Multispectral Resource Sampler: Proof of concept. Literature survey of bidirectional reflectance

A bibliography compiled in order to give a comprehensive review of previous work in scene bidirectional reflectance, particularly those studies relevant to the Multispectral Resource Sampler (MRS) is presented. The bibliography contains 124 abstracts. In addition a synthesis of the literature results is given along with background information concerning MRS

Yield prediction by analysis of multispectral scanner data

A preliminary model describing the growth and grain yield of wheat was developed. The modeled growth characteristics of the wheat crop were used to compute wheat canopy reflectance using a model of vegetation canopy reflectance. The modeled reflectance characteristics were compared with the corresponding growth characteristics and grain yield in order to infer their relationships. It appears that periodic wheat canopy reflectance characteristics potentially derivable from earth satellites will be useful in forecasting wheat grain yield

Global retrieval of bidirectional reflectance and albedo over land from EOS MODIS and MISR data: Theory and algorithm

This paper describes the theory and the algorithm to be used in producing a global bidirectional reflectance distribution function (BRDF) and albedo product from data to be acquired by the moderate resolution imaging spectroradiometer (MODIS) and the multiangle imaging spectroradiometer (MISR), both to be launched in 1998 on the AM-I satellite platform as part of NASA's Earth Observing System (EOS). The product will be derived using the kernel-driven semiempirical Ambrals BRDF model, utilizing five variants of kernel functions characterizing isotropic, volume and surface scattering. The BRDF and the albedo of each pixel of the land surface will be modeled at a spatial resolution of I km and once every 16 days in seven spectral bands spanning the visible and the near infrared. The BRDF parameters retrieved and recorded in the MODIS BRDF/albedo product will be intrinsic surface properties decoupled from the prevailing atmospheric state and hence suited for a wide range of applications requiring characterization of the directional anisotropy of Earth surface reflectance. A set of quality control flags accompanies the product. An initial validation of the Ambrals model is demonstrated using a variety of field-measured data sets for several different land cover types

