Polarimetric data for tropical forest monitoring : studies at the Colombian Amazon

An urgent need exists for accurate data on the actual tropical forest extent, deforestation, forest structure, regeneration and diversity. The availability of accurate land cover maps and tropical forest type maps, and the possibility to update these maps frequently, is of great importance for the development and success of monitoring systems. For areas like the Amazon the use of optical remote sensing systems as the source of information, is impeded by the permanent presence of clouds that affect the interpretation and the accuracy of the algorithms for classification and map production. The capabilities of radar systems to acquire cloud free images and the penetration of the radar waves into the forest canopy make radar systems suitable for monitoring activities and provide additional and complementary data to optical remote sensing systems. Information regarding forest structure, forest biomass, and vegetation cover and flooding can be associated with radar images because of the typical wave-object interaction properties of the radar systems.In this thesis new algorithms for the classification of radar images and the production of accurate maps are presented. The production of specific maps is studied by applying the developed algorithms to two different study areas in the Colombian Amazon. The first site, San José del Guaviare, is a colonisation area with active deforestation activities and dynamic land cover change. The second area is a pristine natural forest with high diversity of landscapes.A detailed statistical description of the polarimetric AirSAR data is made in terms of backscatter (gamma values), polarimetric phase difference and polarimetric correlation. This approach allows a better interpretation of physical backscatter mechanisms important for interpretation of images in relation to ground parameters. Theoretical cumulative probability density distributions (pdf) are used to describe the mean field values and the standard deviation for a class. A Gausian distribution is used to describe the field average gamma values; a circular Gausian distribution is used to describe the field average HH-VV phase difference and a Beta distribution is used to described the field average HH-VV phase correlation. The accuracy of the estimation of the field-averaged values depends on the level of speckle, i.e. number of independent looks. This effect is included in the calculation of the pdf's and therefore can be simulated.For the Guaviare site the classification algorithm is used to assess the AirSAR data in the production of a land cover type map. Classification accuracies are calculated for different combinations of bands and level of speckle. An accuracy of 98.7% was calculated for a map when combining L-HV and P-RR polarisations. Confusion between classes are studied to evaluate the use of radar bands for monitoring activities, e.g. loss of forest or detection of new deforested areas. In addition a biomass map is created by using the empirical relationship between the combination of the same radar bands and the biomass estimations from 28 plots as measured in the field. The agreement of the biomass map with the land cover map is used to evaluate the biomass classification.For the Araracuara site the classification algorithm is used to assess the use of polarimetric data for forest structural type mapping and indirect forest biophysical characterisation. 23 field-measured plots used for forest structural characterisation are used to assess the accuracy of the classification. A new SAR derived legend is more suitable for the SAR map allowing better physical interpretation of results. A method based on iterated conditional modes is introduced to create maps from the classified radar images, increasing in most of the cases the accuracy of the classification. The structural type map with 15 classes can be classified with accuracies ranging from 68% to 94% depending on the classification and the mapping approach. The relationship between forest structure and polarimetric signal properties is studied in detail by using a new decomposition of polarimetric coherence, based on a simple physical description of the wave-object interactions. The accuracy of the complex coherence is described using the complex Wishart distribution. In addition for the same area, a biomass map is created using the previous structural type characterisation as the basis for the classification, overcoming problems as the well know radar signal saturation.The possibilities and restrictions of creating biomass maps with AirSAR polarimetric images are deeply investigated. Two different approaches are proposed depending on the terrain conditions. A theoretical exploration on the physical limits for radar biomass inversion is made by using a new interface model, called LIFEFORM that describes the layered tropical forest in terms of scatterers. The UTARTCAN scattering model is used to analyse the effect of flooding, forest structure and terrain roughness in the biomass inversion

Estimation of Forest Biomass and Faraday Rotation using Ultra High Frequency Synthetic Aperture Radar

