be available following the launch of EOS-AM-1 in 1999. Historical black and white panchromatic aerial photos flown by the US military in the 1940's and 1950's are available through national mapping agencies to support historical LCLU change and second-growth forest investigations at intensive study sites. Topographic and life-zone data are available for ecological stratification and topographic corrections of radar data.

JERS-1 data were acquired in 1996 as part of the Global Rain Forest mapping project and are currently waiting to be pre-processed at the Alaska SAR facility. There are more than 200 images (12.5 m ground pixel) covering an area from The Panama Canal to Southern Mexico (22 degrees north latitude) including the entire Yucatan Peninsula. The images will be calibrated and post-processed at JPL. The images will be multi-looked to 50 m and 100 m resolutions. Software developed for the Shuttle Radar Topography Mission (SRTM) at JPL will use the equal-angle projection to mosaic the images for the entire area. Mosaicing utilizes mathematical simulations that mimic a wallpapering approach. Image scenes will float freely with respect to one another (within a global coordinate system) until the location of all scenes are calculated simultaneously. This ensures minimization of error propagation in any direction and results in a seamless mosaic.

The 3 arc-sec topography data derived from stereo satellite imagery from unclassified military sources (available at JPL) will be used to georectify and radiometrically correct the images. The SAR mosaic will be projected in the same plane as the topography data and registered using tie points and an automatic spatial optimization approach. After co-registration, the SAR backscatter data are radiometrically calibrated by correcting the slope effect on the local incidence angle. This will improve the quality of radar data and will allow improved classification and thematic interpretation. We will use the SRTM digital high-resolution topographic data if it becomes available during the lifetime of this proposed project.

A general land cover classification scheme appropriate for the region will be selected to crosswalk with the Federal Geographic Data Committee scheme. The JERS-1 images and derived texture measures will be used to develop a classification technique for mapping land use and land cover types in Meso America. SAR data, being sensitive to penetration in vegetation canopy, surface and vegetation structure, and moisture can separate several vegetation classes depending on their structure or moisture regime (Saatchi et al., 1997, 1998; Rignot et al., 1997). Since JERS-1 is a single channel data and texture is an intrinsic characteristic of radar images, the classification approach will use several texture measure (first and second order spatial statistics of data) as inputs to the classifier. This technique was developed for mapping the land cover types in Amazon basin, West and Central Africa (Saatchi et al., 1998; Podest, 1998). The modification of the method for the Meso American region requires the selection of training and test data sets based on available ground data and existing maps compiled by CCAD.

We will examine the existing AVHRR cover maps (reference) for their utility in pre-stratification of major vegetation and land cover types and as correlary evidence to compare the SAR mapping results. Areas of disagreement can help direct sampling efforts for revisions of the land cover classification in the early stages. If MODIS data are available, we will compute forest density using Landsat-TM forest area (data from intensive sites) regressed against MODIS, 250 or 500 m data (Zhu, 1994). Forest density maps will be compared with SAR land cover classification and evaluated for possible merging of the optical and SAR results for forest classification and biomass estimation improvements.

We will collaborate with Meso American scientists to validate the maps over test sites selected in each country. Validation plots will be selected and interpreted using a combination of high resolution imagery, aerial photography and ground visits. Mutual information index (Finn, 1993) and an error matrix approach (Congalton, 1991) will be employed for accuracy assessment.

Land Cover/Land Use Change and Forest Biomass Estimation

A LCLU change detection technique will be applied to multi-temporal, optical data acquired over selected test sites. The technique will employ both digital (NDVI image differencing and principal component analysis) and visual methods and will rely on integration of GIS coverages and stratification/masking procedures. On the basis of recent experience in the region (Sader, 1997), completely automated change detection procedures are not likely to isolate only the change events of interest. For example, a seasonal forest wetland can contain surface water at one image acquisition date but not another, thus indicating a change. Also a brushland fire will be detected by a change detection algorithm but it may not represent a land use change. The methodology will take into account the current and future remote sensing capabilities and propose a monitoring scenario for the entire region. MODIS, Landsat and ASTER will likely be the primary sensors to monitor LCLU change. Landsat MSS and TM will provide the historical mega-changes (70's, 80's, 90's) and 10 day to monthly MODIS NDVI composites will provide updated change within the annual cycle. Change detection maps will be validated using historical aerial photos (if available) or independent visual interpretation of multidate Landsat color composite images (Cohen et al., 1998).

