A multispectral remote sensing study of coastal waters off Vancouver Island

In March 1996, a multispectral aircraft survey of the coastal waters off Vancouver Island was carried out using a Compact Airborne Spectrographic Imager (CASI). This survey was combined with in situ measurements of water properties (phytoplankton composition, phytoplankton pigments, absorption spectra of phytoplankton, and concentration of dissolved organic carbon, or DOC). Comparison of the phytoplankton absorption data from this experiment with similar data from other regions shows that phytoplankton community has a significant impact on the spectral form and magnitude of absorption spectra, when normalized to unit chlorophyll-a. Concurrent measurements of in situ properties and aircraft data were obtained at eight stations. The in situ measurements of phytoplankton absorption and estimates of downwelling irradiance based on a clear-sky atmospheric-transmission model are used as inputs to a model of water-leaving irradiance. The modelled irradiances are compared with the remotely sensed values of water-leaving radiances. The observed differences between model and observation are used to evaluate the potential influence of DOC on water-leaving radiance. Practical difficulties of separating the phytoplankton signal from that of the coloured component of DOC (also known as yellow substance) are examined. Algorithms for estimation of the concentration of chlorophyll-a (the major phytoplankton pigment) can be based on their absorption or fluorescence properties. The distribution of chlorophyll-a in the study area is estimated using both these approaches, and possible causes for the observed discrepancies are discussed.     

Extending one-dimensional models for deep lakes to simulate the impact of submerged macrophytes on water quality

Submerged macrophytes can stabilise clear water conditions in shallow lakes. However, many existing models for deep lakes neglect their impact. Here, we tested the hypothesis that submerged macrophytes can affect the water clarity in deep lakes. A one-dimensional, vertically resolved macrophyte model was developed based on PCLake and coupled to SALMO-1D and GOTM hydrophysics and validated against field data. Validation showed good coherence in dynamic growth patterns and colonisation depths. In our simulations the presence of submerged macrophytes resulted in up to 50% less phytoplankton biomass in the shallowest simulated lake (11 m) and still 15% less phytoplankton was predicted in 100 m deep oligotrophic lakes. Nutrient loading, lake depth, and lake shape had a strong influence on macrophyte effects. Nutrient competition was found to be the strongest biological interaction. Despite a number of limitations, the derived dynamic lake model suggests significant effects of submerged macrophytes on deep lake water quality.     

Spatio-temporal dynamics of phytoplankton and primary production in Lake Tanganyika using a MODIS based bio-optical time series

Lake Tanganyika, the second largest freshwater ecosystem in Africa, is characterised by a significant heterogeneity in phytoplankton concentration linked to its particular hydrodynamics. To gather a proper understanding of primary production, it is necessary to consider spatial and temporal dynamics throughout the lake. In the present work, daily MODIS-AQUA satellite measurements were used to estimate chlorophyll-a concentrations and the diffuse attenuation coefficient (K490) for surface waters. The spatial regionalisation of Lake Tanganyika, based on Empirical Orthogonal Functions of the chlorophyll-a dataset (July 2002-November 2005), allowed for the separation of the lake in 11 spatially coherent and co-varying regions, with 2 delocalised coastal regions. Temporal patterns of chlorophyll-a showed significant differences between regions. Estimation of the daily primary production in each region indicates that the dry season is more productive than the wet season in all regions with few exceptions. Whole-lake daily primary productivity calculated on an annual basis (2003) was 646 ± 142 mg C m^− 2 day^− 1. Comparing our estimation to previous studies, photosynthetic production in Lake Tanganyika appears to be presently lower (about 15%), which is consistent with other studies which used phytoplankton biovolume and changes of δ¹³C in the lake sediments. The decrease in lake productivity in recent decades may be associated to changes in climate conditions.     

