Land surface temperature (LST) is a crucial parameter for global climate change studies. LST changes are also directly associated with the large-scale changes in land cover. Previous studies carried out a comparative analysis of satellite-derived LST response between periods before and after homogenous land cover changes. We present an alternative approach that quantifies long-term LST variability in response to various land use/land cover change (LULCC) patterns over Phuket Island, Thailand, from 2003 to 2017. First, four Moderate Resolution Imaging Spectroradiometer (MODIS) overpass times of LST time series were adjusted for seasonal effects using a cubic spline function to preserve the number of original data and enable estimates of LST dynamics and trends using the generalized least squared models. Second, LULCC patterns were classified according to land cover type conversion and spatial pattern transformations between the years 2000 and 2016. Spatial homogeneity and heterogeneity were quantified by the coverage percentage for each land use and land cover (LULC) type within a given location. Finally, the influence of LULCC patterns on the long-term spatiotemporal behavior of LST was assessed using the generalized estimating equation model. Results showed that different land cover transitions influence the dynamics of daytime LST but not the nighttime LST. The proportion of different land cover types within an LST pixel and transition amounts contributed to the quantity of increasing surface temperature, especially over impervious surface types. Diverse LULCC patterns with considerations of spatial heterogeneity improved our insight about a relatively strong effect of combined LULC types on LST responses. The climatic effect through the gradual conversion of heterogeneous land cover is necessary to be considered in climate research studies.
This study investigated the mechanisms underlying the scaling effects that apply to a fraction of vegetation cover (FVC) estimates derived using two-band spectral vegetation index (VI) isoline-based linear mixture models (VI isoline-based LMM). The VIs included the normalized difference vegetation index, a soil-adjusted vegetation index, and a two-band enhanced vegetation index (EVI2). This study focused in part on the monotonicity of an area-averaged FVC estimate as a function of spatial resolution. The proof of monotonicity yielded measures of the intrinsic area-averaged FVC uncertainties due to scaling effects. The derived results demonstrate that a factor ξ, which was defined as a function of “true” and “estimated” endmember spectra of the vegetated and nonvegetated surfaces, was responsible for conveying monotonicity or nonmonotonicity. The monotonic FVC values displayed a uniform increasing or decreasing trend that was independent of the choice of the two-band VI. Conditions under which scaling effects were eliminated from the FVC were identified. Numerical simulations verifying the monotonicity and the practical utility of the scaling theory were evaluated using numerical experiments applied to Landsat7-Enhanced Thematic Mapper Plus (ETM+) data. The findings contribute to developing scale-invariant FVC estimation algorithms for multisensor and data continuity.
We developed a unique methodology that spectrally translates the enhanced vegetation index (EVI) across sensors for data continuity based on vegetation isoline equations and derived a moderate resolution imaging spectroradiometer (MODIS)-compatible EVI for the visible/infrared imager/radiometer suite (VIIRS) sensor. The derived equation had four coefficients that were a function of soil, canopy, and atmosphere, e.g., soil line slope, leaf area index (LAI), and aerosol optical thickness (AOT). The PROSAIL canopy reflectance and 6S atmospheric models were employed to numerically characterize the MODIS-compatible VIIRS EVI. MODIS-compatible VIIRS EVI values only differed from those of MODIS EVI by, at most, 0.002 EVI units, whereas VIIRS and MODIS EVI values differed by 0.018 EVI units. The derived coefficients were sensitive mainly to LAI and AOT for the full- and a partial-covered canopy, respectively. The MODIS-compatible EVI resulted in a reasonable level of accuracy when the coefficients were fixed at values found via optimization for model-simulated and actual sensor data (83 and 41% reduction in the root mean square error, respectively), demonstrating the potential practical utility of the derived equation. The developed methodology can be used to obtain a spectrally compatible EVI for any pair of sensors in the data continuity context.
Vegetation indices (VIs) are widely used in long-term measurement studies of vegetation changes, including seasonal vegetation activity and interannual vegetation-climate interactions. There is much interest in developing cross-sensor/multi-mission vegetation products that can be extended to future sensors while maintaining continuity with present and past sensors. In this study we investigated multi-sensor spectral bandpass dependencies of the enhanced vegetation index (EVI), a 2-band EVI (EVI2), and the normalized difference vegetation index (NDVI) using spectrally convolved Earth Observing-1 (EO-1) Hyperion satellite images acquired over a range of vegetation conditions. Two types of analysis were carried out, including (1) empirical relationships among sensor reflectances and VIs and (2) decomposition of bandpass contributions to observed cross-sensor VI differences. VI differences were a function of cross-sensor bandpass disparities and the integrative manner in which bandpass differences in red, near-infrared (NIR), and blue reflectances combined to influence a VI. Disparities in blue bandpasses were the primary cause of EVI differences between the Moderate Resolution Imaging Spectroradiometer (MODIS) and other course resolution sensors, including the upcoming Visible Infrared Imager / Radiometer Suite (VIIRS). The highest compatibility was between VIIRS and MODIS EVI2 while AVHRR NDVI and EVI2 were the least compatible to MODIS.
