ID: 53062
Title: Assessing land-use and carbon stock in slash-and-burn ecosystems in tropical mountain of Laos based on time-series satellite images
Author: Yoshio Inoue, Yoshiyuki Kiyono, Hidetoshi Asai, Yukihito Ochiai, Jiaguo Qi, Albert Olioso, Tatsuhiko Shiraiwa, Takeshi Horie, Kazuki Saito, Linkham Dounagsavanh
Editor: Alfred Stein
Year: 2010
Publisher: Elsevier, Vol 12, issue 4, August 2010
Source: Centre for Ecological Science,Indian Institute of Science, Bangalore-12
Reference: None
Subject: International Journal of Applied Earth Observation and Geoinformation
Keywords: Carbon sequestration, Remote sensing, shifting cultivation, Slash-and -burn, Laos, Southeast Asia
Abstract: In the tropical mountains of Southeast Asia, slash-and-burn (S/B) agricuture is a widely practiced and important food production system. The ecosystem carbon stock in this land-use is linked not only to the carbon exchange with the atmosphere but also with food and resource security. The objective of this study was to provide quantitative information on the land-use and ecosystem carbon stock in the region as well as to infer the impacts of alternative land-use and ecosystem management scenarios on the carbon sequestration potential at a regional scale. The study area was selected in a typical slash-and -burn region in the northern part of Laos. The chrono-sequential changes of land-use such as the relative areas of community age and cropping (C) + fallow (F) patterns were derived from the analysis of time-series satellite images. The chrono-sequential analysis showed that a consistent increase of S/B area during the past three decades and a rapid increase after 1990. Approximately 37% of the whole area was with the community age of 1-5 years, whereas 10% for 6-10 years in 2004. The ecosystem carbon stock at a regional scale was estimated by synthesizing the land-use patterns and semi-empirical carbon stock model derived from in situ measurements where the community age was used as a clue to the linkage. The ecosystem carbon stock in the region was strongly affected by the land-use patterns; the temporal average of carbon stock in 1C + 10 F cycles, for example, was greater by 33 MgCha-1 compared to that in 1C + 2F land-use pattern. The amount of carbon lost from the regional ecosystems during 1990-2004 periods periods was estimated to be 42 MgCha-1. The study approach proved to be useful especially in such regions with low data-availability and accessibility. This study revealed the dynamic change of land-use and ecosystem carbon stock in the tropical mountain of Laos as affected by land-use. Results suggest the significant potential of carbon sequestration through changing land-use and ecosystem management scenarios. These quantitative estimates would be useful to better understand and manage the land-use and ecosystem carbon stock towards higher sustainability and food security in similar ecosystems.
Location: 231
Literature cited 1: None
Literature cited 2: None
ID: 53061
Title: Universal reconstruction method for radiometric quality improvement of remote sensing images
Author: Huanfeng Shen, Yaolin Liu, Tinghua Ai, Yi Wang, Bo Wu
Editor: Alfred Stein
Year: 2010
Publisher: Elsevier, Vol 12, issue 4, August 2010
Source: Centre for Ecological Science,Indian Institute of Science, Bangalore-12
Reference: None
Subject: International Journal of Applied Earth Observation and Geoinformation
Keywords: Remote sensing image, Radiometric quality improvement, Universal reconstruction method
Abstract: The performance of remote sensing images in some applications is often affected by the existence of noise, blurring, stripes and corrupted pixels, as well as the hardware limits of the sensor with respect to spatial resolution. This paper presents a universal reconstruction method that can be used to improve the image quality by performing image denoising, deconvolution, destriping, impainting, interpolation and super-resolution reconstruction. The proposed method consists of two parts: a universal image observation model and a universal image reconstruction model. In the observation model, most degradation processes in remote sensing imaging are considered in order to relate the desired image to the observed images. For the reconstruction model, we use the maximum a posteriori (MAP) framework to set up the minimization energy equation. The likelihood probability density function (PDF) is constructed based on the image observation model, and a robust Huber-Markov model is employed as the prior PDF. Experimental results are presented to illustrate the effectiveness of the proposed method.