Kahden varpukasvin spektrien kaksisuuntaiset heijastussuhdetekijämittaukset

Recent studies have shown the benefits of multiangular remote sensing techniques for characterizing vegetation reflection properties. The study of spectral anisotropy of understory vegetation enables methods for improved plant species identification, and provides valuable input data for radiation scattering models of forests. This thesis presents the applied methods and results of a research effort carried out over the growing season of 2017 for the temporal spectral characterization of two of the economically most important wild berry species in Finland: lingonberry (Vaccinium vitis-idaea) and blueberry (Vaccinium myrtillus). The spectral bidirectional reflectance factor (BRF) data on lingonberry and blueberry shrub samples were collected in a multidirectional measurement geometry using the Finnish Geodetic Institute Goniospectrometer (FIGIFIGO) in laboratory conditions. Leaf reflectance and transmittance spectra on both species were collected with SpectroClip-TR spectral probe. The anisotropic characteristics were analysed in the spectral range from 400 to 2200 nm for view angle dependence (-40° to +40°), illumination angle dependence (+40°, +55°), seasonal dynamics over the growing season (2017), and for berry and flower detection. Both lingonberry and blueberry shrubs have strong backward and notable forward scattering characteristics on the principal plane. In the interspecies comparison, lingonberry is brighter into all view direction in the visible and near infrared wavelengths but darker in the short-wave infrared. Increasing the illumination zenith angle by 15° improves the spectral discrimination of the two dwarf shrub species by inducing a 12% ratio of the spectral responses. Vegetation indices that are commonly used in remote sensing of forests (NDVI, NDVI705, MSI, PSRI) show low sensitivity to the changes in the view- and illumination angles. The presence of lingonberries and lingonberry flowers is indicated as a spectral peak around 679 nm in the spectral ratio of samples with berries or flowers to samples without berries or flowers. It was shown that the analysis of spectral data on the reflectance anisotropy improves the spectral discrimination of the dwarf shrub species. The contribution of the berries on the obtained shrub spectra was shown to be notable enough to justify further studies by applying unmanned aerial vehicle (UAV) platforms. Future studies on the aerial spectral data are suggested to evaluate the potential of berry mapping in larger-scale.Viimeaikaiset tutkimukset ovat osoittaneet monisuunta-spektrometrian hyödyt kasvillisuuden heijastusominaisuuksien karakterisoinnissa kaukokartoituksessa. Aluskasvillisuuden spektrien anisotropian tutkiminen edesauttaa kehittämään menetelmiä kasvilajien tunnistamiseksi ja tarjoaa validointiaineistoa metsien sirontamalleihin. Tämä diplomityö esittää menetelmät ja tulokset Suomen kahden taloudellisesti tärkeimmän luonnonmarjoja tuottavan varpukasvin, mustikan (Vaccinium myrtillus) ja puolukan (Vaccinium vitis-idaea), spektrien temporaalisesta karakterisointikampanjasta kasvukauden 2017 yli. Kaksisuuntainen heijastussuhdetekijä spektriaineisto mitattiin mustikan ja puolukan varpunäytteistä monisuuntamittausgeometriassa FIGIFIGO (Finnish Geodetic Institute Goniospectrometer) goniospektrometrillä laboratorio-olosuhteissa. Lehtien heijastus- ja läpäisyspektrit mitattiin molemmista lajeista käyttäen SpectroClip-TR mittalaitetta. Anisotropiset ominaispiirteet analysointiin aallonpituuksien 400 - 2200 nm välillä katselukulmariippuvuudelle (-40° to +40°), valaistuskulmariippuvuudelle (+40°, +55°), vuodenajan aiheuttamille muutoksille (kasvukausi 2017) sekä marja ja kukintojen tunnistamiselle. Sekä puolukka että mustikka osoittavat voimakasta taaksepäin suuntautuvaa ja huomattavaa eteenpäin suuntautuvaa ominaissirontaa päätasossa. Lajien välisessä vertailussa puolukka on kirkkaampi kaikkiin mitattuihin katselukulmiin näkyvän valon ja lähi-infrapunan aallonpituuksilla, mutta tummempi lyhytaaltoisen infrapunan alueella. Valaistuskulman zeniitin kasvattaminen 15° parantaa lajien spektrien erotettavuutta aiheuttamalla 12 %:n eron lajien heijastusvasteisiin. Yleisesti metsän kaukokartoituksessa käytetyt kasvillisuusindeksit (NDVI, NDVI705, MSI, PSRI) osoittavat matalaa herkkyyttä katselu- ja valaistuskulman muutoksille. Näytteessä olevat puolukanmarjat ja -kukat erottuvat spektrissä piikkinä 679 nm:n kohdalla, kun tarkastellaan marjallisten ja kukallisten näytteiden suhdetta marjattomiin ja kukattomiin. Spektriaineiston heijastus-anisotropian analysoinnin näytettiin edesauttavan varpukasvien erotettavuutta. Marjojen vahva kontribuutio varpunäytteistä mitattuihin spektreihin osoitettiin niin selkeästi, että jatkotutkimuksia UAV (unmanned aerial vehicle) -alustalla voidaan pitää perusteltuina. Ilma-aluksilla kerättyä aineistoa ehdotetaan käytettävän marjojen laajemman kartoituksen potentiaalin selvittämiseksi

Characterization and Discrimination of Selected Vegetation Canopies from Field Observations of Bidirectional Reflectances

