Dec 23 2005

Land use change in Sumatra

The Swiss NGO Paneco is engaged in Sumatra (Indonesia) where anthropogenic land use changes threaten local fauna and flora. The collaboration with Paneco has started with a small project that aimed at diagnosing rainfall intensity of a storm from TRMM 3B42 space radar data due to a lack of locally available rain gauges. The project continued with analysis of land use changes due to increasing oil palm plantations in northern Sumatra.

 

Mangroves woody trees or shrubs that grow in coastal areas and swamps. They occupy shallow water zones in tropical and subtropical coastal regions, usually where protected from direct wave action. The roots of the mangrove plants stabilize sand and mud. They also provide a habitat for wildlife, including several commercially important species of fish. Mangroves serve as a natural buffer to strong winds and waves produced by cyclones and they can protect against tsunamis.

 

image on the left was acquired by Landsat 5 in 1990 and shows a coastal mangrove area of around 400 km2 in south-western Aceh (Sumatra, Indonesia). The natural habitat is divided by the meandering Seumayam river which discharges into the Indian Ocean to the south. Dark red colors show natural vegetation, bright red indicate farming areas and blue colors mark bare soil and urban areas while blue-white colors are clouds. The image on the right was acquired by Landsat 7 in the year 2000 and shows the same area as above, but 10 years later: Almost all of the mangroves have been replaced with oil palm plantations. The rectangular and regular structures now show irrigation channels since oil palms have a high water demand. In the eastern part of the image a gradual expansion of farmland into the mangrove area can be witnessed.

Over 50% of the World’s mangroves have been lost during the past decades. In areas of the world where mangroves have been removed for development purposes, the coastline has been subject to rapid erosion. In Sumatra most of the mangrove trees have been replaced by oil palm plantations. Palm oil is a form of edible vegetable oil obtained from the fruit of the oil palm tree. Indonesia has become the second largest world producer of palm oil producing approximately 36% of world palm oil volume. There is increasing concern about environmental impacts of the palm oil industry since threatened species (such as the orang utan) live in the few remaining natural ecosystems on Borneo and Sumatra.

The false-color image from 5 August 2000 was acquired by the Enhanced Thematic Mapper plus (ETM+) aboard NASA’s Landsat 7 satellite. The one from 6 January 1990 was acquired by the Thematic Mapper (TM) aboard NASA’s Landsat 5 satellite. The Landsat satellites enable scientists to monitor land use and land cover change since 1972. Images by Reto Stöckli, based on Landsat data from the Global Land Cover Facility.

Further Reading

Paneco. Wanted: Undamaged Rainforest

Sumatran Orangutan Society

Earth Observatory: Deforestation in Indonesia

 

Jan 01 2005

Blue Marble Next Generation

The Blue Marble Next Generation (BMNG, 2005) is a cloud-free global satellite data set offering great spatial detail. It spans a longer data collection period than the original Blue Marble from 2000 and 2002. The original Blue Marble was a composite of four months of MODIS observations with a spatial resolution (level of detail) of 1 square kilometer per pixel. The Blue Marble Next Generation offers a year’s worth of monthly composites at a spatial resolution of 500 meters. These monthly images reveal seasonal changes to the land surface: the green-up and dying-back of vegetation in temperate regions such as North America and Europe, dry and wet seasons in the tropics, and advancing and retreating Northern Hemisphere snow cover.  Modified and shortened text from: http://bluemarble.nasa.gov

Space exploration changed our visual perception of planet Earth. In 1950s, satellites revolutionized weather forecasting when they began beaming home television images of cloud patterns. Astronaut photography in the early 1970s showed us the whole Earth“disk” for the first time in true-color – the so-called “Blue Marble”. Since 1972, satellite sensors have been acquiring atmosphere, land, ice and ocean data with increasing spectral, spatial and temporal resolution. Satellite remote sensing systems like NASAs Earth Observing System (EOS) help us to understand and monitor Earth’s physical, chemical, and biological processes.