Location: 231
Literature cited 1: None
Literature cited 2: None
ID: 53060
Title: Spectral mixture anlaysis to monitor defoliation in mixed-aged Eucalyptus globulus Labill plantations in southern Australia using Landsat 5-TM and EO-1 Hyperion data
Author: B.Somers, J. Verbesselt, E.M.Ampe, N. Sims, W.W.Verstraeten, P.Coppin
Editor: Alfred Stein
Year: 2010
Publisher: Elsevier, Vol 12, issue 4, August 2010
Source: Centre for Ecological Science,Indian Institute of Science, Bangalore-12
Reference: None
Subject: International Journal of Applied Earth Observation and Geoinformation
Keywords: Defoliation, Unmixing, Hyperspectral, Multi-spectral, MESMA, Weighted spectral mixture analysis, Forest, LANDSAT, Hyperion, Leaf area index
Abstract: Defoliation is a key parameter of forest health and is associated with reduced productivity and tree mortality. Assessing the health of forests requires regular observations over large areas. Satellite remote sensing provides a cost-effective alternative to traditional ground-based assessment of forest health, but assessing defoliation can be difficult due to mixed pixels where vegetation cover is low or fragmented. In this study we apply a novel spectral unmixing technique, referred to as weighted Multiple Endmember Spectral Mixture Analysis (wMESMA), to Landsat 5-TM and EO-1 Hyperion data acquired over a Eucalyptus globulus (Labill) plantation in southern Australia. This technique combines an iterative mixture analysis cycle allowing endmembers to vary on a per pixel basis (MESMA) and a weighting algorithm that prioritizes wavebands based on their robustness against endmember variability. Spectral mixture analysis provides an estimate of the physically interpretable canopy cover, which is not necessarily correlated with defoliation in mixed-aged plantations due to natural variation in canopy cover as stands age. There is considerable variability in the degree of defoliation as well as in stand age among sites and in this study we found that results were significantly improved by the inclusion of an age correction algorithm for both the multi-spectral (R2 no age correction = 0.55 vs R2age correction = 0.73 for Landsat) and hyperspectral (R2 no age correction = 0.12 vs R2 age correction = 0.50 for Hyperion) image data. The improved accuracy obtained from Landsat compared to the Hyperion data illustrates the potential of applying SMA techniques for analysis of multi-spectral datasets such as MODIS and SPOT-VEGETATION.
Location: 231
Literature cited 1: None
Literature cited 2: None
ID: 53059
Title: Hyperspectral image classification by a variable interval spectral average and spectral curve matching combined algorithm
Author: A. Senthil Kumar, V. Keerthi, A.S. Manjunath, Harald van der Werff, Freek van der Meer
Editor: Alfred Stein
Year: 2010
Publisher: Elsevier, Vol 12, issue 4, August 2010
Source: Centre for Ecological Science,Indian Institute of Science, Bangalore-12
Reference: None
Subject: International Journal of Applied Earth Observation and Geoinformation
Keywords: Hyperspectral image, Spectral curve matching, Multiresolution analysis, Mixed pixel classification
Abstract: Classification of hyperspectral images has been receiving considerable attention with many new applications reported from commercial and military sectors. Hyperspectral images are composed of a large number of spectral channels, and have the potential to deliver a great deal of information about a remotely sensed scene. However, in addition to high dimensionality, hyperspectral image classification is compounded with a coarse ground pixel size of the sensor for want of adequate sensor signal to noise ratio within a fine spectral passband. This makes multiple ground features jointly occupying a single pixel. Spectral mixture analysis typically begins with pixel classification with spectral matching techniques, followed by the use of spectral unmixing algorithms for estimating endmembers abundance values in the pixel. The spectral matching techniques are analogous to supervised pattern recognition approaches, and try to estimate some similarity between spectral signatures of the pixel and reference target. In this paper, we propose a spectral matching approach by combining two schemes-variable interval spectral average (VISA) method and spectral curve matching (SCM) method. The VISA method helps to detect transient spectral features at different scales of spectral windows, while the SCM method finds a match between these features of the pixel and one of library spectra by least square fitting. Here we also compare the performance of the combined algorithm with other spectral matching techniques using a simulated and the AVIRIS hyperspectral data sets. Our results indicate that the proposed combination technique exhibits a stronger performance over the other methods in the classification of both teh pure and mixed class pixels simultaneously.
