ID: 53902
Title: Functional assessment of Wetlands Towards evaluation of ecosystem services
Author: Edward Maltby
Editor: Edward Maltby
Year: 2009
Publisher: Woodhead publishing Limited & CRC Press LLC, 2009
Source: Centre for Ecological Sciences
Reference: None
Subject: Functional assessment of Wetlands Towards evaluation of ecosystem services
Keywords: None
Abstract: None
Location: 215
Literature cited 1: None
Literature cited 2: None
ID: 53901
Title: Ground Object Identification-based on Absorption-band Position using EO-1 Hyperion Data
Author: Xu Yuanjin . Zhang Zhenfei . Hu Guangdao
Editor: Prof B.L.Deekshatulu
Year: 2010
Publisher: Indian Society of Remote Sensing, Vol 38, No 2, June 2010
Source: Centre for Ecological Sciences
Reference: None
Subject: Journal of the Indian Society of Remote Sensing
Keywords: Hyperspectral imagery . Ground object identification . Absorption-band position
Abstract: In order to accurately identify ground
objects in the hyperspectral imagery by spectral
matching, it is important to analyze the absorptionband
parameters. This paper presents a new spectral
matching method which is based mainly on analysis
of the absorption-band position. A measured
spectrum of a ground object can be subject to shifts
from its real wavelength position; meanwhile an
absorption band in the spectrum can also be shifted
relatively. Both these shifts are due to theenvironmental effects. Our spectral matching method
stresses the quantification of the total shift of the
absorption-band position, thus to get a possible offset
range of the measured absorption bands. This offset
range is taken as a constraint on the matching process.
The pixel spectrum in the image is then compared to
each known reference spectrum in a spectral library
previously built, so that the ground object
corresponding to the reference spectrum is identified.
A case study is conducted in Pulang Porphyry Copper
deposit, Zhongdian county, Yunnan, China. Five
types of ground objects were studied and it is shown
that our methods can get more accurate identification
results than the approach which does not consider
the shift ranges.
Location: 215
Literature cited 1: None
Literature cited 2: None
ID: 53900
Title: Exploring full-waveform LiDAR parameters for tree species classification
Author: Johannes Heinzel, Barbara Koch
Editor: Alfred Stein
Year: 2011
Publisher: Elsevier, Vol 13, Issue 1, February 2011
Source: Centre for Ecological Sciences
Reference: None
Subject: International Journal of Applied Earth Observation and Geoinformation
Keywords: Full-waveform, LiDAR, Intensity, Width, Amplitude, Tree species
Abstract: Precise tree species classification with high density full-waveform LiDAR data is a key research topic for automated forest inventory. Most approaches constrain to geometric features and only a few consider intensity values. Since full-waveform data offers a much larger amount of deducible information this study explores a high number of parameter and feature conbinations. Those variables having the highest impact on species differentiation are determined. To handle the large amount of airborne full-waveform data and to extract a comprehensive number of variable combinations an improved algorithm was developed. The full-waveform point parameters amplitude, width, range corrected intensity and total number of targets within a beam are transferred into raster covering a test site of 10 km2. It was possible to isolate the three most important variables based on the intensity, the width and the total number of targets. Up to six tree species were classified with an overall accuracy of 57%, limiting to the four main species accuracy was improved to 78% and constraining just to conifers and broadleaved trees even 91% could be classified correctly.
Location: 231
Literature cited 1: None
Literature cited 2: None
ID: 53899
Title: A simple retrieval method of land surface temperature from AMSR-E passive microwave data-A case study over Southern China during the strong snow disaster of 2008
Author: Shui-sen Chen, Xiu-zhi Chen, Wei-qi Chen, Yong-xian Su, Da Li
Editor: Alfred Stein
Year: 2011
Publisher: Elsevier, Vol 13, Issue 1, February 2011
Source: Centre for Ecological Sciences
Reference: None
Subject: International Journal of Applied Earth Observation and Geoinformation
Keywords: AMSR-E, LST, MPDI, Vegetation classification, Microwave remote sensing, GIS, Southern China
Abstract: The analysis of the passive microwave radiance transfer equation certifies that there is a linear relationship between satellite-generated brightness temperatures (BT) and in situ observation temperature and that land surface temperature (LST) is largely influenced by vegetation cover conditions. Microwave polarization difference index (MPDI) is an effective indicator for characterizing the land surface vegetation cover density. Based on the analysis of LST models from AMSR-E BT with 6.9 GHz MPDI intervals at 0.04, 0.02 and 0.01, respectively, this paper developed a simplified LST regression model with MPDI-based five land cover types, combining observation temperatures from 86 meterological observation stations. The study shows that smaller MPDI intervals can obtain higher accuracy of AMSR-E LST simulation, and that the combination of HDF Explorer and ArcGIS software was useful for automatically processing the pixel latitude, longitude and BT information from the AMSR-E HDF imagery files. The RMSE of the five LST simulation algorithms is between 1.47 and 1.920C, with an average LST retrieval error of 0.91-1.300C. Besides, only 7 polarization bands and 5 land surface types are required by the proposed simplified model. The new LST simulation models appears to be more effective for producing LST compared to past most studies, of which the accuracy used to be more than 20C. This study is one of the rare applications that combine the meterological observation temperature with MPDI to produce the LST regression analysis algorithms with less RMSE from AMSR-E data. The results can be referred to similar areas of the world for LST retrieval or land surface process research, in particular under extreme bad weather conditions.
