The Landsat program—a series of satellites that continuously record information about the earth's surface—has contributed to research on an enormous variety of land-use, ecosystem and development issues.
Landsat data have been used to study mitigation of deforestation and eutrophication; prediction of the effects of climate change; quantification of urban sprawl; and early-warning and response to extreme weather, natural disasters, and vector-borne diseases.
While earth-observing satellites record a range of data types, Landsat technology, in particular, produces pictures of earth’s surface through remote sensing. When solar radiation reaches the earth surface it can be either reflected or absorbed and re-emitted. Remote sensing satellites measure specific wavelength ranges of reflected or re-emitted radiation from earth’s surface and create a picture based on these measurements.
When the first Landsat satellite was launched in the early 1970s, the pixel resolution of Landsat data was only eighty square meters, meaning each pixel of data represented an 80m x 80m square. Since then the technology has improved, and sensors can now more finely distinguish among wavelengths of radiation from earth’s surface. Landsat-5 and Landsat-7—-the two satellites currently in operation—-can measure wavelengths at a fine enough scale to create images with a pixel resolution of thirty square meters. These more finely rendered images allow scientists to distinguish slight differences that represent, for instance, healthy and unhealthy vegetation.
Though a pixel resolution of thirty square meters isn’t enough to identify the individual houses visible in some parts of Google Earth, it does allow for the identification of both natural and anthropogenic changes in the environment. It is also produces data files on a global scale that are small enough to be manageable. Landsat data has recently been used to map rift valley fever outbreak risk areas in the Arabian Peninsula and to monitor rain forest disturbance in the Amazon. These two projects are described below:
Mapping Rift Valley Fever Outbreak Risk Areas
Rift Valley Fever (RVF) is a mosquito-borne virus that can affect both humans and livestock, resulting in illness, economic losses, and even death. The disease is prevalent in East Africa and the Arabian Penninsula, particularly during rainy periods that allow mosquitos to breed in heavily vegetated areas.
Between August and September of 2000, widespread RVF in both humans and livestock was reported in the western coastal plain of Yemen. In order to prevent future outbreaks, Yemeni and international epidemiological experts wished to pinpoint the areas where mosquitoes were breeding.
High resolution Landsat-7 images from May and September of 2000 were acquired and compared to find regions of intensive vegetation growth during the epidemic (Figure 1). Using these images in addition to aerial surveys, a team of experts were able to identify locations most conducive to growth of mosquito larvae and the transmission of RVF. The outcome of this study allowed for disease control operations to be sent to regions with highest risk for the virus.
Monitoring Rainforests in the Amazon
Landsat technology has been used to measure disturbance and deforestation of the Amazon rainforest in both Brazil and Peru. Over years of studying satellite data for information about disturbance and deforestation in Brazil, scientists developed the Carnegie Landsat Analysis System (CLAS). CLAS allows scientists to manipulate Landsat data in order to penetrate the canopy of leaves in the rainforest and measure the consequences of logging activities on the ground. Extensive development of this system in Brazilian rainforest studies allowed scientists to apply it in Peru and complete an analysis of anthropogenic alteration of the Peruvian rainforest in only one year.
In Peru, CLAS scientists found that areas designated as protected areas had very little disturbance between 1999 and 2005 whereas substantial rainforest disturbance occurred in locations adjacent to both roads and legally designated logging areas (Figure 2). Results such as these are indispensable tools to Amazonian countries attempting to reduce rates of disturbance and deforestation and curb overall carbon emissions.
The success of CLAS has lead to the development of a more user-friendly version of the system that can be operated on desktop computers. The intention is that CLASLite—the user-friendly version—will allow forest monitoring to become an everyday activity, rather than a costly exercise requiring specialized equipment and expertise.
While much of the Landsat data still requires technical manipulation to make it compatible with most GIS software, systems such as CLASLite are making this information increasingly accessible. Data describing earth’s surface can be applied to a variety of disciplines and can be used to answer questions that will lead to better informed decisions concerning natural resources, disaster relief and public health.
Related Links:
NASA, The Landsat Program
USGS Landsat Data
EarthTrends Data Derived from Satellite Observations:
Ecosystem Area: Permanent wetlands
Ecosystem Area: Snow and ice covered area
Ecosystem Area: Urban and built-up areas (2000 data)