Location: 231
Literature cited 1: None
Literature cited 2: None
ID: 53058
Title: A community-based urban forest inventory using online mapping services and consumer-grade digital images
Author: Amr H. Abd-Elrahman, Mary E. Thornhill, Michael G. Andreu, Francisco Escobedo
Editor: Alfred Stein
Year: 2010
Publisher: Elsevier, Vol 12, issue 4, August 2010
Source: Centre for Ecological Science,Indian Institute of Science, Bangalore-12
Reference: None
Subject: International Journal of Applied Earth Observation and Geoinformation
Keywords: i-Tree, UFORE, Web mapping, Community involvement, Digital image, Photogrammetry
Abstract: Community involvement in gathering and submitting spatially referenced data via web mapping aplications has recently been gaining momentum. Urban forest inventory data analyzed by programs such as the i-Tree ECO inventory method is a good candidate for such an approach. In this research, we tested the feasibility of using spatially referenced data gathered and submitted by non-professional individuals through a web application to augment urban forest inventory data. We examined the use of close range photogrammetry solutions of images taken by consumer -grade cameras to extract quantitative metric information such as crown diameter, tree heights and trunk diameters. Several tests were performed to evaluate the accuracy of the photogrammetric solutions and to examine their use in addition to existing aerial image data to supplement or partially substitute for standard i-Tree ECO field measurements. Digital images of three sample sites were acquired using different consumer-grade cameras. Several photogrammetric solutions were performed using the acquired image sets. Each model was carried out using a relative orientation process followed by baseline model scaling. Several distances obtained through this solution were compared to the corresponding distances obtained through direct measurements in order to assess the quality of the model scaling appraoch. Measured i-Tree ECO field plot inventory data, online aerial image measurements and photogrammetric observations were compared. The results demonstrate the potential for using aerial image digitizing in addition to ground images to assist in participatory urban forest inventory efforts.
Location: 231
Literature cited 1: None
Literature cited 2: None
ID: 53057
Title: Leaf area index retrieval using gap fractions obtained from high resolution satellite data: Comparisons of approaches, scales and atmospheric effects
Author: Alemu Gonsamo
Editor: Alfred Stein
Year: 2010
Publisher: Elsevier, Vol 12, issue 4, August 2010
Source: Centre for Ecological Science,Indian Institute of Science, Bangalore-12
Reference: None
Subject: International Journal of Applied Earth Observation and Geoinformation
Keywords: Atmospheric correction, High resolution satellite data, Large scale LAI inversion, Scale effect, Vegetation index
Abstract: This study is aimed at demonstrating the feasibility of the large scale LAI inversion algorithms using red and near infrared reflectance obtained from high resolution satellite imagery. Radiances in digital counts were obtained in 10 m resolution acquired on cloud free day of August 23, 2007, by the SPOT 5 high resolution geometric (HRG) instrument on mostly temperate hardwood forest located in the Great Lakes-St. Lawrence forest in Southern Quebec. Normalized difference vegetation index (NDVI), scaled difference vegetation index (SDVI) and modified soil-adjusted vegetation index (MSAVI) were applied to calculate gap fractions. LAI was inverted from the gap fraction using the common Beer-Lambert ' s law of light extinction under forest canopy. The robustness of teh algorithm was evaluated using the ground-based LAI measurements and by applying the methods for the independently simulated reflectance data using PROSPECT + SAIL coupled radiative transfer models. Furthermore, the high resolution LAI was compared with MODIS LAI product. The effects of atmospheric corrections and scales were investigated for all of the LAI retrieval methods. NDVI was found to be not suitable index for large scale LAI inversion due to the sensitivity to scale and atmospheric effects. SDVI was virutally scale and atmospheric correction invariant. MSAVI was also scale invariant. Considering all sensitivity analysis, MSAVI performed best followed by SDVI for robust LAI inversion from high resolution imagery.
Location: 231
Literature cited 1: None
Literature cited 2: None
ID: 53056
Title: Study on estimating the evapotranspiration cover coefficient for stream flow simulation through remote sensing techniques
Author: Chihda Wu, Chichuan Cheng, Hannchung Lo, Yeongkeung Chen
Editor: Alfred Stein
Year: 2010
Publisher: Elsevier, Vol 12, issue 4, August 2010
Source: Centre for Ecological Science,Indian Institute of Science, Bangalore-12
Reference: None
Subject: International Journal of Applied Earth Observation and Geoinformation
Keywords: Evapotranspiration cover coefficient, Remote sensing, Stream flow simulation, SEBAL model
Abstract: This study focuses on using remote sensing techniques to estimate the evapotranspiration cover coefficient (CV) which is an important parameter for stream flow. The objective is to derive more accurate stream flow from the estimated CV. The study area is located in the Dan-Shuei watershed in northern Taiwan. The processes include the land-use classification using hybrid classification and four Landsat-5 TM images; the CV estimations based on remote sensing and traditional approaches; comparison of stream flow simulation according to the above two CV values. The result indicated that the study area was classified into seven land-use types with 88.35 classification accuracy. The simulated stream flow using remote sensing approach could represent more accurate hydrological characteristics than a traditional approach. Obviously integrating remote sensing technique and the SEBAL model is a useful approach to estimate the CV. The CV parameter estimated by remote sensing technique did improve the accuracy of the stream flow simulation. Therefore, the results can be extended to further studies such as forest water management.
