Aplicação de dados multisensor (SAR e ETM+) no reconhecimento de padrões de uso e ocupação do solo em costas tropicais: Costa Amazônica, Amapá, Brasil

Os estudos ambientais necessitam de informações sobre a cobertura e o uso da terra. Este trabalho apresenta a aplicação de dados de sensores remotos orbitais (óticos e de radares) na validação de padrões de uso e cobertura do solo na planície costeira amapaense para fins de mapeamento e reconhecimento da dinâmica natural e antrópica. Esta costa é submetida a uma dinâmica intensa devido à magnitude dos processos costeiros (marés-pororoca), sua localização geográfica, influenciada pelo rio Amazonas e pela Zona de Convergência Intertropical, e processos antrópicos associados à bubalinocultura. A análise foi realizada aplicando-se dados de satélite (JERS-1, RADARSAT-1, Landsat 7 e DEM do SRTM) digitalmente processados em abordagem multisensor, multiescala e multitemporal, correlacionada com dados pretéritos e informações de campo. A análise dos produtos gerados e dados colaterais permitiu distinguir oito padrões de uso e cobertura do solo: florestas de mangue, florestas de várzeas, campos arbustivos, áreas de vegetação campestre, campo antrópico, zona de intermaré, canal estuarino e lagos, além de feições morfológicas lineares associadas a estes padrões. Estas informações são importantes para o mapeamento dos ambientes costeiros e fundamentais para o reconhecimento da dinâmica na região.

Robust trends of landscape dynamics in the Arctic Lena Delta with temporally dense Landsat time-series stacks, with links to GeoTIFFs

Arctic permafrost landscapes are among the most vulnerable and dynamic landscapes globally, but due to their extent and remoteness most of the landscape changes remain unnoticed. In order to detect disturbances in these areas we developed an automated processing chain for the calculation and analysis of robust trends of key land surface indicators based on the full record of available Landsat TM, ETM +, and OLI data. The methodology was applied to the ~ 29,000 km**2 Lena Delta in Northeast Siberia, where robust trend parameters (slope, confidence intervals of the slope, and intercept) were calculated for Tasseled Cap Greenness, Wetness and Brightness, NDVI, and NDWI, and NDMI based on 204 Landsat scenes for the observation period between 1999 and 2014. The resulting datasets revealed regional greening trends within the Lena Delta with several localized hot-spots of change, particularly in the vicinity of the main river channels. With a 30-m spatial resolution various permafrost-thaw related processes and disturbances, such as thermokarst lake expansion and drainage, fluvial erosion, and coastal changes were detected within the Lena Delta region, many of which have not been noticed or described before. Such hotspots of permafrost change exhibit significantly different trend parameters compared to non-disturbed areas. The processed dataset, which is made freely available through the data archive PANGAEA, will be a useful resource for further process specific analysis by researchers and land managers. With the high level of automation and the use of the freely available Landsat archive data, the workflow is scalable and transferrable to other regions, which should enable the comparison of land surface changes in different permafrost affected regions and help to understand and quantify permafrost landscape dynamics.

Classified Adélie penguin colonies from Landsat data

Breeding distribution of the Adelie penguin, Pygoscelis adeliae, was surveyed with Landsat-7 Enhanced Thematic Mapper Plus (ETM+) data along the coastline of Antarctica, an area covering approximately 330° of longitude. An algorithm was designed to minimize the radiometric contribution from exogenous sources and to retrieve Adelie penguin colony location and spatial extent from the ETM+ data. In all, 9143 individual pixels were classified as belonging to an Adelie penguin colony class out of the entire dataset of 195 ETM+ scenes, where the dimension of each pixel is 30 m by 30 m, and each scene is approximately 180 km by 180 km. Pixel clustering identified a total of 187 individual Adelie penguin colonies, ranging in size from a single pixel (900 m**2) to a maximum of 875 pixels (0.788 km**2). Colony retrievals have a very low error of commission, on the order of 1 percent or less, and the error of omission was estimated to be 2.9 percent by population based on comparisons with direct observations from surveys across east Antarctica. Thus, the Landsat retrievals can successfully locate Adelie penguin colonies that account for ~97 percent of a regional population. Geographic coordinates and the spatial extent of each colony retrieved from the Landsat data are available publically. Regional analysis found several areas where the Landsat retrievals suggest populations that are significantly larger than published estimates. Six Adelie penguin colonies were found that are believed to be unreported in the literature.

Drought estimation maps by means multidate landsat fused images

The purpose of this paper is to analyze the usefulness of traditional indexes, such as NDVI and NDWI along with a recently proposed index (NDDI) using merged data for multiple dates, with the aim of obtaining drought data to facilitate the analysis for government premises. In this study we have used Landsat 7 ETM+ data for the month of June (2001-2009), which merged to get bands with twice the resolution. The three previous indices were calculated from these new bands, getting in turn drought maps that can enhance the effectiveness of decision making.

