LINEAR SPECTRAL MIXING MODEL APPLIED IN IMAGES FROM PROBA-V SENSOR: A SPATIAL MULTIRESOLUTION APPROACH

The complexity of pixel composition of orbital images has been commonly referred to the spectral mixture problem. The acquisition of endmembers (pure pixels) direct from image under study is one of the most commonly employed approaches. However, it becomes limited in low or moderate spatial resolutions due to the lower probability of finding those pixels. In this way, this work proposes the combined use of images with different spatial resolutions to estimate the spectral responses of the endmembers in low spatial resolution image, from the obtained proportions derived from the spatial higher-resolution images. The proposed methodology was applied to products provided by PROBA-V satellite with spatial resolution of 100 m and 1 km in the Pantanal region of Mato Grosso state. Initially, the fraction images (proportions) were generated from the 100 m dataset using the endmembers selected directly in the image, considering the higher probability of finding pure pixels in such images. Following the spectral responses of the endmembers in 1 km were estimated by multiple linear regression, using the proportions of the endmembers in the pixels derived from 100 m images. For the evaluation, the endmembers fraction images were compared and field data was used. These analyses indicated that the spectral responses estimated allowed to improve the results with regard to error, to variability, and to the identification of endmembers proportions, considering that inadequate choice of pixels considered as pure in low spatial resolution images can affect the quality of the fraction images for operational use

Sub-pixel change detection for urban land-cover analysis via multi-temporal remote sensing images

Conventional change detection approaches are mainly based on per-pixel processing, which ignore the sub-pixel spectral variation resulted from spectral mixture. Especially for medium-resolution remote sensing images used in urban land-cover change monitoring, land use/cover components within a single pixel are usually complicated and heterogeneous due to the limitation of the spatial resolution. Thus, traditional hard detection methods based on pure pixel assumption may lead to a high level of omission and commission errors inevitably, degrading the overall accuracy of change detection. In order to address this issue and find a possible way to exploit the spectral variation in a sub-pixel level, a novel change detection scheme is designed based on the spectral mixture analysis and decision-level fusion. Nonlinear spectral mixture model is selected for spectral unmixing, and change detection is implemented in a sub-pixel level by investigating the inner-pixel subtle changes and combining multiple compositi..

Determining class proportions within a pixel using a new mixed-label analysis method

Land-cover classification is perhaps one of the most important applications of remote-sensing data. There are limitations with conventional (hard) classification methods because mixed pixels are often abundant in remote-sensing images, and they cannot be appropriately or accurately classified by these methods. This paper presents a new approach in improving the classification performance of remote-sensing applications based on mixed-label analysis (MLA). This MLA model can determine class proportions within a pixel in producing soft classification from remote-sensing data. Simulated images and real data sets are used to illustrate the simplicity and effectiveness of this proposed approach. Classification accuracy achieved by MLA is compared with other conventional methods such as linear spectral mixture models, maximum likelihood, minimum distance, and artificial neural networks. Experiments have demonstrated that this new method can generate more accurate land-cover maps, even in the presence of uncertainties in the form of mixed pixels.published_or_final_versio

Seasonality of vegetation types of South America depicted by moderate resolution imaging spectroradiometer (MODIS) time series

The development, implementation and enforcement of policies involving the rational use of the land and the conservation of natural resources depend on an adequate characterization and understanding of the land cover, including its dynamics. This paper presents an approach for monitoring vegetation dynamics using high-quality time series of MODIS surface reflectance data by generating fraction images using Linear Spectral Mixing Model (LSMM) over South America continent. The approach uses physically-based fraction images, which highlight target information and reduce data dimensionality. Further dimensionality was also reduced by using the vegetation fraction images as input to a Principal Component Analysis (PCA). The RGB composite of the first three PCA components, accounting for 92.9% of the dataset variability, showed good agreement with the main ecological regions of South America continent. The analysis of 21 temporal profiles of vegetation fraction values and precipitation data over South America showed the ability of vegetation fractions to represent phenological cycles over a variety of environments. Comparisons between vegetation fractions and precipitation data indicated the close relationship between water availability and leaf mass/chlorophyll content for several vegetation types. In addition, phenological changes and disturbance resulting from anthropogenic pressure were identified, particularly those associated with agricultural practices and forest removal. Therefore the proposed method supports the management of natural and non-natural ecosystems, and can contribute to the understanding of key conservation issues in South America, including deforestation, disturbance and fire occurrence and management