Location: 231
Literature cited 1: None
Literature cited 2: None
ID: 53055
Title: Assessment of SPOT 5 and QuickBird remotely sensed imagery for mapping tree cover in savannas
Author: G.S.Boggs
Editor: Alfred Stein
Year: 2010
Publisher: Elsevier, Vol 12, issue 4, August 2010
Source: Centre for Ecological Science,Indian Institute of Science, Bangalore-12
Reference: None
Subject: International Journal of Applied Earth Observation and Geoinformation
Keywords: Tree cluster, Canopy cover, SPOT 5, QuickBird, Object based image analysis, NDVI threshold, Savanna, Southern Africa
Abstract: The relative abundance and distribution of trees in savannas has important implications for ecosystem function. High spatial resolution satellite sensors, including QuickBird and IKONOS, have been successfully used to map tree cover patterns in savannas. SPOT 5, with a 2.5 m panchromatic band and 10m multispectral bands, represents a relatively coarse resolution sensor within this context, but has the advantage of being relatively inexpensive and more widely available. This study evaluates the performance of NDVI threshold and object based image analysis techniques for mapping tree canopies from QuickBird and SPOT 5 imagery in two savanna systems in southern Africa. High thematic mapping accuracies were obtained with the QuickBird imagery, independent of mapping technique. Geometric properties of the mapping indicated that the NDVI threshold produced smaller patch sizes, but that overall patch size distributions were similar. Tree canopy mapping using SPOT 5 imagery and an NDVI threshold approach performed poorly, however acceptable thematic accuracies were obtained from the object based iamge analysis. Although patch sizes were generally larger than those mapped from the QuickBird image data, patch size distributions mapped with object based image analysis of SPOT 5 have a similar form to the QuickBird mapping. This indicates that SPOT 5 imagery is suitable for regional studies of tree canopy cover patterns.
Location: 231
Literature cited 1: None
Literature cited 2: None
ID: 53054
Title: Estimating canopy water content using hyperspectral remote sensing data
Author: J.G.P.W.Clevers, L. Kooistra, M.E.Schaepman
Editor: Alfred Stein
Year: 2010
Publisher: Elsevier, Vol 12, issue 2, April 2010
Source: Centre for Ecological Science,Indian Institute of Science, Bangalore-12
Reference: None
Subject: International Journal of Applied Earth Observation and Geoinformation
Keywords: Remote sensing, Hyperspectral, Canopy water content, Water absorption features, PROSAIL, First derivative
Abstract: Hyperspectral remote sensing has demonstrated great potential for accurate retrieval of canopy water content (CWC). This CWC is defined by the product of the leaf equivalent water thickness (EWT) and the leaf area index (LAI). In this paper, in particular the spectral information provided by the canopy water absorption feature at 970 nm for estimating and predicting CWC was studied using a modelling approach and in situ spectroradiometric measurements. The relationship of the first derivative at the right slope of the 970nm water absorption feature with CWC was investigated with the PROSAIL radiative transfer model and tested for field spectroradiometer measurements on two test sites. The first site was a heterogeneous floodplain with natural vegetation like grasses and various shrubs. The second site was an extensively grazed fen meadow. PROSAIL simulations (using coupled SAIL/PROSPECT-5 models) showed a linear relationship between the first derivative over the 1015-1050 nm spectral interval and CWC (R2=0.97). For 8 plots at the floodplain site the spectral derivative over the 1015-1050 nm interval obtained with an ASD FieldSpec spectroradiometer yielded an R2 of 0.51 with CWC. For 40 plots at the fen meadow ASD FieldSpec spectral measurements yielded an R2 of 0.68 for the derivative over the 1015-1050 nm interval with CWC. Consistency of the results confirmed the potential of using simulation results for calibrating the relationship between this first derivative and CWC.
