Atmospheric Correction of a Seasonal Time Series of Hyperion EO-1 Images and Red Edge Inflection Point Calculation

While spaceborne satellite data has been extensively used to extract biophysical forest characteristics through reflectance features and indices, there are still many questions regarding seasonal changes in reflectance. Boreal forests have already seen changes in growth patterns from climate change, and the large scale monitoring of these forests is becoming more important. Understanding seasonal changes in reflectance in the boreal region will allow for the monitoring of seasonal biophysical changes using satellite imagery. For this monitoring to be possible the satellite imagery needs to be preprocessed and atmospherically corrected to create a time series of hemispherical-directional reflectance factors. The red edge is the abrupt change in reflectance between 680 and 740 nm seen in vegetation spectra. The red edge inflection point is the wavelength, at which the slope is steepest in the red edge. The red edge inflection point is sensitive to plant chlorophyll content and has been extensively used for estimating vegetation biophysical parameters including: leaf-area index, biomass and plant health levels. Hyperion is a narrowband imaging spectrometer aboard the Earth Observer-1 satellite. Hyperion captures data across 242 spectral bands covering a spectral range of 356 to 2577 nm resulting in a nominal spectral range of 10 nm. While the high spectral resolution of Hyperion makes it possible to calculate the REIP, there is no consensus on how this should be done, with different methods producing conflicting results. This study explains the preprocessing and atmospheric correction of a seasonal time series of five Hyperion EO-1 images (Provided courtesy of the USGS) from Hyytiälä, Southern Finland (61° 51 N, 24° 17 E). The time series ranges from 31.5.2010 to 12.8.2010, covering much of the growing season and the seasonal changes in reflectance. The first derivative, four-point linear interpolation, Lagrangian interpolation, and fifth-order polynomial fitting methods for calculating the REIP are looked at to determine their applicability for Hyperion imagery using this time series. Hyperion data requires considerable preprocessing before atmospheric correction can be done. In this study the preprocessing covered: destriping, desmiling, atmospheric correction and finally geocorrection. Atmospheric correction was done using both FLAASH and ATCOR, both of which are MODTRAN based absolute atmospheric correction algorithms. The final atmospherically corrected HDRF images were evaluated using in situ handheld spectrometer reference measurements of a grass field in the area. An average RMSE value of around 3% was achieved with both algorithms. The corrected Hyperion images were also compared against two MODIS products, which also showed good agreement. The aerosol retrieval however did not work with either algorithm, on any scene. The use of a sun photometer for aerosol level estimation was also not effective. Due to the dynamics of the red edge and expected seasonal red edge inflection point trends, the fifth-order polynomial fitting method was seen as the best method for calculating the red edge inflection point. The red edge inflection point did not correlate strongly with leaf area index overall, however there was a strong correlation with individual plots. A strong correlation was