Depth-integrated primary production in the eastern tropical and subtropical North Atlantic basin from ocean colour imagery

Existing satellite numerical models and data interpolation methods were used to evaluate the scales of variation in the vertical distribution of the phytoplankton biomass and the daily rate of primary production within the subtropical and tropical NE Atlantic Ocean (5-40 N; 6-30 W). The choice of this area was dictated principally by its economical importance, as it represents a large fishing ground due to upwelling activities along the coast of Morocco and Mauritania. After geographical partition of the area of study into 'bio-geochemical provinces' based on bathymetry and surface circulation, up to 300 satellite scenes obtained from the defunct Coastal Zone Color Scanner (CZCS) and covering the full year 1983 were processed into maps of surface chlorophyll and were further analysed into integrated primary production. Analyses of satellite data included a specific method to account for the sensitivity loss of the sensor during the studied period. The results of the productivity model are presented in terms of seasonal variations in the daily and annual photosynthetic carbon production in the various provinces and their contribution to the productivity of the overall study area. A total of 0.82 GtC y-1 was computed for the subtropical and tropical NE Atlantic. One third of this is produced within the coastal zone although it accounts for only 14% of the total area.     

A three component classification of phytoplankton absorption spectra: Application to ocean-color data

A method is presented to identify absorption characteristics of three optically-distinct phytoplankton classes from a suite of measurements of total phytoplankton absorption coefficient and chlorophyll-a concentration by successive application of the two-population absorption model of Sathyendranath et al. (2001) and Devred et al. (2006a). The total phytoplankton absorption coefficient at multiple wavelengths is expressed as the weighted sum of the absorption coefficients of each class at those wavelengths. The resultant system of equations is solved under some constraints to derive the fraction of each class present in any given sample of seawater, given the spectrum of total phytoplankton absorption coefficient. When applied to a large database, the results compare well with phytoplankton size-classes derived from pigment composition, so that we can assume that the three phytoplankton classes derived from absorption coefficients are representative of the pico-, nano- and microphytoplankton size classes. A modification is proposed to the pigment-based phytoplankton size classification of Uitz et al. (2006) to account for the effect of fucoxanthin associated with nanophytoplankton. Comparison between satellite and in situ data demonstrates the potential of satellite ocean-color data to yield the distribution of phytoplankton size classes from space. The algorithm is applied to phytoplankton absorption coefficients derived from remotely-sensed reflectance values collected by SeaWiFS over the Northwest Atlantic in 2007. Monthly composites for April, August and November, representative of Spring, Summer and Fall, give synoptic views of the phytoplankton community structure: a Spring bloom dominated by microphytoplankton is followed by a second, less intense, bloom in the Fall dominated by nanophytoplankton. Picophytoplankton are dominant in the study area in Summer.     

Seasonal and spatial distribution of chlorophyll-a concentrations and water conditions in the Gulf of Tonkin, South China Sea

The Gulf of Tonkin is a semi-closed gulf northwest of the South China Sea, experiencing reversal seasonal monsoon. Previous studies of water conditions have been conducted in the western waters of the gulf, but very few studies of the Chlorophyll-a (Chl-a) distribution have been carried out for the entire gulf. The present study investigates seasonal and spatial distributions of Chl-a and water conditions in the Gulf of Tonkin by analyzing Sea-viewing Wide Field-of-View Scanner (SeaWiFS) derived Chlorophyll-a (Chl-a), in situ measurements, sea surface temperatures (SST), and other oceanographic data obtained in 1999 and 2000. The results show seasonality of Chl-a and SST variations in the Gulf of Tonkin, and reveal phytoplankton blooming events in the center part of the gulf during the northeast monsoon season. In summer, Chl-a concentrations were relatively low (<0.3 mg m⁻³) and distributed uniformly throughout most of the area, with a belt of higher Chl-a concentrations along the coast, particularly the coast of Qiongzhou Peninsula; in winter, Chl-a concentration increased (0.5 mg m⁻³) in the entire gulf, and phytoplankton blooms offshore-ward from the northeast coast to the center of the gulf, while Chl-a concentrations reached high levels (0.8-1 mg m⁻³) in the center of the blooms. One peak of Chl-a concentrations was observed during the northeast monsoon season in the year. SST were high (27-29 °C) and distributed uniformly in summer, but lower with a large gradient from northeast (17 °C) to southwest (25 °C) in winter, while strong northeast winds (8-10 m/s) were parallel to the east coast of the gulf. Comparison of Chl-a values shows that SeaWiFS derived Chl-a concentrations match well with in situ measurements in most parts of the gulf in May 1999, but SeaWiFS derived Chl-a are higher than in situ data in river mouth waters. The seasonal variation of Chl-a concentrations and SST distribution were associated with the seasonally reversing monsoon; the winter phytoplankton blooms were related to vertical mixing and upwelling nutrients drawn by the northeast wind.     

