Relationships between percent vegetation cover and vegetation indices

In this paper, percent vegetation cover is estimated from vegetation indices using simulated Advanced Very High Resolution Radiometer (AVHRR) data derived from in situ spectral reflectance data. Spectral reflectance measurements were conducted on grasslands in Mongolia and Japan. Vegetation indices such as the normalized difference, soil-adjusted, modified soil-adjusted and transformed soil-adjusted vegetation indices (NDVI, SAVI, MSAVI and TSAVI) were calculated from the spectral reflectance of various vegetation covers. Percent vegetation cover was estimated using pixel values of red, green and blue bands of digitized colour photographs. Relationships between various vegetation indices and percent vegetation cover were compared using a second-order polynomial regression. TSAVI and NDVI gave the best estimates of vegetation cover for a wide range of grass densities.     

Assessing the seasonal dynamics of the Brazilian Cerrado vegetation through the use of spectral vegetation indices

In this study, the response of vegetation indices (VIs) to the seasonal patterns and spatial distribution of the major vegetation types encountered in the Brazilian Cerrado was investigated. The Cerrado represents the second largest biome in South America and is the most severely threatened biome as a result of rapid land conversions. Our goal was to assess the capability of VIs to effectively monitor the Cerrado and to discriminate among the major types of Cerrado vegetation. A full hydrologic year (1995) of composited AVHRR, local area coverage (LAC) data was converted to Normalized Difference Vegetation Index (NDVI) and Soil Adjusted Vegetation Index (SAVI) values. Temporal extracts were then made over the major Cerrado vegetation communities. Both the NDVI and SAVI temporal profiles corresponded well to the phenological patterns of the natural and converted vegetation formations and depicted three major categories encompassing the savanna formations and pasture sites, the forested areas, and the agricultural crops. Secondary differences in the NDVI and SAVI temporal responses were found to be related to their unique interactions with sun-sensor viewing geometries. An assessment of the functional behaviour of the VIs confirmed SAVI responds primarily to NIR variations, while the NDVI showed a strong dependence on the red reflectance. Based on these results, we expect operational use of the MODIS Enhanced Vegetation Index (EVI) to provide improved discrimination and monitoring capability of the significant Cerrado vegetation types.     

A model for the estimation of fractional vegetation cover based on the relationship between vegetation and soil moisture

Based on surface temperature and the normalized difference vegetation index (NDVI), we calculated the temperature vegetation dryness index (TVDI). Using the relationship between TVDI and NDVI, we established a vegetation–soil moisture response model that captures the sensitivity of NDVI's response to changes in TVDI using a linear unmixing approach, and validated the model using Landsat Thematic Mapper (TM) images acquired in 1997, 2004 and 2006 and a Landsat Enhanced Thematic Mapper Plus (ETM+) image acquired in 2000. We determined the correlations between TVDI and field-measured soil moisture in 2006. TVDI was correlated significantly with soil moisture at depths of 0 to 10 cm and 10 to 20 cm, so TVDI can be used as an index that captures changes in soil moisture at these depths. By using fractional vegetation cover (FVC) data measured in the field to validate the estimated values, we estimated mean absolute errors of 0.043 and 0.137 for shrub and grassland vegetation coverage, respectively, demonstrating acceptable estimation accuracy. Based on these results, it is possible to estimate a region's FVC using the linear unmixing model. The results show bare land coverage values distributed similarly to TVDI values. In mountain areas, grassland coverage mostly ranged from 0.4 to 0.6. Shrub coverage mostly ranged from 0.4 to 0.6. Forest coverage was zero in most parts of the study area.     