Land cover classification of Samoylov Island and Landsat subpixel water cover of Lena River Delta, Siberia, with links to ESRI grid files

Ignoring small-scale heterogeneities in Arctic land cover may bias estimates of water, heat and carbon fluxes in large-scale climate and ecosystem models. We investigated subpixel-scale heterogeneity in CHRIS/PROBA and Landsat-7 ETM+ satellite imagery over ice-wedge polygonal tundra in the Lena Delta of Siberia, and the associated implications for evapotranspiration (ET) estimation. Field measurements were combined with aerial and satellite data to link fine-scale (0.3 m resolution) with coarse-scale (upto 30 m resolution) land cover data. A large portion of the total wet tundra (80%) and water body area (30%) appeared in the form of patches less than 0.1 ha in size, which could not be resolved with satellite data. Wet tundra and small water bodies represented about half of the total ET in summer. Their contribution was reduced to 20% in fall, during which ET rates from dry tundra were highest instead. Inclusion of subpixel-scale water bodies increased the total water surface area of the Lena Delta from 13% to 20%. The actual land/water proportions within each composite satellite pixel was best captured with Landsat data using a statistical downscaling approach, which is recommended for reliable large-scale modelling of water, heat and carbon exchange from permafrost landscapes.

Comparação entre imagens Aster e Landsat 7 na classificação de níveis de degradação de pastagens utilizando redes neurais artificiais.

O presente estudo teve como objetivo comparar a eficiência dos dados dos sensores Aster e ETM+/Landsat 7 na classificação do uso e cobertura da terra, com ênfase nos níveis de degradação das pastagens na Zona da Mata Mineira, através da utilização de redes neurais artificiais. Foram testadas três composições de uma imagem do sensor Aster e uma do ETM+/Landsat 7, para definição das melhores feições discriminantes para o classificador. As classes de uso e cobertura consideradas foram: floresta, café, área urbana/solo exposto e três níveis de degradação das pastagens (moderado, forte e muito forte). Utilizou-se o simulador de redes neurais Java Neural Network Simulator e o algoritmo empregado foi o back-propagation. Dentre as composições de imagens testadas o melhor resultado foi alcançado com a utilização das 9 bandas do Aster (30m) como variáveis discriminantes, que também permitiu uma melhor discriminação dos níveis de degradação das pastagens considerados. Este resultado é atribuído à melhor resolução espectral desta composição de imagem quando comparada às demais. Dentre as classes consideradas, a pastagem no nível de degradação muito forte foi a que apresentou o maior erro de classificação, em todas as composições, sendo bastante confundida com a pastagem no nível de degradação forte.

Motivation, development and validation of a new spectral greenness index : a spectral dimension related to foliage projective cover

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.

Constrained Linear Spectral Unmixing Technique for Regional Land Cover Mapping Using MODIS Data

Over the last few decades, there has been a significant land cover (LC) change across the globe due to the increasing demand of the burgeoning population and urban sprawl. In order to take account of the change, there is a need for accurate and up-to-date LC maps. Mapping and monitoring of LC in India is being carried out at national level using multi-temporal IRS AWiFS data. Multispectral data such as IKONOS, Landsat-TM/ETM+, IRS-ICID LISS-III/IV, AWiFS and SPOT-5, etc. have adequate spatial resolution (similar to 1m to 56m) for LC mapping to generate 1:50,000 maps. However, for developing countries and those with large geographical extent, seasonal LC mapping is prohibitive with data from commercial sensors of limited spatial coverage. Superspectral data from the MODIS sensor are freely available, have better temporal (8 day composites) and spectral information. MODIS pixels typically contain a mixture of various LC types (due to coarse spatial resolution of 250, 500 and 1000 in), especially in more fragmented landscapes. In this context, linear spectral unmixing would be useful for mapping patchy land covers, such as those that characterise much of the Indian subcontinent. This work evaluates the existing unmixing technique for LC mapping using MODIS data, using end-members that are extracted through Pixel Purity Index (PPI), Scatter plot and N-dimensional visualisation. The abundance maps were generated for agriculture, built up, forest, plantations, waste land/others and water bodies. The assessment of the results using ground truth and a LISS-III classified map shows 86% overall accuracy, suggesting the potential for broad-scale applicability of the technique with superspectral data for natural resource planning and inventory applications. Index Terms-Remote sensing, digital

Landscape-scale soil erosion modelling and ecological restoration for a mountainous watershed in Sichuan, China

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.

Separating grazing and rainfall effects at regional scale using remote sensing imagery: A dynamic reference-cover method

Remote detection of management-related trend in the presence of inter-annual climatic variability in the rangelands is difficult. Minimally disturbed reference areas provide a useful guide, but suitable benchmarks are usually difficult to identify. We describe a method that uses a unique conceptual framework to identify reference areas from multitemporal sequences of ground cover derived from Landsat TM and ETM+ imagery. The method does not require ground-based reference sites nor GIS layers about management. We calculate a minimum ground cover image across all years to identify locations of most persistent ground cover in years of lowest rainfall. We then use a moving window approach to calculate the difference between the window's central pixel and its surrounding reference pixels. This difference estimates ground-cover change between successive below-average rainfall years, which provides a seasonally interpreted measure of management effects. We examine the approach's sensitivity to window size and to cover-index percentiles used to define persistence. The method successfully detected management-related change in ground cover in Queensland tropical savanna woodlands in two case studies: (1) a grazing trial where heavy stocking resulted in substantial decline in ground cover in small paddocks, and (2) commercial paddocks where wet-season spelling (destocking) resulted in increased ground cover. At a larger scale, there was broad agreement between our analysis of ground-cover change and ground-based land condition change for commercial beef properties with different a priori ratings of initial condition, but there was also some disagreement where changing condition reflected pasture composition rather than ground cover. We conclude that the method is suitably robust to analyse grazing effects on ground cover across the 1.3 x 10(6) km(2) of Queensland's rangelands. Crown Copyright (c) 2012 Published by Elsevier Inc. All rights reserved.

