970 resultados para Daedalus Thematic Mapper
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Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
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"September 1991."
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We address the practical issue of using thermal image data without adjustment or calibration for projects which do not require actual temperatures per se. Large scale airborne scanning in the thermal band at 8.5–13 μm was obtained for a mangrove and salt marsh in subtropical eastern Australia. For open sites, the raw image values were strongly positively correlated with ground level temperatures. For sites under mangrove canopy cover, image values indicated temperatures 2–4°C lower than those measured on the ground. The raw image was useful in identifying water bodies under canopy and has the potential for locating channel lines of deeper water. This could facilitate modification to increase flushing in the system, thereby reducing mosquito larval survival.
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A method is presented for the development of a regional Landsat-5 Thematic Mapper (TM) and Landsat-7 Enhanced Thematic Mapper plus (ETM+) spectral greenness index, coherent with a six-dimensional index set, based on a single ETM+ spectral image of a reference landscape. The first three indices of the set are determined by a polar transformation of the first three principal components of the reference image and relate to scene brightness, percent foliage projective cover (FPC) and water related features. The remaining three principal components, of diminishing significance with respect to the reference image, complete the set. The reference landscape, a 2200 km2 area containing a mix of cattle pasture, native woodland and forest, is located near Injune in South East Queensland, Australia. The indices developed from the reference image were tested using TM spectral images from 19 regionally dispersed areas in Queensland, representative of dissimilar landscapes containing woody vegetation ranging from tall closed forest to low open woodland. Examples of image transformations and two-dimensional feature space plots are used to demonstrate image interpretations related to the first three indices. Coherent, sensible, interpretations of landscape features in images composed of the first three indices can be made in terms of brightness (red), foliage cover (green) and water (blue). A limited comparison is made with similar existing indices. The proposed greenness index was found to be very strongly related to FPC and insensitive to smoke. A novel Bayesian, bounded space, modelling method, was used to validate the greenness index as a good predictor of FPC. Airborne LiDAR (Light Detection and Ranging) estimates of FPC along transects of the 19 sites provided the training and validation data. Other spectral indices from the set were found to be useful as model covariates that could improve FPC predictions. They act to adjust the greenness/FPC relationship to suit different spectral backgrounds. The inclusion of an external meteorological covariate showed that further improvements to regional-scale predictions of FPC could be gained over those based on spectral indices alone.
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Land condition monitoring information is required for the strategic management of grazing land and for a better understanding of ecosystem processes. Yet, for policy makers and those land managers whose properties are situated within north-eastern Australia's vast Great Barrier Reef catchments, there has been a general lack of geospatial land condition monitoring information. This paper provides an overview of integrated land monitoring activity in rangeland areas of two major Reef catchments in Queensland: the Burdekin and Fitzroy regions. The project aims were to assemble land condition monitoring datasets that would assist grazing land management and support decision-makers investing public funds; and deliver these data to natural resource management(NRM) community groups, which had been given increased responsibility for delivering local environmental outcomes. We describe the rationale and processes used to produce new land condition monitoring datasets derived from remotely sensed Landsat thematic mapper (TM) and high resolution SPOT 5 satellite imagery and from rapid land condition ground assessment. Specific products include subcatchment groundcover change maps, regional mapping of indicative very poor land condition, and stratified land condition site summaries. Their application, integration, and limitations are discussed. The major innovation is a better understanding of NRM issues with respect to land condition across vast regional areas, and the effective transfer of decision-making capacity to the local level. Likewise, with an increased ability to address policy questions from an evidence-based position, combined with increased cooperation between community, industry and all levels of government, a new era has emerged for decision-makers in rangeland management.