Remote sensing of phytoplankton community composition along the northeast coast of the United States

Satellite imagery has proven to be a powerful tool for measuring chlorophyll a in surface waters. While this provides an estimate of total phytoplankton biomass, it does not distinguish between phytoplankton groups, many of which have functional differences and therefore affect biogeochemical cycles differently. Phytoplankton pigment analysis has been used to quantify a wide range of photosynthetic and accessory pigments, and chemotaxonomic analysis (e.g. CHEMTAX) has been used to successfully quantify functional taxonomic groups in nature based on pigment distributions. Here, we combine CHEMTAX analysis with satellite-derived distributions of specific phytoplankton pigments to describe the distributions of particular components of the phytoplankton community in the northeast coast of the United States from space. The spatial and seasonal variations in phytoplankton community structure elucidated through satellite remote sensing methods generally agreed with observations of abundance estimates of cell counts. Diatoms were generally the most abundant phytoplankton in this region, especially during Winter-Spring and in the inner shelf, but phytoplankton populations shifted to increasing abundance of other taxa during Summer, especially offshore. While still preliminary, satellite-derived taxa-specific information with proper regional controls holds promise for providing information on phytoplankton abundance to a taxonomic group level which would greatly improve our understanding of the impacts of human activity and climate change on ecosystems.     

Ecological indicators for the pelagic zone of the ocean from remote sensing

It is generally accepted that responsible stewardship of the ocean implies ecosystem-based management. A requirement then arises for ecosystem indicators that can be applied in serial fashion with a view to detection of ecosystem change in response to environmental perturbations such as climate change or overfishing. The status of ecological indicators for the pelagic ecosystem is reviewed. The desirable properties of such indicators are listed and it is pointed out that remote sensing (ocean colour, supplemented by sea-surface temperature) is an important aid to achieving them. Some ecological indicators that can be developed from remotely-sensed data on ocean colour are tabulated. They deal with the seasonal cycle of phytoplankton biomass, production and loss terms, annual production, new production, ratio of production to respiration, spatial variances in phytoplankton biomass and production, spatial distribution of phytoplankton functional types, delineation of ecological provinces and phytoplankton size structure.     

Remote sensing of phytoplankton functional types

The principal goal in early missions of satellite-borne visible spectral radiometry (ocean colour) was to create synoptic fields of phytoplankton biomass indexed as concentration of chlorophyll-a. In the context of climate change, a major application of the results has been in the modelling of primary production and the ocean carbon cycle. It is now recognised that a partition of the marine autotrophic pool into a suite of phytoplankton functional types, each type having a characteristic role in the biogeochemical cycle of the ocean, would increase our understanding of the role of phytoplankton in the global carbon cycle. At the same time, new methods have been emerging that use visible spectral radiometry to map some of the phytoplankton functional types. Here, we assess the state of the art, and suggest paths for future work.     

Characterization of mesoscale spatio‐temporal patterns and variability of remotely sensed Chl a and SST in the Interior Sea of Chiloe (41.4–43.5° S)