The enhanced vegetation index (EVI) has been found useful in improving linearity with biophysical vegetation
properties and in reducing saturation effects found in densely vegetated surfaces, commonly encountered in the
normalized difference vegetation index (NDVI). However, EVI requires a blue band and is sensitive to variations in blue
band reflectance, which limits consistency of EVI across different sensors. The objectives of this study are to develop a
2-band EVI (EVI2) without a blue band that has the best similarity with the 3-band EVI, and to investigate the crosssensor
continuity of the EVI2 from the Moderate Resolution Imaging Spectroradiometer (MODIS) and Advanced Very
High Resolution Radiometer (AVHRR). A linearity-adjustment factor (β) was introduced and coupled with the soil
adjustment factor (L) used in the soil-adjusted vegetation index (SAVI) in the development of the EVI2 equation. The
similarity between EVI and EVI2 was validated at the global scale. After a linear adjustment, the AVHRR EVI2 was
found to be comparable with the MODIS EVI2. The good agreement between the AVHRR and MODIS EVI2 suggests
the possibility of extending the current MODIS EVI time series to the historical AVHRR data, providing another longterm
vegetation record different from the NDVI counterpart.
Current earth observing satellite sensors have different temporal, spectral and spatial characteristics that present
problems in the establishment of long term, time series data records. Vegetation indices (VI's) are commonly used in
deriving long term measures of vegetation biophysical properties, which have been shown useful in interannual climate
studies and phenology studies. While significant improvements have been made with new sensors, and algorithms, and
processing methods, backward compatibility of VI's is desired so that the long term record can extend back and utilize
the AVHRR record to 1981. Conversely, any reprocessing of the AVHRR record should consider steps to allow forward
compatibility with newer sensors and products. In this study we evaluated the use of sensor-specific enhanced vegetation
index (EVI) and normalized difference vegetation index (NDVI) data sets, using a time sequence of Hyperion images
over Tapajos National Forest in Brazil over the 2001 and 2002 dry seasons. We computed NDVI, EVI, and a 2-band
version of EVI (EVI2) for different sensor systems (AVHRR, MODIS, VIIRS, SPOT-VGT, and SeaWiFS) and
evaluated their differences and continuity in the characterization of tropical forest phenology. We also analyzed the
influence of different atmosphere correction scenarios to assess noise in the phenology signal. Our analyses show that
EVI2 maintains the desirable properties of increased sensitivity in high biomass forests across all sensor systems
evaluated in this study. We further conclude that EVI2 can be extended to the AVHRR time series record and
compliment that current NDVI time series record.
In red-NIR reflectance space, the Modified Soil Adjusted Vegetation Index (MSAVI) isolines, representing similar vegetation biophysical quantities, are neither convergent to a point nor parallel to each other. Consequently, the treatment of the MSAVI isolines is distinctly different from those of other vegetation index isolines, such as the normalized difference vegetation index (NDVI), the perpendicular vegetation index (PVI), and the soil-adjusted vegetation index (SAVI). In this study, the MSAVI isolines are shown to be the tangent lines of the parabola, (NIR-0.5)2+2Red=0, and the values of the MSAVI isolines are equal to the ordinates of their tangent points plus 0.5. These findings provide a graphic interpretation of the MSAVI and are useful in understanding the biophysical characteristics of the MSAVI. The MSAVI isolines are shown to better approximate field data and simulated vegetation biophysical isolines than the other 2-band vegetation index isolines. As the treatment of the MSAVI isolines can be depicted by the parabola curve, the MSAVI can be referred to as a parabola-based vegetation index.
Long term data records require the effective integration of new sensor technologies and improved algorithms to better characterize global and climate change impacts on ecosystems, while preserving the fundamental attributes of the existing data record. In this study, we investigated key determinants in the spectral translation and extension of MODIS Vegetation Index products across current sensor systems and to the NPOESS (VIIRS) era. We used simulated sensor-specific data sets derived from hyperspectral data using field spectroroadiometers and Hyperion sensors to investigate inter-sensor translation and continuity issues of the normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI). We also investigated the use of data fusion of satellite VI time series with in-situ flux tower time series measurements of photosynthesis, and the use of data fusion with tower-based continuous measures of broadband/hemispherical VI's as possible reference data sets for the inter-calibration of satellite VI time series from different sensor systems. Preliminary comparisons are presented with actual satellite VI measurements from SPOT-VEGETATION, Terra- and Aqua-MODIS, and AVHRR sensors. We found that with a consistent atmosphere correction scheme and a generalized compositing procedure, translation of multi-sensor datasets can be achieved with certain limitations.