One of our first false-color Earth images from back in the year 2000 is named the “Blue Marble”. That image was created for public outreach and it demonstrated the high value of such visualizations. New sensors like MODIS, aboard NASAs Terra and Aqua satellites, observe and measure a wide range of geophysical parameters. In 2002 we created a successor true-color Earth image using MODIS land, ocean, ice and atmosphere science products. This image has been widely used in museums, print media, TV documentaries, in movies, by mapping agencies, and in NASA’s public communications about its missions and research initiatives.

The success of the Blue Marble imagery motivated us to continue the project. The Blue Marble Next Generation (BMNG) is a cloud-free true-color dataset at 500-m spatial resolution and monthly temporal resolution. The BMNG aims at providing freely available data from the Earth surface in true color, derived from scientific data as a value-added product. Although the spatial resolution of the BMNG is comparable to other datasets, seasonal variations with monthly time-steps have not been shown before in seamless true-color composites. A visualization of seasonal variations (snowfall, droughts, wet seasons, spring greening, etc.) has good potential to enhance education. Furthermore, the BMNG can help to increase public understanding (and therefore acceptance) of satellite missions and awareness of causes and effects of changes in Earth’s climate system.

Visit the NASA’s official Blue Marble Next Generation Website: http://bluemarble.nasa.gov

Jan 01 2005

Blue Marble Next Generation Monthly Composites

A monthly collection of earth close-up images

Click on the respectiv images to get larger versions.

January 2004 Kenya

February 2004 Lake Etosha

March 2004 Sahara Desert

April 2004 The Alps

May 2004 The Himalayas

June 2004 The Andes

July 2004 Salt Lake City

August 2004 Eastern Greenland

September 2004 Patagonia

October 2004 The Okawango Delta

November 2004 Indonesian Islands

December 2004 The Bahamas

Oct 01 2004

PhD Thesis

Modeling and observations of seasonal land-surface heat and water exchanges at local and catchment scales over Europe (Doctoral thesis ETH No. 15742)

The land surface plays an important role in the global climate system, because it interacts dynamically with the atmosphere through manifold feedback mechanisms on a wide range of spatial and temporal scales. While on the one hand, weather and climate are known to influence vegetation phenology and its geographical distribution, soil and vegetation actively control land surface heat, water, momentum and carbon exchanges, thus influencing boundary layer development and convection. Evapotranspiration and runoff, in particular, which are balanced by precipitation, constitute the land portion of the water cycle, which is known to be a main contributor to climate variability. Knowledge about these processes and the ability to realistically model them is therefore of central importance in climate research. Simulated climate (and variability) are indeed sensitive to land surface parameterizations. There is, however, a gap between the local scale, at which land surface models and parameters are usually developed and evaluated, and the larger scales at which they are applied. This scale-gap needs to be bridged so that the high spatial and temporal dynamics of the land surface water cycle becomes part of modeled climate.

In order to help narrow the uncertainties in the modeling of seasonal-scale land-surface heat and water exchanges, local and catchment scale modeling experiments are performed in this study. Concurrently, different parameterizations are tested regarding their applicability in climate modeling, by exercising them on a wide range of climatic environments. All considered model formulations are embedded in a framework which includes ground and satellite remote sensing measurements, serving as an integration tool for the assessment of land surface processes. Satellite remote sensing is initially used to monitor vegetation state variables over Europe with a high temporal resolution, so that vegetation dynamics in land surface models can be prescribed with observed quantities. In a second stage local-scale measurements from FLUXNET are used for a process-based analysis of model results. Land surface models are applied at local scale using un-tuned large-scale and satellite-derived parameter sets. It is shown that soil storage processes play an important role in the seasonal heat and water fluxes and that vegetation biochemistry is a key component controlling the seasonal land surface water cycle. Finally, the Rhone-AGG initiative provides hydrological measurements on the catchment-scale, allowing for the exploration of scaling issues in the simulated water cycle. Catchment-scale simulations including lateral water fluxes, show that soil moisture drives runoff on the monthly time-scale and is largely controlled by evapotranspiration. While evapotranspiration was not found to be overly sensitive to runoff processes, the use of subgrid-scale topography-driven runoff provides a good simulation of the timing and magnitude of runoff at the daily to seasonal scale.

