Satellite Imagery Activism: Sharpening the Focus on Tropical Deforestation

The Rise of Satellite Imagery Activism
According to the authors (from the RAND Corp. & George Washington University), the first Landsat satellite was launched by the U.S. in 1972 and since then has opened up the doors to satellite remote sensing, especially in terms of natural resource sustainability and human activity monitoring. The original users of satellite imagery were state agencies (civil & military), natural resource related businesses and scientific research institutes. However today the groups that use satellite imagery run the gamut from NGO's to multinational organizations. Since the early days of satellite imagery there have been dramatic improvements in availability and quality that have lead to increased accessibility. First, cheap and highly powerful computing abilities have broadened the range of individuals that can work with imagery data. Second, "the advent of user-friendly software for image processing and analysis has made imagery analysis much less the purview of remote sensing specialists". Third, the cost of imagery data has drastically declined with the onset of cheaper Landsat/SPOT images, declassified U.S. & Russian intelligence imagery and many other forms of commercially available imagery. Lastly, the internet and CD-ROM's have facilitated the spread of imagery with giants like Google moving into the arena.

Imagery Activism & Tropical Forests
Due to "rising levels of global transparency" and recent software and internet abilities, there are many avenues in which citizens can bring awareness to public policy issues through the use of satellite imagery. A lot of environmental factors can be analyzed via satellite imagery such as changes in vegetation, biological stress and habitat characterization. Out of these issues, there has been the greatest focus on monitoring trends in tropical deforestation. Besides simple deforestation, "satellite imagery can be used to monitor growth, as well as the effects of human activity and exploitation...on the surrounding ecosystems". Satellite images can provide objective proof of potential logging infractions and at the same time be built up into databases to provide long-term monitoring of forests. In the last several years, the international community's gaze has fallen on the tropical rainforests of Brazil and Indonesia. Brazil in particular has seen the dramatic expansion of road networks, farms, pastures and plantations, all at the expense of the rainforests.

Conclusions
In summary, this paper examined the rise of satellite imagery in today's society and how this imagery affects the world's tropical rainforests. The paper's focus was on deforestation in specifically Brazil and Indonesia. Furthermore, the paper's aim is to attract global attention to the problem of deforestation by utilizing satellite imagery data and other geospatial technologies.

Source
http://www.aseanenvironment.info/Abstract/41016424.pdf

Tropical Cyclone Intensity Analysis and Forecasting from Satellite Imagery

Introduction
This article by Vernon F. Dvorak, discusses the implications of analyzing cyclones with intensity analysis, by using satellite platforms. Dvorak notes that satellite imagery is highly useful in monitoring tropical cyclones because methods have been developed that allow analysts to estimate the intensity of the cyclone based on certain distinguishing cloud features.
The Technique
The technique of combining intensity analysis with satellite imagery yields rather good estimates in terms of forecasting storm intensity. The specific cloud features that are used to estimate cyclone intensity are derived from two categories of features: "central features" and "outer-banding features". Each of these features is assigned a "T-number" ("T" for tropical) which denotes tropical disturbances. T1 describes disturbances "exhibiting minimal but significant signs of tropical cyclone intensity", whereas T8 denotes the "maximum possible" cyclone intensity. Next, each of the cloud features is analyzed to determine whether the cyclone will remain at its modeled intensity over the next 24 hours.

The Analysis Procedure
The intensity analysis via the satellite platform consists of three stages. The first stage "requires a qualitative judgment as to how cloud features related to cyclone intensity have changed between yesterday's picture and today's". The second and third stages of analysis involve examining the overall cloud pattern and component features to see if they agree with the modeled intensity estimate that the forecasters arrived at during the first stage of analysis.
The Forecast Procedure
To come up with the actual intensity forecast, the researchers have to use either the cyclone's model curve to obtain tomorrow's intensity or adjust the curve when an interuption due to landfall or the approach of some unfavorable circumstance is near. These changes can be detected with an unexplainable change of T-number or in changes to the cloud features.
Performance
A researcher named Erickson conducted the first tests using intensity analysis via satellite imagery in 1972, and his results have shown good consistency. Another researcher by the name of Arnold (1972)used the T-number estimates in tandem with the Joint Typhoon Warning System estimates for the west Pacific and had pretty good results as well.

