Process Modeling, Remote Monitoring, and Forecasting
Thesis By
Gregory S. Okin
In Partial Fulfillment of the Requirements
for the Degree of
Doctor of Philosophy
California Institute of Technology
Pasadena, California
2001
(Submitted October 13, 2000)
© 2001
Gregory S. Okin
All Rights Reserved
ABSTRACT
Arid and semiarid landscapes comprise nearly a third of the Earth’s total land surface. These areas are coming under increasing land use pressures. Despite their low productivity these lands are not barren. Rather, they consist of fragile ecosystems vulnerable to anthropogenic disturbance.
The purpose of this thesis is threefold: (I) to develop and test a process model of wind-driven desertification, (II) to evaluate next-generation process-relevant remote monitoring strategies for use in arid and semiarid regions, and (III) to identify elements for effective management of the worlds drylands.
In developing the process model of wind-driven desertification in arid and semiarid lands, field, remote sensing, and modeling observations from a degraded Mojave Desert shrubland are used. This model focuses on aeolian removal and transport of dust, sand, and litter as the primary mechanisms of degradation: killing plants by burial and abrasion, interrupting natural processes of nutrient accumulation, and allowing the loss of soil resources by abiotic transport. This model is tested in field sampling experiments at two sites and is extended by Fourier Transform and geostatistical analysis of high-resolution imagery from one site.
Next, the use of hyperspectral remote sensing data is evaluated as a substantive input to dryland remote monitoring strategies. In particular, the efficacy of spectral mixture analysis (SMA) in discriminating vegetation and soil types and determining vegetation cover is investigated. The results indicate that hyperspectral data may be less useful than often thought in determining vegetation parameters. Its usefulness in determining soil parameters, however, may be leveraged by developing simple multispectral classification tools that can be used to monitor desertification.
Finally, the elements required for effective monitoring and management of arid and semiarid lands are discussed. Several large-scale multi-site field experiments are proposed to clarify the role of wind as a landscape and degradation process in drylands. The role of remote sensing in monitoring the world’s drylands is discussed in terms of optimal remote sensing platform characteristics and surface phenomena which may be monitored in order to identify areas at risk of desertification. A desertification indicator is proposed that unifies consideration of environmental and human variables.
Table of Contents
CHAPTER 1: Introduction
The Potential Cost of Inaction
Organization of the thesis
History and features of principal study sites
The Manix Basin, San Bernardino County, California
The Jornada Del Muerto Basin, Doña Ana County, New Mexico
PART I: PROCESS MODELING
CHAPTER 2: Degradation of Sandy Arid Shrubland Environments: Observations, Process Modeling, and Management Implications
1. INTRODUCTION
2. Methods
3. Results and Discussion
3.1 Remote Observations from the Manix Basin
3.2 Field Observations in the Manix Basin
Direct Disturbance
Indirect Disturbance
Anthropogenic Additions
3.3 Quantitative Assessmen
4. Conclusions
4.1 Anthropogenic Desertification Of Arid Shrublands
4.2 Nutrient Relations and Soil Resources
4.3 Lessons for Land Managers
4.4 Regional Drivers and Effects
4.5 Extrapolation to Other Areas
Other Land Forms in the Arid Southwest
4.6 Global Implications
5. SUMMARY
CHAPTER 3: Desertification in an Arid Shrubland in thS outhwestern United States: Process Modeling and Validation
1. Introduction
2. Model Hypothesis and Approach
3. Methods
3.1 Preliminary Observations
3.1.1 Manix Basin, southeastern California, USA
3.1.2 Jornada Basin, south-central New Mexico, USA
3.2 Image Acquisition
3.3 Sample Collection
3.3.1 Manix
3.3.2 Jornada
3.4 Laboratory Analysis
3.5 Statistical Analysis
4. Results
4.1 Spatial information: Remote Sensing
4.2 Available Nitrogen (NAvail)
4.2.1 Manix
4.2.2 Jornada
4.3 Phosphorus
4.3.1 Manix
4.3.2 Jornada
4.4 Other Species: Cl-, SO4-2, Mg+2, Ca+2, K+, Na+, Li+
4.4.1 Manix
4.4.2 Jornada
5. Discussion
5.1 Salinization
5.2 Fertilization
5.3 Material Transport
6. Implications for Biogeochemical Desertification in Arid Shrublands
CHAPTER 4: Distribution of vegetation in wind-dominated landscapes: Implications for wind erosion modeling and landscape processes
1. Introduction
2. Experimental Methods
2.1 Experimental Sites
2.3 Image acquisition and processing
2.3.1 Digital Orthophoto Quads
2.3.2 Fourier Transform Analysis
2.3.3 Geostatistical Analysis
3. Results
3.2 Fourier Transform Analysis
3.3 Geostatistical Analysis
4. Discussion
4.1 The length and spacing of streets
4.2 Implications for wind erosion modeling
4.3 Implications for landscape processes
5. Conclusions
PART II: REMOTE MONITORING
CHAPTER 5: Practical Limits on Hyperspectral Vegetation Discrimination in Arid and Semiarid Environments
