Estimativa de biomassa e carbono em floresta com araucaria utilizando imagens do satélite Ikonos II.

The implications of forest reduction in the carbon cycle have created a demand for development of non-destructive methods to biomass estimates, a key component for modeling biogeochemical. Results from the application of a new methodology using satellite images from IKONOS II to estimate biomass and organic carbon in Araucarian Forest are present. The methodology included statistical analysis (correlation analysis) of the bans and vegetation indices with the biomass and organic carbon data as well as adjustments and selections of equations to estimate the biomass and organic carbon (dependent variables) in function of data from the satellite images. The reflectance of the MS-1, MS-2, MS-3, MS-4 bands, the ratio among bans (MS-4/MS-3), NDVI and SAVI were used as independent variables. The use of satellites images from IKONOS II presented satisfactory results, although it still needs adjustment from mathematical models for better results.A implicação da redução das florestas no ciclo do carbono vem criando uma demanda de desenvolvimento de métodos não destrutivos para a determinação de biomassa, elemento importante na modelagem dos ciclos biogeoquímicos. Apresenta-se resultados de teste de metodologia utilizando-se imagens do satélite IKONOS II para estimar biomassa e carbono orgânico em Floresta com Araucária. O desenvolvimento metodológico englobou análise estatística (matriz de correlação) das bandas e índices de vegetação com os dados de biomassa e carbono orgânico, o ajuste e seleção de equações para estimar a biomassa e o carbono orgânico (variáveis dependentes) em função de variáveis provenientes das imagens de satélite, sendo a reflectância das bandas MS-1, MS-2, MS-3, MS-4, Razão de Bandas (MS-4/MS-3), NDVI e SAVI (variáveis independentes), e a quantificação das variáveis dependentes para toda a área de estudo. A utilização de imagens provenientes do satélite IKONOS II apresentou resultados bastante satisfatório, necessitando, no entanto, que modelos matemáticos sejam ajustados de modo a obterem-se melhores resultados

Carbon and biomass estimate in araucarian forest using satellite images from Ikonos II

A implica\ue7\ue3o da redu\ue7\ue3o das florestas no ciclo do carbono vem criando uma demanda de desenvolvimento de m\ue9todos n\ue3o destrutivos para a determina\ue7\ue3o de biomassa, elemento importante na modelagem dos ciclos biogeoqu\uedmicos. Apresenta-se resultados de teste de metodologia utilizando-se imagens do sat\ue9lite IKONOS II para estimar biomassa e carbono org\ue2nico em Floresta com Arauc\ue1ria. O desenvolvimento metodol\uf3gico englobou an\ue1lise estat\uedstica (matriz de correla\ue7\ue3o) das bandas e \uedndices de vegeta\ue7\ue3o com os dados de biomassa e carbono org\ue2nico, o ajuste e sele\ue7\ue3o de equa\ue7\uf5es para estimar a biomassa e o carbono org\ue2nico (vari\ue1veis dependentes) em fun\ue7\ue3o de vari\ue1veis provenientes das imagens de sat\ue9lite, sendo a reflect\ue2ncia das bandas MS-1, MS-2, MS-3, MS-4, Raz\ue3o de Bandas (MS-4/MS-3), NDVI e SAVI (vari\ue1veis independentes), e a quantifica\ue7\ue3o das vari\ue1veis dependentes para toda a \ue1rea de estudo. A utiliza\ue7\ue3o de imagens provenientes do sat\ue9lite IKONOS II apresentou resultados bastante satisfat\uf3rio, necessitando, no entanto, que modelos matem\ue1ticos sejam ajustados de modo a obterem-se melhores resultados.The implications of forest reduction in the carbon cycle have created a demand for development of non-destructive methods to biomass estimates, a key component for modeling biogeochemical. Results from the application of a new methodology using satellite images from IKONOS II to estimate biomass and organic carbon in Araucarian Forest are present. The methodology included statistical analysis (correlation analysis) of the bans and vegetation indices with the biomass and organic carbon data as well as adjustments and selections of equations to estimate the biomass and organic carbon (dependent variables) in function of data from the satellite images. The reflectance of the MS-1, MS-2, MS-3, MS-4 bands, the ratio among bans (MS-4/MS-3), NDVI and SAVI were used as independent variables. The use of satellites images from IKONOS II presented satisfactory results, although it still needs adjustment from mathematical models for better results