In summary, this study shows how satellite remote sensing, observations of boundary layer fluxes and ecosystem measurements can assist in developing models of the land surface water cycle which bridge the scale gaps between the processes involved; above-ground biophysics, relevant aspects of biochemistry and soil hydrology should be equally well represented in climate modeling applications.

Cover pictures: True color visualization of the cloud-free European land surface (August and February 2003): NASA’s Blue Marble Next Generation dataset, showing the seasonal changes in land surface reflectance, was derived from MODIS (MODerate resolution Imaging Spectroradiometer) MOD09A1 data by Reto Stöckli, NASA Earth Observatory.

The PDF of my PhD thesis is available here.

Jul 15 2004

Aura satellite launch & deploy

Aura is a earth observing satellite and it is part of NASA Earth Observing System (EOS). Aura’s four instruments study the atmosphere’s chemistry and dynamics. Aura’s measurements will enable us to investigate questions about ozone trends, air quality changes and their linkage to climate change.

Aura’s measurements will provide accurate data for predictive models and provide useful information for local and national agency decision support systems.

The Aura spacecraft was successfully launched on July 15, 2004 aboard a Delta II 7920-10L, a two stage expendable rocket, from the Vandenberg Western Test Range.

I have created the Aura launch and deploy animation for the Aura science team. The animations were used for public outreach purposes prior to launch.

 

Jan 01 2004

The European Fourier-adjusted and Interpolated NDVI (EFAI NDVI)

The EFAI NDVI is a high resolution land surface parameter dataset for climate modeling and mesoscale weather simulations. It was derived from the 20 year long NASA/NOAA AVHRR Pathfinder NDVI dataset, which is available in 10 day temporal and 0.1 degree spatial resolution for global area coverage.

The variability of vegetation state and function motivates the creation of high-resolution vegetation parameters varying dynamically over space and time. These parameters can be used to model the complex soil-vegetation-atmosphere interactions but also to assess the long-term changes in land use and vegetation physiology over a large area. The knowledge of the temporal variability of vegetation is a key requirement to quantify the carbon pools and exchanges on local and global scale. On global scale, satellite remote sensing now offers the possibility to estimate vegetation phenology with a high spatial and temporal revisiting frequency.

The NOAA/NASA Pathfinder dataset (James and Kalluri 1994) uses the NOAA polar orbiting satellites and is corrected for Rayleigh scattering and ozone absorption as well as for instrument degradation. It is however not corrected for aerosols or viewing geometry effects. High solar zenith angles in high latitudes causes long term data dropouts during winter. The cloud screening is problematic and leads to temporal inconsistencies in the original Pathfinder dataset. To create a spatio-temporally consistent vegetation phenology useful for climate research we used the Pathfinder NDVI (Normalized Difference Vegetation Index) over Europe and applied the following correction methodology:

  • Replacement of processing artifacts and data gaps in the dataset by spatial interpolation
  • Adjustment of the NDVI time-series by using a temporal interpolation procedure

The temporal interpolation procedure is a modification of Los (1998) and Sellers et al. (1996) and uses second order discrete fourier series to extract the seasonal variability of vegetation state and function from the Pathfinder NDVI dataset. The resulting spatio-temporally consistent 0.1 degree and 10 day NDVI dataset is called EFAI NDVI (European Fourier-Adjusted and Interpolated NDVI). A full description of this methodology is available in Stöckli and Vidale (2004). Biophysical land surface parameters were derived from the EFAI NDVI by applying simple empirical relationships between satellite radiometry and vegetation physiology.

The EFAI Dataset has also been processed at global scale and is available here. It has been superseded by higher quality and better gap-filled products. For instance our NASA Energy and Water Cycle MODIS-based FPAR/LAI data assimilation project called Pheno Analysis is a suitable successor of the EFAI NDVI.