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Sichuanissa Tiibetin ylängön metsäkato on pysähtynyt mutta eroosio-ongelmat jatkuvat Viikin tropiikki-instituutin tutkija Ping ZHOU kartoitti trooppisen metsänhoidon alaan kuuluvassa väitöskirjatyössään maaperän eroosioalttiutta ja sen riippuvuutta metsäkasvillisuudesta Jangtsen tärkeää sivuhaaraa Min-jokea ympäröivällä n. 7400 neliökilometrin suuruisella valuma-alueella Sichuanin Aba-piirikunnassa. Aineistonaan hän käytti muun muassa satelliittikartoitustietoja ja mittaustuloksia yli 600 maastokoealalta. Tutkimuksen nimi suomeksi on "Maaperän eroosion mallinnus ja vuoristoisen valuma-alueen ekologinen ennallistaminen Sichuanissa Kiinassa". Aikaisempien tutkimusten perusteella oli tiedossa että metsien häviäminen tällä alueella pysähtyi jo 1980-luvun alussa. Sen jälkeen on metsien pinta-ala hitaasti kasvanut etupäässä sen vuoksi, että teollinen puunhakkuu luonnonmetsissä kiellettiin kokonaan v. 1998 ja 25 astetta jyrkemmillä rinteillä myös maatalouden harjoittaminen on saatu lopetetuksi viljelijöille tarjottujen taloudellisten houkuttimien avulla. Täten myös pelto- ja laidunmaata on voitu ennallistaa metsäksi. Ping Zhou pystyi jakamaan 5700 metrin korkeuteen saakka kohoavan vuoristoalueen eroosioalttiudeltaan erilaisiin vyöhykkeisiin rinteen kaltevuuden, sademäärän, kasvipeitteen ja maalajin perusteella. Noin 15 prosentilla tutkitun valuma-alueen pinta-alasta, lähinnä Min-joen pääuomaa ympäröivillä jyrkillä rinteillä, eroosioriski oli suuri tai erittäin suuri. Eri tyyppisellä kasvillisuudella oli hyvin erilainen vaikutus eroosioalttiuteen, ja myös alueen sijainti vuoriston eri korkeuksilla vaikutti eroosioon. Säästyneet lähes luonnontilaiset havumetsät, joita on etupäässä vuoriston ylimmissä osissa 2600-4000 metrin korkeudella, edistävät tehokkaasti metsän luontaista uudistumista ja levittäytymistä vaurioituneille alueille. Säilyneiden metsien puulajikoostumus antoi tutkimuksessa mahdollisuuden ennustaa metsien tulevaa kehitystä koko tutkitulla valuma-alueella sen eri korkeusvyöhykkeissä ja eri maaperätyypeillä. Ennallistamisen kannalta ongelmallisimpia olivat alueet joilta metsäpeite oli lähinnä puiden teollisen hakkuun vuoksi kokonaan hävinnyt ja joilla maaperä yleisesti oli eroosion pahoin kuluttama. Näillä alueilla ei ole tehty juuri mitään uudistamis- tai ennallistamistoimenpiteitä. Niillä metsien ennallistaminen vaatii myös puiden tai pensaiden istuttamista. Tähän sopivia ovat erityisesti ilmakehän typpeä sitovat lajit, joista alueella kasvaa luontaisena mm. sama tyrnilaji joka esiintyy myös Suomessa. Työssä tutkittiin yli kahdeksankymmenen paikallisen luontaisen puulajin (joista peräti noin kolmannes on havupuulajeja) ekologisia ominaisuuksia ja soveltuvuutta metsien ennallistamiseen. Avainasemassa työn onnistumisen kannalta ovat nyt paikalliset asukkaat, joiden maankäytön muutokset ovat jo selvästi edistänet luonnonmetsän ennalleen palautumista. Suomen Akatemia rahoitti vuosina 2004-2006 VITRI:n tutkimushanketta, josta Ping Zhou'n väitöskirjatyö muodosti keskeisen osan. Kenttätyö Sichuanissa avasi mahdollisuuden hedelmälliseen monitieteiseen yhteistyöhön ja tutkijavaihtoon Kiinan tiedeakatemian alaisen Chengdun biologiainstituutin (CIB) kanssa; tämä tieteellinen kanssakäyminen jatkuu edelleen.
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This paper investigates a new Glowworm Swarm Optimization (GSO) clustering algorithm for hierarchical splitting and merging of automatic multi-spectral satellite image classification (land cover mapping problem). Amongst the multiple benefits and uses of remote sensing, one of the most important has been its use in solving the problem of land cover mapping. Image classification forms the core of the solution to the land cover mapping problem. No single classifier can prove to classify all the basic land cover classes of an urban region in a satisfactory manner. In unsupervised classification methods, the automatic generation of clusters to classify a huge database is not exploited to their full potential. The proposed methodology searches for the best possible number of clusters and its center using Glowworm Swarm Optimization (GSO). Using these clusters, we classify by merging based on parametric method (k-means technique). The performance of the proposed unsupervised classification technique is evaluated for Landsat 7 thematic mapper image. Results are evaluated in terms of the classification efficiency - individual, average and overall.