Location: 231
Literature cited 1: None
Literature cited 2: None
ID: 53898
Title: Comined use of groundwater modeling and potential zone analysis for management of groundwater
Author: Shishir Gaur, B.R.Chahar, Didier Graillot
Editor: Alfred Stein
Year: 2011
Publisher: Elsevier, Vol 13, Issue 1, February 2011
Source: Centre for Ecological Sciences
Reference: None
Subject: International Journal of Applied Earth Observation and Geoinformation
Keywords: GIS, Groundwater modeling, Remote Sensing, Groundwater evaluation, Watershed management
Abstract: A methodology for groundwater evaluation has been developed by teh combined use of numerical model and spatial modeling using GIS. The developed methodology has been applied on the sub-basin of the Banganga River, India. Initially, the groundwater potential zones have been delineated by spatial modeling. Different thematic maps of the basin like geology, geomorphology, soil, drainage, slope factor and landuse/landcover have been used to identify the groundwater potential zones. Further, the groundwater flow model for the study area has been developed in teh MODFLOW. The groundwater flow vector map has been developed and superimposed on the potential zone map to validate the results of spatial modeling. Finally, the different scenarios have been conceptualized by varying the discharge of the wells and purposing the location for new rainwater harvesting structures. Results reveal that increasing the discharge of the wells in the potential zones put less stress on the aquifer. The suggested locations of rainwater harvesting structures also help to reduce the overall decline of groundwater in the area. The hydrological and spatial modeling presented in this study is highly useful for the evaluation of groundwater resources and for deciding the location of rainwater harvesting structures in semi-arid regions.
Location: 231
Literature cited 1: None
Literature cited 2: None
ID: 53897
Title: Modeling of multi-strata forest fire severity using Landsat TM data
Author: Qingmin Meng, Ross K. Meentemeyer
Editor: Alfred Stein
Year: 2011
Publisher: Elsevier, Vol 13, Issue 1, February 2011
Source: Centre for Ecological Sciences
Reference: None
Subject: International Journal of Applied Earth Observation and Geoinformation
Keywords: Fire severity, Multi-strata, dNBR, Heterogeneous landscapes, Landsat TM
Abstract: Most of the severity studies use field measures of composite burn index (CBI) to represent forest fire severity and fit the relationships between CBI and Landsat imagery derived differenced normalized burn ratio (dNBR) to predict and map fire severity at unsampled locations. However, less attention has been paid on the multi-strata forest fire severity, which represents fire activities and ecological responses at different forest layers. In this study, using field measured fire severity across five forest strata of dominant tree, intermediate-sized tree, shrub, herb, substrate layers, and the agrgregated measure of CBI as response variables, we fit statistical models with predictors of Landsat TM bands. Landsat derived NBR or dNBR, image differencing and image ratioing data. We model multi-strata forest fire in the historical recorded largest wildfire in California, teh Big Sur Basin Complex fire. We explore the potential contributions of the post-fire Landsat bands, image differencing, image ratioing to fire severity modeling and compare with the widely used NBR and dNBR . Models using combinations of post-fire Landsat bands perform much better than NBR, dNBR, image differencing , and image ratioing. We predict and map multi-strata forest fire severity across the whole Big Sur fire area, and find that the overall measure CBI is not optimal to represent multi-strata forest fire severity.