Reference: R. Stöckli and P. L. Vidale (2004). European plant phenology and climate as seen in a 20-year avhrr land-surface parameter dataset, International Journal of Remote Sensing, 25(17), 3303–3330

Jan 01 2004

The European Fourier-Adjusted and Interpolated Normalized Difference Vegetation Index (EFAI NDVI)

Jan 07 2003

NDVI Measurements with Simple Radiometric and photographic Methods

A remote sensing tutorial for teachers and students

In July 2003 our institute IACETH (Insitute for Atmospheric and Climate Science, ETH Zurich) offered a field course on hydrology and climate science taking place in our research catchment “Rietholzbach”. In this course I teached students how to measure the surface radiation balance using pyrano- and pyrgeometers. During this course I have also evaluated a number of different methods to estimate biophysical vegetation parameters like LAI and NDVI. The document includes a review and a technical description of these experiments. The methods make use of calibrated radiation measurement equipment available at most climate measurement stations, but also use very inexpensive material that can help students to understand environmental satellite remote sensing science, e.g. performed by NASA’s Earth Observing System.

Theoretical background

In ecosystem research and climate modeling vegetation parameters are used to describe the state and function of land surface vegetation. The temporal evolution of these vegetation parameters follows the phenological cycle of plants (thus leaf out in spring, senescence in autumn or leaf coloring due to drought stress). This phenological cycle is the result of an interaction between the local climate and the plants. Vegetation parameters are a means to prescribe land surface vegetation in physical representations of the land surface used in climate research. These so called land surface models include soil physics and plants biochemistry processes that are strongly dependent on the state of vegetation. One of the most important vegetation parameter is LAI (Leaf Area Index) and is widely used to parameterize vegetation density and cover in biophysical land surface models.

LAI tells us how many leaf layers we find in a plant canopy. Values range from 0 to a maximum of 8 and the parameter is dimensionless. The parameter is derived by measuring the leaf area above a certain ground area and taking the fraction of these two values. LAI has been historically measured by destructive methods, thus taking apart a certain area of canopy and counting the total area of leaves.

LAI=(leaf area)/(surface area) [m2/m2] (1)

Especially satellite remote sensing methods have shown in the last two decades that land surface parameters can also be remotely estimated from space with high accuracy. Remote sensing offers the possibility to map large areas or the whole planet with a large spatial and temporal resolution, like this wouldn’t be possible by ground measurements. But in these indirect measurements new relationships between the measured quantity and the state of vegetation are needed. As it can be seen in Figure 1, healthy plants absorb light for their photsynthesis process in the visible part of the spectrum (especially red and blue) and strongly reflect light in the near-infrared part of the spectrum. Mainly, the green chlorophyl in the leaves does absorb a lot of visible light and the mesophyl cells scatter light in near-infrared wavelengths. Inactive vegetation (winter deciduous trees), dry vegetation, bare soil and snow do not show this spectral response.

Figure 1: Spectral reflectance of natural surfaces

Because of this spectral behavior of plants the remote sensing community created the NDVI (Normalized Difference Vegetation Index) parameter, which exploits the spectral properties of land surface vegetation:

NDVI= (NIR-VIS)/(NIR+VIS) [-] (2)

Where NIR and VIS are the near-infrared and visible reflectances of the land surface. A reflectance is calculated by dividing the outgoing (reflected) light by the incident (solar) light. Green and active vegetation does have an NDVI of 0.2-0.8, stressed vegetation, soil and snow does have NDVI values of -0.2 to 0.2. In satellite remote sensing the wavelengths in the red (NOAA AVHRR: 620-700nm) and the near-infrared (NOAA AVHRR: 740-1100nm) are commonly used to derive the NDVI.