(Satellite Imagery of Tropical Cyclone patterns with "T" Numbers)
Source
http://journals.ametsoc.org/doi/pdf/10.1175/1520-0493%281975%29103%3C0420%3ATCIAAF%3E2.0.CO%3B2

Validation of SPOT-5 Satellite Imagery for Geological hazard Identification

Summary
This study was developed to determine whether SPOT-5 satellite imagery could be used to develop geographic hazard maps for areas lacking available resource. The study took place in Nicaragua, and compared SPOT-5 analysis with newly created threat maps, and additional geographic monitoring. Maps were created using SPOT-5 imagery for hazard inventory, hazard susceptibility, and vulnerability mapping. The study concluded that SPOT-5 imagery could be used to effectively develop geographic hazard maps. The system was however limited by the level analysis needed to create the hazard maps.
Strengths
Satellite imagery was capable of providing necessary data in remote areas that lack accurate geographic maps. Additionally it allowed for direct GIS system and an ability to gather data despite poor weather conditions.
Limitations
Despite the SPOT-5 test success, the full process of creating the maps required large amounts of additional analysis. The system was also unable to easily identify certain features needed for hazard mapping, which could lead to possible issues without additional support from standard measurement systems

Source:http://www.cartesia.org/geodoc/isprs2004/comm1/papers/51.pdf

The Use Of Satellite Imagery In Landslide Studies In High Mountain Areas

Summary

This is a case-study that compares the application of Landsat ETM+ and IKONOS imagery when assessing a natural terrain's susceptibility to landslides. This study looked at six areas located in the upland areas of Nepal and Bhutan. In each case, the imagery has been used both to directly map landslides and to examine the occurrence of factors that might be important in landslide initiation, such as water seepage. The results from the imagery were bench-marked using field surveys.

The results of this study demonstrated that Landsat ETM+ continues to be the most cost-effective imagery tool for mapping landslide susceptibility. This is due to the fact that it is relatively low-cost and has high spectral resolution. However, the researchers state that the spatial resolution is still a significant limitation to Landsat ETM+.

The high resolution, multispectral IKONOS imagery is not limited in the same way. With IKONOS, even small landslides are able to be mapped in great detail. However, according to the report, this type of imagery is less useful for factor type mapping. The report concluded that of the high cost of IKONOS will prevent most developing countries from developing and utilizing the tool, resulting in Landsat ETM+ being the better tool for mapping landslide susceptibility.

Sources: http://www.securinglivelihoods.org/nepal/files/nepal%20case%20study.pdf

Processing Satellite Imagery To Detect Waste Tire Piles

Summary

This article, posted on www.techbriefs.com, discusses the development of a new methodology in satellite imagery analysis. The developers, Joseph Skiles, Cynthia Schmidt, Becky Quinlan, and Catherine Huybrechts, state that they have created a new methodology for processing commercially available satellite spectral imagery to identify and map waste tire piles in California. The methodology uses a combination of previously commercially available image-processing and georeferencing software. This is then used to develop a model that identifies tire piles.

Tire piles are difficult to distinguish in satellite imagery because of their low reflectance levels. Often times tire piles are mistaken for shadows or deep water. The developers of this methodology claim it attempts to correct these misinterpretations by using software that implements the Tire Identification from Reflectance (TIRe) model. The development of the TIRe model incorporated lessons learned in previous research on the detection and mapping of tire piles by various methods of analysis (manual/visual and/or computational) of aerial and satellite imagery.

Strengths

• Reduces time spent surveying regions for tire sites

Weaknesses

• This methodology still requires a trained analyst to go over the tentative findings and discriminate between tires, water, vegetation, etc.

Source: http://www.techbriefs.com/component/content/article/2486

High-Resolution Satellite Imagery and the Conflict in Sri Lanka

In May 2009, the Science and Human Rights Program of the American Association for the Advancement of Science (AAAS) acquired and analyzed commercial high-resolution satellite imagery of the Civilian Safety Zone (CSZ) and surrounding area in northeastern Sri Lanka. The project was done at the request of Human Rights Watch and Amnesty International, who expressed concern over the status and safety of civilians due to the heavy fighting occurring 9-10 May, 2009. Comparing the May 6 and May 10, 2009 images of the CSZ, AAAS found significant removal of IDP shelters. In addition, imagery showed evidence of bombshell craters, destroyed permanent structures, mortar positions, and 1,346 individual graves. AAAS’s analysis was based on images from various publically accessible commercial satellites, US Army Field Manuals, and open-source information from public statements and media reports.