Location: 231
Literature cited 1: None
Literature cited 2: None
ID: 53896
Title: Path-finding through flexible hierarchical road networks: An experiential approach using taxi trajectory data
Author: Qingquan Li, Zhe Zeng, Tong Zhang, Jonathan Li, Zhongheng Wu
Editor: Alfred Stein
Year: 2011
Publisher: Elsevier, Vol 13, Issue 1, February 2011
Source: Centre for Ecological Sciences
Reference: None
Subject: International Journal of Applied Earth Observation and Geoinformation
Keywords: Path-finding, Road network, Hierarchy, Taxi trajectory, Navigation system
Abstract: Opimal paths computed by conventional path-planning algorithms are usually not "optimal" since realistic traffic information and local road network characteristics are not considered. We present a new experiential approach that computes optimal paths based on the experience of taxi drivers by mining a huge number of floating car trajectories. The approach consists of three steps. First, routes are recovered from original taxi trajectories. Second, an experiential road hierarchy is constructed using travel frequency and speed information for road segments. Third, experiential optimal paths are planned based on the experiential road hierarchy. Compared with conventional path-planning methods, the proposed method provides better experiential optimal path identification. Experiments demonstrate that the travel time is less for these experiential paths than for paths planned by conventional methods. Results obtained for a case study in the city of Wuhan, China, demonstrate that experiential optimal paths can be flexibly obtained in different time intervals, particularly during peak hours.
Location: 231
Literature cited 1: None
Literature cited 2: None
ID: 53895
Title: Spatio-temporal evaluation matrices for geospatial data
Author: Joc Triglav, Dusan Petrovic, Bojan Stopar
Editor: Alfred Stein
Year: 2011
Publisher: Elsevier, Vol 13, Issue 1, February 2011
Source: Centre for Ecological Sciences
Reference: None
Subject: International Journal of Applied Earth Observation and Geoinformation
Keywords: Geospatial data quality, Spatio-temporal, Visualization, Fitness for use, Communication feedback
Abstract: The global geospatial community is investing substantial effort in providing tools for geospatial data- quality information analysis and systematizing the criteria for geospatial data quality. The importance of these activities is increasing, especially in the last decade, which has witnesses an enormous expansion of geospatial data use in general and especially among mass users. Although geospatial data producers are striving to define and present data-quality standards to users and users increasingly need to assess the fitness for use of the data, the success of these activities is still far from what is expected or required. As a consequence, neglect or misunderstanding of data quality among users results in misuse or risks. This paper presents an aid in spatio-temporal quality evaluation through the use of spatio-temporal evaluation matrices (STEM) and the index of spatio-temporal anticipations (INSTANT) matrices. With the help of these two simple tools, geospatial data producers can systematically categorize and visualize the granularity of their spatio-temporal data, and users can present their requirements in the same way using business intelligence principles and a Web 2.0 approach. The basic principles and some examples are presented in the paper, and potential further applied research activities are briefly described.
Location: 231
Literature cited 1: None
Literature cited 2: None
ID: 53894
Title: A study of supervised classification accuracy in fuzzy topological methods
Author: Wenzhong Shi, Kimfung Liu, Hua Zhang
Editor: Alfred Stein
Year: 2011
Publisher: Elsevier, Vol 13, Issue 1, February 2011
Source: Centre for Ecological Sciences
Reference: None
Subject: International Journal of Applied Earth Observation and Geoinformation
Keywords: Characteristic posterior probabilities matrix, Fuzzy topology, Multiple classifier system, Eigen-value
Abstract: The multiple classifier system (MCS) is an effective automatic classification method, useful in connection with remote sensing analysis techniques. Combining MSC with induced fuzzy topology enables a decomposition of image classes. This fuzzy topological MCS then provides a new and improved approach to classification. The basic classification methods discussed in this paper include maximum likelihood classification (MLC), minimum distance classification (MIND) and Mahalanobis distance classification (MAH).
In this paper, the use of the fuzzy topology techniques in combination with the current classification methods is discussed. The methods included are 1) ordinary single classifier classification methods; 2) fuzzy single classifier classification methods; 3) simple average MCS; 4) fuzzy topological simple average MCS; 5)eigen-value MCS; 6) fuzzy topology and eigen-values MCS. This new experimental approach, involving such combinations for comparing the kappa values and overall accuracies is also discussed.
After comparing the kappa values and overall accuracies of these classification methods, the experimental results, demonstrated that a) methods combining with fuzzy topology concepts produced better classification accuracy than the ordinary methods; b) the eigen-value MCS method produces better classification accuracy than the non-fuzzy method and c) the best classifier combination was found to be MLC+MIND+MAH fuzzy eigen-value MCS.