Estimating ground fractional vegetation cover using the double-exposure method

Fractional vegetation cover (FVC) is a key parameter in ecological models. It is important to determine the ground FVC quickly and accurately in studies of soil erosion, surface energy balance, and carbon cycling. As one of the FVC ground measurement methods, the photographic method is easy to operate with relatively high precision. However, its classification result showed poor accuracy when an image of a high-contrast scene contained a shadow region where a low signal-to-noise ratio (SNR) existed, because the single-exposure image in the photographic method did not contain sufficient surface information about both the illuminated and shadowed parts. This article presents application of a double-exposure photographic method to determine vegetation cover in the shadow region of an image. It consists of two measurements used in acquiring images (normal and over-exposure) and one image-processing part to handle the obtained images. Illuminated vegetation and soil, as well as the shadow region, was classified with the normally exposed image in the intensity, hue, and saturation (IHS) colour space, and the shadow region was further classified as shadowed vegetation and shadowed soil using the over-exposed image. The results indicate that the over-exposed image reduced the average bias of the FVC in the shadow region from 15.40% to ?4.14% and the root mean square error (RMSE) from 0.174 to 0.066. The RMSE of the entire scene was 0.055 in the over-exposed image and 0.092 in the single-exposed image. The double-exposure method also showed a better classification result than the high dynamic range method in deep shadow regions. This study shows that this method is capable of distinguishing vegetation and soil in the shadow region and thus it is an effective and accurate method for ground FVC measurement.     

Dynamics of the relationship between NDVI and SWIR32 vegetation indices in southern Africa: implications for retrieval of fractional cover from MODIS data

Fractional cover of photosynthetic vegetation (F_PV), non-photosynthetic vegetation (F_NPV), and bare soil (F_BS) has been retrieved for Australian tropical savannah based on linear unmixing of the two-dimensional response envelope of the normalized difference vegetation index (NDVI) and short wave infrared ratio (SWIR)32 vegetation indices (VI) derived from Moderate Resolution Imaging Spectroradiometer (MODIS) reflectance data. The approach assumes that cover fractions are made up of a simple mixture of green leaves, senescent leaves, and bare soil. In this study, we examine retrieval of fractional cover using this approach for a study area in southern Africa with a more complex vegetation structure. Region-specific end-members were defined using Hyperion images from different locations and times of the season. These end-members were applied to a 10-year time series of MODIS-derived NDVI and SWIR32 (from 2002 to 2011) to unmix F_PV, F_NPV, and F_BS. Results of validation with classified high-resolution imagery indicated major bias in estimation of F_NPV and F_BS, with regression coefficients for predicted versus observed data substantially less than 1.0 and relatively large intercept values. Examination with Hyperion images of the inverse relationship between the MODIS-equivalent SWIR32 index and the Hyperion-derived cellulose absorption index (CAI) to which it nominally approximates revealed: (1) non-compliant positive regression coefficients for certain vegetation types; and (2) shifts in slope and intercept of compliant regression curves related to day of year and geographical location. The results suggest that the NDVI–SWIR32 response cannot be used to approximate the NDVI–CAI response in complex savannah systems like southern Africa that cannot be described as simple mixtures of green leaves, dry herbaceous material high in cellulose, and bare soil. Methods that use a complete set of multispectral channels at higher spatial resolution may be needed for accurate retrieval of fractional cover in Africa.     

Analysis of NDVI and scaled difference vegetation index retrievals of vegetation fraction

The normalized difference vegetation index (NDVI) is the most widely used vegetation index for retrieval of vegetation canopy biophysical properties. Several studies have investigated the spatial scale dependencies of NDVI and the relationship between NDVI and fractional vegetation cover, but without any consensus on the two issues. The objectives of this paper are to analyze the spatial scale dependencies of NDVI and to analyze the relationship between NDVI and fractional vegetation cover at different resolutions based on linear spectral mixing models. Our results show strong spatial scale dependencies of NDVI over heterogeneous surfaces, indicating that NDVI values at different resolutions may not be comparable. The nonlinearity of NDVI over partially vegetated surfaces becomes prominent with darker soil backgrounds and with presence of shadow. Thus, the NDVI may not be suitable to infer vegetation fraction because of its nonlinearity and scale effects. We found that the scaled difference vegetation index (SDVI), a scale-invariant index based on linear spectral mixing of red and near-infrared reflectances, is a more suitable and robust approach for retrieval of vegetation fraction with remote sensing data, particularly over heterogeneous surfaces. The proposed method was validated with experimental field data, but further validation at the satellite level would be needed.     