This study characterizes the spatial and temporal distribution of chlorophyll a (Chl a) and sea surface temperature (SST) in the Interior Sea of Chiloe in Chile at a moderate spatial resolution using SeaWiFS and MODIS‐Aqua time series data from the Goddard Earth Science (GES) Data and Information Services Center (DISC) imported into the GES DISC Interactive Online Visualization and Analysis System (‘Giovanni’). The Interior Sea of Chiloe is home to Chile's salmon farming industry, the world's second largest salmonid producer. This study undertakes the characterization of the main patterns of spatial and temporal variability of Chl a and SST in the Interior Sea of Chiloe using a continuous set of time series ocean colour and SST data. Both Chl a and SST exhibit a marked spatial and temporal distribution, with values being significantly higher in the northern area (41.4–42.7° S; total area 41.4–43.5° S) and during the spring–summer period. Peak Chl a concentrations tend to occur in a temporal interval from October to April (austral spring–austral autumn), whereas monthly averaged peak SST values occur consistently in the month of February (austral summer). Chl a concentrations exhibit strong interannual variations, with monthly averaged peak Chl a concentrations experiencing a twofold increase between the year with the lowest and highest Chl a peak concentration in the time series. Results suggest that at the present scale of analysis two spatial domains can be distinguished, regarding the differential behaviour of SST and Chl a in the northern and southern areas of the Interior Sea of Chiloe. The concurrent analysis of Chl a time series data with accumulated rainfall time series data as a proxy of solar radiation, on the other hand, supports previous hypothesis suggesting solar radiation to be a limiting factor for phytoplankton development in the Interior Sea of Chiloe. The role of geomorphological factors on pattern formation and the results presented in this study in relation to results from seasonal oceanographic cruises in the area are briefly discussed.     

Remote sensing of sea surface Sun-induced chlorophyll fluorescence: consequences of natural variations in the optical characteristics of phytoplankton and the quantum yield of chlorophyll a fluorescence

The rate of Sun-induced chlorophyll a fluorescence (SICF) observed at sea surface is determined by chlorophyll a concentration, incident irradiance, and optical and fluorescence properties of the phytoplanktonic population. In this study, the impact of natural variations in the two latter on the use of remotely sensed SICF to determine ocean surface chlorophyll a concentration, is assessed using a simple parameterization of phytoplankton optical properties and a model describing the variations in the quantum yield of chlorophyll a fluorescence as a function of environmental factors, such as excess irradiance and nutrient limitations. It is shown that (1) variations in the optical properties of phytoplankton are the main cause of non-linearity in the relationship between SICF and chlorophyll a concentration, (2) the extent of spatial variations in the rate of fluorescence per unit chlorophyll a concentration and irradiance, at the level of a typical sensor scene, prevents the use of a linear relationship between SICF and chlorophyll a concentration even at local scales and (3) the optical properties of the ocean surface layer play an important role in modifying the SICF signal. Nevertheless, the parameterizations presented in this paper may represent a reasonably sound approach for a meaningful use of SICF in view of detecting the chlorophyll a concentration within the upper layer of the ocean. Interestingly, it is also shown that the detection threshold of SICF could be significantly lower than the one currently expected.     

Two-component modeling of the optical properties of a diatom bloom in the Southern Ocean

Diatom cells have distinctive optical characteristics, originating from their relatively large cell size, fucoxanthin content and silica cell wall. It has been proposed that diatom-dominated phytoplankton blooms can be identified by optical remote sensing and that specifically tuned chlorophyll and primary production algorithms should be applied in regions where these blooms are present. However there have been few studies on how the optical properties of diatom blooms change as they progress from active growth to senescence, and it is unlikely that measurements on laboratory cultures encompass the full range of physiological states found in natural waters. We have therefore examined the inherent optical properties (IOPs) of the waters around the island of South Georgia at the end of the spring diatom bloom. Considerable variability was found in the relationships between the inherent optical properties and analytically determined chlorophyll a concentrations even in the surface layer, which meant that the usual bio-optical assumptions for Case 1 waters did not apply. To account for this variability, phytoplankton absorption and scattering were modeled as a two-component mixture, with the components representing actively growing and senescent material. The specific inherent optical properties of the two components were derived by linear regression of total IOPs against chlorophyll concentration and a fraction of the suspended mineral concentration. These specific IOPs were used to develop radiative transfer models of diatom blooms in varying stages of growth and senescence. Remote sensing reflectances calculated using this technique confirmed the tendency of the standard algorithms employed in SeaWiFS, MODIS and MERIS data processing to under-estimate near-surface chlorophyll concentrations in diatom blooms. However the inclusion of increasing proportions of senescent material had a significant effect on algorithm performance only at chlorophyll concentrations below 10 mg m^− 3. Optical depths predicted by the model around South Georgia were 9 +/− 2 m at 512 nm, indicating that a large fraction of the phytoplankton biomass was located below the depth from which the remote sensing signals originated.     