Location: 231
Literature cited 1: None
Literature cited 2: None
ID: 53053
Title: Coupling urbanization analysis for studying urban thermal environment and its interplay with biophysical parameters based on TM/ETM+ imagery
Author: Ya Ma, Yaoqiu Kuang, Ningsheng Huang
Editor: Alfred Stein
Year: 2010
Publisher: Elsevier, Vol 12, issue 2, April 2010
Source: Centre for Ecological Science,Indian Institute of Science, Bangalore-12
Reference: None
Subject: International Journal of Applied Earth Observation and Geoinformation
Keywords: Urbanization, Urban heat island, Vegetation abundance, Percent impervious surface, Geostatistics, TM/ETM+
Abstract: Studies of urbanization and urban thermal environment are now attracting wide interests among scientists all over the world. This study investigated the influences of urbanization on urban thermal environment as well as the relationships of thermal characteristics to other biophysical variables in Guangzhou, China utilizing three dates of Landsat TM/ETM+ images acquired in 1990, 2000, and 2005, respectively. Vegetation abundances and percent impervious surfaces were derived by means of linear spectral mixture model, and a method for effectively enhancing impervious surface has been developed to accurately examine the urban enlargement. As a key parameter for studying urban thermal characteristics, the land surface temperature (LST) was also retrieved from the thermal infrared band of each TM/ETM+ dataset. Based on these parameters, the urgan expansion, urban heat island effect and the relationships of LSTs to other biophysical parameters were then anlayzed. Results indicated that the area ratio of impervious surface in Guangzhou increased significantly, which grew from 20.56% in 1990, to 34.72% in 2000, and further to 41.12% in 2005, however , the intensity of urban heat island was not always enlarged in observed years. In addition, Geostatistical analyses showed that the mean-centre of the impervious surface was moving towards the northwest during 1990-2005. And correlation analyses revealed that, the pixel-scale, the association of LST to other two variables ( vegetation abundance and percent impervious surface) was not straightforward, while LSTs possessed a strong positive correlation with percetn impervious surfaces and negative correlation with vegetation abundances at the regional scale, respectively. This study provided an integrated research scheme and the findings can be very useful for urban ecosystem modeling
Location: 231
Literature cited 1: None
Literature cited 2: None
ID: 53052
Title: Variability of SeaWiFS-derived chlorophyll-a concentrations in waters off central east coast of India, 1998-2003
Author: K. Muni Krishna
Editor: Alfred Stein
Year: 2010
Publisher: Elsevier, Vol 12, issue 2, April 2010
Source: Centre for Ecological Science,Indian Institute of Science, Bangalore-12
Reference: None
Subject: International Journal of Applied Earth Observation and Geoinformation
Keywords: Coastal upwelling, Central east coast of India, Chlorophyll-a concentration
Abstract: Seasonal and inter-annual variability in satellite-derived estimates of near-surface chlorophyll-a concentration off the central east coast of India from 1998 to 2003 is examined. Wind -induced upwelling predominates in late spring and winter, coinciding with the maximum in solar radiation, leading to increased accumulations of phytoplankton biomass. Chlorophyll concentrations varied from 2 to 10 mg/m3 over the central east coast of India and were generally lower in June and maximal in March. Chlorophyll concentrations along the coast followed a similar seasonal pattern (ranging from 0.5 to 6 mg/m3); however, concentrations were always greater on the Machilipatnam and Nellore compared with the Visakhapatnam and Chennai. The lack of upwelling favorable conditions results in the majority of the southern side of the central east cost of Indida waters being insufficient, which is reflected in low or moderate productivity. The possible reasons and observed correlations between chlorophyll-a and upwelling index during the study period was discussed.