Synthetic Aperture Radar (SAR) data in the Ultra High Frequency (UHF; 300 MHz – 3 GHz)) band have been shown to be strongly dependent of forest biomass, which is a poorly estimated variable in the global carbon cycle. In this thesis UHF-band SAR data from the fairly flat hemiboreal test site Remningstorp in southern Sweden were analysed. The data were collected on several occasions with different moisture conditions during the spring of 2007. Regression models for biomass estimation on stand level (0.5-9 ha) were developed for each date on which SAR data were acquired. For L-band (centre frequency 1.3 GHz) the best estimation model was based on HV-polarized backscatter, giving a root mean squared error (rmse) between 31% and 46% of the mean biomass. For P-band (centre frequency 340 MHz), regression models including HH, HV or HH and HV backscatter gave an rmse between 18% and 27%. Little or no saturation effects were observed up to 290 t/ha for P-band. A model based on physical-optics has been developed and was used to predict HH-polarized SAR data with frequencies from 20 MHz to 500 MHz from a set of vertical trunks standing on an undulating ground surface. The model shows that ground topography is a critical issue in SAR imaging for these frequencies. A regression model for biomass estimation which includes a correction for ground slope was developed using multi-polarized P-band SAR data from Remningstorp as well as from the boreal test site Krycklan in northern Sweden. The latter test site has pronounced topographic variability. It was shown that the model was able to partly compensate for moisture variability, and that the model gave an rmse of 22-33% when trained using data from Krycklan and evaluated using data from Remningstorp. Regression modelling based on P-band backscatter was also used to estimate biomass change using data acquired in Remningstorp during the spring 2007 and during the fall 2010. The results show that biomass change can be measured with an rmse of about 15% or 20 tons/ha. This suggests that not only deforestation, but also forest growth and degradation (e.g. thinning) can be measured using P-band SAR data. The thesis also includes result on Faraday rotation, which is an ionospheric effect which can have a significant impact on spaceborne UHF-band SAR images. Faraday rotation angles are estimated in spaceborne L-band SAR data. Estimates based on distributed targets and calibration targets with high signal to clutter ratios are found to be in very good agreement. Moreover, a strong correlation with independent measurements of Total Electron Content is found, further validating the estimates

THE AMBIGUITY IN FOREST PROFILES AND XTINCTION ESTIMATED FROM MULTIBASELINE INTERFEROMETRIC SAR

This paper demonstrates by simulation that in the estimation of vegetation profiles from multibaseline interferometric synthetic aperture radar (InSAR), the peak extinction coefficient is poorly determined for typical interferometric coherence and phase accuracies. This coefficient determines overall density and affects the relative density profiles estimated from interferometry. This paper shows that a given radar power profile gives rise to a family of vegetation density profiles, depending on the peak extinction assumed. It is further  demonstrated that estimating the peak extinction requires coherence accuracies of better than 0.1% and phase accuracies of better than a few tenths of a degree, both of which exceed the performance of typical or envisioned SAR systems. Two recommended approaches to profile production with InSAR are 1) use the radar power profile instead of the vegetation density profile for biomass estimation and other ecosystem characterization (in analogy to LIDAR power which is most frequently used for lidar studies of biomass) or 2) apply external information to establish the extinction characteristics needed for vegetation density profiles.Esse artigo procura demonstrar, por simulação, que na estimativa de perfis de volume da vegetação por interferometria  com múltiplas linhas de base, o pico de extinção não é adequadamente determinado pela coerência interferométrica e fase, com acurácias típicas de InSAR. Esse pico determina a densidade global, afetando os perfis de densidade relativa da vegetação estimados por interferometria. Esse trabalho mostra que para um dado perfil de potência-radar há uma série de perfis de densidade da vegetação, dependendo do pico de extinção assumido. É ainda demonstrado que a estimativa do pico de  extinção requer exatidões de coerência melhores que 0,1%, bem como, de acurácias de fases que alguns décimos de graus, valores esses que atualmente excedem o desempenho de sistemas SAR em operação ou aqueles previstos. As duas abordagens recomendadas para a produção de perfis com InSAR são: (1) utilizar o perfil-radar, ao invés do perfil de densidade de vegetação, para estimação de biomassa e outras caracterizações de ecossistema (em nalogia à potência-lidar, a qual é mais  frequentemente utilizada nos estudos de biomassa baseados em LIDAR); ou (2) aplicar informação externa para estabelecer as características de extinção necessárias aos perfis de densidade de vegetação

ANALYSIS OF STRUCTURAL PARAMETERS OF FOREST TYPOLOGIES USING L-BAND SAR DATA

O objetivo principal desse trabalho é investigar a relação entre o retroespalhamento (σ°) de dados SAR polarimétricos de banda L, em diferentes ângulos de incidência (coletado pelo sensor aerotransportado R99-B/SIPAM) e os parâmetros estruturais de sítios de floresta primária e sucessão  secundária. A área selecionada para esse estudo está localizada na região da Floresta Nacional do Tapajós (Estado do Pará, Brasil) e áreas circunvizinhas. É utilizada a  técnica de decomposição de alvos de Freeman-Durden na avaliação dos mecanismos básicos de espalhamento, para verificar a contribuições das componentes fisionômico-estruturais dos alvos florestais na resposta-radar de banda L. Como conclusão, é possível verificar que a variável “altura das árvores” teve  melhor relação com os valores de retroespalhamento, quando comparado com outras variáveis biofísicas, especialmente quando o modelo também incluiu variações do ângulo de incidência na direção em range. A técnica de decomposição de Freeman-Durden indicou que a componente volumétrica de espalhamento tem uma forte influência na resposta embanda L para florestas tropicais primárias  e secundárias,em ângulos de incidência entre 52 e 70 graus, devido principalmente ao elevado ângulo de incidência e, consequentemente a baixa profundidade de penetração vertical da onda incidente.