An integrated approach to grassland productivity modelling using spectral mixture analysis, primary production and Google Earth Engine

Thesis (MA)--Stellenbosch University, 2020.ENGLISH ABSTRACT: Grassland degradation can have a severe impact on condition, productivity and consequently grazing potential. Current conventional methods for monitoring and managing grasslands are time-consuming, destructive and not applicable at large-scale. These constraints could be addressed using a remote sensing (RS)-based approach, however, current RS-based approaches also have technological and scientific limitations in the context of grassland management. The inability of RS-based primary production models to discriminate between herbaceous and woody production at sub-pixel level poses constraints for use in grazing capacity (GC) calculation. The integration of fractional vegetation cover (FVC) is posed as a promising solution, specifically estimation using spectral mixture analysis (SMA). Current grassland monitoring approaches are limited by the technological constraints of traditional, desktop-based RS approaches, but the implementation of analysis in a Google Earth Engine (GEE) web application can address these limitations by providing dynamic, continuous productivity estimates. Field data collection and analysis of biophysical parameters were performed to establish crucial relationships between vegetation productivity and RS signals. Biophysical parameters obtained include FVC, leaf area index (LAI), fraction of absorbed photosynthetically active radiation (fAPAR) and grass dry matter (DM) production. An important outcome was the improvement of the normalised difference vegetation index (NDVI) and fAPAR regression relationship, achieved by scaling fAPAR using the proportion of green, living biomass. The relationship proved useful in subsequent vegetation productivity modelling. The potential of SMA for FVC estimation using medium resolution imagery (Landsat 8 and Sentinel-2) and relatively few field points, was explored. A linear spectral mixture model (LSMM) was calibrated, implemented and evaluated on accuracy and transferability. A number of bands and spectral indices were identified as core features, specifically the dry bare-soil index (DBSI). DBSI improved discrimination between bare ground and dry vegetation, a common challenge in semi-arid conditions. The calibrated LSMM performed well, with Sentinel-2 providing the most accurate results. The research proved the transferability of the LSMM approach, as accurate FVC estimates were obtained for both arid, dry season conditions and green, growing season conditions. The LSMM-estimated FVC was combined with primary production to improve GC calculation for grassland and rangelands. Annual grassland production was calculated using the Regional Biosphere Model (RBM). Although a water stress factor is a well-known source of uncertainty, the research found its inclusion crucial to the transferability of the model between different climatic conditions. FVC was used to determine the grazable primary production from RBM estimates, thus mitigating the effects of woody components on GC calculations. A comparison of model-estimated GC to the most recent national GC map showed good agreement. Slight discrepancies were likely due to the inability of the model to include species composition and palatability in GC calculations. The final FVC-integrated productivity model was implemented in a GEE web app to demonstrate the practical contribution of the research for continuous, dynamic, multi-scale and sustainable grassland management. Overall, the findings of the research provide valuable insights into improving RS-based modelling of grassland condition and productivity. Operationalisation of this research can aid in identifying potential degradation, highlighting regions vulnerable to food shortages and establishing sustainable productivity levels. Recommendations include investigating alternative methods for estimating water stress and exploring the incorporation of species composition in GC calculation using RS.AFRIKAANSE OPSOMMING: Agteruitgang