We anticipate some confusion in distinguishing differences between abandoned over-grown pasture and short fallow agriculture using only Landsat imagery. Early successional forest may be distinguishable from older second growth but the distinction is more difficult after approximately 10-15 years of regrowth (Sader et al., 1989). On intensive study sites, we will develop time-series data bases to test the capability of SAR data and Landsat-TM merged with SAR to distinguish regeneration age classes. We will stratify second growth forest age classes up to approximately 20 years with archival Landsat data and use it as a basis for testing the relationships between SAR backscatter and vegetation structure measurements.

Field data from regenerating forests will be collected in test sites throughout the entire region and will be integrated with SAR backscatter and texture data to develop a biomass retrieval technique for the secondary regrowth in the region. Species composition, basal area, stem diameter (dbh >5 cm), height measurements, site conditions and GPS readings will be recorded for a representative sample of these different age classes. Plot size will vary by age class with young age classes (<5 years) composed of 2 m diameter circular plots along a transect and older age classes (5-25) with rectangular plots (e.g., 20x 50 m). We will select intensive sites to capitalize on existing forest inventory data to supplement our ground sampling (next section).

The general form of the biomass algorithm exists and has been tested over sites in the Amazon basin(Luckman et al., 1997; Saatchi et al., 1998). This model will be modified for major life zones and forest environments throughout Meso America. Previous research indicates that L-band SAR has a strong linear relation to above ground biomass below the range of 60 to 100 tons per hectare (Sader, 1987; Luckman et al., 1997). We will concentrate our biomass sampling in stands less than 20 years where the SAR signal saturation occurs. We will test the algorithms with other existing forest volume data (e.g. FAO), and optical remote sensing data (MODIS-TM) in order to generate a regional vegetation biomass map. Other allometric equations available in the open literature (e.g., Brown et al., 1989) will be tested for old secondary and primary forest.

Intensive Study Site Selection

The intensive study sites selected throughout Meso America will serve several purposes. First, the region is composed of many life zones and topographic conditions. Forest clearing rates, agriculture and land abandonment, secondary regrowth and biomass accumulation will vary between life zones. To develop an effective strategy to monitor the biological corridor, all life zones and land cover types will need to be represented. Second, the intensive sites will provide calibration data sets to test and revise land cover maps and biomass estimates during the early and intermediate stages of the project. Thirdly, the sites will be selected to represent all countries in the region to engage Meso American cooperators in the data collection and analysis of data. Ground data collection and GIS data base development will be led by the investigator teams in each country. All of the intensive sites encompassed within a Landsat frame will overlap two countries to foster data collection and research cooperation between neighboring countries.

Although the location of intensive study sites will not be decided until the project investigators and Meso American scientists convene the first team meeting; some criteria for selection of sites are offered for consideration:

I.	Full range of Holdridge Life Zones represented.
II.	Sites contain biological reserves, protected areas, range of land cover/use and high, medium, low LCLU conversion zones over the past 20-25 years.
III.	Each of 8 Central American countries represented by at least one study site.
IV.	Each site contained within one Landsat frame and at least three relatively cloud free scenes available one each from 1970's, 1980's, and 1990's.
V.	Complementary GIS data available, established forest research sites with existing forest inventory data and historical aerial photos available.

Based on life zone criteria and the investigator's knowledge of the region, some candidate sites are suggested as follows:

Location	Life Zone	Landsat TM path/row
Mexico, Guatemala	tropical dry, tropical moist	20/48
Guatemala, Belize	tropical moist, tropical wet	19/49
Honduras, El Salvador	tropical dry, tropical moist, premontane moist	18/51 or 19/51
Nicaragua, Costa Rica	tropical wet, premontane wet and rain, montane wet	15/53 or 16/52
Costa Rica, Panama	premontane wet and rain, montane wet and rain	14/54

Landscape Metrics and Biological Corridor Monitoring

During year 3, on intensive sites, spatial indices (patch size, shape, fractal dimension) using Fragstats (McGarigal and Marks, 1993) will be employed (Olsen et al., 1993; O'Neill et al., 1993; LaGro, 1991) to analyze landscape metrics and their relationship to forest conversion and land cover types. Quantification of patchiness and spatial patterns across the landscape over time may reveal some interesting correlates linked to physical and socio-economic driving forces operating in the region.