1. INTRODUCTION
2. METHODS
2.1 Field Spectroscopy
2.2 Spectral Simulations
2.3 Spectral mixture analysis of Simulated Spectra
2.4 AVIRIS Image Preprocessing
2.5 Multiple Endmember Spectral Mixture Analysis (MESMA)
3. Results and Discussion
3.1 Spectral Simulations
3.1.1 Vegetation Retrievals
3.1.2 Soil Retrievals
3.2 MESMA of AVIRIS data
4. Conclusions and IMplications
5. Future Work
CHAPTER 6: The Role of Optical Remote Sensing in Dryland Monitoring
1. Introduction
1.1 Remote Sensing Monitorables—Desertification vs. its Causes
1.2 Remote Monitoring as a Planning/Management Tool
1.3 Process-Relevant Observation and Monitoring- An Integrated Approach
2. Remote Sensing Data Requirements: reasonable expectations
2.1 Spatial Issues—Large Pixels or Small?
2.2 Spectral issues—Hyperspectral or Multispectral?
2.3 Temporal Issues—Toward Multitemporal Monitoring
2.4 Integrative Issues—Possible Data Sources
3. NEar-Term Prospects for Improved Remote Monitoring Techniques
3.1 Concentrating on the Soil
3.1.1 Grain-size distribution
3.1.2 Soil armoring
3.1.3 Soil nutrient concentrations
3.2 Possibilities for Retrieval of Vegetation Information
3.2.1 Two-Step MESMA
3.2.2 The Use of Spatial Information
3.2.3 The Use of Temporal Information
4. Summary
PART III: TOWARD SUSTAINABLE LAND USE IN ARID AND SEMIARID ENVIRONMENTS
CHAPTER 7: Obstacles on the Road to Sustainability
1. Wind-Driven Desertification: Process Modeling
1.1 The Role of Wind vs. Water in Dryland Degradation
1.2 The Role of Climate
1.3 The Role of Disturbance
2. Remote Monitoring of Desertification
3. Forecasting Desertification
4. Conclusion
Appendix A: Hyperspectral Enhancement of Multispectral Indices: Potential Use for Mapping Wind-Erodible Soils in an Arid Shrubland
1. INTRODUCTION
2. Methods
2.1 MESMA of AVIRIS Data
2.2 Simulation of Multispectral Data
2.3 Choice and Evaluation of Candidate Indices
3. Results and Discussion
3.1 MESMA Modeling Results
3.2 Multispectral Simulations and Spectral Indices
3.3 Application to a Landsat TM scene
4. Potential Application to Studies of Land Degradation
Appendix B: Identifying Areas at Risk of Desertification: The Leading Desertification Indicator Concept
1. Introduction
2. The Leading Desertification Indicator Concept
2.1 Global Information Level
2.2 Geopolitical Information Level
2.3 National Information Level
2.3 Local Information Level
3. Managing uncertainty and Suiting Users
4. The Role of Satellite Remote Monitoring
5. Conclusion
APPENDIX C: Correlation Between ENSO Anomaly and Wind Erosion in the Manix Basin
APPENDIX D: Dust Emission and Nutrient Losses from Semiarid Grasslands: Relation to Climate Change and Desertification
1. Introduction
2. Does wind erosion matter?
3. The State of the Art
3.1 General Wind Erosion Relations
3.2 The Impact of Soil Characteristics on Wind Erosion
3.3 The Role of Vegetation in Controlling Wind Erosion
3.4 Conclusion
4. Why Climate Matters
4.1 Landscape Response to Regional Climate
4.2 Climate Response to Landscape Change
5. The Role of Disturbance
6. Proposed Experiments to Better Delineate the role of wind erosion
6.1 Bioclimatic Transect
6.1.1 Jornada LTER Site
6.1.2 Sevilleta LTER Site
6.1.3 Shortgrass Steppe LTER
6.2 Disturbance and Scale
6.3 Timescales
6.4 Experimental Set-up and Required Measurements
6.4.1 Site Selectio
6.4.2 Experimental Treatments at Each Locality
6.4.3 Measurements
6.5 Aeolian Sediment and Nutrient Budgets
6.6 Redistribution of Nutrients Within the Landscape
6.7 Measuring Vertical Dust Flux, FA
6.7.1 Finding horizontal mass flux, q
6.7.2 Finding values of k
6.8 Modeling Regional Effect of Wind on Landscapes
7. Conclusion
CITED REFERENCES