van grasvelde kan 'n ernstige invloed op kondisie, produktiwiteit en gevolglik weidingspotensiaal hê. Huidige konvensionele metodes vir die monitering en bestuur van grasvelde is tydrowend, vernietigend en nie op groot skaal toepasbaar nie. Hierdie beperkinge kan met behulp van 'n afstandwaarnemings (AW)-gebaseerde benadering aangespreek word, maar huidige AW-metodes het egter ook tegnologiese en wetenskaplike beperkings, veral in die konteks van veldbestuur. Die onvermoë van AW-gebaseerde primêre produksiemodelle om tussen kruidagtige en houtagtige produksie op sub-pixelvlak te onderskei, hou beperkings in vir die berekening van drakapasiteit (DK). Die integrasie van fraksionele plantegroeibedekking (FPB) word aangebied as 'n belowende oplossing. Beraming van FPB deur gebruik te maak van spektrale mengselanalise (SMA) het veral potensiaal. Huidige benaderings vir die monitering van grasvelde word beperk deur die tegnologiese beperkings van tradisionele, rekenaargebaseerde AW-metodes, maar die implementering van analise in 'n Google Earth Engine (GEE) webtoepassing kan hierdie beperkings aanspreek deur dinamiese, deurlopende produktiwiteitsramings te verskaf. Velddata is ingesamel en analise van biofisiese parameters is uitgevoer om belangrike verwantskappe tussen plantproduktiwiteit en AW-seine te bepaal. Die biofisiese parameters sluit in FPB, blaaroppervlakte-indeks (BOI), fraksie van geabsorbeerde fotosinteties aktiewe bestraling (fAFAB) en droë materiaal (DM) produksie. Die verbetering van die genormaliseerde verskilplantegroei-indeks (NVPI) en fAFAB -regressie-verhouding, wat verkry is deur fAFAB te skaleer met behulp van die hoeveelheid groen, lewende biomassa was ‘n belangrike uitkoms. Die verwantskap was nuttig in die daaropvolgende modellering van plantegroei. Die potensiaal van SMA vir die bepaling van FPB deur middel van medium resolusiebeelde (Landsat 8 en Sentinel-2) met relatief min veldpunte is ondersoek. 'n Lineêre spektrale mengelmodel (LSMM) is gekalibreer, geïmplementeer en vir akkuraatheid en oordraagbaarheid geëvalueer. 'n Aantal bande en spektrale indekse is as kernkenmerke geïdentifiseer, spesifiek die droë kaal-grondindeks (DKGI). DKGI het die onderskeid tussen kaal grond en droë plantegroei, 'n algemene uitdaging in semi-droë landskappe, verbeter. Die gekalibreerde LSMM het goed gevaar, met Sentinel-2 wat die akkuraatste resultate gelewer het. Die navorsing het bewys dat die LSMM-benadering oorgedra kan word, aangesien akkurate FPB-ramings vir beide droë seisoen en groen, groeiseisoen toestande verkry is. Die LSMM-beraamde FPB is met primêre produksie ramings gekombineer om die DK-berekening vir grasveld te verbeter. Die jaarlikse grasveldproduksie is met behulp van die Streeks Biosfeer Model (SBM) bereken. Alhoewel 'n waterstresfaktor 'n bron van onsekerheid is, het die navorsing bevind dat dit die gebruik daarvan vir die oordraagbaarheid van die model tussen verskillende klimaatstoestande belangrik is. FPB is gebruik om die weibare primêre produksie volgens SBMramings te bepaal, en het die effekte van houtagtige komponente op DK-berekeninge verminder. 'n Vergelyking van die gemodelleerde DK met die nuutste nasionale DK-kaart het 'n goeie ooreenkoms getoon. Klein afwykings was waarskynlik te wyte aan die onvermoë van die model om spesiesamestelling en eetbaarheid by DK-berekeninge in te sluit. Die finale FPB-geïntegreerde produktiwiteitsmodel is in 'n GEE webtoep geïmplementeer om die praktiese bydrae van die navorsing vir deurlopende, dinamiese, meervoudige en volhoubare grasveldbestuur te demonstreer. In die geheel bied die bevindinge van die navorsing waardevolle insigte in die verbetering van die AW-gebaseerde modellering van veldtoestand en produktiwiteit. Operasionalisering van hierdie navorsing kan tot die identifisering van potensiële agteruitgang, die uitlig van streke wat kwesbaar is vir voedseltekorte en die bepaling van volhoubare produktiwiteitsvlakke bydra. Aanbevelings sluit in die ondersoek van alternatiewe metodes vir die beraming van waterstres en die gebruik van spesiesamestelling in DK-berekening met behulp van AW.Master