Location: 231
Literature cited 1: None
Literature cited 2: None
ID: 53051
Title: Exploring hyperspectral bands and estimation indices for leaf nitrogen accumulation in wheat
Author: Xia Yao, Yan Zhu, YongChao Tian, Wei Feng, WeiXing Cao
Editor: Alfred Stein
Year: 2010
Publisher: Elsevier, Vol 12, issue 2, April 2010
Source: Centre for Ecological Science,Indian Institute of Science, Bangalore-12
Reference: None
Subject: International Journal of Applied Earth Observation and Geoinformation
Keywords: Spectral analysis, Sensitive hyperspectral bands, Estimation indices, Leaf nitrogen accumulation, Wheat
Abstract: Hyperspectral sensing can provide an effective means for fast and non-destructive estimation of leaf nitrogen (N) status in crop plants. The objectives of this study were to design a new method to extract hyperspectral spectrum information, to explore sensitive spectral bands, suitable bandwidth and best vegetation indices based on precise analysis of ground-based hyperspectral information, and to develop regression models for estimating leaf n accumulation per unit soil area (LNA, g N m-2) in winter wheat (Triticum aestivum L.). Three field experiments were conducted with different N rates and cultivar types in three consecutive growing seasons, and time-course measurements were taken on canopy hyperspectral reflectance and LNA under the various treatments. Then, normalized difference spectral indices (NDSI) and ratio spectral indices (RSI) based on the original spectrum and the first derivative spectrum were constructed within the range of 350-2500nm, and their relationships with LNA were quantified. The results showed that both LNA and canopy hyperspectral reflectance in wheat changed with varied N rates, with consistent patterns across different cultivars and seasons. The sensitive spectral bands for LNA existed mainly within visible and near infrared regions. The best spectral indices for estimating LNA in wheat were found to be NDSI (R860, R720), RSI (R990, R720), NDSI (FD736, FD526) and RSI (FD 725, FD 516) and the regression models based on the above four spectral indices were formulated as Y=26.34x1.887, Y = 5.095x - 6.040, Y = 0.609 e3.008x and Y=0.388x1.260, respectively, with R2 greater than 0.81. Furthermore, expanding the bandwidth of NDSI (R860, R720) and RSI (R990, R720) from 1 nm to 100nm at 1nm interval produced the LNA monitoring models with similar performance within about 33 nm and 23 nm bandwidth, respectively, over which the statistical parameters of the models became less stable. From testing of the derived equations, the model for LNA estimation on NDSI (R860, R720), RSI (R990, R720), NDSI (FD736, FD526) and RSI (FD725, FD516) gave R2 over 0.79 with more satisfactory performance than previously reported models and physical models in wheat. It can be concluded that the present hyperspectral parameters of NDSI (R860, R720), RSI (R990,R720), NDSI (FD736, FD526) and RSI (FD725, FD516) can be reliably used for estimating LNA in winter wheat.
Location: 231
Literature cited 1: None
Literature cited 2: None
ID: 53050
Title: Monitoring agricultural cropping patterns across the Laurentian Great Lakes Basin using MODIS-NDVI data
Author: Ross S. Lunetta, Yang Shao, Jayantha Ediriwickrema, John G. Lyon
Editor: Alfred Stein
Year: 2010
Publisher: Elsevier, Vol 12, issue 2, April 2010
Source: Centre for Ecological Science,Indian Institute of Science, Bangalore-12
Reference: None
Subject: International Journal of Applied Earth Observation and Geoinformation
Keywords: Multi-temporal imagery analysis, Cropland categorization, MODIS-NDVI
Abstract: The Moderate Resolution Imaging Spectrometer (MODIS) Normalized Difference Vegetation Index (NDVI) 16-day composite data product (MOD12Q) was used to develop annual cropland and crop-specific map products (corn, soybeans and wheat) for the Laurentian Great Lakes Basin (GLB). The crop area distributions and changes in crop rotations were characterized by comparing annual crop map products for 2005, 2006 and 2007. The total acreages for corn and soybeans were relatively balanced for calender years 2005 (31,462 km2 and 31,283 km2, respectively) and 2006 (30,766 km2 and 30,972 km2, respectively). Conversely, corn acreage increased approximately 21% from 2006 to 2007, while soybean and wheat acreage decreased approximately 9% and 21%, respectively. Two-year crop rotational change analyses were conducted for the 2005-2006 and 2006-2007 time periods. The large increase in corn acreages for 2007 introduced crop rotation changes across the GLB. Compared to 2005-2006, crop rotation patterns for 2006-2007 resulted in increased corn-corn, soybean-corn, and wheat -corn rotations. The increased corn acreages could have potential negative impacts on nutrient loadings,pesticide exposures, and sediment-mediated habitat degradation. Increased in US corn acreage in 2007 were related to new biofuel mandates, while Canadian increases were attributed to higher world-wide corn prices. Additional study is needed to determine the potential impacts of increases in corn-based ethanol agricultural production on watershed ecosystems and receiving waters.