Figure 2: Global map of satellite derived NDVI (on land) and chlorophyll concentration (in the oceans) by using radiometric methods

NDVI is neither a parameter for direct use in numerical models nor can ecosystem researchers directly compare it with their ground based vegetation studies. Nevertheless, it is strongly related to the amount of radiation used by the photosynthesis process in plants. FPAR (Fraction of Photosynthetically Active Radiation absorbed by the green leaves of the canopy) can be estimated from NDVI by a linear scaling dependent on vegetation class:

FPAR=(NDVI-NDVImin)/(NDVImax-NDVImin)*(FPARmax-FPARmin)+FPARmin [-] (3)

  • FPARmax: maximum FPAR (0.95)
  • FPARmin: minimum FPAR (0.01)
  • NDVImin: minimum NDVI for a give vegetation type (around 0)
  • NDVImax: maximum NDVI for a given vegetation type (0.6-0.7)

Figure 3: Radiation transmission and absorption through the canopy

Radiation used by the photosynthesis process decays exponentially by passing through the canopy. The higher the LAI, the less radiation will be reflected. This decay can be described by using Beer’s law:

I=I0*exp(-k*LAI) [W/m2] (4)

  • I0: Incident radiation at the top of the canopy [W/m2]
  • I: transmitted radiation [W/m2]
  • k: canopy extinction coefficient [-]

If only the PAR (Photosynthetically Active Radiation) and the APAR (Available Photosynthetically Active Radiation) is used from the radiation, then a logarithmic relationship between FPAR and LAI can be found:

APAR=FPAR*PAR [W/m2] (5)

I/I0=(1-FPAR) [-] (6)

LAI=-1/k*ln(1-FPAR) [m2/m2] (7)

This was a very brief and simplified explanation and allows the reader to relate radiometric properties of plants qualitatively and quantitatively to their biophysical properties. For scientific use, all of the above formulations do need an in-depth calibration dependent on light conditions, solar zenith angle, vegetation type, leaf properties and other factors. A more sophisticated description of these relationships can be found in Tucker (1979), Stockli and Vidale (2003), Los (1998) and Pontailler et al. (2003).

Figure 4: European LAI during a mean of July 1982-2001, derived from satellite remote sensing NDVI and FPAR

Measurement methods and examples

A. Estimation of the LAI using a pyranometer

Figure 5: The pyranometer for measuring incident solar radiation (the one measuring the reflected radiation is attached underneath)

We used two pyranometers and two pyrgeometers to measure the radiation balance at the land surface. A pyranometer does measure in the shortwave (0.4-4.0um) and the pyrgeometer does measure radiation in the longwave frequencies (4.0-40.0um). Of each instrument, one is pointing upwards, measuring the top hemisphere (incident radiation) and one is pointing downwards, measuring the lower hemisphere, thus the reflected radiation.

LAI can be estimated using equation 5, and by solving it for LAI. Using this methodology, the radiation has to be measured above and under the canopy, what is only possible for trees or e.g. fully grown corn fields. Grass and other short vegetation does not allow to fit the pyranometer under the canopy. The incident radiation I0 is measured above or beneath the canopy. The transmitted radiation is measured under the canopy. It is best to take a number of measurement samples within the same canopy to get a good relationship between incident and transmitted radiation. The extinction coefficient can be estimated by using true measured LAI values (from destructive sampling). From Pontailler et al. (2003) it can be seen that k varies between 0.8 and 1.2, and a value of 1.0 should be a good choice. This method can overestimate LAI because it does not differentiate between leaves, stems and branches.

B.Estimation of LAI using hemispherical photography

The gap fraction method as described by Gower and Norman (1991) can be used to estimate LAI from hemispherical photography. We used a CI-110 (CID Inc. Vancouver USA) hemispherical camera with a 150degree viewing area. Actually, any fisheye lens can be used for this method, but the CI-110 already had the analysis software included. The hemispherical image is a grayscale image and a threshold is applied to mark the differentiation between canopy and sky. The gaps within the canopy are determined from this digital fisheye image and the transmission fractions (I/I0) are determined by zenith angle. The LAI is also determined by the Beer’s law equation. This method does also consider stems and branches as part of the canopy and can overestimate LAI. On the other side, this method will underestimate clumped foliage because it uses a threshold function between canopy and sky.