Estimation and evaluation of multi-decadal fire severity patterns using Landsat sensors

Retrospective identification of fire severity can improve our understanding of fire behaviour and ecological responses. However, burnt area records for many ecosystems are non-existent or incomplete, and those that are documented rarely include fire severity data. Retrospective analysis using satellite remote sensing data captured over extended periods can provide better estimates of fire history. This study aimed to assess the relationship between the Landsat differenced normalised burn ratio (dNBR) and field measured geometrically structured composite burn index (GeoCBI) for retrospective analysis of fire severity over a 23 year period in sclerophyll woodland and heath ecosystems. Further, we assessed for reduced dNBR fire severity classification accuracies associated with vegetation regrowth at increasing time between ignition and image capture. This was achieved by assessing four Landsat images captured at increasing time since ignition of the most recent burnt area. We found significant linear GeoCBI–dNBR relationships (R2 = 0.81 and 0.71) for data collected across ecosystems and for Eucalyptus racemosa ecosystems, respectively. Non-significant and weak linear relationships were observed for heath and Melaleuca quinquenervia ecosystems, suggesting that GeoCBI–dNBR was not appropriate for fire severity classification in specific ecosystems. Therefore, retrospective fire severity was classified across ecosystems. Landsat images captured within ~ 30 days after fire events were minimally affected by post burn vegetation regrowth.

Change detection and controlling forest information using multi-temporal Landsat TM imagery.

A method was developed for relative radiometric calibration of single multitemporal Landsat TM image, several multitemporal images covering each others, and several multitemporal images covering different geographic locations. The radiometricly calibrated difference images were used for detecting rapid changes on forest stands. The nonparametric Kernel method was applied for change detection. The accuracy of the change detection was estimated by inspecting the image analysis results in field. The change classification was applied for controlling the quality of the continuously updated forest stand information. The aim was to ensure that all the manmade changes and any forest damages were correctly updated including the attribute and stand delineation information. The image analysis results were compared with the registered treatments and the stand information base. The stands with discrepancies between these two information sources were recommended to be field inspected.

Contrained linear spectral unmixing technique for regional land cover mapping using MODIS data

Over the last few decades, there has been a significant land cover (LC) change across the globe due to the increasing demand of the burgeoning population and urban sprawl. In order to take account of the change, there is a need for accurate and up- to-date LC maps. Mapping and monitoring of LC in India is being carried out at national level using multi-temporal IRS AWiFS data. Multispectral data such as IKONOS, Landsat- TM/ETM+, IRS-1C/D LISS-III/IV, AWiFS and SPOT-5, etc. have adequate spatial resolution (~ 1m to 56m) for LC mapping to generate 1:50,000 maps. However, for developing countries and those with large geographical extent, seasonal LC mapping is prohibitive with data from commercial sensors of limited spatial coverage. Superspectral data from the MODIS sensor are freely available, have better temporal (8 day composites) and spectral information. MODIS pixels typically contain a mixture of various LC types (due to coarse spatial resolution of 250, 500 and 1000 m), especially in more fragmented landscapes. In this context, linear spectral unmixing would be useful for mapping patchy land covers, such as those that characterise much of the Indian subcontinent. This work evaluates the existing unmixing technique for LC mapping using MODIS data, using end- members that are extracted through Pixel Purity Index (PPI), Scatter plot and N-dimensional visualisation. The abundance maps were generated for agriculture, built up, forest, plantations, waste land/others and water bodies. The assessment of the results using ground truth and a LISS-III classified map shows 86% overall accuracy, suggesting the potential for broad-scale applicability of the technique with superspectral data for natural resource planning and inventory applications.

A simple model for spatial disaggregation of evaporative fraction: Comparative study with thermal sharpened land surface temperature data over India

[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.

Estimation of ice thickness using surface velocities and slope: case study at Gangotri Glacier, India

We estimate the distribution of ice thickness for a Himalayan glacier using surface velocities, slope and the ice flow law. Surface velocities over Gangotri Glacier were estimated using sub-pixel correlation of Landsat TM and ETM+ imagery. Velocities range from similar to 14-85 m a(-1) in the accumulation region to similar to 20-30 ma(-1) near the snout. Depth profiles were calculated using the equation of laminar flow. Thickness varies from similar to 540 m in the upper reaches to similar to 50-60 m near the snout. The volume of the glacier is estimated to be 23.2 +/- 4.2 km(3).