A Review of Landsat TM/ETM based Vegetation Indices as Applied to Wetland Ecosystems

A review of vegetation indices as applied to Landsat-TM and ETM+ multispectral data is presented. The review focuses on indices that have been developed to produce biophysical information about vegetation biomass/greenness, moisture and pigments.In addition, a set of biomass/greenness and moisture content indices are tested in a Mediterranean semiarid wetland environment to determine their appropriateness and potential for carrying redundant information.The results indicate that most vegetation indices used for biomass/greenness mapping produce similar information and are statistically well correlated.

A New Approach for the Analysis of Hyperspectral Data: Theory and Sensitivity Analysis of the Moment Distance Method

We present the Moment Distance (MD) method to advance spectral analysis in vegetation studies. It was developed to take advantage of the information latent in the shape of the reflectance curve that is not available from other spectral indices. Being mathematically simple but powerful, the approach does not require any curve transformation, such as smoothing or derivatives. Here, we show the formulation of the MD index (MDI) and demonstrate its potential for vegetation studies. We simulated leaf and canopy reflectance samples derived from the combination of the PROSPECT and SAIL models to understand the sensitivity of the new method to leaf and canopy parameters. We observed reasonable agreements between vegetation parameters and the MDI when using the 600 to 750 nm wavelength range, and we saw stronger agreements in the narrow red-edge region 720 to 730 nm. Results suggest that the MDI is more sensitive to the Chl content, especially at higher amounts (Chl \u3e 40 mg/cm2) compared to other indices such as NDVI, EVI, and WDRVI. Finally, we found an indirect relationship of MDI against the changes of the magnitude of the reflectance around the red trough with differing values of LAI

Performance evaluation of remote sensing data with machine learning technique to determine soil color

The aim of the present research is the determination of soil color by spectral bands and indices obtained from MODIS images. For this purpose, soil samples were collected from East Azerbaijan Province (Iran) and their color and texture were investigated through Munsell color system and hydrometer method, respectively. Stepwise regression, principle component analysis and sensitivity function methods were employed to find the dominant indices and bands using artificial neural network (ANN) as one of the machine learning techniques. The improved indices as the model input had better performance, for example, the calculation of correlation coefficient between indices and hue showed 51.48% increase of correlation coefficient with comparison of the normalized difference vegetation index (NDVI) to modified soil adjustment vegetation index (MSAVI) and 54.54% correlation enhancement of soil adjustment vegetation index (SAVI) compared to MSAVI. Stepwise regression method along with error criteria decline may enhance the performance of soil color model. In comparison with multivariate regression, ANN model exhibited better performance (with a 12.61% mean absolute error [MAE] decline). Temporal variation of modified perpendicular drought index (MPDI) as well as band 31 could justify the Munsell soil color components variations specifically chroma and hue. MPDI and thermal bands could be employed as a precise indicator in soil color analysis. Thus, remote sensing data combined with machine learning technique can clarify the procedure potential for soil color determination

Effect of Relative Spectral Response on Multi-Spectral Measurements and NDVI from Different Remote Sensing Systems

Spectrally derived metrics from remotely sensed data measurements have been developed to improve understanding of land cover and its dynamics. Today there are an increasing number of remote sensing systems with varying characteristics that provide a wide range of data that can be synthesized for Earth system science. A more detailed understanding is needed on how to correlate measurements between sensors. One factor that is often overlooked is the effect of a sensor's relative spectral response (RSR) on broadband spectral measurements. This study examined the variability in spectral measurements due to RSR differences between different remote sensing systems and the implications of these variations on the accuracy and consistency of the normalized difference vegetation index (NDVI). A theoretical model study and a sensor simulation study of laboratory and remotely sensed hyper-spectral data of known land cover types was developed to provide insight into the effect on NDVI due to differences in RSR measurements of various land cover signatures. This research has shown that the convolution of RSR, signature reflectance and solar irradiance in land cover measurements leads to complex interactions and generally small differences between sensor measurements. Error associated with cross-senor calibration of signature measurements and the method of band radiance conversion to reflectance also contributed to measurement discrepancies. The effect of measurement discrepancies between sensors on the accuracy and consistency of NDVI measurements of vegetation was found to be dependent on the increasing sensitivity of NDVI to decreasing band measurements. A concept of isolines of NDVI error was developed as a construct for understanding and predicting the effect of differences in band measurements between sensors on NDVI. NDVI difference of less than 0.05 can be expected for many sensor comparisons of vegetation, however, some cases will lead to higher differences. For vegetation signatures used in this study, maximum effect on NDVI from measurement differences was 0.063 with an average of 0.023. For sensors with well aligned RSRs such as Landsat 7 ETM+ and MODIS, NDVI differences in the range of 0.01 are possible

The Detection of Potato Cyst Nematode (PCN) Infestation Using Remotely Sensed Imagery