Unusual arson fires in the United States

A total of 722 Coastal Zone Color Scanner (CZCS) images obtained during 1979-1985 were used to investigatethe variability of surface phytoplankton in the Portugal Current System off western Iberia. Seasonal fluctuations of pigment biomass are clearly identified with amplitudes and phases varying from the shelf offshore and also with latitude. Interannual fluctuations are also present. The spatial distribution of phytoplankton varies strongly with topographic constraints and dynamical factors, such as the topography of the shelf and upper slope, pronounced coastline features, stratification due to the heating-cooling seasonal cycle and to river runoff, fronts, coastal upwelling, mesoscale filaments and jet-like structures, and surface currents. Surface pigment biomass is generally higher in the north, mainly in relation to the Galicia Front, and on the shelf, especially in winter (due to stratification and nutrient enrichment induced by river outflow) and in summer (as a result of coastal upwelling). Wavenumber analysis reveals a strong influence of mesoscaleand submesoscale processes on phytoplankton growth and aggregation. The relevance of this study for the global carbon cycle and for fisheries is briefly described.     

Using MODIS images to examine the surface extents and variations derived from the DEM and laser altimeter data in the Danjiangkou Reservoir,China

We used publicly available digital spatial datasets to study the area extents and their horizontal variations of two water bodies within the Danjiangkou Reservoir, China. Between 2003 and 2005, the water levels varied from 140 to 149 m above mean sea level as measured by the Geoscience Laser Altimeter System (GLAS). Detailed procedures to derive the horizontal extents and variations from the Moderate Resolution Imaging Spectroradiometer (MODIS) and Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM) coupled with GLAS data and to verify the extents and variations spatially were provided. For the water bodies on the north and west, the surface water extents derived from four MODIS images varied between 174 and 218 km² and from 96 to 135 km², respectively. The extents by inundating the DEM using the GLAS data were 178–212 km² for the water body on the north and 104–118 km² for the water body on the west. The spatial verifications of surface water extents derived from the MODIS images versus DEM coupled with GLAS data agreed 83–93%. Within the ring areas between water/land boundaries at elevations of 140 and 147 m, and 140 and 149 m, the spatial agreement was 52–75%.     

Space-borne study of seasonal,multi-year,and decadal phytoplankton dynamics in the Bay of Biscay

Seasonal and inter-annual variations in phytoplankton community abundance in the Bay of Biscay are studied. Preliminarily processed by the National Aeronautics and Space Administration (NASA) to yield normalized water-leaving radiance and the top-of-the-atmosphere solar radiance, Sea-viewing Wide Field-of-View Sensor (SeaWiFS), Moderate Resolution Imaging Spectroradiometer (MODIS), and Coastal Zone Color Scanner (CZCS) data are further supplied to our dedicated retrieval algorithms to infer the sought for parameters. By applying the National Oceanic and Atmospheric Administration's (NOAA's) Advanced Very High Resolution Radiometer (AVHRR) data, the surface reflection coefficient in the only band in the visible spectrum is derived and employed for analysis. Decadal bridged time series of variations of diatom-dominated phytoplankton and green dinoflagellate Lepidodinium chlorophorum within the shelf zone and the coccolithophore Emiliania huxleyi in the pelagic area of the Bay are documented and analysed in terms of impacts of some biogeochemical and geophysical forcing factors.

It is shown that in the shelf zone of the Bay, the diatom-dominated phytoplankton community variations are predominantly controlled by river discharge variations, by water column stratification conditions (forming in winter–early spring), and by wind action (resulting in such phenomena as up-wellings and sediment re-suspension).

Satellite data indicate that while in river deltas and adjoining waters the L. chlorophorum blooming events occur annually, in the Iroise Sea and near the Bailiwick of Guernsey, they happen irregularly. It is thought that such an irregular pattern, possibly, arises from L. chlorophorum competing with other phytoplankton species for nutrients.