Vegetation indices (VI's) are important tools in the seasonal and inter-annual monitoring of the Earth's vegetation. In this study, the vegetation index products from the Moderate Resolution Imaging Spectroradiometer (MODIS) are evaluated over a preliminary set of validation test sites, including a cerrado and rainforest site in Brazil, and two grass/ shrub sites in Arizona and New Mexico, U.S.A. Ground and airborne validation experiments were conducted to assess the performance of the normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI) for vegetation monitoring. Calibrated spectroradiometers were flown for top-of-canopy reflectance retrievals. Vegetation sampling provided the data needed for a biophysical validation of the VI's. Both single-day and 16-day composited MODIS data were processed and corrected for atmosphere at 500m and 1 km resolutions. The MODIS data compared quite well with the validation data with most of the uncertainty associated with the compositing process. Results show the MODIS VI products to offer enhanced sensitivity for land use discrimination and monitoring at both regional and global scales. The EVI was fairly well resistant to residual cloud and aerosol contamination and had a good range of sensitivity over the high biomass, forested areas.
Vegetation indices have emerged as important tools in the seasonal and inter-annual monitoring of the Earth's vegetation. They are radiometric measures of the amount and condition of vegetation. In this study, the Sea-viewing Wide Field-of-View sensor (SeaWiFS) is used to investigate coarse resolution monitoring of vegetation with multiple indices. A 30-day series of SeaWiFS data, corrected for molecular scattering and absorption, was composited to cloud-free, single channel reflectance images. The normalized difference vegetation index (NDVI) and an optimized index, the enhanced vegetation index (EVI), were computed over various 'continental' regions. The EVI had a normal distribution of values over the continental set of biomes while the NDVI was skewed toward higher values and saturated over forested regions. The NDVI resembled the skewed distributions found in the red band while the EVI resembled the normal distributions found in the NIR band. The EVI minimized smoke contamination over extensive portions of the tropics. As a result, major biome types with continental regions were discriminable in both the EVI imagery and histograms, whereas smoke and saturation considerably degraded the NDVI histogram structure preventing reliable discrimination of biome types.
Remotely sensed reflectance data are often acquired at variable view and solar geometric configurations. Vegetation change monitoring with the NDVI (Normalized Difference Vegetation Index) is sensitive to the effects of solar and view angle geometry. However, by using a BRDF (Bidirectional Reflectance Distribution Function) model, the view and sun angle variability in the NDVI can be standardized. If multi- sun angle data are not available, a second method allows us to extrapolate (nadir) satellite observations to a standard sun angle by using predetermined linear regression relationships between sun angle and ground-based nadir NDVI values for a range of vegetation types. Both methods were applied to one month of daily, atmospherically corrected, multi-angle SeaWiFS (Sea viewing Wide Field-of-view Spectroradiometer) land reflectance data, with promising results. The difference in NDVI due to a sun angle change from 20 degrees to 70 degrees can be up to 50%. The NDVI values for very dense vegetated and bare soil surface areas are less affected by changes in solar zenith angles. This research shows that the sun and view angle effects on the widely used spectral indices could be standardized to improve the accuracy of regional and global vegetation and crop monitoring efforts.
We developed a simple technique to evaluate the importance of non-linear mixing in coniferous forests with different overstory structural characteristics and different backgrounds. The methodology consists on using a hybrid forest reflectance model for reflectance simulation, and on using factor analysis and target testing for unmixing the forest reflectance into three components: a forest canopy component (free of background effects), a background component (free of canopy effects) and a background-and-canopy dependent component. This third component is considered responsible for non-linearities since it depends simultaneously on the background reflectance and on the canopy transmittance. After running the model, the contribution of the third (non-linear) component to the total forest reflectance was evaluated and compared over different forest scenarios parameterized with data collected on maritime pine stands in Central Portugal.
In this study we propose a methodology for understory characterization based on the isolation of the forest reflectance that is exclusively due to the background. This is achieved by extracting the contribution of the forest canopy from the overall forest reflectance using forward modeling based on forest reflectance models and existing data on forest structure. Then, the understory can be characterized by applying any methodology developed for open-sky shrub lands, such as vegetation indices or linear mixture models. The developed methodology was successfully applied to simulated data of maritime pine forests with different biophysical stand characteristics and with different understory characteristics.
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