Regression-Based Retrieval of Boreal Forest Biomass in Sloping Terrain using P-band SAR Backscatter Intensity Data

A new biomass retrieval model for boreal forest using polarimetric P-band synthetic aperture radar (SAR) backscatter is presented. The model is based on two main SAR quantities: the HV backscatter and the HH/VV backscatter ratio. It also includes a topographic correction based on the ground slope. The model is developed from analysis of stand-wise data from two airborne P-band SAR campaigns: BioSAR 2007 (test site: Remningstorp, southern Sweden, biomass range: 10-287 tons/ha, slope range: 0-4 degrees) and BioSAR 2008 (test site: Krycklan, northern Sweden, biomass range: 8-257 tons/ha, slope range: 0-19 degrees). The new model is compared to five other models in a set of tests to evaluate its performance in different conditions. All models are first tested on data sets from Remningstorp with different moisture conditions, acquired during three periods in the spring of 2007. Thereafter, the models are tested in topographic terrain using SAR data acquired for different flight headings in Krycklan. The models are also evaluated across sites, i.e., training on one site followed by validation on the other site. Using the new model with parameters estimated on Krycklan data, biomass in Remningstorp is retrieved with RMSE of 40-59 tons/ha, or 22-33% of the mean biomass, which is lower compared to the other models. In the inverse scenario, the examined site is not well represented in the training data set, and the results are therefore not conclusive

Mapping of clear-cuts in Swedish forest using satellite images acquired by the radar sensor ALOS PALSAR

This study presents results for observing forest changes in Sweden using multi-temporal L-band satellite data and is a part of the JAXA’s ALOS Kyoto and Carbon Initiative. An extensive dataset of images acquired by the Advanced Land Observing Satellite Phased Array type L-band Synthetic Aperture Radar (ALOS PALSAR) is investigated for clear-cut detection in boreal forests in northern Sweden (Lat. 64°14’ N, Long. 19°50’ E). Strong forest/non-forest contrast and temporal consistency were found for the Fine Beam Dual HV-polarized backscatter during unfrozen conditions. Thus, a simple thresholding algorithm that exploits the temporal consistency of pair-wise HV-backscatter measurements has been developed for detection of clear-felled areas. When applied to an image pair acquired during favorable weather conditions, the detection algorithm identified 76% of the clear-cut pixels within a reference layer, with zero erroneously detected pixels. With further refinement, the developed methodology can be an option to present operational alternatives for clear-cut detection

Estimating aboveground woody biomass change in Kalahari woodland: combining field, radar, and optical data sets

Maps that accurately quantify aboveground vegetation biomass (AGB) are essential for ecosystem monitoring and conservation. Throughout Namibia, four vegetation change processes are widespread, namely, deforestation, woodland degradation, the encroachment of the herbaceous and grassy layers by woody strata (woody thickening), and woodland regrowth. All of these vegetation change processes affect a range of key ecosystem services, yet their spatial and temporal dynamics and contributions to AGB change remain poorly understood. This study quantifies AGB associated with the different vegetation change processes over an 8-year period, for a region of Kalahari woodland savannah in northern Namibia. Using data from 101 forest inventory plots collected during two field campaigns (2014–2015), we model AGB as a function of the Advanced Land Observing Satellite Phased Array L-band synthetic aperture radar (PALSAR and PALSAR-2) and dry season Landsat vegetation index composites, for two periods (2007 and 2015). Differences in AGB between 2007 and 2015 were assessed and validated using independent data, and changes in AGB for the main vegetation processes are quantified for the whole study area (75,501 km2). We find that woodland degradation and woody thickening contributed a change in AGB of −14.3 and 2.5 Tg over 14% and 3.5% of the study area, respectively. Deforestation and regrowth contributed a smaller portion of AGB change, i.e. −1.9 and 0.2 Tg over 1.3% and 0.2% of the study area, respectively