Advanced optical technologies for phytoplankton discrimination : application in adaptive ocean sampling networks

There is a lack on ocean dynamics understanding, and that lead oceanographers to the need of acquiring more reliable data to study ocean characteristics. Oceanographic measurements are difficult and expensive but essential for effective study oceanic and atmospheric systems. Despite rapid advances in ocean sampling capabilities, the number of disciplinary variables that are necessary to solve oceanographic problems are large. In addition, the time scales of important processes span over ten orders of magnitude, and due to technology limitations, there are important spectral gaps in the sampling methods obtained in the last decades. Thus, the main limitation to understand these dynamics is an inaccurate measurement of the process due to undersampling. But fortunately, recent advances in ocean platforms and in situ autonomous sampling systems and satellite sensors are enabling unprecedented rates of data acquisition as well as the expansion of temporal and spatial coverage. Many advances in technologies involving different areas such as computing, nanotechnology, robotics, molecular biology, etc. are being developed. There exist the effort that these advantages could be applied to ocean sciences and will prove to extremely beneficial for oceanographers in the next few decades. Autonomous underwater vehicles, in situ automatic sampling devices, high spectral resolution optical and chemical sensors are some of the new advances that are being utilized by a limited number of oceanographers, and in a few years are expected to be widely used. Thanks to new technologies and, for instance, utilization of data assimilation models coupled with autonomous sampling platforms can increase temporal and spatial sampling capabilities. For instance, studies of phytoplankton dynamics in the water column, or the transportation and aggregation of organisms need a high rate of sampling because of their rapid evolution, that is why new strategies and technologies to increase sampling rate and coverage would be really useful. However, other challenges come up when increasing the variety and quantity of ocean measurements. For instance, number of measurements are limited by costs of instruments and their deployment, as well as data processing and production of useful data products and visualizations. In some studies, there exists the necessity to discriminate and detect different phytoplankton species present in sea water, and even track their evolution. The use of their optical properties is one of the approximations used by some of them. Acquiring optical properties is a non-invasive and non-destructive method to study phytoplankton communities. Phytoplankton species are then organized thanks to presenting similar optical characteristics. Fluorescence spectroscopy has been used and found as a really potential technique for this goal, although passive optical techniques such as the study of the absorption can be also useful, or even their combination can be studied. Specifically speaking about fluorescence, the majority of the studies have centered their effort in discriminating phytoplankton groups using their excitation spectra because the emission spectra contains less information. The inconvenient of using this kind of information, is that the acquisition is not instantaneous and it is necessary to spend some time (over a second) exciting the sample at different wavelengths sequentially. In contrast, the whole emission spectra can be acquired instantaneously. Therefore, the aim of this thesis is to explore new and powerful signal processing techniques able to discriminate between different phytoplankton groups from their emission fluorescence spectra. This document presents important results that demonstrate the capabilities of these methods.Existe una falta de conocimiento sobre las dinámicas de los océanos, lo que lleva a los oceanógrafos a la necesidad de adquirir datos fiables para estudiar las características de los océanos. Los datos oceanográficos son difíciles y costosos de adquirir, pero esenciales para estudiar de manera efectiva los sistemas oceánicos i atmosféricos. A causa de los rápidos avances para muestrear este medio tan hostil, es necesario que diversas disciplinas trabajen juntas para solucionar el gran número de problemáticas que se pueden encontrar. Además, los procesos que se tienen que estudiar pueden perdurar hasta diez órdenes de magnitud, y por culpa de las limitaciones tecnológicas existen importantes faltas en los métodos que se llevan utilizando en las últimas décadas. Por eso, la principal limitación para entender estas dinámicas es la imposibilidad de medir procesos correctamente como consecuencia de la baja frecuencia de muestreo. Por suerte, los recientes avances en plataformas oceánicas y sistemas de muestreo autónomos, junto con datos de satélite, están mejorando estas frecuencias de adquisición, i en consecuencia aumentando la cobertura temporal y espacial de estos procesos. Actualmente hay disciplinas como la computación, nanotecnología, robótica, biología molecular, etc. que están protagonizando unos avances tecnológicos sin precedentes. La intención es aprovechar este esfuerzo y aplicarlo en oceanografía. Vehículos autónomos bajo el agua, sistemas automáticos de muestreo, sensores ópticos o químicos de alta resolución son algunas de las tecnologías que se empiezan a utilizar, pero que por culpa de su coste todavía no están extendidas y se espera que lo puedan estar en los próximos años. Gracias a algunas de estas tecnologías, como por ejemplo la utilización de modelos de asimilación de datos conjuntamente con plataformas autónomas de muestreo, se puede incrementas la capacidad de muestreo, tanto temporal como espacial. Un ejemplo claro de aplicación es el estudio de las dinámicas del fitoplancton, así como el transporte de organismos dentro de la columna de agua. No obstante, no todos los aspectos son positivos, otros retos surgen al aumentar la variedad y cantidad de datos oceanográficos. El número de datos queda limitado por el coste de los instrumentos y las campañas. Además, es necesario estudiar nuevos sistemas para procesar y extraer información útil de estos datos, puesto que los métodos conocidos hasta el momento quizá no son los más adecuados. La detección y discriminación de diferentes especies de fitoplancton en el mar es muy importante en ciertos estudios científicos. Algunos de estos estudios se basan en extraer información de sus propiedades ópticas, ya que es un método no invasivo ni destructivo. Espectroscopia a partir de la respuesta de fluorescencia del fitoplancton se utiliza en muchos experimentos y se ha demostrado que es una técnica con gran potencial, aunque el estudio de los espectros de absorción u otras técnicas basadas en métodos pasivos también se pueden utilizar. En el caso de la fluorescencia, la mayoría de los estudios se han centrado en discriminar grupos de fitoplancton a partir de los espectros de excitación, porque los espectros de emisión contienen menos información. La desventaja es que el tiempo necesario para adquirir una muestra puede estar entorno al segundo, porque se necesita estimular la muestra a diferentes longitudes de onda secuencialmente. En el caso de los espectros de emisión, con los avances actuales en sensores ópticos, las respuestas espectrales pueden ser adquiridas casi instantáneamente. Por este motivo, el objetivo principal de esta tesis es explorar nuevas tecnologías de procesado capaces de discriminar diferentes grupos de fitoplancton a partir de sus espectros de emisión de fluorescencia. Este documento presenta importantes resultados que demuestran la capacidad de discriminación de este tipo de información en combinación con las técnicas de procesado adecuadas