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The widely used Bayesian classifier is based on the assumption of equal prior probabilities for all the classes. However, inclusion of equal prior probabilities may not guarantee high classification accuracy for the individual classes. Here, we propose a novel technique-Hybrid Bayesian Classifier (HBC)-where the class prior probabilities are determined by unmixing a supplemental low spatial-high spectral resolution multispectral (MS) data that are assigned to every pixel in a high spatial-low spectral resolution MS data in Bayesian classification. This is demonstrated with two separate experiments-first, class abundances are estimated per pixel by unmixing Moderate Resolution Imaging Spectroradiometer data to be used as prior probabilities, while posterior probabilities are determined from the training data obtained from ground. These have been used for classifying the Indian Remote Sensing Satellite LISS-III MS data through Bayesian classifier. In the second experiment, abundances obtained by unmixing Landsat Enhanced Thematic Mapper Plus are used as priors, and posterior probabilities are determined from the ground data to classify IKONOS MS images through Bayesian classifier. The results indicated that HBC systematically exploited the information from two image sources, improving the overall accuracy of LISS-III MS classification by 6% and IKONOS MS classification by 9%. Inclusion of prior probabilities increased the average producer's and user's accuracies by 5.5% and 6.5% in case of LISS-III MS with six classes and 12.5% and 5.4% in IKONOS MS for five classes considered.
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This paper presents hierarchical clustering algorithms for land cover mapping problem using multi-spectral satellite images. In unsupervised techniques, the automatic generation of number of clusters and its centers for a huge database is not exploited to their full potential. Hence, a hierarchical clustering algorithm that uses splitting and merging techniques is proposed. Initially, the splitting method is used to search for the best possible number of clusters and its centers using Mean Shift Clustering (MSC), Niche Particle Swarm Optimization (NPSO) and Glowworm Swarm Optimization (GSO). Using these clusters and its centers, the merging method is used to group the data points based on a parametric method (k-means algorithm). A performance comparison of the proposed hierarchical clustering algorithms (MSC, NPSO and GSO) is presented using two typical multi-spectral satellite images - Landsat 7 thematic mapper and QuickBird. From the results obtained, we conclude that the proposed GSO based hierarchical clustering algorithm is more accurate and robust.
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This paper presents an improved hierarchical clustering algorithm for land cover mapping problem using quasi-random distribution. Initially, Niche Particle Swarm Optimization (NPSO) with pseudo/quasi-random distribution is used for splitting the data into number of cluster centers by satisfying Bayesian Information Criteria (BIC). Themain objective is to search and locate the best possible number of cluster and its centers. NPSO which highly depends on the initial distribution of particles in search space is not been exploited to its full potential. In this study, we have compared more uniformly distributed quasi-random with pseudo-random distribution with NPSO for splitting data set. Here to generate quasi-random distribution, Faure method has been used. Performance of previously proposed methods namely K-means, Mean Shift Clustering (MSC) and NPSO with pseudo-random is compared with the proposed approach - NPSO with quasi distribution(Faure). These algorithms are used on synthetic data set and multi-spectral satellite image (Landsat 7 thematic mapper). From the result obtained we conclude that use of quasi-random sequence with NPSO for hierarchical clustering algorithm results in a more accurate data classification.
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We investigated area changes in glaciers covering an area of similar to 200 km(2) in the Tista basin, Sikkim, Eastern Indian Himalaya, between similar to 1990 and 2010 using Landsat Thematic Mapper (TM) and Indian Remote-sensing Satellite (IRS) images and related the changes to debris cover, supraglacial lakes and moraine-dam lakes. The glaciers lost an area of 3.3 +/- 0.8% between 1989/90 and 2010. More detailed analysis revealed an area loss of 2.00 +/- 0.82, 2.56 +/- 0.61 and 2.28 +/- 2.01 km(2) for the periods 1989-97, 1997-2004/05 and 2004-2009/10, respectively. This indicates an accelerated retreat of glaciers after 1997. On further analysis, we observed (1) the formation and expansion of supraglacial lakes on many debris-covered glaciers and (2) the merging of these lakes over time, leading to the development of large moraine-dam lakes. We also observed that debris-covered glaciers with lakes lose a greater area than debris-covered glaciers without lakes and debris-free glaciers. The climatic data for 24 years (1987-2011), measured at the Gangtok meteorological station (1812 m a.s.l.), showed that the region experienced a 1.0 degrees C rise in the summer minimum temperature and a 2.0 degrees C rise in the winter minimum temperature, indicating hotter summers and warmer winters. There was no significant trend in the total annual precipitation. We find that glacier retreat is caused mainly by a temperature increase and that debris-covered glaciers can retreat at a faster rate than debris-free glaciers, if associated with lakes.