Location: 231
Literature cited 1: None
Literature cited 2: None
ID: 53049
Title: Applying different inversion techniques to retrieve stand variables of summer barley with PROSPECT+SAIL
Author: M. Vohland, S. Mader, W. Dorigo
Editor: Alfred Stein
Year: 2010
Publisher: Elsevier, Vol 12, issue 2, April 2010
Source: Centre for Ecological Science,Indian Institute of Science, Bangalore-12
Reference: None
Subject: International Journal of Applied Earth Observation and Geoinformation
Keywords: Canopy reflectance modelling, PROSPECT, SAIL, Numerical optimization, Artificial neural network, Look-up table inversion
Abstract: This study describes the retrieval of state variables (LAI, canopy chlorophyll, water and dry matter contents) for summer barley from airborne HyMap data by means of a canopy reflectance model (PROSPECT + SAIL). Three different inversion techniques were applied to explore the impact of the employed method on estimation accuracies: numerical optimization (downhill simplex method), a look - up take (LUT) and an artificial neural network (ANN) approach. By numerical optimization (Num Opt), reliable estimates were obtained for LAI and canopy chlorophyll contents (LAI x Cab) with r2 of 0.85 and 0.94 and RDP values of 1.81 and 2.65, respectively. Accuracies dropped for canopy water (LAI x Cw) and dry matter contents (LAI x Cm). Nevertheless, the range of leaf water contents (Cw) was very narrow in the studied plant material. Prediction accuracies generally decreased in the order Num Opt>LUT>ANN. This decrease in accuracy mainly resulted from an increase in offset in the obtained values, as the retrievals from the different approaches were highly correlated. The same decreasing order in accuracy was found for the difference between the measured spectra and those reconstructed from the retrieved variable values. The parallel application of the different inversion techniques to one collective data set was helpful to identify modelling uncertainties, as shortcomings of the retrieval algorithms themselves could be separated from uncertainties in model structure and parameterisation schemes.
Location: 231
Literature cited 1: None
Literature cited 2: None
ID: 53048
Title: A framework for creating and validating a non-linear spectrum-biomass model to estimate the secondary succession biomass in moist tropical forests
Author: Hui Li, Paul Mausel, Eduardo Brondizio, David Deardorff
Editor: George Vosselman
Year: 2010
Publisher: Elsevier, Vol 65, issue 2, March 2010
Source: Centre for Ecological Science,Indian Institute of Science, Bangalore-12
Reference: None
Subject: ISPRS Journal of Photogrammetry and Remote Sensing
Keywords: Remote sensing, Amazonian forest, Landsat, Modeling, SWIR
Abstract: Uncertainties remain in the use of remote sensing technologies to provide validated model-derived estimates of the biomass of the secondary succession (SS) forests in the Amazon Basin. The objectives of this study were to develop a modeling framework for creating a valid spectrum-biomass model to estimate the SS biomass, to assess the utility of the framework and the accuracy and validity of the model, and to identify the model ' s determinants. Data sources for this study include 1992-1993 vegetation inventory data and 1991 Landsat Thematic Mapper (TM) data on the Altamira region of Para, Brazil, and 1994-1995 vegetation inventory data and 1994 Landsat TM data on the nearby Bragantina region. The allometric approach was used to estimate the biomass of the sampled sites based on the vegetation inventory data. A framework for the spectrum -biomass regression model was developed based on the estimated biomass of the sampled sites and the Landsat data. The framework includes (1) the pooling of data from Bragantina and the use of ANCOVA to justify this approach; (2) image calibration; (3) biomass data age-adjustment (4) selection of independent variables, (5) regression model development and (6) model assessment and validation. The cubic regression model with TM Band5-related predictors was found to best fit the data as evidenced by an adjusted R-squared value of 0.865, mean square error (MSE) of the model, and the analysis of residuals. Residual analysis showed that the model might yield a better estimation on a lower biomass values than on higher biomass values. In addition, further analyses identified several determinants that can impact the accuracy of the spectrum-biomass model. ANCOVA confirmed that the relationship between the biomass and the spectrum is independent of the Altamira and Bragantina regions, and that it was appropriate to pool sampled data from both regions in the proposed model. The model development methodology and the model produced from this research will be of value to researchers using the spectrum-biomass modeling approaches to estimate the biomass and study the SS rates in moist tropical forests.
Location: 231
Literature cited 1: None
Literature cited 2: None