It is proposed that the data base development conducted by CCAD and the individual countries be compatible with the requirements of a GAP analysis project (Scott et al., 1993). This work should be coordinated with the regional Biodiversity Center in San Jose, Costa Rica where relevant data bases already exist. The GAP analysis process provides an overview of the distribution and conservation status of several components of biodiversity (i.e., land cover and terrestrial vertebrate distribution). Conservation zones, reserves and park boundaries are located throughout the region of interest. Digital map overlays in a GIS are used to identify individual species, species-rich areas, and vegetation types that are unrepresented or under-represented in existing management areas (Scott et al., 1993). These are the "gaps" in the overall mix of conservation lands and conservation activities. GAP analysis functions as a preliminary step to the more detailed studies needed to establish actual boundaries of potential biodiversity management areas. These data and results are made available to institutions and resource managers so that they may become more effective stewards through more complete knowledge of the management status of these elements of biodiversity (Scott et al., 1993; Krohn et al., in review).

It is not the intention of the proposed project to complete a GAP analysis of Meso America as the GAP endeavor is a three-year effort at minimum. The proposed project will lay the foundation and compile the appropriate regional data bases including the essential land cover/land use maps to initiate a GAP analysis at the termination of the project. It is anticipated that CCAD would lead this effort and secure the necessary funding to accomplish the goals of the regional ecological monitoring program.

Project Phases

Year 1:
Project coordination, Japanese Earth Resource Sensing (JERS) satellite mosaicing, initiation of land cover/ land use (LCLU) mapping, intensive site selection, data acquisition and pre-processing, regional and intensive site GIS data base development including socio-economic and demographic data, preliminary field work, training and capacity development.

Year 2:
Complete JERS LCLU mapping, accuracy assessment /validation of JERS maps, time-series LCLU analysis, field data collection, second-growth forest SAR analysis, Regional GIS data base development and analysis, biomass regression estimators, interim publications.

Year 3:
LCLU conversion and SAR second-growth biomass validation, spatial analysis/fragmentation/biodiversity indices, initiation of regional GAP analysis (species ranges, conservation zones, biodiversity hotspots, management plans and conservation priorities), map and tabular output, publications, metadata, and data distribution.

Capacity Development/training

Following regional LCLU mapping and GIS ecological data base development, CCAD and Central American national scientists will be in a position at the end of the project to initiate ecological analysis of the biological corridor perhaps using the USGS Biological Resources Division, Geographic Approach to Planning Biological Diversity (GAP) Analysis (Scott et al., 1993, Krohn et al., manuscript in review) as a model. Central American scientists will receive training in remote sensing / image processing / LCLU classification to develop capacity for utilizing remote sensing and GIS/spatial analysis tools in order to conduct ecological monitoring throughout the region. Short term visiting scientist and student exchange programs will be arranged at GHCC and Umaine. Expenses for the training of Meso American scientists and students will be the responsibility of CCAD or the individual country programs.

Data Distribution and Reporting

The 50 and 100 meter images will be available for release in CDROM and through the JPL web site immediately after processing. The high resolution data (12.5 m) will be used for detailed studies over test sites and will be released to national collaborators. This is mainly because of the large volume of data for distribution and the agreement with NASDA (National Space Development Agency of Japan). The data sets and project documentation will be distributed to the Central American Commission on Environment and Development (CCAD). This will ensure that the access and distribution of the results gets to the organizations that will need it to support conservation and sustainable development projects within the region. Data sets and reports will be available on GHCC, JPL and UMaine web sites and links will be established between all investigator team research sites.

Publications

Interim publications and symposium presentations will be prepared with Central American collaborators during the early phases of the project. These will target regional outlets. Results of the SAR LCLU mapping will be prepared for publication before the end of year 2. In year 3, we will complete all analyses and prepare a series of manuscripts for submission to scientific journals and major symposiums.

Project Team and Responsibilities

The project brings together US researchers with extensive remote sensing experience in LCLU mapping, forest biomass and change detection studies in Central America and South America. The Central American scientists have not been identified by name. The research teams for each country are being identified through coordination with CCAD at the current time. Two team meetings will be conducted at GHCC and Guatemala City during the first year and one location in year 2 and 3. Research team meetings will be conducted in each field site in year 1 and 2. Routine correspondence will be by e-mail, fax and telephone.

