Teacher’s Summary: In the paper “Problems in Remote Sensing: An Academic Review,” Will Thompson, an 11th-grade honors environmental science student, examines the significant challenges of error, uncertainty, and scale in remote sensing. Through his experiences and research, Thompson highlights how these issues affect the accuracy and interpretation of remotely sensed data, which is crucial for environmental monitoring and analysis. He discusses the types and implications of errors, the sources and consequences of uncertainty, and the importance of choosing appropriate scales for different research questions. Thompson’s insights, drawn from his internship and hands-on projects, emphasize the need for ongoing advancements in remote sensing technology to improve the reliability of Earth observation methods.
Problems in Remote Sensing: An Academic Review
Will Thompson
Honors Environmental Science, Grade 11
Oakwood High School
Abstract
Remote sensing technology has revolutionized our ability to observe and analyze Earth’s surface from afar. However, the interpretation of remotely sensed data is often fraught with challenges. This paper examines three primary issues in remote sensing: error, uncertainty, and scale. By exploring these concepts and their implications, we can better understand the limitations and potential of remote sensing in environmental science and related fields.
1. Introduction
As an avid hiker and environmental enthusiast, I’ve always been fascinated by how scientists can study vast areas of our planet without setting foot on them. This interest led me to delve deeper into the field of remote sensing during my summer internship at the local university’s Earth Sciences department. Through this experience, I discovered that while remote sensing offers incredible opportunities for environmental monitoring and analysis, it also presents significant challenges.
The goal of remote sensing, as defined by Curtis Woodcock (2002), is “to infer information about objects from measurements” taken at various locations. However, this process is not without its complications. This paper aims to explore three primary issues in remote sensing: error, uncertainty, and scale. By understanding these challenges, we can better appreciate the complexities involved in interpreting remotely sensed data and develop more effective strategies for its use in environmental science.
2. Error in Remote Sensing
Error in remote sensing is defined by Heuvelink (1991) as “the difference between reality and our representation of reality.” During my internship, I learned that errors can significantly impact the accuracy of environmental assessments and land use classifications.
2.1 Types of Error
Jensen (2005) categorizes geometric errors into two main types: internal and external. Internal errors are caused by factors inherent to the Earth’s curvature and the remote sensing system being used. These include:
- Skew: Caused by the Earth’s rotation and the sensor’s orbit, resulting in distorted images.
- Scanning System Issues: Particularly prevalent in sub-orbital craft, leading to distortions in the captured data.
- Relief Displacement: Objects in images are displaced from their true positions due to perspective geometry.
External errors, on the other hand, occur due to unexpected changes in nature through space and time. These include altitude changes and attitude changes (yaw, pitch, and roll) of the sensing platform.
2.2 Implications of Error
During a field trip to a local conservation area, our class used drone-captured imagery to map invasive plant species. We quickly realized how even small errors in georeferencing could lead to significant misclassifications of vegetation types. This experience highlighted the critical importance of error correction and validation in remote sensing applications.
3. Uncertainty in Remote Sensing
Uncertainty in remote sensing refers to the lack of certainty in the interpretation and analysis of remotely sensed data. As May (2001) points out, uncertainty pertains to areas of inference and prediction.
3.1 Sources of Uncertainty
Woodcock (2002) relates uncertainty to three key areas:
- Accuracy: The closeness of results to accepted true values.
- Bias: Systematic over- or under-estimation of true values.
- Precision: The exactness of a value, regardless of its accuracy.
These factors play crucial roles in projects measuring changes in ice sheets, tropical forests, and land classification – topics that fascinate me as I consider pursuing environmental science in college.
3.2 Implications of Uncertainty
The collapse of the Kyoto Protocol negotiations in 2000 due to high levels of uncertainty in carbon emission measurements serves as a stark reminder of the real-world implications of uncertainty in remote sensing data. This example, which we discussed in our AP Environmental Science class, demonstrates how scientific uncertainty can have far-reaching political and environmental consequences.
4. Scale in Remote Sensing
Scale in remote sensing refers to the level of detail at which information is observed and analyzed. As I learned during a workshop on GIS and remote sensing, scale can be a significant constraint in data interpretation.
4.1 Types of Scale
Lillesand (2004) breaks down scale into two main categories:
- Temporal Scale: The time frame over which data is collected and analyzed.
- Spatial Scale: The physical size of the area being studied and the resolution of the data.
4.2 Implications of Scale
During a science fair project where I used Landsat imagery to study urban sprawl in our city, I encountered the challenges of scale firsthand. The 30-meter resolution of Landsat was sufficient for identifying large-scale changes but inadequate for detecting smaller developments like individual buildings. This experience taught me the importance of choosing the appropriate scale for the research question at hand.
5. Conclusion
As I’ve explored the field of remote sensing, from classroom discussions to hands-on projects, I’ve come to appreciate both its potential and its limitations. The issues of error, uncertainty, and scale present significant challenges in the interpretation and application of remotely sensed data. However, understanding these challenges is crucial for developing more accurate and reliable methods of Earth observation.
Looking ahead to my future studies in environmental science, I’m excited about the ongoing advancements in remote sensing technology and methodologies. As sensors become more precise and our understanding of these issues deepens, we’ll be better equipped to address global environmental challenges. The journey from my local hiking trails to analyzing satellite imagery has been eye-opening, and I’m eager to contribute to this field in the years to come.
References
1. Atkinson, M.P., & Foody, M.G. (2002). Uncertainty in Remote Sensing and GIS: Fundamentals. John Wiley & Sons Ltd.
2.Harvey, K.R., & Hill, G.J.E. (2001). “Vegetation mapping of a tropical freshwater swamp in the Northern Territory, Australia: A comparison of aerial photography, Landsat TM and SPOT satellite imagery.” International Journal of Remote Sensing, vol. 22, no. 15, pp. 2911-2925.
3. Heuvelink, G.B.M. (1991). Error Propagation in Environmental Modelling with GIS. Taylor & Francis.
4. Jensen, R.J. (2005). Introductory Digital Image Processing. Pearson Prentice Hall.
5.Lillesand, T.M., Kiefer, R.W., & Chipman, J.W. (2004). Remote Sensing and Image Interpretation. The Lehigh Press.
6.May, R. (2001). “Risk and Uncertainty.” Nature, vol. 411, no. 6838, pp. 891.
7.Woodcock, C.E., & Strahler, A.H. (1987). “The factor of scale in remote sensing.” Remote Sensing of Environment, vol. 21, no. 3, pp. 311-332.
8.Woodcock, Curtis E. (2002). “Remote Sensing in the Environmental Sciences: An Overview.” Remote Sensing and the Environment. Springer.
9.Foody, G.M. (2002). “Status of land cover classification accuracy assessment.” Remote Sensing of Environment. Elsevier, www.elsevier.com/locate/rse.
Online References
1. May, R. (2001). “Risk and Uncertainty.” Nature, vol. 411, no. 6838, pp. 891.
2. Foody, G.M. (2002). “Status of land cover classification accuracy assessment.” Remote Sensing of Environment. Elsevier.