Comparing synthetic aperture radar and LiDAR for above-ground biomass estimation in Glen Affric, Scotland

Quantifying above-ground biomass (AGB) and carbon sequestration has been a significant focus of attention within the UNFCCC and Kyoto Protocol for improvement of national carbon accounting systems (IPCC, 2007; UNFCCC, 2011). A multitude of research has been carried out in relatively flat and homogeneous forests (Ranson & Sun, 1994; Beaudoin et al.,1994; Kurvonen et al., 1999; Austin et al., 2003; Dimitris et al., 2005), yet forests in the highlands, which generally form heterogeneous forest cover and sparse woodlands with mountainous terrain have been largely neglected in AGB studies (Cloude et al., 2001; 2008; Lumsdon et al., 2005; 2008; Erxue et al., 2009, Tan et al., 2010; 2011a; 2011b; 2011c; 2011d). Since mountain forests constitute approximately 28% of the total global forest area (Price and Butt, 2000), a better understanding of the slope effects is of primary importance in AGB estimation. The main objective of this research is to estimate AGB in the aforementioned forest in Glen Affric, Scotland using both SAR and LiDAR data. Two types of Synthetic Aperture Radar (SAR) data were used in this research: TerraSAR-X, operating at X-band and ALOS PALSAR, operating at L-band, both are fully polarimetric. The former data was acquired on 13 April 2010 and of the latter, two scenes were acquired on 17 April 2007 and 08 June 2009. Airborne LiDAR data were acquired on 09 June 2007. Two field measurement campaigns were carried out, one of which was done from winter 2006 to spring 2007 where physical parameters of trees in 170 circular plots were measured by the Forestry Commission team. Another intensive fieldwork was organised by myself with the help of my fellow colleagues and it comprised of tree measurement in two transects of 200m x 50m at a relatively flat and dense plantation forest and 400m x 50m at hilly and sparse semi-natural forest. AGB is estimated for both the transects to investigate the effectiveness of the proposed method at plot-level. This thesis evaluates the capability of polarimetric Synthetic Aperture Radar data for AGB estimation by investigating the relationship between the SAR backscattering coefficient and AGB and also the relationship between the decomposed scattering mechanisms and AGB. Due to the terrain and heterogeneous nature of the forests, the result from the backscatter-AGB analysis show that these forests present a challenge for simple AGB estimation. As an alternative, polarimetric techniques were applied to the problem by decomposing the backscattering information into scattering mechanisms based on the approach by Yamaguchi (2005; 2006), which are then regressed to the field measured AGB. Of the two data sets, ALOS PALSAR demonstrates a better estimation capacity for AGB estimation than TerraSAR-X. The AGB estimated results from SAR data are compared with AGB derived from LiDAR data. Since tree height is often correlated with AGB (Onge et al., 2008; Gang et al., 2010), the effectiveness of the tree height retrieval from LiDAR is evaluated as an indicator of AGB. Tree delineation was performed before AGB of individual trees were calculated allometrically. Results were validated by comparison to the fieldwork data. The amount of overestimation varies across the different canopy conditions. These results give some indication of when to use LiDAR or SAR to retrieve forest AGB. LiDAR is able to estimate AGB with good accuracy and the R2 value obtained is 0.97 with RMSE of 14.81 ton/ha. The R2 and RMSE obtained for TerraSAR-X are 0.41 and 28.5 ton/ha, respectively while for ALOS PALSAR data are 0.70 and 23.6 ton/ha, respectively. While airborne LiDAR data with very accurate height measurement and consequent three-dimensional (3D) stand profiles which allows investigation into the relationship between height, number density and AGB, it's limited to small coverage area, or large areas but at large cost. ALOS PALSAR, on the other hand, can cover big coverage area but it provide a lower resolution, hence, lower estimation accuracy

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

Integration of Multi-Sensor Data to Estimate Plot-Level Stem Volume Using Machine Learning Algorithms–Case Study of Evergreen Conifer Planted Forests in Japan