The principal investigator will be responsible for project management and overall coordination of all research components. S. Sader will be assisted by a post-doctorate research associate on the intensive site LCLU change analysis, forest density modeling (MODIS/TM) and spatial analysis.

S. Saatchi will lead the SAR mosaicing, land cover mapping, topographic correction and SAR biomass estimation with E. Podest, a Ph.D. student (University of Dundee) and E. Rodriguez.

T. Sever and GHCC personnel will lead the optical data collection, pre-processing and coordinate GIS data base training and validation components with CCAD and Meso American scientists in each country. The roles of the investigators team are summarized as follows:

Univ. of Maine (Sader and post-doc)	project management and overall coordination of US and Central American research components Experimental design JERS land cover mapping co-leader analysis of time-series LCLU change and second-growth forest data for biomass estimation spatial analysis /fragmentation/biodiversity studies
JPL ( Saatchi, Podest, Rodriguiz)	JERS mosiacing , calibration and post-processing, topographic correction, JERS land cover mapping SAR and optical analysis and development of regression estimators for biomass/carbon stocks SAR/optical data fusion second-growth SAR/TM analysis and validation
Global Hydrology and Climate Center (GHCC). (Sever, Goodman and UAH personnel)	Intensive site data acquisition and pre-processing of time-series Landsat Accuracy assessment/validation of JERS and LCLU change maps Spatial analysis/fragmentation/biodiversity indices and testing regional GIS data coordination with CCAD training/capacity development/technology transfer CCAD web site development and data archive and distribution
CCAD and Central American scientists	Regional and intensive site GIS data base development field data collection to support LCLU and second-growth forest studies Evaluation/validation of JERS and time-series LCLU maps initiation of GAP analysis study of Central America biological corridor Training/capacity development participation

References:

Brondizio, E., E. Moran, P. Mausel and Y. Wu, 1996. Land cover in the Amazon estuary: linking of thematic mapper with botanical and historical data. Photogrammetric Engineering and Remote Sensing, 62(9):921-929.

Brown, S., C.A.S. Hall, W. Knabe, J. Raich, M.C. Trexler, and P. Woomer, 1993. Tropical forests: their past, present, and potential future role in the terrestrial carbon budget. Water, Air, and Soil Pollution, 70:71-94.

Brown, S. and A.E. Lugo, 1990. Tropical secondary forests. J. of Tropical Ecology 6, 1-32.

Brown, S., A.J.R. Gillespie and A.E. Lugo, 1989. Biomass estimation methods for tropical forests with applications to forest inventory data. Forest Science 35(4):881-902.

Cohen, W.B., M. Fiorella, J. Gray, E. Helmer, and K. Anderson, 1998. An efficient and accurate method for mapping forest clearcuts in the Pacific Northwest using Landsat imagery. Photogrammetric Engineering & Remote Sensing 64:293-300.

Congalton, R.G., 1991. A review of accessing accuracy of classifications of remotely sensed data. Remote Sensing of Environment 37:35-46.

Finn, J.T., 1993. Use of average mutual information index in evaluating classification error and consistency. International Journal of Geographic Information Systems 7(4):349-366.

Food and Agricultural Organization, 1993. Forest resources assessment, 1990, Tropical countries. FAO forestry paper 112, FAO-United Nations, Rome, Italy.

Hall, C.A.S. and J. Uhlig, 1991. Refining estimates of carbon released from tropical land-use change. Canadian Journal of Forest Resources, 21:118-131.

Holdridge, L. R., W. C. Grenke, W. H. Hatheway, T. Liang, and J. A. Tosi, Jr. 1971. Forest environments in the tropical life zones - a pilot study. Pergamon press, Oxford, Great Britain.

Houghton, R.A., 1991. Tropical deforestation and atmospheric carbon cycling. Clim. Change, 19, 99-118.

Krohn, W. B., R. B. Boone, S. A. Sader, J. A. Hepinstall, S. M. Schaefer, and S. L. Painton. Maine Gap Analysis - a geographic analysis of biodiversity. Final contract report to USGS Biological Resources Division, GAP Analysis Program, Moscow, Idaho. (Manuscript in USGS review).

LaGro Jr., J., 1991. Assessing patch shape in landscape mosaics. Photogrammetric Engineering & Remote Sensing 57(3):285-293.