A review of remote sensing applications for oil palm studies

Oil palm becomes an increasingly important source of vegetable oil for its production exceeds soybean, sunflower, and rapeseed. The growth of the oil palm industry causes degradation to the environment, especially when the expansion of plantations goes uncontrolled. Remote sensing is a useful tool to monitor the development of oil palm plantations. In order to promote the use of remote sensing in the oil palm industry to support their drive for sustainability, this paper provides an understanding toward the use of remote sensing and its applications to oil palm plantation monitoring. In addition, the existing knowledge gaps are identified and recommendations for further research are given

Exploration of factors limiting biomass estimation by polarimetric radar in tropical forests

Direct inversion of radar return signals for forest biomass estimation is limited by signal saturation at medium biomass levels (roughly 150 ton/ha for P-band). Disturbing factors such as forest structural differences-and, notably, at low biomass levels, terrain roughness, and soil moisture variation-cause further complications. A new and indirect inversion approach is proposed that may circumvent such problems. Using multifrequency polarimetric radar the forest structure can be assessed accurately. Ecological relationships link these structures with biomass levels, even for high biomass levels. The LIFEFORM model is introduced as a new approach to transform field observations of the complex tropical forest into input files for the theoretical UTARTCAN polarimetric backscatter model. The validity of UTARTCAN for a wide range of forest structures is shown. Backscatter simulations for a wide range of forest structures, terrain roughness, and soil moisture clearly show the limitations of the direct approach and the validity of the proposed indirect approach up to very high levels of biomass

Analysing mangrove forest structure and biomass in the Niger Delta

Mangrove forests are important in providing a range of ecosystem services, including food provision to local communities and carbon storage, while being globally restricted to tropical coastlines. The conservation and sustainability of mangrove forests is thus a globally important topic. Mangrove forests in the Niger Delta are known to be under high pressure from urbanisation, development, logging and oil pollution, and invasive species such as nipa palm (Nypa fruticans). These mangrove forests are poorly understood as a result of difficulty of access, social unrest and security restrictions. For example, there is no data on the relationship between disturbance and mangrove structure in the Delta, current area extent and biomass stocks of mangrove forest, its rate of loss, or the rate of nipa palm colonisation in the Niger Delta. The overall objective of this thesis is to utilise a combination of field data and earth observation to resolve these knowledge gaps. This work will estimate area and biomass of mangrove forests in the Niger Delta, and their changes over recent years through disturbance and invasive species. I used an extensive field data collection in 2016-17 to establish 25 geo-referenced 0.25-ha plots across the Niger Delta and collected 567 ground control points. I estimated aboveground biomass (AGB) from a general allometric equation based on stem surveys. Leaf area index (LAI) was recorded using hemispherical photos. I performed and evaluated a land cover classification using a combination of Advanced Land Observatory Satellite Phased Array L-band SAR (ALOS PALSAR), Landsat ETM+ and the Shuttle Radar Topography Mission Digital Elevation Model (SRTM DEM) data. I also compared two supervised classification methods: Maximum Likelihood (ML) and Support Vector machine (SVM) classifiers. I established a relationship between field estimates of AGB and Advanced Land Observatory Satellite (ALOS) L-band radar backscatter. I also estimated the area of nipa palm and mangrove forests in the Niger Delta and generated the first mangrove biomass map for the region, for 2007 and 2017 to obtain change information. Plot estimated mean AGB was 83.7 Mg ha-1 and I found significantly higher plot biomass in close proximity to protected sites and tidal influence, and the lowest in the sites where urbanisation was actively taking place. The mean LAI was 1.45 and there was a significant positive correlation between AGB and LAI (R2= 0.28). Satellite observations of NDVI for the growing season correlated positively with in-situ LAI (R2= 0.63) and AGB (R2= 0.8). Lower stem sizes (5-15cm) accounted for 70% contribution to the total biomass in disturbed plots, while undisturbed plots had a more even contribution of different size classes to AGB. Nipa palm invasion was significantly correlated to plots with larger variations in LAI (i.e. more patchy cover) and proportion of basal area removed within plots. The classification results showed SVM (overall accuracy 99.9 %) performed better than ML (98.7%) across the Niger Delta. I estimated a 2017 mangrove area of 794 561 ha and nipa extent of 11,419 ha. I discovered a 12% decrease in mangrove area and 694 % increase in nipa palm between 2007 and 2017. The highest radar-AGB relationship was from the combination of HH: HV and HV bands (R2= 0.62, p-value < 0.001). Using this relationship, I estimated a mean and total AGB of 90.5 Mg ha-1 and 82 X 106 Mg in 2007; 83.4 Mg ha-1 and 65 X 106 Mg in 2017. Local wood exploitation is removing larger stems (> 15 cm DBH) preferentially from these mangroves and creates an avenue for nipa palm colonisation. I identified opportunities to use remote sensing to estimate biomass, based on the LAI-AGB-NDVI relationship I found, and can serve as a calibration dataset for radar data to provide effective monitoring of mangrove forest degradation. It is clear from these results that remote sensing can be used to map the extent and changes in these land cover types, and thus such mapping efforts should continue for policy targeting and monitoring. I was able to show that mangroves of the Niger Delta are at risk, from rapid clearance as well as from the invasive species nipa palm. I also provide evidence of mangrove cover loss of 11 000 ha yr-1 over a decade, resulting in biomass loss rate of 100 Mg ha-1 yr-1 while mangrove degradation rate of 56 Mg ha-1 yr-1 in the Niger Delta. Assessing carbon stock of mangrove forests in the Niger Delta can create a baseline for regional conservation and regeneration plans. These plans can create opportunities for generating carbon credits under reducing emissions from deforestation and forest degradation (REDD+)