Spatial-temporal dynamics of land surface temperature in relation to fractional vegetation cover and land use/cover in the Tabriz urban area, Iran

Rapid changes of land use and land cover (LULC) in urban areas have become a major environmental concern due to environmental impacts, such as the reduction of green spaces and development of urban heat islands (UHI). Monitoring and management plans are required to solve this problem effectively. The Tabriz metropolitan area in Iran, selected as a case study for this research, is an example of a fast growing city. Multi-temporal images acquired by Landsat 4, 5 TM and Landsat 7 ETM+ sensors on 30 June 1989, 18 August 1998, and 2 August 2001 respectively, were corrected for radiometric and geometric errors, and processed to extract LULC classes and land surface temperature (LST). The relationship between temporal dynamics of LST and LULC was then examined. The temperature vegetation index (TVX) space was constructed in order to study the temporal variability of thermal data and vegetation cover. Temporal trajectory of pixels in the TVX space showed that most changes due to urbanization were observable as the pixels migrated from the low temperature-dense vegetation condition to the high temperature-sparse vegetation condition in the TVX space. The uncertainty analysis revealed that the trajectory analysis in the TVX space involved a class-dependant noise component. This emphasized the need for multiple LULC control points in the TVX space. In addition, this research suggests that the use of multi-temporal satellite data together with the examination of changes in the TVX space is effective and useful in urban LULC change monitoring and analysis of urban surface temperature conditions as long as the uncertainty is addressed.     

Estimating fractional cover of photosynthetic vegetation, non-photosynthetic vegetation and bare soil in the Australian tropical savanna region upscaling the EO-1 Hyperion and MODIS sensors

Quantitative estimation of fractional cover of photosynthetic vegetation (f_PV), non-photosynthetic vegetation (f_NPV) and bare soil (f_BS) is critical for natural resource management and for modeling carbon dynamics. Accurate estimation of fractional cover is especially important for monitoring and modeling savanna systems, subject to highly seasonal rainfall and drought, grazing by domestic and native animals, and frequent burning. This paper describes a method for resolving f_PV, f_NPV and f_BS across the ~ 2 million km² Australian tropical savanna zone with hyperspectral and multispectral imagery. A spectral library compiled from field campaigns in 2005 and 2006, together with three EO-1 Hyperion scenes acquired during the 2005 growing season were used to explore the spectral response space for f_PV, f_NPV and f_BS. A linear unmixing approach was developed using the Normalized Difference Vegetation Index (NDVI) and the Cellulose Absorption Index (CAI). Translation of this approach to MODerate resolution Imaging Spectroradiometer (MODIS) scale was assessed by comparing multiple linear regression models of NDVI and CAI with a range of indices based on the seven MODIS bands in the visible and shortwave infrared region (SWIR) using synthesized MODIS surface reflectance data on the same dates as the Hyperion acquisitions. The best resulting model, which used NDVI and the simple ratio of MODIS bands 7 and 6 (SWIR3/SWIR2), was used to generate a time series of fractional cover from 16 day MODIS nadir bidirectional reflectance distribution function-adjusted reflectance (NBAR) data from 2000-2006. The results obtained with MODIS NBAR were validated against grass curing measurement at ten sites with good agreement at six sites, but some underestimation of f_NPV proportions at four other sites due to substantial sub-pixel heterogeneity. The model was also compared with remote sensing measurements of fire scars and showed a good matching in the spatio-temporal patterns of grass senescence and posterior burning. The fractional cover profiles for major grassland cover types showed significant differences in relative proportions of f_PV, f_NPV and f_BS, as well as large intra-annual seasonal variation in response to monsoonal rainfall gradients and soil type. The methodology proposed here can be applied to other mixed tree-grass ecosystems across the world.     