A full evaluation of the bidirectional reflectance properties of different vegetated surfaces was limited in past studies by instrumental inadequacies. With the development of the PARABOLA, it is now possible to sample reflectances from a large number of view angles in a short period of time, maintaining an almost constant solar zenith angle. PARABOLA data collected over five different canopies in Texas are analyzed. The objective of this investigation was to evaluate the intercanopy and intracanopy differences in bidirectional reflectance patterns. Particular attention was given to the separability of canopy types using different view angles for the red and the near infrared (NIR) spectral bands. Comparisons were repeated for different solar zenith angles. Statistical and other quantitative techniques were used to assess these differences. For the canopies investigated, the greatest reflectances were found in the backscatter direction for both bands. Canopy discrimination was found to vary with both view angle and the spectral reflectance band considered, the forward scatter view angles being most suited to observations in the NIR and backscatter view angles giving better results in the red band. Because of different leaf angle distribution characteristics, discrimination was found to be better at small solar zenith angles in both spectral bands

Remote sensing of snow : Factors influencing seasonal snow mapping in boreal forest region

Monitoring of snow cover in northern hemisphere is highly important for climate research and for operational activities, such as those related to hydrology and weather forecasting. The appearance and melting of seasonal snow cover dominate the hydrological and climatic patterns in the boreal and arctic regions. Spatial variability (in particular during the spring and autumn transition months) and long-term trends in global snow cover distribution are strongly interconnected to changes in Earth System (ES). Satellite data based estimates on snow cover extent are utilized e.g. in near-real-time hydrological forecasting, water resource management and to construct long-term Climate Data Records (CDRs) essential for climate research. Information on the quantitative reliability of snow cover monitoring is urgently needed by these different applications as the usefulness of satellite data based results is strongly dependent on the quality of the interpretation. This doctoral dissertation investigates the factors affecting the reliability of snow cover monitoring using optical satellite data and focuses on boreal regions (zone characterized by seasonal snow cover). Based on the analysis of different factors relevant to snow mapping performance, the work introduces a methodology to assess the uncertainty of snow cover extent estimates, focusing on the retrieval of fractional snow cover (within a pixel) during the spring melt period. The results demonstrate that optical remote sensing is well suited for determining snow extent in the melting season and that the characterizing the uncertainty in snow estimates facilitates the improvement of the snow mapping algorithms. The overall message is that using a versatile accuracy analysis it is possible to develop uncertainty estimates for the optical remote sensing of snow cover, which is a considerable advance in remote sensing. The results of this work can also be utilized in the development of other interpretation algorithms. This thesis consists of five articles predominantly dealing with quantitative data analysis, while the summary chapter synthesizes the results mainly in the algorithm accuracy point of view. The first four articles determine the reflectance characteristics essential for the forward and inverse modeling of boreal landscapes (forward model describes the observations as a function of the investigated variable). The effects of snow, snow-free ground and boreal forest canopy on the observed satellite scene reflectance are specified. The effects of all the error components are clarified in the fifth article and a novel experimental method to analyze and quantify the amount of uncertainty is presented. The five articles employ different remote sensing and ground truth data sets measured and/or analyzed for this research, covering the region of Finland and also applied to boreal forest region in northern Europe

Remote sensing of leaf area index : enhanced retrieval from close-range and remotely sensed optical observations