observed between Hyperion red edge inflection point and understory red edge inflection point, both overall and for individual plots.Kaukokartoitusmenetelmiä on pitkään käytetty metsän biofyysisten ominaisuuksien arvioinnissa, käyttäen hyväksi niiden heijastusominaisuuksia ja kasvillisuusindeksejä. Metsän heijastuksen muutokset kasvukauden aikana eivät kuitenkaan ole täysin ymmärrettyjä. Boreaalisten metsien kasvussa on havaittu muutoksia ilmastonmuutoksen myötä, minkä vuoksi niiden monitorointi on erityisen tärkeää. Kaukokartoitusaineistoon pohjautuvaan monitorointiin vaaditaan ymmärrystä metsien heijastusominaisuuksien muutoksista kasvukauden aikana. Tätä myöten käytettävän aineiston täytyy olla esikäsitelty ja ilmakehäkorjattu. Niin kutsuttu punainen reuna (red edge) on tyypillinen ominaisuus kasvillisuuden heijastuksessa, joka näkyy äkillisenä muutoksena heijastuksessa 680 ja 740 nm välillä. Punaisen reunan käännepiste (red edge inflection point) on se aallonpituus, jossa heijastuksen muutos on jyrkimmillään. Punaisen reunan käännepiste on herkkä klorofyllin määrälle kasvillisuudessa, ja sitä on käytetty arvioimaan kasvillisuuden biofyysisiä parametreja, kuten lehtialaindeksiä, biomassaa ja kasvillisuuden terveyttä. Hyperion on kapeakanavainen kuvaava spektrometri EO-1 satelliitissa. Hyperion mittaa aallonpituusalueen 356–2577 nm heijastusta 242 kanavalla, ja sensorin nominaalinen spektrinen resoluutio on 10 nm. Korkea spektrinen resoluutio mahdollistaa punaisen reunan käännepisteen laskemisen. Laskemiseen on kuitenkin useita menetelmiä, jotka tuottavat erilaisia tuloksia. Tämä Pro Gradu - tutkielma kattaa viiden Hyperion EO-1 kuvan esikäsittelyn ja ilmakehäkorjauksen. Kuvat ovat Hyytiälästä, Etelä-Suomesta (61° 51 N, 24° 17 E). Aikasarja alkaa 5. toukokuuta 2010 ja päättyy 11. heinäkuuta 2010, kattaen suurimman osan kasvukaudesta ja kasvillisuuden heijastuksen vaihtelusta. Seuraavia punaisen reunan käännepisteen laskentamenetelmien soveltuvuutta testattiin Hyperion-aineistolla: neljän pisteen lineaarinen interpolaatio, Lagrangian interpolaatio, ja viidennen asteen yhtälön sovittaminen. Hyperion-data vaatii paljon esikäsittelyä ennen kuin ilmakehäkorjaus voidaan suorittaa. Tässä tutkielmassa esikäsittely kattoi seuraavat vaiheet: spektrisen hymyn poisto, viivojen poisto, ilmakehäkorjaus, ja lopuksi geometrinen korjaus. Ilmakehäkorjaus toteutettiin käyttäen FLAASH ja ATCOR -algoritmeja, jotka ovat absoluuttisia ilmakehäkorjauksia ja käyttävät MODTRAN -algoritmia ilmakehän mallinnuksessa. Lopullisten, ilmakehäkorjattujen kuvien heijastusta verrattiin maastossa mitattuun tukiaineistoon. Maastoaineisto mitattiin tutkimusalueella sijaitsevalla ruohokentällä. Molemmat algoritmit tuottivat hyvän tuloksen, mutta kummankaan algoritmin automaattinen aerosolin määrän arviointi ei toiminut. Myöskään arviointi aurinkofotometrin avulla ei toiminut. Korjatut Hyperion-kuvat sopivat kuitenkin hyvin yhteen verrattaessa niitä kahteen MODIS-tuotteeseen. Punaisen reunan dynamiikan takia viidennen asteen yhtälön sovittaminen punaiseen reunaan todettiin parhaaksi menetelmäksi laskea punaisen reunan käännepiste. Hyperion-aineistosta johdettu punaisen reunan käännepiste ei korreloitunut voimakkaasti lehtialaindeksin kanssa, vaikka yksittäiset koealat korreloivatkin vahvasti. Sen sijaan Hyperion-aineiston punaisen reunan käännepisteen ja aluskasvillisuuden punaisen reunan käännepisteen välillä oli hyvin vahva korrelaatio