Characterizing fractional vegetation cover and land surface temperature based on sub-pixel fractional impervious surfaces from Landsat TM/ETM+

Estimating the distribution of impervious surfaces and vegetation is important for analysing urban landscapes and their thermal environment. The application of a crisp classification of land-cover types to analyse urban landscape patterns and land surface temperature (LST) in detail presents a challenge, mainly due to the complex characteristics of urban landscapes. In this article, sub-pixel percentage impervious surface areas (ISAs) and fractional vegetation cover (FVC) were extracted from bitemporal Thematic Mapper/Enhanced Thematic Mapper Plus (TM/ETM+) data by linear spectral mixture analysis (LSMA). Their accuracy was assessed with proportional area estimates of the impervious surface and vegetation extracted from high-resolution data. A range approach was used to classify percentage ISA into different categories by setting thresholds of fractional values and these were compared for their LST patterns. For each ISA category, FVC, LST, and percentage ISA were used to quantify the urban thermal characteristics of different developed areas in the city of Fuzhou, China. Urban LST scenarios in different seasons and ISA categories were simulated to analyse the seasonal variations and the impact of urban landscape pattern changes on the thermal environment. The results show that FVC and LST based on percentage ISA can be used to quantitatively analyse the process of urban expansion and its impacts on the spatial–temporal distribution patterns of the urban thermal environment. This analysis can support urban planning by providing knowledge on the climate adaptation potential of specific urban spatial patterns.     

On the relationship between monthly mean and maximum-value composite normalized vegetation indices

Abstract

A relationship between the maximum-value composite and monthly mean normalized difference vegetation index (NDVI) is derived statistically using data over the U.S. Great Plains during 1986. The monthly mean NDVI is obtained using a simple nine-day compositing technique based on the specifics of the scan patterns of the NOAA-9 Advanced Very High Resolution Radiometer (AVHRR). The results indicate that these two quantities are closely related over grassland and forest during the growing season. It is suggested that in such areas a monthly mean NDVI can be roughly approximated by 80 per cent of the monthly maximum NDVI, the latter being a standard satellite data product. The derived relationship was validated using data for the growing season of 1987.     

Sensitivity study of Radarsat-2 polarimetric SAR to crop height and fractional vegetation cover of corn and wheat

Increasing studies have been conducted to investigate the potential of polarimetric synthetic aperture radar (SAR) in crop growth monitoring due to the capability of penetrating the clouds, haze, light rain, and vegetation canopy. This study investigated the sensitivity of 16 parameters derived from C-band Radarsat-2 polarimetric SAR data to crop height and fractional vegetation cover (FVC) of corn and wheat. The in-situ measured crop height and FVC were collected from 29 April to 30 September 2013, at the study site in southwest Ontario, Canada. A total of 10 Radarsat-2 polarimetric SAR images were acquired throughout the same growing season. It was observed that at the early growing stage, the corn height was strongly correlated with the SAR parameters including HV (R² = 0.88), HH-VV (R² = 0.84), and HV/VV (R² = 0.80), and the corn FVC was significantly correlated with HV (R² = 0.79) and HV/VV (R² = 0.92), but the correlation became weaker at the later growing stage. The sensitivity of the SAR parameters to wheat variables was very low and only HV and Yamaguchi helix scattering showed relatively good but negative correlations with wheat height (R² = 0.57 and R² = 0.39) at the middle growing stage. These findings indicated that Radarsat-2 polarimetric SAR (C-band) has a great potential in crop height and FVC estimation for broad-leaf crops, as well as identifying the changes in crop canopy structures and phenology.     