A wide range of models used in agriculture, ecology, carbon cycling, climate and other related studies require information on the amount of leaf material present in a given environment to correctly represent radiation, heat, momentum, water, and various gas exchanges with the overlying atmosphere or the underlying soil. Leaf area index (LAI) thus often features as a critical land surface variable in parameterisations of global and regional climate models, e.g., radiation uptake, precipitation interception, energy conversion, gas exchange and momentum, as all areas are substantially determined by the vegetation surface. Optical wavelengths of remote sensing are the common electromagnetic regions used for LAI estimations and generally for vegetation studies. The main purpose of this dissertation was to enhance the determination of LAI using close-range remote sensing (hemispherical photography), airborne remote sensing (high resolution colour and colour infrared imagery), and satellite remote sensing (high resolution SPOT 5 HRG imagery) optical observations. The commonly used light extinction models are applied at all levels of optical observations. For the sake of comparative analysis, LAI was further determined using statistical relationships between spectral vegetation index (SVI) and ground based LAI. The study areas of this dissertation focus on two regions, one located in Taita Hills, South-East Kenya characterised by tropical cloud forest and exotic plantations, and the other in Gatineau Park, Southern Quebec, Canada dominated by temperate hardwood forest. The sampling procedure of sky map of gap fraction and size from hemispherical photographs was proven to be one of the most crucial steps in the accurate determination of LAI. LAI and clumping index estimates were significantly affected by the variation of the size of sky segments for given zenith angle ranges. On sloping ground, gap fraction and size distributions present strong upslope/downslope asymmetry of foliage elements, and thus the correction and the sensitivity analysis for both LAI and clumping index computations were demonstrated. Several SVIs can be used for LAI mapping using empirical regression analysis provided that the sensitivities of SVIs at varying ranges of LAI are large enough. Large scale LAI inversion algorithms were demonstrated and were proven to be a considerably efficient alternative approach for LAI mapping. LAI can be estimated nonparametrically from the information contained solely in the remotely sensed dataset given that the upper-end (saturated SVI) value is accurately determined. However, further study is still required to devise a methodology as well as instrumentation to retrieve on-ground green leaf area index . Subsequently, the large scale LAI inversion algorithms presented in this work can be precisely validated. Finally, based on literature review and this dissertation, potential future research prospects and directions were recommended.Ei saatavill

Estimating leaf area index in savanna vegetation using remote sensing and inverse modelling

Leaf area index (LAI), defined as the one sided green leaf area per unit ground area, is a key parameter in ecosystem process models. Owing to the large area of the earth's surface that they occupy, savanna ecosystems represent the third largest terrestrial carbon sink. There is considerable uncertainty however, as to the functioning of these ecosystems, particularly as they respond to land cover changes. Consequently, ecosystem process models constitute one of the best methods available for investigating the effect this may have on terrestrial carbon cycling. If these models are to be used over large areas however, they need to be parameterised.This thesis develops a methodology to estimate LAI in savanna ecosystems, using remotely sensed earth observation (EO) data, laboratory bidirectional reflectance measurements (BRDF), physically based canopy reflectance models (CRMs), and artificial neural networks (ANN). First, the scattering behaviour of Kalahari soils was characterised, by making laboratory BRDF measurements. Soils were shown to be highly non-Lambertian. These measurements were then used to parameterise three different CRMs. Modelled reflectances were assessed with respect to Landsat ETM+ and Terra-MODIS reflectances. Results showed that a 1-D turbid medium provided the closest fit to the measurements. A series of model sensitivity analyses (SA) were performed, and it was shown that reflectance in the red and shortwave infrared displayed greatest sensitivity to LAI, sensitivity in the near-infrared was negligible. Model inversions were performed with ANN and different waveband combinations, and LAI was estimated. The results showed that LAI could be estimated with high accuracy, an RMSE of 0.3 1, and 0.18, from ETM+ and MODIS measurements, respectively. These results were promising, and with further improvements to models, coupled with more accurate input data, will see the use of EO data play an increasingly important role in understanding the functioning of these savanna ecosystems

Multiangular crop differentiation and LAI estimation using PROSAIL model inversion

xiii, 161 leaves : ill., map ; 29 cmUnderstanding variations in remote sensing data with illumination and sensor angle changes is important in agricultural crop monitoring. This research investigated field bidirectional reflectance factor (BRF) in crop differentiation and PROSAIL leaf area index (LAI) estimation. BRF and LAI data were collected for planophile and erectophile crops at three growth stages. In the solar principal plane, BRF differed optimally at 860 nm 60 days after planting (DAP) for canola and pea, at 860 nm 45 and 60 DAP for wheat and barley, and at 860 nm and 670 nm 45 and 60 DAP for planophiles versus erectophiles. The field BRF data helped better understand PROSAIL LAI estimation. NDVI was preferred for estimating LAI, however the MTVI2 vegetation index showed high sensitivity to view angles, particularly for erectophiles. The hotspot was important for crop differentiation and LAI. Availability of more along-track, off-nadir looking spaceborne sensors was recommended for agricultural crop monitoring