Crop Growth Monitoring by Hyperspectral and Microwave Remote Sensing

Methoden und Techniken der Fernerkundung fungieren als wichtige Hilfsmittel im regionalen Umweltmanagement. Um diese zu optimieren, untersucht die folgende Arbeit sowohl die Verwendung als auch Synergien verschiedener Sensoren aus unterschiedlichen Wellenlängenbereichen. Der Fokus liegt auf der Modellentwicklung zur Ableitung von Pflanzenparametern aus fernerkundlichen Bestandsmessungen sowie auf deren Bewertung. Zu den verwendeten komplementären Fernerkundungssystemen zählen die Sensoren EO-1 Hyperion und ALI, Envisat ASAR sowie TerraSAR-X. Für die optischen Hyper- und Multispektralsysteme werden die Reflexion verschiedener Spektralbereiche sowie die Performanz der daraus abgeleiteten Vegetationsindizes untersucht und bewertet. Im Hinblick auf die verwendeten Radarsysteme konzentriert sich die Untersuchung auf Parameter wie Wellenlänge, Einfallswinkel, Radarrückstreuung und Polarisation. Die Eigenschaften verschiedener Parameterkombinationen werden hierbei dargestellt und der komplementäre Beitrag der Radarfernerkundung zur Wachstumsüberwachung bewertet. Hierzu wurden zwei Testgebiete, eines für Winterweizen in der Nordchinesischen Tiefebene und eines für Reis im Nordosten Chinas ausgewählt. In beiden Gebieten wurden während der Wachstumsperioden umfangreiche Feldmessungen von Bestandsparametern während der Satellitenüberflüge oder zeitnah dazu durchgeführt. Mit Hilfe von linearen Regressionsmodellen zwischen Satellitendaten und Biomasse wird die Sensitivität hyperspektraler Reflexion und Radarrückstreuung im Hinblick auf das Wachstum des Winterweizens untersucht. Für die optischen Daten werden drei verschiedene Modelvarianten untersucht: traditionelle Vegetationsindices berechnet aus Multispektraldaten, traditionelle Vegetationsindices berechnet aus Hyperspektraldaten sowie die Berechnung von Normalised Ratio Indices (NRI) basierend auf allen möglichen 2-Band Kombinationen im Spektralbereich zwischen 400 und 2500 nm. Weiterhin wird die gemessene Biomasse mit der gleichpolarisierten (VV) C-Band Rückstreuung des Envisat ASAR Sensors linear in Beziehung gesetzt. Um den komplementären Informationsgehalt von Hyperspektral und Radardaten zu nutzen, werden optische und Radardaten für die Parameterableitung kombiniert eingesetzt. Das Hauptziel für das Reisanbaugebiet im Nordosten Chinas ist das Verständnis über die kohärente Dualpolarimetrische X-Band Rückstreuung zu verschiedenen phänologischen Wachstumsstadien. Hierfür werden die gleichpolarisierte TerraSAR-X Rückstreuung (HH und VV) sowie abgeleitete polarimetrische Parameter untersucht und mit verschiedenen Ebenen im Bestand in Beziehung gesetzt. Weiterhin wird der Einfluss der Variation von Einfallswinkel und Auflösung auf die Bestandsparameterableitung quantifiziert. Neben der Signatur von HH und VV ermöglichen vor allem die polarimetrischen Parameter Phasendifferenz, Ratio, Koherenz und Entropy-Alpha die Bestimmung bestimmter Wachstumsstadien. Die Ergebnisse der Arbeit zeigen, dass die komplementären Fernerkundungssysteme Optik und Radar die Ableitung von Pflanzenparametern und die Bestimmung von Heterogenitäten in den Beständen ermöglichen. Die Synergien diesbezüglich müssen auch in Zukunft weiter untersucht werden, da neue und immer variablere Fernerkundungssysteme zur Verfügung stehen werden und das Umweltmanagement weiter verbessern können

The potential for using remote sensing to quantify stress in and predict yield of sugarcane (Saccharum spp. hybrid)

Thesis (Ph.D.)-University of KwaZulu-Natal, Pietermaritzburg, 2010

Evaluating and Developing Methods for Non-Destructive Monitoring of Biomass and Nitrogen in Wheat and Rice Using Hyperspectral Remote Sensing