Investigating the accuracy of vegetation index-based models for estimating the fractional vegetation cover and the effects of varying soil backgrounds using in situ measurements and the PROSAIL model

Fractional vegetation cover (FVC) is an important variable for describing the quality and changes of vegetation in terrestrial ecosystems. The simplest and most widely used model for the estimation of FVC is the dimidiate pixel model. The normalized difference vegetation index (NDVI) is commonly used as a vegetation index (VI) in this model. A range of VIs is possible alternative to the use of NDVI in the dimidiate pixel model. In this article, six VI-based dimidiate pixel models were compared using in situ measurements and canopy reflectances simulated by the PROSAIL model over nine different soil backgrounds. A comparison with in situ measurements showed that the Gutman–Ignatov method overestimated FVC, with a mean root mean square error (RMSE) of 0.14. The mean RMSE had an intermediate value of 0.08 in the Carlson–Ripley method and was further reduced to 0.05 in the method proposed by Baret et al. The use of both modified soil-adjusted vegetation index (MSAVI) and a mixture of NDVI and the ratio vegetation index (RVI) to replace NDVI in the Gutman–Ignatov model reduced the RMSE to 0.06. The mean RMSE in the difference vegetation index (DVI)-based model was 0.08. The simulated results indicated that soil backgrounds have significant effects on these VI-based models. The sensitivity of the first three models and the NDVI plus RVI-based model to soil backgrounds decreased with an increase in soil reflectance. In contrast, the DVI-based model is sensitive to soil backgrounds with high reflectances. MSAVI, which is less sensitive to soil backgrounds, represents a feasible alternative to the use of NDVI in the Gutman–Ignatov model.     

Assessing the response of the MODIS vegetation indices to landscape disturbance in the forested areas of the legal Brazilian Amazon

In this study we assessed the impacts of forest fragmentation on the Amazon landscape using remote sensing techniques. Landscape disturbance, obtained for an area of approximately 3.5 × 10⁶ km² through simple spatial metrics (i.e. number of fragments, mean fragment area and border size) and principal component transformation were then compared to the MODIS (Moderate Resolution Imaging Spectroradiometer) NDVI (Normalized Difference Vegetation Index) and EVI (Enhanced Vegetation Index) seasonal responses. As expected, higher disturbance values prevail in the southern border of the Amazon, near the intensively converted deforestation arc, and close to the major roads. NDVI seasonal responses more closely follow human-induced patterns, i.e. forest remnants from areas more intensively converted were associated with higher NDVI seasonal values. The significant correlation between NDVI seasonal responses and landscape disturbances were corroborated through analysis of geographically weighted regression (GWR) parameters and predictions. On the other hand, EVI seasonal responses were more complex with significant variations found over intact, less fragmented forest patches, thus restricting its utility to assess landscape disturbance. Although further research is needed, our results suggest that the degree of fragmentation of the forest remnants can be remotely sensed with MODIS vegetation indices. Thus, it may become possible to upscale field-based data on overall canopy condition and fragmentation status for basin-wide extrapolations.     

Multi-scale linkages between topographic attributes and vegetation indices in a mountainous landscape

This paper addresses a few issues that are fundamental for the understanding of vegetation-topography relations: scale dependency, seasonal variability, and importance of observing individual properties. Particularly, it uses two statistical tools - Pearson's r and Moran's I - to define relationships of several topographic attributes with the Normalized Difference Vegetation Index (NDVI), the Normalized Difference Infrared Index (NDII), and their seasonal changes (from May to July and then September) in the Mediterranean-type landscape of the Santa Monica Mountains, California. The analyses are conducted at both the original data resolution and 20 aggregated resolutions, covering a total range of 30 m to 1500 m, so that topography-vegetation relationships can be compared at different scales. Large sample sizes have supported the significance of the following main findings for this landscape. First, elevation, slope, and southness are the most relevant primary topographic attributes among the tested attributes and their importance changes across seasons. Second, NDVI, NDII, and their seasonal variations have notably different relationships (including no relationship) with topography. Third, the observed topography-vegetation correlations (r) tend to change - typically increase - with the coarsening of spatial scale. Lastly, the spatial autocorrelation of vegetation variables and topographic attributes are often comparable, and the comparability is more apparent when topography-vegetation correlations are stronger. In all, the topography-NDVI/NDII relations defined in this paper may improve the understanding of the multi-scale and property-specific role that mountain topography plays in the formation and seasonal change of vegetation patterns.     