Strengths - Satellite imagery analysis is a useful way to assess the situation on the ground during conflicts in which no outside parties are allowed in the area.

Weaknesses – None

Source: http://shr.aaas.org/geotech/srilanka/srilanka.shtml

Tsunami Satellite Image Analysis Reveals Dramatic Water Quality Changes

Satellite Imagery Analysis

Summary:
An article in XPress Press, via Applied Analysis Inc. describes how Satellite Imagery Analysis was used to determine water quality levels after the Tsunami that hit Sri Lanka and India. Applied Analysis Inc., an American company, used satellite imagery analysis processes originally designed for military use, to determine the clarity of the water. Applied Analysis Inc. used the IKONOS imagery software for their analysis, and believes that the technology they used will be able to better identify other problems with water supplies in the near future.

Strengths:

• Can be used on multiple types of problems/issues.
• Continually upgraded technology.
• Has relatively clear data that is visible.

Weaknesses:

• None noted.

Link

http://www.xpresspress.com/news/AppliedAnalysis_011305.html

Satellite Image Analysis Reveals South Ossetian Damage

Satellite Imagery Analysis

Summary:
An article from ScienceDaily on October 9, 2008 discusses the usage of satellite imagery analysis to determine the damage done in the South Ossetian during the Russian-Georgian conflict. The article uses analysis done by the American Association for the Advancement of Science at the request of Amnesty International USA. The AAAS study examined damage to 24 villages near the city of Tskhinvali, which is considered the main city in South Ossetia. The AAAS looked at the damage of structures on August 10th, and compared them to the damage on August 19th. They determined that Tskhinvali sustained the greatest amount of damage (182 structures) between the 10th and 19th of August. The details of how they believe the buildings were destroyed corroborated the stories from the ground that fires were the main cause. The AAAS study combined eye-witness accounts of destruction with objectively interpreted satellite images purchased through three commercial vendors: GeoEye, DigitalGlobe and ImageSat International. They also used the software packages ERDAS Imagine and ArcView to leverage the power of remote sensing technology and Geographic Information Systems (GIS), respectively.

Strengths:

• Used to corroborate reports on the ground.
• Can be very detailed.
• Easy to compare images from different dates.
  

Weaknesses:

• Need of trained professionals.
• Software can be expensive.
  

Source:

http://www.sciencedaily.com/releases/2008/10/081009144105.htm

Using A Time Series Of Satellite Imagery To Detect Land Use And Land Cover Changes In The Atlanta, Georgia Metropolitan Area

Application of Satellite Imagery Analysis:

This article detailed a study in which researchers used a series of Landsat MSS and Landsat TM images from between 1973-1998 to assess the impact of land use/cover change on temperature change and air quality in Atlanta. The study indicated disturbing trends of deforestation and urban sprawl, but it also yielded worth-while information about the use of satellite photograph interpretation.

Strengths:

• Provides an excellent overview of a region
• Allows a retrospective view
• Can provide highly accurate results

Weaknesses:

• Relies on interpretation
• Limited by resolution, image quality, atmospheric haze, and contrast

How to (Steps):

1. Geometric Rectification- align landmarks (necessary for analysis over time)
2. Radiometric Normalization (RRN)- necessary when images were taken by different sensors, to standardize radiometry, or contrast within an image
3. Design of Classification Scheme- decide on classification categories ex. cultivated land or grassland
4. Image Classification- divide the photograph into "clusters", spatial areas with the same characteristics, then decide into which category they should be placed
5. Spatial Reclassification- an effort to reduce errors in classification involving techniques such as having a human check portions of computer-classified work
6. Accuracy Assessment- compare classification results to what the ground areas truly are as revealed by aerial photographs

Accuracy of Method:

The study results stated that user accuracy was over 80% in 5 out of 6 land-type classification categories. The accuracy for the sixth was in the high seventies. Landsat TM images which have a higher resolution yielded slightly more accurate results than those from the Landsat MSS images especially for high and low density urban areas and forests. User accuracy for these categories was 96%, 98%, and 98% respectively.

Source:

http://vk.cs.umn.edu/sboriah/LandCoverBib/YangYL2002.pdf