Location: 231
Literature cited 1: None
Literature cited 2: None
ID: 53893
Title: Soil organic carbon mapping of partially vegetated agricultural fields with imaging spectroscopy
Author: Harm Bartholomeus, Lammert Kooistra, Antoine Stevens, Martin Van Leeuwen, Bas Van Wesemael, Eyal Ben-Dor, Bernard Tychon
Editor: Alfred Stein
Year: 2011
Publisher: Elsevier, Vol 13, Issue 1, February 2011
Source: Centre for Ecological Sciences
Reference: None
Subject: International Journal of Applied Earth Observation and Geoinformation
Keywords: Imaging spectroscopy, Soil organic carbon, Residual Spectral Unmixing
Abstract: Soil Organic Carbon (SOC) is one of the key soil properties, but the large spatial variation makes continuous mapping a complex task. Imaging spectroscopy has proven to be an useful technique for mapping of soil properties, but the applicability decreases rapidly when fields are partially covered with vegetation. In this paper we show that with only a few percent fractional maize cover the accuracy of a Partial Least Square Regression (PLSR) based SOC prediction model drops dramatically. However, this problem can be solved with the use of spectral unmixing techniques. First, the fractional maize cover is determined with linear spectral unmixing, taking the illumination and observation angles into account. In a next step the inflence of maize is filtered out from the spectral signal by a new procedure termed Residual Spectral Unmixing (RSU). The residual soil spectra resulting from this procedure are used for mapping of SOC using PLSR, which could be done with accuracies comparable to studies performed on bare soil surfaces (Root Mean Standard Error of Calibration = 1.34g/kg and Root Mean Standard Error of Prediction = 1.65 g/kg). With the presented RSU approach it is possible to filter out the influence of maize from the mixed spectra, and the residual soil spectra contain enough information for mapping of the SOC distribution within agricultural fields. This can improve the applicability of airborne imaging spectroscopy for soil studies in temperate climates, since the use of the RSU approach can extend the flight-window which is often constrained by the presence of vegetation.
Location: 231
Literature cited 1: None
Literature cited 2: None
ID: 53892
Title: Burnt area delineation from a uni-temporal perspective based on Landsat TM imagery classification using Support Vector Machines
Author: George P. Petropoulos, Charalambos Kontoes, Iphigenia Keramitsoglou
Editor: Alfred Stein
Year: 2011
Publisher: Elsevier, Vol 13, Issue 1, February 2011
Source: Centre for Ecological Sciences
Reference: None
Subject: International Journal of Applied Earth Observation and Geoinformation
Keywords: Burnt area mapping, Landsat TM, Support Vector Machines, Risk-EOS, Greek fires 2007, Remote sensing
Abstract: Information on burnt area is of critical importance in many applications as for example in assessing the disturbance of natural ecosystems due to a fire or in proving important information to policy makers on the land cover changes for establishing restoration policies of fire-affected regions. Such information is commonly obtained through remote sensing image thematic classification and a wide range of classifiers have been suggested for this purpose. The objective of the present study has been to investigate the use of Support Vector Machines (SVMs) classifier combined with multispectral Landsat TM image for obtaining burnt area mapping. As a case study a typical Mediterranean landscape in Greece was used, in which occurred one of the most devastating fires during the summer of 2007. Accuracy assessment was based on the classification overall statistical accuracy results and also on comparisons of the derived burnt area estimates versus validated estimates from the Risk-EOS Burnt Scar Mapping service. Results from the implementation of the SVM usign diverse kernel functions showed an average overall classification accuracy of 95.87% and a mean kappa coefficient of 0.948, with the burnt area class always clearly separable from all the other classes used in the classification scheme. Total burnt area estimate computed from the SVM was also in close agreement with that from Risk-EOS (mean difference of less than 1%). Analysis also indicated that, at least for the studied here fire, the inclusion of the two middle infrared spectral bands TM5 and TM7 of TM sensor as well as the selection of the kernel function in SVM implementation have a negligible effect in both the overall classification performance and in the delineation of total burnt area. Overall, results exemplified the appropriateness of the spatial and spectral resolution of the Landsat TM imagery combined with the SVM in obtaining rapid and cost-effective post-fire analysis. This is of considerable scientific and practical value, given the present open access to the archived and new observations from this satellite radiometer globally.