Relating spatial patterns of fractional land cover to savanna vegetation morphology using multi-scale remote sensing in the Central Kalahari

Understanding, monitoring, and managing savanna ecosystems requires characterizing both functional and structural properties of vegetation. From a functional perspective, in savannas, quantitative estimation of fractional cover of photosynthetic vegetation (f_PV), non-photosynthetic vegetation (f_NPV), and bare soil (f_BS) is important as it relates to carbon dynamics and ecosystem function. On the other hand, vegetation morphology classes describe the structural properties of the ecosystem. Due to high functional diversity and structural heterogeneity in savannas, accurately characterizing both these properties using remote sensing is methodologically challenging. While mapping both fractional cover and vegetation morphology classes are important research themes within savanna remote sensing, very few studies have considered systematic investigation of their spatial association across different spatial resolutions. Focusing on the semi-arid savanna ecosystem in the Central Kalahari, this study utilized f_PV, f_NPV, and f_BS derived in situ and estimated from spectral unmixing of high- (GeoEye-1), medium- (Landsat TM), and coarse- (MODIS) spatial resolution imagery to investigate: (i) the impact of reducing spatial resolution on both magnitude and accuracy of fractional cover; and (ii) how fractional-cover magnitude and accuracy are spatially associated with savanna vegetation morphology classes. Endmembers for Landsat TM and GeoEye-1 were derived from the image based on purity measures; for MODIS (MCD43A4), the challenge of finding spectral endmembers was addressed following an empirical multi-scale hierarchical approach. GeoEye-1-derived fractional estimates showed comparatively closest agreement with in situ measurements and were used to evaluate Landsat TM and MODIS. Overall results indicate that increasing pixel size caused consistent increases in variance of and error in fractional-cover estimates. Even at coarse spatial resolution, f_PV was estimated with higher accuracy compared with f_NPV and f_BS. Assessment considering vegetation morphology of samples revealed both morphology- and cover-specific differences in accuracy. At larger pixel sizes, in areas with dominant woody vegetation, f_PV was overestimated at the cost of mainly underestimating f_BS; in contrast, in areas with dominant herbaceous vegetation, f_NPV was overestimated with a corresponding underestimation of both f_PV and f_BS. These results underscore that structural and functional heterogeneity in semi-arid savanna both impact retrieval of fractional cover, suggesting that comprehensive remote sensing of savannas needs to take both structure and cover into account.     

Seasonal landscape and spectral vegetation index dynamics in the Brazilian Cerrado: An analysis within the Large-Scale Biosphere-Atmosphere Experiment in Amazônia (LBA)

The Brazilian Cerrado biome comprises a vertically structured mosaic of grassland, shrubland, and woodland physiognomies with distinct phenology patterns. In this study, we investigated the utility of spectral vegetation indices in differentiating these physiognomies and in monitoring their seasonal dynamics. We obtained high spectral resolution reflectances, during the 2000 wet and dry seasons, over the major Cerrado types at Brasilia National Park (BNP) using the light aircraft-based Modland Quick Airborne Looks (MQUALS) package, consisting of a spectroradiometer and digital camera. Site-intensive biophysical and canopy structural measurements were made simultaneously at each of the Cerrado types including Cerrado grassland, shrub Cerrado, wooded Cerrado, Cerrado woodland, and gallery forest. We analyzed the spectral reflectance signatures, their first derivative analogs, and convolved spectral vegetation indices (VI) over all the Cerrado physiognomies. The high spectral resolution data were convolved to the MODIS, AVHRR, and ETM⁺ bandpasses and converted to the normalized difference vegetation index (NDVI) and the enhanced vegetation index (EVI) to simulate their respective sensors. Dry and wet season comparisons of the measured biophysical attributes were made with the reflectance and VI data for the different Cerrado physiognomies. We found that three major domains of Cerrado could be distinguished with the dry and wet season spectral signatures and vegetation indices. The EVI showed a higher sensitivity to seasonality than the NDVI; however, both indices displayed seasonal variations that were approximately one-half that found with the measured landscape green cover dynamics. Inter-sensor comparisons of seasonal dynamics, based on spectral bandpass properties, revealed the ETM⁺-simulated VIs had the best seasonal discrimination capability, followed by MODIS and AVHRR. Differences between sensor bandpass-derived VI values, however, varied with Cerrado type and between dry and wet seasons, indicating the need for inter-sensor VI translation equations for effective multi-sensor applications.     