Deep Image Prior for Disentangling Mixed Pixels

A mixed pixel in remotely sensed images measures the reflectance and emission from multiple target types (e.g., tree, grass, and building) from a certain area. Mixed pixels exist commonly in spaceborne hyper-/multi-spectral images due to sensor limitations, causing the signature ambiguity problem and impeding high-resolution remote sensing mapping. Disentangling mixed pixels into the underlying constituent components is a challenging ill-posed inverse problem, which requires efficient modeling of spatial prior information and other application-dependent prior knowledge concerning the mixed pixel generation process. The recent deep image prior (DIP) approach and other application-dependent prior information are integrated into a Bayesian framework in the research, which allows comprehensive usage of different prior knowledge. The research improves mixed pixel disentangling using the Bayesian DIP in three key applications: spectral unmixing (SU), subpixel mapping (SPM), and soil moisture product downscaling (SMD). The main contributions are summarized as follows. First, to improve the decomposition of mixed pixels into pure material spectra (i.e., endmembers) and their constituting fractions (i.e., abundances) in SU, a designed deep fully convolutional neural network (DCNN) and a new spectral mixture model (SMM) with heterogeneous noise are integrated into a Bayesian framework that is efficiently solved by a new iterative optimization algorithm. Second, to improve the decomposition of mixed pixels into class labels of subpixels in SPM, a dedicated DCNN architecture and a new discrete SMM are integrated into the Bayesian framework to allow the use of both spatial prior and the forward model. Third, to improve the decomposition of mixed pixels into soil moisture concentrations of subpixels in SMD, a new DIP architecture and a forward degradation model are integrated into the Bayesian framework that is solved by the stochastic gradient descent approach. These new Bayesian approaches improve the state-of-the-art in their respective applications (i.e., SU, SPM, and SMD), which can be potentially utilized for solving other ill-posed inverse problems where simultaneously modeling of the spatial prior and other prior knowledge is needed