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[1] Evaporative fraction (EF) is a measure of the amount of available energy at the earth surface that is partitioned into latent heat flux. The currently operational thermal sensors like the Moderate Resolution Imaging Spectroradiometer (MODIS) on satellite platforms provide data only at 1000 m, which constraints the spatial resolution of EF estimates. A simple model (disaggregation of evaporative fraction (DEFrac)) based on the observed relationship between EF and the normalized difference vegetation index is proposed to spatially disaggregate EF. The DEFrac model was tested with EF estimated from the triangle method using 113 clear sky data sets from the MODIS sensor aboard Terra and Aqua satellites. Validation was done using the data at four micrometeorological tower sites across varied agro-climatic zones possessing different land cover conditions in India using Bowen ratio energy balance method. The root-mean-square error (RMSE) of EF estimated at 1000 m resolution using the triangle method was 0.09 for all the four sites put together. The RMSE of DEFrac disaggregated EF was 0.09 for 250 m resolution. Two models of input disaggregation were also tried with thermal data sharpened using two thermal sharpening models DisTrad and TsHARP. The RMSE of disaggregated EF was 0.14 for both the input disaggregation models for 250 m resolution. Moreover, spatial analysis of disaggregation was performed using Landsat-7 (Enhanced Thematic Mapper) ETM+ data over four grids in India for contrasted seasons. It was observed that the DEFrac model performed better than the input disaggregation models under cropped conditions while they were marginally similar under non-cropped conditions.
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The inversion of canopy reflectance models is widely used for the retrieval of vegetation properties from remote sensing. This study evaluates the retrieval of soybean biophysical variables of leaf area index, leaf chlorophyll content, canopy chlorophyll content, and equivalent leaf water thickness from proximal reflectance data integrated broadbands corresponding to moderate resolution imaging spectroradiometer, thematic mapper, and linear imaging self scanning sensors through inversion of the canopy radiative transfer model, PROSAIL. Three different inversion approaches namely the look-up table, genetic algorithm, and artificial neural network were used and performances were evaluated. Application of the genetic algorithm for crop parameter retrieval is a new attempt among the variety of optimization problems in remote sensing which have been successfully demonstrated in the present study. Its performance was as good as that of the look-up table approach and the artificial neural network was a poor performer. The general order of estimation accuracy for para-meters irrespective of inversion approaches was leaf area index > canopy chlorophyll content > leaf chlorophyll content > equivalent leaf water thickness. Performance of inversion was comparable for broadband reflectances of all three sensors in the optical region with insignificant differences in estimation accuracy among them.
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The non-availability of high-spatial-resolution thermal data from satellites on a consistent basis led to the development of different models for sharpening coarse-spatial-resolution thermal data. Thermal sharpening models that are based on the relationship between land-surface temperature (LST) and a vegetation index (VI) such as the normalized difference vegetation index (NDVI) or fraction vegetation cover (FVC) have gained much attention due to their simplicity, physical basis, and operational capability. However, there are hardly any studies in the literature examining comprehensively various VIs apart from NDVI and FVC, which may be better suited for thermal sharpening over agricultural and natural landscapes. The aim of this study is to compare the relative performance of five different VIs, namely NDVI, FVC, the normalized difference water index (NDWI), soil adjusted vegetation index (SAVI), and modified soil adjusted vegetation index (MSAVI), for thermal sharpening using the DisTrad thermal sharpening model over agricultural and natural landscapes in India. Multi-temporal LST data from Landsat-7 Enhanced Thematic Mapper Plus (ETM+) and Moderate Resolution Imaging Spectroradiometer (MODIS) sensors obtained over two different agro-climatic grids in India were disaggregated from 960 m to 120 m spatial resolution. The sharpened LST was compared with the reference LST estimated from the Landsat data at 120 m spatial resolution. In addition to this, MODIS LST was disaggregated from 960 m to 480 m and compared with ground measurements at five sites in India. It was found that NDVI and FVC performed better only under wet conditions, whereas under drier conditions, the performance of NDWI was superior to other indices and produced accurate results. SAVI and MSAVI always produced poorer results compared with NDVI/FVC and NDWI for wet and dry cases, respectively.