Luckman, A., J. Baker, M. Honzak, and R. Lucus, 1998. Tropical forest biomass density estimation using JERS-1 SAR: seasonal variation, confidence limits, and application to image mosaics, Remote Sensing of Environment, 63:126-139.

Lugo, A.E. and S. Brown, 1992. Tropical forests as sinks of atmospheric carbon. Forest Ecology and Management, 54:239-255.

McGarigal, K. and B.J. Marks, 1993. FRAGSTATS: spatial pattern analysis program for quantifying landscape structure. Unpubl. Software, Dept. Forest Science, Oregon State University.

Olsen, E.R., R.D. Ramsey, and D.S. Winn, 1993. A modified fractal dimension as a measure of landscape diversity. Photogrammetric Engineering and Remote Sensing, 59(10):1517-1520.

O'Neill, R.V., J.R. Krummel, R.H. Gardner, G. Sugihara, B. Jackson, D.L. DeAngelis, B.T. Milne, M.G. Turner, B. Zygmunt, S.W. Christensen, V.H. Dale, and R.L. Graham, 1988. Indices of landscape pattern. Landscape Ecology 1(3):153-162.

Podest, E., 1998. SAR texture analysis for land cover classification of tropical rainforests using JERS-1 data, Master Thesis, University of Dundee/JPL.

Rignot, E., W. A. Salas, and D. L. Skole, 1997. Mapping deforestation and secondary growth in Rondonia, Brazil, using imaging radar and thematic mapper data. Remote Sensing of Environment 59: 167-179.

Saatchi, S., J Chen, and J. Cihlar, 1997. Estimating foliage biomass and LAI from microwave and optical remote sensing data. Remote Sensing of Environment (submitted).

Saatchi, S., 1996 Application of SAR remote sensing in land surface processes over tropical regions, proc. of VIII Simposio Brasileiro de Sennsoriemento Remoto. April 14-19, 1996, Salvador , Brazil, 213-222.

Saatchi, S., B. Nelson, E. Podest, and J. Holt, 1998. Mapping land cover types in the Amazon basin using 1 km JERS-1 mosaic, submitted to Special Issue of Int. J. Remote Sens.

Sader, S. A., M. Coan and D. Hayes. Time-series tropical forest change detection for the Maya Biosphere Reserve: updated estimates for 1995 to 1997. (manuscript in internal review).

Sader, S. A., C. Reining , T. Sever and C. Soza, 1997. Human migration and agricultural expansion: a threat to the Maya tropical forests. Journal of Forestry 95 (12): 27-30.

Sader, S.A., T. Sever, J.C. Smoot and M. Richards, 1994. Forest change estimates for the northern Peten region of Guatemala - 1986-1990. Human Ecology, 22(3):317-332.

Sader, S.A., 1995. Spatial characteristics of forest clearing and vegetation regrowth as detected by Landsat Thematic Mapper imagery. Photogrammetric Engineering and Remote Sensing, 61:1145-1151.

Sader, S.A., T.A. Stone, and A.T. Joyce, 1990. Remote sensing of tropical forests: an overview of research applications using non-photographic sensors. Photogrammetric Engineering and Remote Sensing 56:(10)1343-1351.

Sader, S. A. and A. T. Joyce, 1988. Deforestation rates and trends in Costa Rica, 1940 to 1983. Biotropica 20(1):11-19.

Sader, S.A., R.B. Waide, W.T. Lawrence, and A.T. Joyce, 1989. Tropical forest biomass and successional age class relationships to a vegetation index derived from Landsat-TM data. Remote Sensing of Environment, 28(4):143-156.

Sader, S.A., 1987. Forest biomass, canopy structure and species composition relationships with multipolarization L-band synthetic aperture radar data. Photogrammetric Engineering and Remote Sensing 53(2):193-202.

Schwartz, N.B., 1990. Forest society. University of Pennsylvania Press, Philadelphia.

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Skole, D.L., W.H. Chomentowski, W.A. Salas, and A.D. Nobre, 1994. Physical and human dimensions of deforestation in Amazonia. Bioscience, 44(5):314-321.

