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Lu D.,Indiana University Bloomington | Li G.,Indiana University Bloomington | Moran E.,Indiana University Bloomington | Batistella M.,Embrapa Satellite Monitoring | Freitas C.C.,National Institute for Space Research
ISPRS Journal of Photogrammetry and Remote Sensing | Year: 2011

This research explored the integrated use of Landsat Thematic Mapper (TM) and radar (i.e., ALOS PALSAR L-band and RADARSAT-2 C-band) data for mapping impervious surface distribution to examine the roles of radar data with different spatial resolutions and wavelengths. The wavelet-merging technique was used to merge TM and radar data to generate a new dataset. A constrained least-squares solution was used to unmix TM multispectral data and multisensor fusion images to four fraction images (high-albedo, low-albedo, vegetation, and soil). The impervious surface image was then extracted from the high-albedo and low-albedo fraction images. QuickBird imagery was used to develop an impervious surface image for use as reference data to evaluate the results from TM and fusion images. This research indicated that increasing spatial resolution by multisensor fusion improved spatial patterns of impervious surface distribution, but cannot significantly improve the statistical area accuracy. This research also indicated that the fusion image with 10-m spatial resolution was suitable for mapping impervious surface spatial distribution, but TM multispectral image with 30. m was too coarse in a complex urban-rural landscape. On the other hand, this research showed that no significant difference in improving impervious surface mapping performance by using either PALSAR L-band or RADARSAT C-band data with the same spatial resolution when they were used for multi-sensor fusion with the wavelet-based method. © 2011 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS). Source

Brandao Z.N.,Brazilian Agricultural Research Corporation Embrapa Cotton | Grego C.R.,Embrapa Satellite Monitoring | Inamasu R.Y.,Embrapa Instrumentation | Jorge L.A.,Embrapa Instrumentation
Proceedings of SPIE - The International Society for Optical Engineering | Year: 2014

The objective of this study was the spatial identification of the NDVI index and cotton yield distributions through different crop phenological stages using geostatistical methods in Goiás state, Brazil. The experiment was carried out in a commercial field with 47.4 ha, in 80x80m georeferenced grid with 74 plots. Yield monitor data and multispectral satellite images at 56 m spatial resolution were collected in a rainfed cotton field in two dates to monitor the plant vigor. Satellite images of AWiFS sensor were acquired on 08/02/2011 and 01/04/2011, during the first flowering and fruiting cotton stages, respectively, corresponding to 70 and 120DAE (days after emergence). Measures of canopy reflectance, plant height and leaf nitrogen content were determined and cotton yield was obtained by mechanical harvest in August, 2011. Data were analyzed using descriptive statistics, correlation and geostatistical analyses by building and setting semivariograms and kriging interpolation. Best correlation was found between NDVI and cotton yield at 120DAE. At first flowering, the NDVI and cotton yield showed strong spatial dependence, while for 120DAE there was no dependence, probably due to the enlargement of vegetated coverage. There were similarities in the bottom left of the study area with high values of NDVI, as well as the highest values of cotton yield due to excellent plant vigor in the cotton flowering stage. Identifications of spatial differences were possible using geostatistical methods with remote sensing data obtained from medium resolution satellite images, allowing to identify distinct stages of plant growth and also to predict the cotton yield. © 2014 SPIE. Source

Li G.,Indiana University | Lu D.,Indiana University | Moran E.,Indiana University | Dutra L.,National Institute for Space Research | Batistella M.,Embrapa Satellite Monitoring
ISPRS Journal of Photogrammetry and Remote Sensing | Year: 2012