Figure 6: Hemispherical photography of grass: grayscale image (left) and the canopy threshold painted in green (right). Figure 7: Hemispherical photography of a small tree: grayscale image (left) and the canopy threshold painted in green (right)

Two examples are presented in Figure 6 and 7. In Figure 6 we have put the hemispherical camera in 20-50 cm long grass. A threshold of 50% grayscale was used. The LAI was estimated to be 1.5 at this site. The second example resulted in a LAI of 1.9. In the second example the sun is shining through the canopy in the lower left corner. This can lead to an underestimation of LAI through light scattering. Generally, overcast sky is preferrable for this method.

C.Estimating NDVI using a Radiometer

In comparison to the two previous methods NDVI only accounts for the green part of vegetation and does not measure branches and stems. A multispectral radiometer MSR16 (Cropscan Inc., Rochester USA) with 16 channels was used. Two channels were equiped with a red and near-infrared band.

Figure 8: Students using the MSR radiometer to determine the NDVI of a small tree

We have used this equipment to determine the NDVI of a number of surfaces, including asphalt, trees, short grass, hay and tall grass. NDVI varied between 0 and 0.8 but saturated very quickly above a certain canopy thickness as this is widely known and observed. Recently, the EVI was developed, also using the blue wavelengths. From NDVI the LAI can be derived by using equations 5,6 and 7.

D. Estimating NDVI using Infrared Photography

A Radiometer like the one used in the previous example is not a common piece of equipment and very expensive to buy. Furthermore, its operation is usually not transparent to the user since the data processing of the radiometer signal requires special calibration algorithms which are hard coded in the equipment’s computer. There is a much simpler and more fun method for students to measure NDVI in the field:

Photography of visible and infrared wavelength can be used to estimate the reflectances of the two bands needed to calculate the NDVI. Infrared film and an 35mm SLR camera can be used for such an experiment. For doing both visible and infrared imagery of the same area, the film does have to be changed for every measurement. With today’s digital cameras, this experiment is even simpler. Digital cameras capture light with CCD devices that are generally sensitive to infrared wavelengths. This is actually an unwanted effect in photography since the photographic image is supposed to neither catch the UV nor the IR components of the radiation spectrum because they will mess up the colors in the image. Some professional digital cameras do therefore filter IR in their lenses, but most consumer digital cameras don’t.

We used an Olympus Camedia C-730 digital camera (Thanks very much to Eva for lending the camera during the field course) which did a perfect job for NDVI photography. We also used a Hoya R72 infrared filter, which blocks visible light below 720nm. Figure 9 shows the results of this experiment.

Figure 9: Examples of visible (left), infrared (center) and derived NDVI (right) measurements using a digital photo camera

All pictures were taken using a tripod and manual settings were used, thus it is important to have a digital camera with the possibility to set manual exposure for both aperture and exposure time. Also, it essential to not move the camera between taking the infrared and the visible picture since they will then not line up anymore. A remote control would help, but was not used here. The visible radiation image can be taken using the camera’s light metering system. It is essential to achieve a good contrast in the image since vegetation shows up very dark in visible wavelengths. Usually an aperture of f5.6 to f8 was used with an ISO-100 film speed setting, resulting in an exposure time of 1/250 or lower. For the infrared image, the IR filter was manually held in front of the lens. For this kind of imagery the light metering system of the camera is unusable because only the red channel is of interest and very little light is getting through the filter. It is important to try a few speed settings (without changing the aperture setting that was used for the visible wavelength picture). I used 1s or longer exposure times to achieve good results in the IR picture. After shooting the VIS and IR images of the scene (which are my reflected radiances), the incident radiances in both wavelengths were also captured by setting the camera to a wide angle lens position and pointing it to the sky (so that the sun was included in the image). Using the same exposure settings as before two pictures (one with the IR filter and one without) were taken.

 

For every scene we then had four pictures. They were uploaded to a computer and a small program was written to calculate the reflectances of every wavelengths and the NDVI. From both wavelength bands only the red channel of the pictures were used. The reflectance of each wavelength was calculated by dividing the outgoing (reflected) image by the image mean of the incident (sky) picture. Reflectances range from 0.0 (totally absorbing surface) to 1.0 (perfectly reflecting surface). Then the NDVI was calculated by applying equation 2.