Location: 231
Literature cited 1: None
Literature cited 2: None
ID: 53891
Title: Generation and evaluation of gross primary productivity using Landsat data through blending with MODIS data
Author: Devendra Singh
Editor: Alfred Stein
Year: 2011
Publisher: Elsevier, Vol 13, Issue 1, February 2011
Source: Centre for Ecological Sciences
Reference: None
Subject: International Journal of Applied Earth Observation and Geoinformation
Keywords: Landsat, MODIS, Blending, Gross primary productivity, Chlorophyll index, Evapotranspiration
Abstract: Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) has been used for the blending of Landsat and MODIS data. Specifically, the 30m Landsat-7 ETM+ (Enhanced Thematic Mapper Plus) surface reflectance was predicted for a period of 10 years (2000-2009) as the product of observed ETM+ and MODIS surface reflectance (MODO9A1) on the predicted and observed ETM+ dates. A pixel based analysis for six observed ETM+ dates covering winter and summer crops showed that the prediction method was more accurate for NIR band (mean r2=0.71, p< 0.01) compared to green band (mean r2=0.53, p< 0.01). A recently proposed chlorophyll index (CI), which involves NIR and green spectral bands, was used to retrieve gross primary productivity (GPP) as the product of CI and photosynthetic active radiation (PAR). The regression analysis of GPP derived from closed observed and synthetic ETM + showed a good agreement (r2=0.85, p< 0.01and r2=0.86, p< 0.01) for wheat and sugarcane crops, respectively. The difference between the GPP derived from synthetic and observed ETM + (prediction residual) was compared with the difference in GPP values from observed ETM+ on the two dates (temporal residual). The prediction residuals (mean value of 1.97 g C/m2 in 8 days) was found to be significantly lower than the temporal residuals (mean value of 4.46 g C/m2 in 8 days) that correspondence to 12% and 27%, respectively, of GPP values (mean value of 16.53 g C/m2 in 8 days) from observed ETM+ data, implying that the prediction method was better than temporal pixel substitution. Investigating the trend in synthetic ETM + GPP values over a growing season revealed that phenological patterns were well captured for wheat and sugarcane crops. A direct comparison between the GPP values derived from MODIS and synthetic ETM+ data showed a good consistency of the temporal dynamics but a systematic error that can be read as bias (MODIS GPP over estimation). Further, the regression analysis between observed evaportranspiration and synthetic ETM+GPP showed good agreement ( r2=0.66, p< 0.01)
Location: 231
Literature cited 1: None
Literature cited 2: None
ID: 53890
Title: A time-integrated MODIS burn severity assessment using the multi-temporal differenced normalized burn ration (dNBRMT)
Author: S. Veraverveke, S. Lhermitte, W.W.Verstraeten, R. Goossens
Editor: Alfred Stein
Year: 2011
Publisher: Elsevier, Vol 13, Issue 1, February 2011
Source: Centre for Ecological Sciences
Reference: None
Subject: International Journal of Applied Earth Observation and Geoinformation
Keywords: Differenced nomralized burn ratio, Fire severity, MODIS, Burn severity, Landsat Thematic Mapper, Composite burn index, Multi-temporal, Vegetative regeneration
Abstract: Burn severity is an important parameter in post-fire management. It incorporates both the direct fire impact (vegetation depletion) and ecosystem responses (vegetation regeneration). From a remote sensing perspective, burn severity is traditionally estimated using Landsat ' s differenced normalized burn ratio (dNBR). In this case study of the large 2007 Peloponnese (Greece) wildfires, Landsat dNBR estimates correlated reasonably well with Geo composite burn index (GeoCBI) field data of severity (R2=0.56). The usage of Landsat imagery is, however, restricted by cloud cover and image-to- image normalization constraints. Therefore a multi-temporal burn severity approach based on coarse spatial, high temporal resolution moderate resolution imaging spectroradiometer (MODIS) imagery is presented in this study. The multi-temporal dNBR (dNBRMT) is defined as the 1-year integrated difference between burned pixels and their unique control pixels. These control pixels were selected based on time series similarity and spatial context and reflect how burned pixels would have behaved in the case no fire had occurred. Linear regression between downsampled Landsat dNBR and dNBRMT estimates resulted in a moderate high coefficient of determination R2=0.54. dNBRMT estimates are indicative for the change in vegetation productivity due to the fire. This change is considerably higher for forests than for more sparsely vegetated areas like shrub lands. Although Landsat dNBR is superior for spatial detail, MODIS-derived dNBRMT estimates present a valuable alternative for burn severity mapping at continental to global scale without image availability constraints. This is beneficial to compare trends in burn severity across regions and time. Moreover, thanks to MODIS ' s repeated temporal sampling, the dNBRMT accounts for both first-and second-order fire effects.