Aboveground plant biomass and plant nitrogen are two important parameters for plant growth monitoring, which have a decisive influence on the final yield. Mismanagement of fertilizer or pesticide inputs leads to poor plant growth, environmental pollution, and accordingly, yield loss. Biomass development is driven by nutrient supply, temperature, and phenology. Crop biomass reaches its highest weight at the harvest time. In contrast, plant nitrogen is dependent from fertilizer inputs to the soil and from biomass. Destructive measurement of both parameters is time-consuming and labor-intensive. Remote sensing offers remotely non-direct observation methods from outer space, air space, or close-range in the field by sensors. This dissertation focuses on non-destructive monitoring of plant biomass (the primary parameter) and plant nitrogen (the secondary parameter) using hyperspectral data from non-imaging field spectrometers and the imaging EO-1 Hyperion satellite. The study was conducted on two field crops: winter wheat of two growing seasons of the Huimin test site in the North China Plain; and rice of three growing seasons of the Jiansanjiang test site in the Sanjiang Plain of China. Study fields were set up in different spatial scales, from small experimental scale to large farmers' scale. Extensive field measurements were carried out, including both destructive measuring and non-destructive hyperspectral remote sensing of biomass and plant nitrogen. Besides, two years' Hyperion images were acquired at the Huimin test site. Four different approaches were used to develop the estimation models, which include: vegetation indices (VIs), band combinations, Optimum Multiple Narrow Band Reflectance (OMNBR) and stepwise Multiple Linear Regression (MLR), and derivatives of reflectance. Based on these four approaches, models were constructed, compared, and improved step by step. Additionally, a multiscale approach and a new VI, named GnyLi, were developed. Since experimental and farmers' fields were differently managed, several calibration and validation methods were tested and the field datasets were pooled. All tested approaches and band selections were greatly influenced by single growth stages. The broad band VIs saturated for both crops at the booting stage at the latest and were greatly outperformed by the narrow band VIs with optimized band combinations. Model applications from experimental to farmers' scale using the narrow bands measured by field spectrometers mostly failed due to the effects of different management practices and crop cultivars at both spatial scales. In contrast, the multiscale approach was successfully applied in winter wheat monitoring to transfer data and knowledge from field spectrometer measurements from the experimental scale to the farmers' field scale and the scale that is covered by the Hyperion imagery. The GnyLi and the Normalized Ratio Index (NRI) based on the optimized band combinations performed the best in the up-scaling process in the winter wheat study. In the rice study, MLR or OMNBR models based on 4–6 narrow bands better explained biomass variability compared to VIs based on broad bands and optimized band combinations. The models were more robust when data from different scales were pooled and then randomly divided into calibration and validation datasets. Additional model improvements were obtained using derivatives of reflectance. This dissertation evaluates different hyperspectral remote sensing approaches for non-destructive biomass and plant nitrogen monitoring, with the main focus on biomass estimation. The results and comparisons of different approaches revealed their potentials and limits. Development of new VIs, such as GnyLi, is advantageous due to the saturation problem of broad band VIs. However, the developed VIs need to be tested and improved for different crops and sites. Detection of optimized band combinations facilitates the development of new VIs, which are site-specific and crop-specific. MLR-based models may better explain the biomass variability; nevertheless, with more bands, they are prone to the issues of over-fitting and collinearity. Hence, no more than six bands were recommended to select from the hyperspectral data. Derivatives of reflectance were beneficial at the early growing season of rice when the canopy was strongly influenced by background signals from soil and water. However, their benefits were reduced when more bands were used