In Figure 9 it can be seen that vegetation has a very bright appearance in the IR. It is very contrasted to the dark color of most non-vegetated surfaces. The dandelion leaf marks a single leaf layer on top of gravel and has an NDVI value of around 0.3. The gravel appears brighter in the visible image than in the IR image. Exactly the opposite is seen for the the dandelion leaf. Trees with multiple leaf layers show NDVI of up to 0.7 like seen in the bottom row. People usually estimate the albedo of vegetation to be lower than the one of e.g. gravel or concrete. The human eye only sees wavelengths up to around 700nm and misses the high reflectivity healthy vegetation has in the near-infrared. The climatological albedo includes the wavelengths of 0.4-4.0 um and thus shows a larger value for e.g. grass (0.20-0.25) than for gravel (0.15-0.20). This experiment can be easily conducted with students by using a broadband pyranometer (Experiment A) and a narrowband radiometer (Experiment C). Furthermore LAI and could be derived from the photography NDVI using equations 5-7.

Final comments

Four methods were presented that allow to relate radiometric properties of plants to biophysical vegetation parameters widely used in climate research. Basically the same methdology is applied in satellite remote sensing applications. The most prominent satellite system performing this task is currently operated by NASA and consists of the TERRA and the AQUA spacecrafts. On both spacecrafts the MODIS (MODerate resolution Imaging Spectroradiometer) does aquire land, ocean and atmosphere data with 36 narrow spectral bands in the visible, near-infrared and infrared band. The presented methods show that broad band (using a pyranometer or hemispherical photography) and narrow band (using a radiometer or infrared photography) remote sensing methods can be used in the field to determine vegetation properties. In addition to the point measurements performed by the three first methods, the infrared photography is also able to create images (and maps if used from e.g. an airplane), which helps to evaluate the spatial heterogeneity of vegetation properties.

Dependent on equipment availability, the application of these methods in a field course can help to understand remote sensing of land surface vegetation and the experiments can motivate for further research.

References

Los, S.O., 1998, Linkages between Global Vegetation and Climate: An Analysis Based on NOAA-Advanced Very High Resolution Radiometer Data. Ph.D. Dissertation, Vrije Universiteit Amsterdam.

Gower, S. T. and Norman, J. M. (1991) Rapid estimation of leaf area index in conifer and broad-leaf plantations. Ecology, 72: 1896-1900.

Pontailler J-Y, Hymus G.J., and Drake B.G. (2003) Estimation of leaf area index using ground-based remote sensing NDVI measurements: validation and comparison with two indirect techniques. Can. J. Remote Sensing, 29 (3): 381-387.

Stockli R., and Vidale P.L., 2004, European plant phenology and climate as seen in a 20 year AVHRR land-surface parameter dataset. International Journal of Remote Sensing. 25(17), 3303–3330.

Tucker C.J. (1979) Red and photographic infrared linear combinations for monitoring vegetation. Remote Sensing of the Environment, 8: 127-150.

May 04 2002

Aqua satellite launch & deploy

Aqua, Latin for water, is a NASA Earth Science satellite mission named for the large amount of information that the mission is collecting about the Earth’s water cycle, including evaporation from the oceans, water vapor in the atmosphere, clouds, precipitation, soil moisture, sea ice, land ice, and snow cover on the land and ice. Additional variables also being measured by Aqua include radiative energy fluxes, aerosols, vegetation cover on the land, phytoplankton and dissolved organic matter in the oceans, and air, land, and water temperatures.

The Aqua mission is a part of the NASA-centered international Earth Observing System (EOS). Aqua was formerly named EOS PM, signifying its afternoon equatorial crossing time. A timeline of Aqua on-orbit progress through the initial 120 day check-out period can be found here.

I have created the launch & deploy animation for the Aqua science team prior to the launch of the satellite for public outreach purposes.

Jan 01 2002

MODIS Satellite versus GOCART Model Aerosol Animation