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World Resources Institute, 1990. World Resources 1990-91. Oxford University Press, Inc. Biographical Sketches

Principal Investigator

Steven A. Sader, 1987 to present: Professor of Forest Resources and Director - Maine Image Analysis Laboratory, Dept. of Forest Management, U. Maine, Orono, ME; B.S.F., Northern Arizona University, M.S.F., Mississippi State University, Ph.D., Forest Resources Management, University of Idaho. Former Positions: Research Forester/Remote Sensing NASA-Stennis Space Center, MS; Natural Resource Specialist/Remote Sensing, BLM-Branch of Remote Sensing, Denver, CO; U.S. Government contractor on remote sensing/rural development projects in Costa Rica, Peru, the Sudan (USAID).

Dr. Sader has over 20 years experience in forest related remote sensing research in Central America. He was principal investigator on migratory bird habitat monitoring investigations in Belize, Costa Rica and Guatemala (US Fish and Wildlife Service) and Southern Mexico (Smithsonian Institution). He has been conducting tropical forest research in Guatemala since 1991. Steve was a consultant on the U.S. Forest Service's (Southern Forest Experiment Station) AVHRR forest mapping project of Central America and served as a facilitator for the Forest Service's Global 2000 workshop on Remote Sensing Inventories of Global Forests in 1995. He was guest editor of the special issue of Photogrammetric Engineering and Remote Sensing on "Remote Sensing of Tropical Forests" published in 1990.

From 1983 to 1987, Steve was principal investigator for tropical forest research conducted at NASA-Stennis Space Center. His research involved analysis of airborne sensors including JPL L-band SAR, LIDAR, TIMS, TMS, CAMS and satellite imagery directed to investigations of forest canopy structure, biomass, species composition and time-series forest change monitoring.

He is a member of the American Society of Photogrammetry and Remote Sensing, International Society of Tropical Foresters, The International Union of Forest Research Organizations, and chair of the Society of American Foresters Remote Sensing Working Group (A2).

Selected Publications: (other than those in reference section)

Sader, S.A., 1995. Big picture of land use change. Book review of W.B. Meyer and B.L. Turner II (Eds.). Changes in Land Use and Land Cover: A Global Perspective. Conservation Biology 9(6):1-2.

Sader, S.A., and J.C. Winne, 1992. RGB-NDVI Color Composites for Visualizing Forest Change Dynamics. International Journal of Remote Sensing 13(16): 3055-3067.

Sader, S.A., G.V.N. Powell, and J.H.Rappole, 1991. Migratory Bird Habitat Monitoring Through Remote Sensing. International Journal of Remote Sensing 12(3): 363-372.

Sader, S.A., and T. Stone (Eds.), 1990. Remote Sensing for Monitoring Tropical Moist Forests. Special issue Photogrammetric Engineering and Remote Sensing 56(10).

Sader, S.A. 1987. Forest Biomass, Canopy Structure and Species Composition Relationships with Multipolarization L-band Synthetic Aperture Radar Data. Photogrammetric Engineering and Remote Sensing 53(2): 193-202.

Wilson, M. and S.A. Sader (Eds.), 1995. Conservation of neotropical migratory birds in Mexico. Maine Agriculture and Forestry Experiment Station, Misc. Pub. 727. Orono, ME. 288 pp.

Co-Investigators: Co-Investigators:

Sasan S. Saatchi

Sasan Saatchi received his B.S. and M.S. degrees in electrical engineering from the University of Illinois in 1981 and 1983 respectively, and the Ph.D. degree from the George Washington University in 1988 with the concentration in electrophysics and modeling of wave propagation in natural media.

From 1989-1991, he was a postdoctoral fellow at the National Research Council and worked at the Laboratory for Terrestrial Physics at NASA/Goddard Space Flight Center on the hydrological application of active and passive microwave remote sensing. Since April of 1991, he has been with the Radar Science and Engineering Section of the Jet Propulsion Laboratory, California Institute of Technology where as a scientist, he is involved in developing microwave scattering and emission models for soil and vegetation surfaces and retrieval algorithms for estimating geophysical parameters from spaceborne remote sensing instruments. He has been a principal or co-investigator in several interdisciplinary international projects such as FIFE, EFEDA, Magellan, Mac-Hydro, Hpex-Sahel, BOREAS, and LCLUC. His present research activities include land cover classification, biomass and soil moisture estimation in boreal forests, and land use and land cover change, and forest regeneration monitoring over tropical rain forests. His research interests also include wave propagation in disordered/random media and EM scattering theory.

Recent Journal Publications

Saatchi, S.S., 1996. Application of SAR remote sensing in studying land surface processes in tropics. Proceedings of VIII Simposio Brasileiro de Sensoriemento Remoto, April 14-19, 1996, Salvador, Bahia, Brazil, pp. 213-222.