Mapping landscape function with hyperspectral remote sensing of natural grasslands on gold mines

Thesis submitted in fulfilment of the requirements for the degree of Doctor of Philosophy. School of Animal, Plant and Environmental Science, University of the Witwatersrand, Johannesburg, South Africa. October 2016.Mining has negative impacts on the environment in many different ways. One method developed to quantify some of these impacts is Landscape Function Analysis (LFA) and this has been accepted by some mining companies and regulators. In brief, LFA aims at quantifying the organization of vegetative and landscape components in a landscape into patches along a transect and quantifying, in a relative manner, three basic processes important to landscape functioning, namely: soil stability or susceptibility to erosion, infiltration or runoff, and nutrient cycling or organic matter decomposition. However, LFA is limited in large heterogeneous environments, such as those around mining operations, due to its localized nature, and the man hours required to collect a representative set of measurements for such large and complex environments. Remote sensing using satellite-acquired data can overcome these limitations by sampling the entire environment in a rapid and objective manner. What is required is a method of connecting these satellite-based measurements to LFA measurements and then being able to extrapolate these measurements across the entire mine surface. The aim of this research was to develop a method to use satellite-based hyperspectral imagery to predict landscape function analysis (LFA) using partial least squares regression (PLSR). This was broken down into three objectives: (1) Collection of the LFA data in the field and validation of the LFA indices against other environmental variables collected at the same time, (2) validation of PLSR models predicting LFA indices and various environmental variables from ground-based spectra, and (3) production of risk maps based on predicting LFA indices and above-ground biomass using PLSR models and Hyperion satellite-based hyperspectral imagery. Although the study was based in grasslands at two mining regions, West Wits and Vaal River, a suitable Hyperion image was only available for Vaal River. A minimum of 374 points were sampled for LFA indices, ground-based spectra, above-ground biomass and soil cores along 2880 m of LFA transect from both mine sites. Soil cores were weighed fresh before sieving with a 2 mm sieve to separate root and stone fractions. The sieved soil fraction was tested for pH, EC, SOM, and for the West Wits samples, organic nitrogen and total extractable inorganic nitrogen. There was one modification to the LFA method where grass patches were collapsed into homogenous units as it was deemed not feasible to sample 180 m transects at grass tuft scales of 10 – 30 cm, but other patch definitions followed the LFA manual (Tongway and Hindley, 2004). Evidence suggested that some of the different patch types, in particular the bare/biological soil crust – bare grass – sparse grass patch types, represented successional stages in a continuum although this was not conclusive. There also was evidence that the presence or absence of cattle play a role in some processes active in these grasslands and erosion is mainly through deflation, rain splash and sheet wash. Generally the environmental variables supported the LFA indices although the nutrient cycling index was representative of above-ground nutrient cycling but not below-ground nutrient cycling. Models derived with PLSR to predict the LFA indices from ground-based spectral measurements were strong at both mine sites (West Wits: LFA stability r2 = 0.63, P < 0.0001; LFA infiltration r2 = 0.75, P < 0.0001; LFA nutrient cycling r2 = 0.73, P < 0.0001; Vaal River: LFA stability r2 = 0.39, P < 0.0001, LFA infiltration r2 = 0.72, P < 0.0001, LFA nutrient cycling r2 = 0.54, P < 0.0001), as were PLSR models predicting above-ground biomass (West Wits above-ground biomass r2 = 0.55, P = 0.0003; Vaal River above-ground biomass r2 = 0.79, P < 0.0001) and soil moisture (West Wits soil moisture r2 = 0.45, P = 0.0017; Vaal River soil moisture r2 = 0.68, P < 0.0001). However, for soil organic matter (r2 = 0.50, P < 0.0001) and EC (r2 = 0.63, P < 0.0001), Vaal River had strong prediction models while West Wits had weak models for these variables (r2 = 0.31, P = 0.019 and r2 = 0.10 and P < 0.18, respectively). For EC, the wide range of soil values at Vaal River in association with gypsum crusts, and low values throughout West Wits explained these model results but for soil organic matter, no clear explanation for these site differences was identified. Patch-based models could accurately discriminate between spectrally well-defined patch types such S. plumosum patches but were less successful with patch types that were spectrally similar such as the bare/biological soil crust – bare grass – sparse grass patch continuum. Clustering similar patch types together before PLSR modelling did improve these patch-based spectral models. To test the method proposed to predict LFA indices from satellite-based hyperspectral imagery, a Hyperion image matching 6 transects at Vaal River was acquired by NASA’s EO-1 satellite and downloaded from the USGS Glovis website. LFA transects were partitioned to match and extract pixel spectra from the Hyperion data cube. Thirty-one spectra were separated into calibration (20) and validation (11) data. PLSR models were derived from the calibration data, tested with validation data to select the optimum model, and then applied to the entire Hyperion data cube to produce prediction maps for five LFA indices and above-ground biomass. The patch area index (PAI) produced particularly strong models (r2 = 0.79, P = 0.0003, n =11) with validation data, whereas the landscape organization index (LOI) produced weak models. It is argued that this difference between these two essentially similar indices is related to the fact that the PAI is a 2-dimensional index and the LOI is a 1-dimensional index. This difference in these two indices allowed the PAI to compensate for some burned pixels on the transects by “seeing” the density pattern of grass tufts and patches whereas the linear nature of the LOI was more susceptible to the changing dimensions of patch structure due to the effects of fire. Although validation models for the three LFA indices of soil stability, infiltration and nutrient cycling were strong (r2 = 0.72, P = 0.004; r2 = 0.66, P = 0.008; r2 = 0.70, P = 0.005, n = 9 respectively), prediction maps were confounded by the presence of fire on some transects. The poor quality of the Hyperion imagery also meant great care had to be taken in the selection of models to avoid poor quality prediction maps. The 31 bands from the VNIR (478 – 885 nm) portion of the Hyperion spectra were generally the best for PLSR modelling and prediction maps, presumably because of better signal-to-noise ratios due to higher energy in the shorter wavelengths. With two satellite-based hyperspectral sensors already operational, namely the US Hyperion and the Chinese HJ-1A HSI, and a number expected to be launched by various space agencies in the next few years, this research presents a method to use the strengths of LFA and hyperspectral imagery to model and predict LFA index values and thereby produce risk maps of large, heterogeneous landscapes such as mining environments. As this research documents a method of partitioning the landscape rather than the pixel spectra into pure endmembers, it makes a valuable contribution to the fields of landscape ecology and hyperspectral remote sensing.LG201

Evaluating Canopy Spectral Invariants Derived from Imaging Spectroscopy Data : A Case Study on Southern Boreal Forests