Enhancing spectral unmixing by considering the point spread function effect

The point spread function (PSF) effect exists ubiquitously in real remotely sensed data and such that the observed pixel signal is not only determined by the land cover within its own spatial coverage but also by that within neighboring pixels. The PSF, thus, imposes a fundamental limit on the amount of information captured in remotely sensed images and it introduces great uncertainty in the widely applied, inverse goal of spectral unmxing. Until now, spectral unmixing has erroneously been performed by assuming that the pixel signal is affected only by the land cover within the pixel, that is, ignoring the PSF. In this paper, a new method is proposed to account for the PSF effect within spectral unmxing to produce more accurate predictions of land cover proportions. Based on the mechanism of the PSF effect, the mathematical relation between the coarse proportion and sub-pixel proportions in a local window was deduced. Area-to-point kriging (ATPK) was then proposed to find a solution for the inverse prediction problem of estimating the sub-pixel proportions from the original coarse proportions. The sub-pixel proportions were finally upscaled using an ideal square wave response to produce the enhanced proportions. The effectiveness of the proposed method was demonstrated using two datasets. The proposed method has great potential for wide application since spectral unmixing is an extremely common approach in remote sensing

Uncertainty Assessment of Spectral Mixture Analysis in Remote Sensing Imagery

Spectral mixture analysis (SMA), a scheme of sub-pixel-based classifications, is one of the widely used models to map fractional land use and land cover information in remote sensing imagery. It assumes that: 1) a mixed pixel is composed by several pure land cover classes (endmembers) linearly or nonlinearly, and 2) the spectral signature of each endmember is a constant within the entire spatial extent of analysis. SMA has been commonly applied to impervious surface area extraction, vegetation fraction estimation, and land use and land cover change (LULC) mapping. Limitations of SMA, however, still exist. First, the existence of between- and within-class variability prevents the selection of accurate endmembers, which results in poor accuracy of fractional land cover estimates. Weighted spectral mixture analysis (WSMA) and transformed spectral mixture analysis (TSMA) are alternate means to address the within- and between- class variability. These methods, however, have not been analyzed systematically and comprehensively. The effectiveness of each WSMA and TSMA scheme is still unknown, in particular within different urban areas. Second, multiple endmember SMA (MESMA) is a better alternative to address spectral mixture model uncertainties. It, nonetheless, is time consuming and inefficient. Further, incorrect endmember selections may still limit model performance as the best-fit endmember model might not be the optimal model due to the existence of spectral variability. Therefore, this study aims 1) to explore endmember uncertainties by examining WSMA and TSMA modeling comprehensively, and 2) to develop an improved MESMA model in order to address the uncertainties of spectral mixture models. Results of the WSMA examination illustrated that some weighting schemes did reduce endmember uncertainties since they could improve the fractional estimates significantly. The results also indicated that spectral class variance played a key role in addressing the endmember uncertainties, as the better performing weighting schemes were constructed with spectral class variance. In addition, the results of TSMA examination demonstrated that some TSMAs, such as normalized spectral mixture analysis (NSMA), could effectively solve the endmember uncertainties because of their stable performance in different study areas. Results of Class-based MEMSA (C-MESMA) indicated that it could address spectral mixture model uncertainties by reducing a lot of the calculation burden and effectively improving accuracy. Assessment demonstrated that C-MEMSA significantly improving accuracy. Major contributions of this study can be summarized as follow. First, the effectiveness of addressing endmember uncertainties have been fully discussed by examining: 1) the effectiveness of ten weighted spectral mixture models in urban environments; and 2) the effectiveness of 26 transformed spectral mixture models in three locations. Constructive guidance regarding handling endmember uncertainties using WSMA and TSMA have been provided. Second, the uncertainties of spectral mixture model were reduced by developing an improved MESMA model, named C-MESMA. C-MESMA could restrict the distribution of endmembers and reduce the calculation burden of traditional MESMA, increasing SMA accuracy significantly