This paper explores the use of ALOS (Advanced Land Observing Satellite) PALSARL-band (Phased Array type L-band Synthetic Aperture Radar) and RADARSAT-2 C-band data for land-cover classification in a tropical moist region. Transformed divergence was used to identify potential textural images which were calculated with the gray-level co-occurrence matrix method. The standard deviation of selected textural images and correlation coefficients between them were then used to determine the best combination of texture images for land-cover classification. Classification results based on different scenarios with maximum likelihood classifier were compared. Based on the identified best scenarios, different classification algorithms - maximum likelihood classifier, classification tree analysis, Fuzzy ARTMAP (a neural-network method), k-nearest neighbor, object-based classification, and support vector machine were compared for examining which algorithm was suitable for land-cover classification in the tropical moist region. This research indicates that the combination of radiometric images and their textures provided considerably better classification accuracies than individual datasets. The L-band data provided much better land-cover classification than C-band data but neither L-band nor C-band was suitable for fine land-cover classification system, no matter which classification algorithm was used. L-band data provided reasonably good classification accuracies for coarse land-cover classification system such as forest, succession, agropasture, water, wetland, and urban with an overall classification accuracy of 72.2%, but C-band data provided only 54.7%. Compared to the maximum likelihood classifier, both classification tree analysis and Fuzzy ARTMAP provided better performances, object-based classification and support vector machine had similar performances, and k-nearest neighbor performed poorly. More research should address the use of multitemporal radar data and the integration of radar and optical sensor data for improving land-cover classification. © 2012 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS). Source

Swann A.L.S.,University of Washington | Longo M.,Embrapa Satellite Monitoring | Knox R.G.,Lawrence Berkeley National Laboratory | Lee E.,Harvard University | Moorcroft P.R.,Harvard University
Agricultural and Forest Meteorology | Year: 2015

Ongoing agricultural expansion in Amazonia and the surrounding areas of Brazil is expected to continue over the next several decades as global food demand increases. The transition of natural forest and savannah ecosystems to pastureland and agricultural crops is predicted to create warmer and drier atmospheric conditions than the native vegetation. Using a coupled ecosystem regional atmospheric model (EDBRAMS) we investigate the expected impacts of predicted future land use on the climate of South America. The climate response in the model simulations is generally consistent with expectations from previous global modeling simulations with drier conditions resulting from deforestation, however the changes in precipitation are relatively small (on order of a few percent). Local drying is driven primarily by decreases in evapo-transpiration associated with the loss of forest, and concomitant increases in runoff. Significant changes in convectively available potential energy (CAPE) and convective inhibition (CIN) during the transition to the wet season indicate that the decrease in surface latent heat flux is indeed leading to a drier atmosphere, however these changes occur around a mean climatological state that is already very favorable for convection, and thus lead to relatively small changes in precipitation. If, however, these land use changes were to occur under a background state of drier conditions, such as those predicted for the future global climate model experiments, this additional atmospheric drying may be sufficient to decrease precipitation more substantially. © 2015 Elsevier B.V. Source

Nogueira S.F.,Embrapa Satellite Monitoring | Pereira B.F.F.,Federal University of Amazonas | Gomes T.M.,University of Sao Paulo | de Paula A.M.,Federal University of Parana | And 2 more authors.
Agricultural Water Management | Year: 2013

This study investigated the effects of irrigation using treated sewage effluent (TSE) combined with nitrogen (N) fertilization on the productivity and quality of bermudagrass, and on its economic feasibility under tropical conditions. The treatments employed were SI - no irrigation and no fertilization; A100 (control) - irrigation with potable water plus 520kgNha-1year-1 provided as NH4NO3; E0, E33, E66, and E100: irrigation with treated sewage effluent plus 0, 172, 343 and 520kgNha-1year-1 as NH4NO3, respectively. Chemical properties of TSE, shoot dry matter production, N concentration in bermudagrass were determined, and benefit-cost and economic viability analyses were carried out. Tree years of irrigation with TSE had agronomical benefits to bermudagrass such as: (i) saving 33% in N fertilizer by adding of 275kgNha-1year-1, increasing N accumulation in the soil; (ii) providing 70% of the N as NH4 +, which is the form most quickly assimilated by the plants; (iii) building up dry matter production with 7Mgha-1year-1 and (iv) increasing leaf N concentration in leaf tissue. The main benefit of TSE irrigation occurs in drought seasons with the increase in N concentration in bermudagrass shoots. Higher N concentration in leaf tissue elevates the quality and the sales price for the grass harvested, thus optimizing the benefit-cost ratio for the producer. Therefore, TSE irrigation is a viable cost-effective alternative if the N concentration in the leaf tissue is considered in the sales price. © 2012 Elsevier B.V. Source

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