Biomass Representation in Synthetic Aperture Radar Interferometry Data Sets

This work makes an attempt to explain the origin, features and potential applications of the elevation bias of the synthetic aperture radar interferometry (InSAR) datasets over areas covered by vegetation. The rapid development of radar-based remote sensing methods, such as synthetic aperture radar (SAR) and InSAR, has provided an alternative to the photogrammetry and LiDAR for determining the third dimension of topographic surfaces. The InSAR method has proved to be so effective and productive that it allowed, within eleven days of the space shuttle mission, for acquisition of data to develop a three-dimensional model of almost the entire land surface of our planet. This mission is known as the Shuttle Radar Topography Mission (SRTM). Scientists across the geosciences were able to access the great benefits of uniformity, high resolution and the most precise digital elevation model (DEM) of the Earth like never before for their a wide variety of scientific and practical inquiries. Unfortunately, InSAR elevations misrepresent the surface of the Earth in places where there is substantial vegetation cover. This is a systematic error of unknown, yet limited (by the vertical extension of vegetation) magnitude. Up to now, only a limited number of attempts to model this error source have been made. However, none offer a robust remedy, but rather partial or case-based solutions. More work in this area of research is needed as the number of airborne and space-based InSAR elevation models has been steadily increasing over the last few years, despite strong competition from LiDAR and optical methods. From another perspective, however, this elevation bias, termed here as the “biomass impenetrability”, creates a great opportunity to learn about the biomass. This may be achieved due to the fact that the impenetrability can be considered a collective response to a few factors originating in 3D space that encompass the outermost boundaries of vegetation. The biomass, presence in InSAR datasets or simply the biomass impenetrability, is the focus of this research. The report, presented in a sequence of sections, gradually introduces terminology, physical and mathematical fundamentals commonly used in describing the propagation of electromagnetic waves, including the Maxwell equations. The synthetic aperture radar (SAR) and InSAR as active remote sensing methods are summarised. In subsequent steps, the major InSAR data sources and data acquisition systems, past and present, are outlined. Various examples of the InSAR datasets, including the SRTM C- and X-band elevation products and INTERMAP Inc. IFSAR digital terrain/surface models (DTM/DSM), representing diverse test sites in the world are used to demonstrate the presence and/or magnitude of the biomass impenetrability in the context of different types of vegetation – usually forest. Also, results of investigations carried out by selected researchers on the elevation bias in InSAR datasets and their attempts at mathematical modelling are reviewed. In recent years, a few researchers have suggested that the magnitude of the biomass impenetrability is linked to gaps in the vegetation cover. Based on these hints, a mathematical model of the tree and the forest has been developed. Three types of gaps were identified; gaps in the landscape-scale forest areas (Type 1), e.g. forest fire scares and logging areas; a gap between three trees forming a triangle (Type 2), e.g. depending on the shape of tree crowns; and gaps within a tree itself (Type 3). Experiments have demonstrated that Type 1 gaps follow the power-law density distribution function. One of the most useful features of the power-law distributed phenomena is their scale-independent property. This property was also used to model Type 3 gaps (within the tree crown) by assuming that these gaps follow the same distribution as the Type 1 gaps. A hypothesis was formulated regarding the penetration depth of the radar waves within the canopy. It claims that the depth of penetration is simply related to the quantisation level of the radar backscattered signal. A higher level of bits per pixels allows for capturing weaker signals arriving from the lower levels of the tree crown. Assuming certain generic and simplified shapes of tree crowns including cone, paraboloid, sphere and spherical cap, it was possible to model analytically Type 2 gaps. The Monte Carlo simulation method was used to investigate relationships between the impenetrability and various configurations of a modelled forest. One of the most important findings is that impenetrability is largely explainable by the gaps between trees. A much less important role is played by the penetrability into the crown cover. Another important finding is that the impenetrability strongly correlates with the vegetation density. Using this feature, a method for vegetation density mapping called the mean maximum impenetrability (MMI) method is proposed. Unlike the traditional methods of forest inventories, the MMI method allows for a much more realistic inventory of vegetation cover, because it is able to capture an in situ or current situation on the ground, but not for areas that are nominally classified as a “forest-to-be”. The MMI method also allows for the mapping of landscape variation in the forest or vegetation density, which is a novel and exciting feature of the new 3D remote sensing (3DRS) technique. Besides the inventory-type applications, the MMI method can be used as a forest change detection method. For maximum effectiveness of the MMI method, an object-based change detection approach is preferred. A minimum requirement for the MMI method is a time-lapsed reference dataset in the form, for example, of an existing forest map of the area of interest, or a vegetation density map prepared using InSAR datasets. Preliminary tests aimed at finding a degree of correlation between the impenetrability and other types of passive and active remote sensing data sources, including TerraSAR-X, NDVI and PALSAR, proved that the method most sensitive to vegetation density was the Japanese PALSAR - L-band SAR system. Unfortunately, PALSAR backscattered signals become very noisy for impenetrability below 15 m. This means that PALSAR has severe limitations for low loadings of the biomass per unit area. The proposed applications of the InSAR data will remain indispensable wherever cloud cover obscures the sky in a persistent manner, which makes suitable optical data acquisition extremely time-consuming or nearly impossible. A limitation of the MMI method is due to the fact that the impenetrability is calculated using a reference DTM, which must be available beforehand. In many countries around the world, appropriate quality DTMs are still unavailable. A possible solution to this obstacle is to use a DEM that was derived using P-band InSAR elevations or LiDAR. It must be noted, however, that in many cases, two InSAR datasets separated by time of the same area are sufficient for forest change detection or similar applications