E miliania huxleyi blooms are found to occur nearly every year in the northern part of the Bay, whereas in the central area, this phenomenon occurs very irregularly. Satellite data indicate that variations in the water chemistry (variations in the nitrogen : phosphorus ratio due to preceding blooms of diatoms), and the incident irradiance level (degree of cloudiness), are important factors controlling the occurrence of E. huxleyi blooming in the central part of the Bay. Covering a 30 year period, the bridged data from CZCS, AVHRR, SeaWiFS, and MODIS imply that climate change might be responsible for the observed increase in E. huxleyi blooming events in the Bay since 1979.     

Retrieval of oceanic chlorophyll concentration with relevance vector machines

In this communication, we evaluate the performance of the relevance vector machine (RVM) for the estimation of biophysical parameters from remote sensing data. For illustration purposes, we focus on the estimation of chlorophyll-a concentrations from remote sensing reflectance just above the ocean surface. A variety of bio-optical algorithms have been developed to relate measurements of ocean radiance to in situ concentrations of phytoplankton pigments, and ultimately most of these algorithms demonstrate the potential of quantifying chlorophyll-a concentrations accurately from multispectral satellite ocean color data. Both satellite-derived data and in situ measurements are subject to high levels of uncertainty. In this context, robust and stable non-linear regression methods that provide inverse models are desirable.Lately, the use of the support vector regression (SVR) has produced good results in inversion problems, improving state-of-the-art neural networks. However, the SVR has some deficiencies, which could be theoretically alleviated by the RVM. In this paper, performance of the RVM is evaluated in terms of accuracy and bias of the estimations, sparseness of the solutions, robustness to low number of training samples, and computational burden. In addition, some theoretical issues are discussed, such as the sensitivity to training parameters setting, kernel selection, and confidence intervals on the predictions.Results suggest that RVMs offer an excellent trade-off between accuracy and sparsity of the solution, and become less sensitive to the selection of the free parameters. A novel formulation of the RVM that incorporates prior knowledge of the problem is presented and successfully tested, providing better results than standard RVM formulations, SVRs, neural networks, and classical bio-optical models for SeaWIFS, such as Morel, CalCOFI and OC2/OC4 models.     

Effects of seasonal hydrologic patterns in south Florida wetlands on radar backscatter measured from ERS-2 SAR imagery

A multi-year study was carried out to evaluate ERS synthetic aperture radar (SAR) imagery for monitoring surface hydrologic conditions in wetlands of southern Florida. Surface conditions (water level, aboveground biomass, soil moisture) were measured in 13 study sites (representing three major wetland types) over a 25-month period. ERS SAR imagery was collected over these sites on 22 different occasions and correlated with the surface observations. The results show wide variation in ERS backscatter in individual sites when they were flooded and non-flooded. The range (minimum vs. maximum) in SAR backscatter for the sites when they were flooded was between 2.3 and 8.9 dB, and between 5.0 and 9.0 dB when they were not flooded. Variations in backscatter in the non-flooded sites were consistent with theoretical scattering models for the most part. Backscatter was positively correlated to field measurements of soil moisture. The MIchigan MIcrowave Canopy Scattering (MIMICS) model predicts that backscatter should decrease sharply when a site becomes inundated, but the data show that this drop is only 1-2 dB. This decrease was observed in both non-wooded and wooded sites. The drop in backscatter as water depth increases predicted by MIMICS was observed in the non-wooded wetland sites, and a similar decrease was observed in wooded wetlands as well. Finally, the sensitivity of backscatter and attenuation to variations in aboveground biomass predicted by MIMICS was not observed in the data.The results show that the inter- and intra-annual variations in ERS SAR image intensity in the study region are the result of changes in soil moisture and degree of inundation in the sites. The correlation between changes in SAR backscatter and water depth indicates the potential for using spaceborne SAR systems, such as the ERS for monitoring variations in flooding in south Florida wetlands.     