As an alternative to complex 3D-modelling of structure, the canopy spectral invariants are a novel concept to describe the average behavior of photons in a vegetated canopy. The probabilities of canopy absorption and scattering can be summarized with only three parameters (?L, p and R): The green leaf single scattering albedo (?L) describes the wavelength-dependent probabilities of absorption for each time a photon interacts with a leaf. In the event of scattering, a photon’s probabilities of reinteraction (photon recollision probability p) and exiting the canopy in a given direction (directional escape factor R) can be described as independent of wavelength; as the size of the scattering elements is considerably larger than wavelengths in the shortwave radiation budget, p and R depend only upon the structural arrangement of the scattering elements. In this work, a recently published (2011) approach to infer remotely sensed (spaceborne) hyperspectral imagery (also referred to as imaging spectroscopy data) based on the canopy spectral invariants was tested in a case study on southern boreal forests at full leaf development. An atmospherically corrected image taken with the Hyperion imaging spectrometer aboard the National Aeronautics and Space Administration’s (NASA) Earth Observing-1 (EO-1) spacecraft was interpreted with a single reference transformed green leaf scattering albedo. Transforming of a traditionally defined leaf albedo means correcting the measurements for the effect of surface reflectance, resulting in probabilities of leaf scattering and absorption given a photon interacts with the leaf internal constituents. Utilizing such transformed albedo as reference results in reference (canopy) spectral invariants describing the relative difference between the reference and the scattering properties of (theoretical) mean leaves at the scale of inference (pixel). The results of the study are parallel to those of previously published and ongoing research: In essence, even while the individual parameters p and R depend on the reference, the ratio R/(1–p) (directional escape factor to total escape probability) was found practically independent of the selection of the reference, thus implicating a possibility to develop a physically-based algorithm to infer hyperspectral imagery in vegetated areas. Moreover, the reference (canopy) spectral invariants were found as highly applicable in retrieval of forest structural properties such as dominant forest type (broadleaved, coniferous, mixed) and a quantitative estimate of the broadleaf fraction of a forest area.“Spectral invariants” -teorian mukaan metsikön rakenteen kuvaus voidaan yksinkertaistaen tiivistää kolmeen rakenteelliseen tunnuslukuun (?L, p and R) perustuen fotonien ja lehtien keskimääräisiin vuorovaikutussuhteisiin. Tällaista lähestymistapaa voidaan kaukokartoitussovelluksissa käyttää vaihtoehtona yksityiskohtaiselle, kolmiulotteiselle mallintamiselle. Aallonpituudelle herkkiä absorptio- ja sirontatodennäköisyyksiä kuvataan vihreän lehden albedolla (?L) eli sirontakertoimella. Mikäli fotoni siroaa osuessaan lehteen, se voi joko törmätä toiseen lehteen (photon recollision probability p) tai poistua latvuksesta satunnaiseen suuntaan (directional escape factor R). Koska fotoneja sirottavat latvuksen rakenneyksiköt (lehdet) ovat kooltaan paljon suurempia kuin lyhytaaltoisen sähkömagneettisen säteilyn aallonpituudet, tunnusluvut p ja R riippuvat ainoastaan rakenneyksiköiden tilajakaumasta, eivätkä aallonpituudesta. Tässä pro gradu -tutkielmassa on sovellettu vuonna 2011 julkaistua, spectral invariants -teoriaan perustuvaa kuvantavan spektroskopian keinoin kaukokartoitetun aineiston (tai hyperspektristen kaukokartoituskuvien) tulkintamenetelmää. Työ on toteutettu eteläboreaalisia metsiä käsittelevänä tapaustutkimuksena, jonka maasto-aineisto on kerätty Hyytiälästä, Keski-Suomesta. Kaukokartoitusaineistona on käytetty ilmakehäkorjattua Hyperion-kuvaa. (Hyperion, kuvantava spektrometri, on yksi Yhdysvaltain avaruushallinnon eli NASA:n EO-1-satelliitin kolmesta pääsensorista.) Hyperion-kuvan tulkinta on suoritettu perustuen yhteen vertailuspektrinä käytettävään, muunnettuun lehden albedoon. Albedon muuntaminen tarkoittaa mitatun albedon korjaamista lehden pintaheijastukseen perustuen siten, että muunnettu albedo kuvaa lehden sisäisistä rakenneosasista siroavan säteilyn osuutta. Käytettäessä vertailukohtana muunnettua sirontakerrointa, kuvaavat tulkinnan välivaiheena syntyvät vertailutunnusluvut p and R kyseisen muunnetun albedon sekä tulkintayksikkökohtaisten (pikselikohtaisten) latvuston (teoreettisten) keskiarvolehtien sirontaominaisuuksien suhteellista eroa. Työn tulokset ovat yhdenmukaisia aiemmin julkaistujen- sekä meneillään olevien tutkimusten tulosten kanssa: Vaikka vertailutunnusluvut p and R riippuvat valitusta vertailukohdasta, havaittiin niistä muodostetun suhdeluvun R/(1–p) olevan käytännössä vertailukohdasta riippumaton. Tämä antaa viitteitä siitä, että työssä sovelletun teorian pohjalta olisi mahdollista kehittää fysikaalisesti pätevä malli, jota voitaisiin soveltaa metsäkasvillisuuden tulkinnassa hyperspektrisiltä kaukokartoituskuvilta. Erityisen hyvin vertailutunnuslukuihin perustuvalla tulkinnalla näytettäisiin voitavan arvioida vallitseva metsikkötyyppi (lehtimetsä, havumetsä, sekametsä), sekä metsien lehti- ja havupuuosuudet