Evaluation of the potential of ALOS PALSAR L-band quadpol radar data for the retrieval of growing stock volume in Siberia

Because of the massive wood trade, illegal logging and severe damages due to fires, insects and pollution, it is necessary to monitor Siberian forests on a large-scale, frequently and accurately. One possible solution is to use synthetic aperture radar (SAR) remote sensing technique, in particular by combining polarimetric technique. In order to evaluate the potentiality of ALOS PALSAR L-band full polarimetric radar for estimation of GSV, a number of polarimetric parameters are investigated to characterise the polarisation response of forest cover. Regardless of the weather conditions, a high correlation (R=-0.87) is achieved between polarimetric coherence and GSV. The coherence in sparse forest is always higher than in dense forest. The coherence level and the dynamic range strongly depends on the weather conditions. The four-component polarimetric decomposition method has been applied to the ALOS PALSAR L-band data to compare the decomposition powers with forest growing stock volume (GSV). Double-bounce and volume scattering powers show significant correlation with GSV. The correlation between polarimetric decomposition parameters and GSV is enhanced if the ratio of ground-to-volume scattering is used instead of considering polarimetric decomposition powers separately. Two empirical models have been developed that describe the ALOS PALSAR L-band polarimetric coherence and ground-to-volume scattering ratio as a function of GSV. The models are inverted to retrieve the GSV for Siberian forests. The best RMSE of 38 m³/ha and R²=0.73 is obtained based on polarimetric coherence. On the other hand, using the ratio of ground-to-volume scattering the best retrieval accuracy of 44 m³/ha and R²=0.62 is achieved. The best retrieval results for both cases are observed under unfrozen condition. Saturation effects for estimated GSV versus ground-truth GSV are not observed up to 250 m³/ha