Location: 231
Literature cited 1: None
Literature cited 2: None
ID: 53889
Title: Effects of lossy compression on remote sensing image classification of forest areas
Author: A. Zabala, X. Pons
Editor: Alfred Stein
Year: 2011
Publisher: Elsevier, Vol 13, Issue 1, February 2011
Source: Centre for Ecological Sciences
Reference: None
Subject: International Journal of Applied Earth Observation and Geoinformation
Keywords: Image compression, Image classification, Forestry management, JPEG, JPEG 2000
Abstract: Lossy compression is being increasingly used in remote sensing; however, its effects on classification have scarcely been studied. This paper studies the implications of JPEG (JPG) and JPEG 2000 (J2K) lossy compression for image classification of forests in Mediterranean areas. Results explore the impact of the compression on the images themselves as well as on the obtained classification. The results indicate that classifications made with previously compressed radiometrically corrected images and topoclimatic variables are not negatively affected by compression, even at quite high compression ratios. Indeed, JPG compression can be applied to images at a compression ratio (CR, ratio between the size of the original file and the size of the compressed file) of 10:1 or even 20:1 ( for both JPG and J2K). Nevertheless, the fragmentation of the study area must be taken into account: in less fragmented zones, high CR are possible for both JPG and J2K, but in fragmented zones, JPG is not advisable, and when J2K is used, only a medium CR is recommended (3.33:1 to 5:1). Taking into account that J2K produces fewer artefacts at higher CR, the study not only contributes with optimum CR recommendations, but also found that the J2K compression standard (ISO 15444-1) is bettre than the JPG (ISO 10918-1) when applied to image classification. Although J2K is computationally more expensive, this is no longer a critical issue with current computer technology.
Location: 231
Literature cited 1: None
Literature cited 2: None
ID: 53888
Title: Directional effects on NDVI and LAI retrievals from MODIS: A case study in Brazil with soybean
Author: Fabio Marcelo Breunig, Lenio Soares Galvao, Antonio Roberto Formaggio, Jose Carlos Neves Epiphanio
Editor: Alfred Stein
Year: 2011
Publisher: Elsevier, Vol 13, Issue 1, February 2011
Source: Centre for Ecological Sciences
Reference: None
Subject: International Journal of Applied Earth Observation and Geoinformation
Keywords: MODIS, LAI, Soybean, Viewing geometry, NDVI
Abstract: The Moderate Resolution Imaging Spectroradiometer (MODIS) is largely used to estimate Leaf Area Index (LAI) using radiative transfer modeling (the "main" algorithm). When this algorithm fails for a pixel, which frequently occurs over Brazilian soybean areas, an empirical model (the "backup" algorithm) based on the relationship between the Normalized Difference Vegetation Index (NDVI) and LAI is utilized. The objective of this study is to evaluate directional effects on NDVI and subsequent LAI estimates using global (biome 3) and local empirical models, as a function of the soybean development in two growing seasons (2004-2005 and 2005-2006). The local model was derived from the pixels that had LAI values retrieved from the main algorithm. In order to keep the reproductive stage for a given cultivar as a constant factor while varying the viewing geometry, pairs of MODIS images acquired in close dates from opposite directions (backscattering and forward scattering) were selected. Linear regression relationships between the NDVI values calculated from these two directions were evaluated for different view angles (0-250;25-450; 45-600) and development stages (<45; 45-90;>90 days after planting). Impacts on LAI retrievals were analyzed. Results showed higher reflectance values in backscattering direction due to the predominance of sunlit soybean canopy components towards the sensor and higher NDVI values in forward scattering direction due to stronger shadow effects in the red waveband. NDVI differences between the two directions were statistically significant for view angles larger than 250. The main algorithm for LAI estimation failed in the two growing seasons with gradual crop development. As a result, up to 94% of the pixels had LAI values calculated from the backup algorithm at the peak of canopy closure. Most of the pixels selected to compose the 8-day MODIS LAI product came from the forward scattering view because it displayed larger LAI values than the backscattering. Directional effects on the subsequent LAI retrievals were stronger at the peak of the soybean development (NDVI values between 0.70 and 0.85). When the global empirical model was used. LAI differences up to 3.2 for consecutive days and opposite viewing directions were observed. Such differences were reduced to values up to 1.5 with the local model. Because of the predominance of LAI retrievals from the MODIS backup algorithm during the Brazilian soybean development, care is necessary if one considers using these data in agronomic growing /yield models.
Location: 231
Literature cited 1: None
Literature cited 2: None