Atmospheric correction of a seasonal time series of Hyperion EO-1 images and red edge inflection point calculation

This study covers the preprocessing and atmospheric correction of a seasonal time series five Hyperion EO-1 images from Hyytiälä, Southern Finland (61° 51′N, 24° 17′E). The time series ranges from May 5th 2010 to July 11th 2010, covering much of the growing season and the seasonal changes in vegetation reflectance. Atmospheric correction of the time series was done with Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes (FLAASH) and ATmospheric CORrection (ATCOR) algorithms for comparison. Both algorithms performed well with Hyperion imagery. Different red edge inflection point (REIP) calculation methods were analyzed to determine their applicability for Hyperion imagery. REIP was calculated using four-point interpolation, Lagrangian interpolation, and fifth order polynomial fitting. Due to the dynamics of the red edge, polynomial fitting was seen as the best method for calculating the REIP. REIP did not correlate strongly with Leaf Area Index (LAI) but a stronger correlation was observed with understory REIP.</p

Refinement of the method for using pseudo-invariant sites for long term calibration trending of Landsat reflective bands

The long term calibration history of the Landsat 5 TM instrument has recently been defined using a time series of desert sites in Northern Africa. This correction is based on the assumption that the atmosphere is invariant and the reflectance of each site is approximately constant and Lambertian over time. As a result, the top of the atmosphere reflection is assumed constant when corrected for variations in the solar elevation angle and earth-sun distance. While this is true to first order and is the basis for all current temporal calibration, there are multiple known sources of residual error in the data. A methodology is presented for reducing the variation in pseudo-invariant site trending data based on correction for the BRDF. This work establishes a means to use DIRSIG to model the L5 calibration site. It combines a digital elevation map and desert atmosphere with a surface BRDF to reduce the residual errors in the calibration data. A set of Landsat 7 ETM+ calibration days is utilized to optimize the surface reflectance properties used in DIRSIG. These optimized parameters are then used to model the L5 TM calibration days. The results of the DIRSIG modeling are compared to the solar elevation angle and time of year trends of the original data and analyzed for their effectiveness at describing and reducing the residual errors. A major goal of this effort is to understand the contribution that BRDFs make to the current calibration errors and to develop methods that are robust enough to be applicable to a wider range of sites to enable extension of the methodology to earlier data sets (e.g. Landsat MSS). Additionally, while Landsat has a 30 m reflective resolution, the pseudo-invariant site calibration approach is valid for all spatial resolutions. Depending on another instrument\u27s field of view, the BRDF error reduction technique used by L5 TM could either be used on the same desert calibration site or on a subsection of the area

An investigation in the use of advanced remote sensing and geographic information system techniques for post-fire impact assessment on vegetation.