Estimating the above-ground biomass of mangrove forests in Kenya

Robust estimates of forest above-ground biomass (AGB) are needed in order to constrain the uncertainty in regional and global carbon budgets, predictions of global climate change and remote sensing efforts to monitor large scale changes in forest cover and biomass. Estimates of AGB and their associated uncertainty are also essential for international forest-based climate change mitigation strategies such as REDD+. Mangrove forests are widely recognised as globally important carbon stores. Continuing high rates of global mangrove deforestation represent a loss of future carbon sequestration potential and could result in significant release into the atmosphere of the carbon currently being stored within mangroves. The main aims of this thesis are 1) to provide information on the current AGB stocks of mangrove forests in Kenya at spatial scales relevant for climate change research, forest management and REDD+ and 2) to evaluate and constrain the uncertainty associated with these AGB estimates. This thesis adopted both a ground-based statistical approach and a remote sensing based approach to estimating mangrove AGB in Kenya. Allometric equations were developed for Kenyan mangroves using mixed-effects regression analysis and uncertainties were fully propagated (using a Monte Carlo based approach) to estimates of AGB at all spatial scales (tree, plot, region and landscape). In this study, species and site effects accounted for a large proportion (41%) of the total variability in mangrove AGB. The generic biomass equation produced for Kenyan mangroves has the potential for broad application as it can be used to estimate the AGB of new trees where there is no pre-existing knowledge of the specific species-site allometric relationship. The 95% prediction intervals for landscape scale estimates of total AGB suggest that between 5.4 and 7.2 megatonnes (Mt) of AGB is currently held in Kenyan mangrove forests. An in-depth evaluation of the relative contribution of various components of uncertainty (measurement, parameter and residual uncertainty) to the magnitude of the total uncertainty of AGB estimates was carried out. This evaluation was undertaken using both the mixed-effects regression model and a standard ordinary least squares (OLS) regression model. The exclusion of measurement uncertainty during the biomass estimation process had negligible impact on the magnitude of the uncertainty regardless of spatial scale or tree size. Excluding the uncertainty due to species and site effects (from the mixed-effects model) consistently resulted in a large reduction (~ 70%) in the overall uncertainty. Estimates of the uncertainty produced by the OLS model were unrealistically low which is illustrative of the general need to account for group effects in biomass regression models. L-band Synthetic Aperture Radar (SAR) was used to estimate the AGB of Kenyan mangroves. There was an observable relationship (R2 = 0.45) between L-band HH and AGB with HH backscatter found to decrease as a function of increasing AGB. There was no significant relationship found between L-band HV and AGB. The negative relationship between HH and AGB in this study can possibly be attributed to enhanced backscatter at lower AGB due to strong double-bounce and direct surface scattering from short stature/open forests and attenuation of the SAR signal at higher AGB. The SAR-derived estimate of total AGB for Kenyan mangroves was 5.32 Mt ± 18.6%. However, due to the unexpected nature of the HH-AGB relationship found in this study the SAR-derived estimates of mangrove AGB in this study should be considered with caution

Radar backscatter modelling of forests using a macroecological approach

This thesis provides a new explanation for the behaviour of radar backscatter of forests using vegetation structure models from the field of macroecology. The forests modelled in this work are produced using allometry-based ecological models with backscatter derived from the parameterisation of a radiative transfer model. This work is produced as a series of papers, each portraying the importance of macroecology in defining the forest radar response. Each contribution does so by incorporating structural and dynamic effects of forest growth using one of two allometric models to expose variations in backscatter as a response to vertical and horizontal forest profiles. The major findings of these studies concern the origin of backscatter saturation effects from forest SAR surveys. In each work the importance of transition from Rayleigh to Optical scattering, combined with the scaling effects of forest structure, is emphasised. These findings are administered through evidence including the transition’s emergence as the region of dominant backscatter in a vertical profile (according to a dominant canopy scattering layer), also through the existence of a two trend backscatter relationship with volume in the shape of the typical “saturation curve” (in the absence of additional attenuating factors). The importance of scattering regime change is also demonstrated through the relationships with volume, basal area and thinning. This work’s findings are reinforced by the examination of the relationships between forest height and volume, as collective values, providing evidence to suggest the non-uniqueness of volume-toheight relationships. Each of the studies refer to growing forest communities not single trees, so that unlike typical studies of radar remote sensing of forests the impact of the macroecological structural aspects are more explicit. This study emphasises the importance of the overall forest structure in producing SAR backscatter and how backscatter is not solely influenced by electrical properties of scatteres or the singular aspects of a tree but also by the collective forest parameters defining a dynamically changing forest