Saatchi, S., J.V. Soares, and D.S. Alves, 1997. Mapping deforestation and land cover in Amazon rainforest using SIR-C imagery. Remote Sensing of Environment, Vol. 59, No. 2, 191-202.

Saatchi, S. and E. Rignot, 1997. Land cover classification of BOREAS modeling grid using AIRSAR images. Remote Sensing of Environment, Vol. 35, No. 6.

Saatchi, S. and M. Moghaddam, 1998. Estimation of canopy water content and biomass of boreal forest from polarimetric radar data. J. Geophysical Research.

Saatchi, S., J. Chen and J. Cihlar, 1997. Estimating foliage biomass and LAI from microwave and optical remote sensing data. Submitted to Remote Sensing of Environment.

Saatchi, S.S., B. Nelson, E. Podest, and J. Holt, 1998. Mapping land cover types in Amazon basin using JERS-1 SAR mosaic. Submitted to International Journal of Remote Sensing.

Thomas L. Sever Thomas L. Sever

1997 to present, Remote Sensing/Archeologist, Global Hydrology and Climate Center, NASA-Marshall Space Flight Center, AL. Ph.D., M.A., Anthropology/Archeology. Former positions: 1981-1997, Remote Sensing/Archeologist, Earth Systems Science Group, NASA-Stennis Space Center, MS; Assistant Professor, U. of So. Mississippi; Scientific Supervisor, Lockheed Electronics Corp.; National Space Technologies Laboratories, Bay St. Louis, MS; Archeology Supervisor, U. of Illinois, Chicago.

Dr. Tom Sever has over 20 years experience in environmental/archeological research. He has been a pioneer in bringing remote sensing/GIS technology to the disciplines of anthropology and archeology. He has been conducting research in northern Guatemala since 1987 and his satellite images and research influenced the President and Congress of Guatemala in establishing the Maya Biosphere Reserve in 1990. He has worked with airborne and satellite systems conducting research in Peru, Chile, Mexico, Costa Rica, Guatemala, and the American Southwest. His awards include the Society of Professional Archeologists (SOPA) Exceptional Achievement Award (1994), NASA Exceptional Achievement Award (1993), and NASA Exceptional Scientific Achievement Award (1992).

Selected Publications

Sever, T.L., P.Sheets, and L.Conyers, (in press). Remote Sensing in Central America: Arenal in Costa Rica and Ceren in El Salvador. In Manual of Remote Sensing, Archeological Applications of Remote Sensing: The Mapping and Measurement of Early Landscapes and Settlements.

Sever, T.L., (in press) Remote Sensing Methods. In Advances in Science and Technology for Historic Preservation. Ray Williamson (ed.). Plenum Press.

Sever, T.L., 1998. Validating Prehistoric and Current Social Phenomena upon the Landscape of the Peten, Guatemala. In People and Pixels: Linking Remote Sensing and Social Science. National Academy of Sciences/National Research Council.

Sever, T.L., 1995. Remote Sensing. American Journal of Archeology 99: 83-84.

Sader, S.A., T.L. Sever, J.C.Smoot, and M.Richards, 1994. Forest Change Estimates for the Northern Peten Region of Guatemala- 1986 to 1990. Human Ecology 22(3): 317-332.

H. Michael Goodman H. Michael Goodman

Atmospheric Scientist, NASA/Marshall Space Flight Center

Education: 1980 M. S. Meteorology, Florida Sate University, Tallahassee FL 1978 B. A. Environmental Science, University of Virginia, Charlottesville VA

Professional Experience (last 5 years): 1989-present Atmospheric Scientist, NASA/Marshall Space Flight Center, AL

Recent Assignments: 1997- present Global Hydrology Resource Center, Project Manager, 1992-1997 MSFC DAAC Project Scientist and User Working Group Co-Chair, 1990-1997 WetNet Project Manager, PIP-1, PIP-2, & PIP3 Executive Committee

Research Areas:

Mr. Goodman has 17 years professional experience in atmospheric science, data analysis, and applied meteorological software development. He successfully initiated and managed the NASA WetNet project which established the first passive microwave data subscription service to the Earth science community. Subsequently, WetNet spawned three algorithm research projects called the Precipitation Intercomparison Project (PIP-1, PIP-2, and PIP-3). The PIP projects performed the first assessment of passive microwave precipitation algorithms on a global scale and examined the strength and weaknesses of passive microwave precipitation algorithms. Following his involvement with the MSFC Distributed Active Archive Center, he became the project manager for the Lightning Imaging Sensor Enhanced Science Computing Facility. Then in 1998 he was named the project manager for the Passive Microwave Earth Science Information Partnership.