Relationship between Landsat TM and SPOT vegetation indices and cumulative spruce budworm defoliation

Thirty‐five stands of mature, closed canopy black spruce (Picea mariana), white spruce (Picea glauca) and balsam fir (Abies balsamea) in Prince Albert National Park, Saskatchewan, were assessed for cumulative defoliation caused by eastern spruce budworm (Choristoneura fumiferana). Multitemporal Landsat 5 TM images (15 June 1992 and 18 July 2004) and a single‐date SPOT 4 HRVIR (high resolution visible and infrared) image (19 August 2004) were obtained over these stands. Correlation analysis suggested that the strength of the relationship between the defoliation and various vegetation indices was generally moderate. The SPOT HRVIR indices were more highly correlated to cumulative defoliation than the Landsat indices, and the multitemporal Landsat TM index outperformed the single‐date Landsat TM index. These results may help in the design of defoliation assessment procedures that integrate satellite remotely‐sensed data and aerial sketch mapping techniques.     

Calibration of satellite radiometers and the comparison of vegetation indices

Satellite technology provides a steadily improving capability to monitor surface land use and vegetation. However, the increasing number of satellite sensors has led to a variety of spectral indices which may be used to characterize vegetation. A basis is developed for comparing results from different sensors using instrument calibration coefficients, and the derived radiances are related to reflectances, principal component variables such as greenness, and spectral vegetation indices.     

In situ examination of the relationship between various vegetation indices and canopy phenology in an evergreen coniferous forest,Japan

We examined the relationship between four vegetation indices and tree canopy phenology in an evergreen coniferous forest in Japan based on observations made using a spectral radiometer and a digital camera at a daily time step during a 4 year period. The colour of the canopy surface of Japanese cedar (Cryptomeria japonica) changed from yellowish-green to whitish-green from late May to July and turned reddish-green in winter. The normalized difference vegetation index (NDVI), enhanced vegetation index (EVI) and plant area index (PAI) showed no seasonality. In contrast, the green–red ratio vegetation index (GRVI) increased from March to June and then decreased gradually from July to December, resulting in a bell-shaped curve. GRVI revealed seasonal changes in the colour of the canopy surface. GRVI correlated more positively with the evaluated maximum photosynthetic rate for the whole forest canopy, A _max, than did NDVI or EVI. These results suggest the possibility that GRVI is more useful than NDVI and EVI for capturing seasonal changes in photosynthetic capacity, as the green and red reflectances are strongly influenced by changes in leaf pigments in this type of forest.     

Cloud cover in Landsat observations of the Brazilian Amazon

High spatial resolution Landsat imagery is employed in efforts to understand the impact of human activities on ecological, biogeochemical and atmospheric processes in the Amazon basin. The utility of Landsat multi-spectral data depends both on the degree to which surface properties can be estimated from the radiometric measurements and on the ability to observe the surface through the atmosphere. Clouds are a major obstacle to optical remote sensing of humid tropical regions, therefore cloud cover probability analysis is a fundamental prerequisite to land-cover change and Earth system process studies in these regions. This paper reports the results of a spatially explicit analysis of cloud cover in the Landsat archive of Brazilian Amazonia from 1984 to 1997. Monthly observations of any part of the basin are highly improbable using Landsat-like optical imagers. Annual observations are possible for most of the basin, but are improbable in northern parts of the region. These results quantify the limitations imposed by cloud cover to current Amazon land-cover change assessments using Landsat data. They emphasize the need for improved radar and alternative optical data fusion techniques to provide time-series analyses of biogeophysical properties for regional modelling efforts.