Potato cyst nematodes (PCN) Globodera pallida Stone and G. rostochiensis (Wollenweber) are economically-important pests which cause significant losses to potato production world-wide. Population control is a major objective of a sustainable management strategy as nematodes are persistent and reproductive rates can be high. Current methods of determining PCN population densities are both expensive and time consuming: an advance on current sampling methods would facilitate the application of precision farming methods to PCN management. The potential for using light reflected from the potato canopy as an assay to detect PCN infestation is investigated. Spectral reflectance was measured from the canopy of commercial potato crops, individual plants and single leaves with a field spectrometer, airborne remote sensing and from SPOT and Ikonos satellite images. Comparisons between the reflectance from healthy and infected plants revealed a significant reduction in green (c. 550nm) reflectance from infested plants, and this was consistent across seasons, cultivars and imaging methods. The strongest results were seen early in the crop cycle, although full canopy development is desirable in airborne and satellite image analysis. Yield-limiting pathogens are frequently reported as increasing visible reflectance by reducing chlorophyll concentration in the leaves of afflicted plants. The spectral response to PCN infestation is distinct from that from common plant stress factors and a PCN- specifrc detector system is proposed. A viable commercial PCN assay would need to be timely, reliable and cost-effective. Whilst aircraft and satellite platforms offer the rapid acquisition of data from large areas, these imaging methods are heavily dependent on weather conditions. Results from data collected using a hand-held chlorophyll meter in this investigation indicate that a closed, or semi-closed imaging system (enclosing or covering the plant canopy) would be suited to the detection of PCN under field conditions and less susceptible to inclement weather

Relation between seasonally detrended shortwave infrared reflectance data and land surface moisture in semi-arid Sahel

In the Sudano-Sahelian areas of Africa droughts can have serious impacts on natural resources, and therefore land surface moisture is an important factor. Insufficient conventional sites for monitoring land surface moisture make the use of Earth Observation data for this purpose a key issue. In this study we explored the potential of using reflectance data in the Red, Near Infrared (NIR), and Shortwave Infrared (SWIR) spectral regions for detecting short term variations in land surface moisture in the Sahel, by analyzing data from three test sites and observations from the geostationary Meteosat Second Generation (MSG) satellite. We focused on responses in surface reflectance to soil- and surface moisture for bare soil and early to mid- growing season. A method for implementing detrended time series of the Shortwave Infrared Water Stress Index (SIWSI) is examined for detecting variations in vegetation moisture status, and is compared to detrended time series of the Normalized Difference Vegetation Index (NDVI). It was found that when plant available water is low, the SIWSI anomalies increase over time, while the NDVI anomalies decrease over time, but less systematically. Therefore SIWSI may carry important complementary information to NDVI in terms of vegetation water status, and can provide this information with the unique combination of temporal and spatial resolution from optical geostationary observations over Sahel. However, the relation between SIWSI anomalies and periods of water stress were not found to be sufficiently robust to be used for water stress detection

Measurement of evapotranspiration with combined reflective and thermal infrared radiance observations

The broad goal of the research summarized in this report was 'To facilitate the evaluation of regional evapotranspiration (ET) through the combined use of solar reflective and thermal infrared radiance observations.' The specific objectives stated by Goward and Hope (1986) were to: (1) investigate the nature of the relationship between surface temperature (T(sub S)) and the normalized difference vegetation index (NDVI) and develop an understanding of this relationship in terms of energy exchange processes, particularly latent flux heat (LE); (2) develop procedures to estimate large area LE using combined T(sub S) and NDVI observations obtained from AVHRR data; and (3) determine whether measurements derived from satellite observations relate directly to measurements made at the surface or from aircraft platforms. Both empirical and modeling studies were used to develop an understanding of the T(sub S)-NDVI relationship. Most of the modeling was based on the Tergra model as originally proposed by Goward. This model, and modified versions developed in this project, simulates the flows of water and energy in the soil-plant-atmosphere system using meteorological, soil and vegetation inputs. Model outputs are the diurnal course of soil moisture, T(sub S), LE and the other individual components of the surface energy balance

Validating and Linking the GIMMS Leaf Area Index (LAI3g) with Environmental Controls in Tropical Africa

The recent Global Inventory Modeling and Mapping Studies (GIMMS) LAI3g product provides a 30-year global times-series of remotely sensed leaf area index (LAI), an essential variable in models of ecosystem process and productivity. In this study, we use a new dataset of field-based LAITrue to indirectly validate the GIMMS LAI3g product, LAIavhrr, in East Africa, comparing the distribution properties of LAIavhrr across biomes and environmental gradients with those properties derived for LAITrue. We show that the increase in LAI with vegetation height in natural biomes is captured by both LAIavhrr and LAITrue, but that LAIavhrr overestimates LAI for all biomes except shrubland and cropland. Non-linear responses of LAI to precipitation and moisture indices, whereby leaf area peaks at intermediate values and declines thereafter, are apparent in both LAITrue and LAIavhrr, although LAITrue reaches its maximum at lower values of the respective environmental driver. Socio-economic variables such as governance (protected areas) and population affect both LAI responses, although cause and effect are not always obvious: a positive relationship with human population pressure was detected, but shown to be an artefact of both LAI and human settlement covarying with precipitation. Despite these complexities, targeted field measurements, stratified according to both environmental and socio-economic gradients, could provide crucial data for improving satellite-derived LAI estimates, especially in the human-modified landscapes of tropical Africa.Peer reviewe

Atmospheric correction of visible to middle-infrared EOS-MODIS data over land surfaces: Background, operational algorithm and validation

The NASA moderate resolution imaging spectroradiometer (MODIS) instrument will provide a global and improved source of information for the study of land surfaces with a spatial resolution of up to 250 m