Modeling airborne laser scanning data for the spatial generation of critical forest parameters in fire behavior modeling

Methods for using airborne laser scanning (also called airborne LIDAR) to retrieve forest parameters that are critical for fire behavior modeling are presented. A model for the automatic extraction of forest information is demonstrated to provide spatial coverage of the study area, making it possible to produce 3-D inputs to improve fire behavior models.The Toposys I airborne laser system recorded the last return of each footprint (0.30-0.38 m) over a 2000 m by 190 m flight line. Raw data were transformed into height above the surface, eliminating the effect of terrain on vegetation height and allowing separation of ground surface and crown heights. Data were defined as ground elevation if heights were less than 0.6 m. A cluster analysis was used to discriminate crown base height, allowing identification of both tree and understory canopy heights. Tree height was defined as the 99 percentile of the tree crown height group, while crown base height was the 1 percentile of the tree crown height group. Tree cover (TC) was estimated from the fraction of total tree laser hits relative to the total number of laser hits. Surface canopy (SC) height was computed as the 99 percentile of the surface canopy group. Surface canopy cover is equal to the fraction of total surface canopy hits relative to the total number of hits, once the canopy height profile (CHP) was corrected. Crown bulk density (CBD) was obtained from foliage biomass (FB) estimate and crown volume (CV), using an empirical equation for foliage biomass. Crown volume was estimated as the crown area times the crown height after a correction for mean canopy cover.     

Scaling of impervious surface area and vegetation as indicators to urban land surface temperature using satellite data

Vegetation and impervious surface as indicators of urban land surface temperature (LST) across a spatial resolution from 30 to 960 m were investigated in this study. Enhanced thematic mapper plus (ETM+) data were used to retrieve LST in Nanjing, China. A land cover map was generated using a decision tree method from IKONOS imagery. Taking the normalized difference vegetation index (NDVI) and percent vegetation area (V) to present vegetated cover, and the normalized difference building index (NDBI) and percent impervious surface area (I) to present impervious surface, the correlation coefficients and linear regression models between the LST and the indicators were simulated. Comparison results indicated that vegetation had stronger correlation with the LST than the impervious surface at 30 and 60 m, a similar magnitude of correlation at 120 and 240 m, and a much lower correlation at 480 and 960 m. In total, the impervious surface area was a slightly better indicator to the LST than the vegetation because all of the correlation coefficients were relatively high (>0.5000) across the spatial resolution from 30 to 960 m. The indicators of LST, V and I are slightly better than the NDVI and NDBI, respectively, based on the correlation coefficients between the LST and the four indices. The strongest correlation of the LST and vegetation at the resolution of 120 m, and the strongest correlation between the LST and impervious surface at 120, 480 and 960 m, denoted the operational scales of LST variations.     

Effect of remote sensing spatial resolution on interpreting tower-based flux observations

Validation comparisons between satellite-based surface energy balance models and tower-based flux measurements over heterogeneous landscapes can be strongly influenced by the spatial resolution of the remote sensing inputs. In this paper, a two-source energy balance model developed to use thermal and visible /near-infrared remotely sensed data is applied to Landsat imagery collected during the 2004 Soil Moisture Experiment (SMEX04) conducted in southern Arizona. Using a two dimensional flux-footprint algorithm, modeled surface fluxes are compared to tower measurements at three locations in the SMEX04 study area: two upland sites, and one riparian site. The effect of pixel resolution on evaluating the performance of the land surface model and interpreting spatial variations of land surface fluxes over these heterogeneous areas is evaluated. Three Landsat scenes were examined, one representing the dry season and the other two representing the relatively wet monsoon season. The model was run at three resolution scales: namely the Landsat visible/near-infrared band resolution (30 m), the Landsat 5 thermal band resolution (120 m), and 960 m, which is nominally the MODIS thermal resolution at near-nadir. Comparisons between modeled and measured fluxes at the three tower sites showed good agreement at the 30 m and 120 m resolutions — pixel scales at which the source area influencing the tower measurement (∼ 100 m) is reasonably resolved. At 960 m, the agreement is relatively poor, especially for the latent heat flux, due to sub-pixel heterogeneity in land surface conditions at scales exceeding the tower footprint. Therefore in this particular landscape, thermal data at 1-km resolution are not useful in assessing the intrinsic accuracy of the land-surface model in comparison with tower fluxes. Furthermore, important spatial patterns in the landscape are lost at this resolution. Currently, there are no definite plans supporting high resolution thermal data with regular global coverage below ∼ 700 m after Landsat 5 and ASTER fail. This will be a serious problem for the application and validation of thermal-based land-surface models over heterogeneous landscapes.