2006/2007Gli incendi boschivi rappresentano uno dei maggiori problemi ambientali nella regione Mediterranea con vaste superfici colpite ogni estate. Una stima dell’impatto ambientale degli incendi (a breve e a lungo termine) richiede la raccolta di informazioni accurate post-incendio relative al tipo di incendio, all’intensità, alla rigenerazione forestale ed al ripristino della vegetazione. L’utilizzo di tecniche avanzate di telerilevamento può fornire un valido strumento per lo studio di questi fenomeni. L’importanza di queste ricerche è stata più volte sottolineata dalla Commissione Europea che si è concentrata sullo studio degli incendi boschivi ed il loro effetto sulla vegetazione attraverso lo sviluppo di adeguati metodi di stima dell’impatto e di mitigazione. Scopo di questo lavoro è la stima dell’impatto post-incendio sulla vegetazione in ambiente Mediterraneo per mezzo di immagini satellitari ad alta risoluzione, di rilievi a terra e mediante tecniche avanzate di analisi dei dati. Il lavoro ha riguardato lo sviluppo di un sistema per l’integrazione di dati telerilevati ad altissima risoluzione spaziale e spettrale. Per la stima dell’impatto a breve termine, un modello di classificazione ad oggetti è stato sviluppato utilizzando immagini Ikonos ad altissima risoluzione spaziale per cartografare il tipo di incendio, differenziando l’incendio radente dall’incendio di chioma. I risultati mostrano che la classificazione ad oggetti potrebbe essere utilizzata per distinguere con elevata accuratezza (87% di accuratezza complessiva) le due tipologie di incendio, in particolare nei boschi Mediterranei aperti. È stata inoltre valutata la capacità della classificazione ad oggetti di distinguere e cartografare tre livelli di intensità del fuoco utilizzando le immagini Ikonos e l’accuratezza del risultato è stimata all’ 83%. Per la stima dell’impatto a lungo termine, la mappatura della rigenerazione post-incendio (pino) e la ripresa della vegetazione arbustiva sono state valutate mediante tre approcci: 1) la classificazione ad oggetti di immagini ad altissima risoluzione QuickBird che ha permesso di mappare la ripresa della vegetazione e l’impatto sulla copertura a seguito dell’incendio distinguendo due livelli di intensità dell’incendio (accuratezza della classificazione 86%). 2) l’analisi statistica di dati iperspettrali rilevati in campo che ha permesso una riduzione del 97% del volume di dati e la selezione delle migliori 14 bande per discriminare l’età e le specie di pino e le 18 migliori bande per la caratterizzazione delle specie arbustive. Successivamente, i dati iperspettrali Hyperion sono stati utlizzati per mappare la rigenerazione forestale e la ripresa della vegetazione. L’accuratezza complessiva della classificazione è stata del 75.1% considerando due diverse specie di pino ed altre specie vegetali. 3) una classificazione ad oggetti che ha combinato l’analisi dei dati QuickBird ed Hyperion. Si è registrato un aumento dell’accuratezza della classificazione pari all’8.06% rispetto all’utilizzo dei soli dati Hyperion. Complessivamente, si osserva che strumenti avanzati di telerilevamento consentono di raccogliere le informazioni relative alle aree incendiate, la rigenerazione forestale e la ripresa della vegetazione in modo accurato e vantaggioso in termini di costi e tempi.Forest fires are a major environmental problem in the Mediterranean region, where large areas are affected each summer. An assessment of the environmental impact of forest fires (in the short-term and in the long-term) requires the collection of accurate and detailed post-fire information related to fire type, fire severity, forest regeneration and vegetation recovery. Advanced tools in remote sensing provide a powerful tool for the study of this phenomenon. The importance of this work was often emphasized by the European Commission, which focused on the studying of forest fires and their effect on vegetation through the development of appropriate impact assessment and mitigation methods. The aim of this study was to assess the post-fire impact on vegetation in a Mediterranean environment by employing high quality satellite and field data and by using advanced data processing techniques. The work entailed the development of a whole system integrating very high spatial and spectral resolution remotely sensed data. For short-term impact assessment, an object-oriented model was developed using very high spatial resolution Ikonos imagery to map the type of fire, namely, canopy fire and surface fire. The results showed that object-oriented classification could be used to accurately distinguish and map areas of surface and crown fire spread (overall accuracy of 87%), especially that occurring in open Mediterranean forests. Also, the performance of object-based classification in mapping three levels of fire severity by employing high spatial resolution Ikonos imagery was evaluated, and accuracy of the obtained results was estimated to be 83%. As for long-term impact assessment, the mapping of post-fire forest regeneration (pine) and vegetation recovery (shrub) was performed by following three different approaches. First, the developed object-based classification of QuickBird (very high spatial resolution) allowed post-fire vegetation recovery and survival mapping of canopy within two different fire severity levels (86% of classification accuracy). The main effect of fire has been to create a more homogeneous landscape. Second, statistical analysis of field hyperspectral data allowed a 97% reduction in data volume and recommended 14 best narrowbands to discriminate among pine trees (age and species) and 18 bands that best characterize the different shrub species. Then, hyperspectral Hyperion was employed for mapping post-fire forest regeneration and vegetation recovery. The overall classification accuracy was found to be 75.81% when mapping two different regenerated pine species and other species of vegetation recovery. Third, an object-oriented combined analysis of QuickBird and Hyperion was investigated for the same objective. An improvement in classification accuracy of 8.06% was recorded when combining both Hyperion and QuickBird imageries than by using only the Hyperion image. Overall, it was observed that advanced tools in remote sensing provided the necessary means for gathering information about the burned areas, the regenerated forests and the recovered vegetations in a successful and a timely/cost effective manner.XX Ciclo197