Selected Publications

Ritchie, A. R., M. R. Smith, H. M. Goodman, R. Schudalla, D. Conway F. LaFontaine, D. Moss, B. Motta, " Critical Analyses of Data Differences Between FNMOC and AFGWC Spawned SSM/I Data Sets", J. Atmos. Sci., (1998)

Smith, E. A., J. Dodge, S. Goodman,. M. Goodman, .E. Zipser, "Interim Report on the Second WetNet Precipitation Intercomparison Project (PIP-2), International Geoscience and Remote Sensing Symposium (IGARSS'95), July 10-14, 1995; Firenze, Italy (1995)

Barrett, E. C., J. Dodge, H. M. Goodman, J. Janowiak, C. Kidd, and E. A. Smith, "The First WetNet Precipitation Intercomparison Project," Remote Sensing Rev., 11(1-4), 49-60 (1994).

Kniveton, D. R., B. C. Motta, H. M. Goodman, M. Smith, and F. J. LaFontaine, "The First WetNet Precipitation Intercomparison Project: Generation of Results," Remote Sensing Rev., 11(1-4), 243-302 (1994).

Dodge, J. and H. M. Goodman, "The WetNet Project, Special Edition: The First Precipitation Intercomparison Project (PIP-1)," Remote Sensing Reviews, 11(1-4), 5-21 (1994).

Spencer, R. W., H. M. Goodman, and R. E. Hood, "Precipitation Retrieval Over Land and Ocean with the SSM/I: Identification and Characteristics of the Scattering Signal," J. Atmos. & Oceanic Technol., 6(2), 254-273 (1989).

Facilities and Equipment

University of Maine

The Maine Image Analysis Laboratory (MIAL) is a research facility for the application of satellite remote sensing and geographic information systems to forestry and natural resources management. The laboratory is equipped with three Silicon Graphics workstations and one 75 MH, Pentium Dell PC, all machines have CD ROM drives and are connected to the internet. Other peripheral hardware in the lab includes one optical drive, DAT and 8mm tape backup, HP 650 C (large format) plotter, a Calcomp X-Y coordinate digitizing tablet, two Bausch and Lomb zoom transfer scopes, four light tables, various stereoscopes and other aerial photo interpretation equipment. A videography workstation with two Sony Hi-8 recorders is available. Two Trimble GPS units, and a 35 mm camera are available within the department. The primary image processing and GIS packages include ERDAS-Imagine (V-8.3) and ARC-Info (V.7.1). A teaching lab in the Forest Management Department contains 6 SGI-02 workstations purchased through the 1997-98 NASA-Remote Sensing Center of Excellence grant.

Jet Propulsion Lab

As part of the GRFM project, we have developed an infrastructure consisting of personnel, workstations, storage space, access to JPL supercomputer and power wall. These will allow the data processing and mosaicing and classification procedures to be implemented efficiently. The mosaic software has been developed for the SRTM and has already been modified and applied to the JERS-1 images over the Amazon basin and Central Africa. We propose to augment this infrastructure by a procurement of a dedicated workstation and storage space to be used by Erika Podest as the Ph.D. student to manage the JERS-1 data over Central America.

Currently there are two workstations (congo and bacchus) used primarily for Central and West African data processing and classification. The mosaic software runs on the JPL supercomputer using the parallel processing feature. Since the account for the supercomputer facility will be closed before the start of this project, we intend to purchase a Sun Ultra 2 machine with 18 GB storage space to implement all the data processing and mosaicing of the Central American JERS-1 data.

Current (C) and Pending (P) Support

S.A. Sader

(C) Time-series land cover/land use change and socio-economic driving forces in northern Guatemala, NASA, 1997-2000, $231,000 (S. Sader (1.50 mo/yr FTE), T. Sever, C. Soza).

(C) Forest monitoring and multisensor research in Maine's industrial forest, NASA, $750,000, 1998-2001 (S. Sader, 4.0 mo/yr FTE)