The development of new methods for estimating precise forest structure parameters is essential for the quantitative evaluation of forest resources. Conventional use of satellite image data, increasing use of terrestrial laser scanning (TLS), and emerging trends in the use of unmanned aerial systems (UASs) highlight the importance of modern technologies in the realm of forest observation. Each technology has different advantages, and this work seeks to incorporate multiple satellite, TLS- and UAS-based remote sensing data sets to improve the ability to estimate forest structure parameters. In this paper, two regression analysis approaches are considered for the estimation: random forest regression (RFR) and support vector regression (SVR). To collect the dependent variable, in situ measurements of individual tree parameters (tree height and diameter at breast height (DBH)) were taken in a Japanese cypress forest using the nondestructive TLS method, which scans the forest to obtain dense and accurate point clouds under the tree canopy. Based on the TLS data, the stem volume was then computed and treated as ground truth information. Topographic and UAS information was then used to calculate various remotely sensed explanatory variables, such as canopy size, canopy cover, and tree height. Canopy cover and canopy shapes were computed via the orthoimages derived from the UAS and watershed segmentation method, respectively. Tree height was computed by combining the digital surface model (DSM) from the UAS and the digital terrain model (DTM) from the TLS data. Topographic variables were computed from the DTM. The backscattering intensity in the satellite imagery was obtained based on L-band (Advanced Land Observing Satellite-2 (ALOS-2) Phased Array type L-band Synthetic Aperture Radar-2 (PALSAR-2)) and C-band (Sentinel-1) synthetic aperture radar (SAR). All satellite (10–25 m resolution), TLS (3.4 mm resolution) and UAS (2.3–4.6 cm resolution) data were then combined, and RFR and SVR were trained; the resulting predictive powers were then compared. The RFR method yielded fitting R2 up to 0.665 and RMSE up to 66.87 m3/ha (rRMSE = 11.95%) depending on the input variables (best result with canopy height, canopy size, canopy cover, and Sentinel-1 data), and the SVR method showed fitting R2 up to 0.519 and RMSE up to 80.12 m3/ha (rRMSE = 12.67%). The RFR outperformed the SVR method, which could delineate the relationship between the variables for better model accuracy. This work has demonstrated that incorporating various remote sensing data to satellite data, especially adding finer resolution data, can provide good estimates of forest parameters at a plot level (10 by 10 m), potentially allowing advancements in precision forestry

Advanced techniques for classification of polarimetric synthetic aperture radar data

With various remote sensing technologies to aid Earth Observation, radar-based imaging is one of them gaining major interests due to advances in its imaging techniques in form of syn-thetic aperture radar (SAR) and polarimetry. The majority of radar applications focus on mon-itoring, detecting, and classifying local or global areas of interests to support humans within their efforts of decision-making, analysis, and interpretation of Earth’s environment. This thesis focuses on improving the classification performance and process particularly concerning the application of land use and land cover over polarimetric SAR (PolSAR) data. To achieve this, three contributions are studied related to superior feature description and ad-vanced machine-learning techniques including classifiers, principles, and data exploitation. First, this thesis investigates the application of color features within PolSAR image classi-fication to provide additional discrimination on top of the conventional scattering information and texture features. The color features are extracted over the visual presentation of fully and partially polarimetric SAR data by generation of pseudo color images. Within the experiments, the obtained results demonstrated that with the addition of the considered color features, the achieved classification performances outperformed results with common PolSAR features alone as well as achieved higher classification accuracies compared to the traditional combination of PolSAR and texture features. Second, to address the large-scale learning challenge in PolSAR image classification with the utmost efficiency, this thesis introduces the application of an adaptive and data-driven supervised classification topology called Collective Network of Binary Classifiers, CNBC. This topology incorporates active learning to support human users with the analysis and interpretation of PolSAR data focusing on collections of images, where changes or updates to the existing classifier might be required frequently due to surface, terrain, and object changes as well as certain variations in capturing time and position. Evaluations demonstrated the capabilities of CNBC over an extensive set of experimental results regarding the adaptation and data-driven classification of single as well as collections of PolSAR images. The experimental results verified that the evolutionary classification topology, CNBC, did provide an efficient solution for the problems of scalability and dynamic adaptability allowing both feature space dimensions and the number of terrain classes in PolSAR image collections to vary dynamically. Third, most PolSAR classification problems are undertaken by supervised machine learn-ing, which require manually labeled ground truth data available. To reduce the manual labeling efforts, supervised and unsupervised learning approaches are combined into semi-supervised learning to utilize the huge amount of unlabeled data. The application of semi-supervised learning in this thesis is motivated by ill-posed classification tasks related to the small training size problem. Therefore, this thesis investigates how much ground truth is actually necessary for certain classification problems to achieve satisfactory results in a supervised and semi-supervised learning scenario. To address this, two semi-supervised approaches are proposed by unsupervised extension of the training data and ensemble-based self-training. The evaluations showed that significant speed-ups and improvements in classification performance are achieved. In particular, for a remote sensing application such as PolSAR image classification, it is advantageous to exploit the location-based information from the labeled training data. Each of the developed techniques provides its stand-alone contribution from different viewpoints to improve land use and land cover classification. The introduction of a new fea-ture for better discrimination is independent of the underlying classification algorithms used. The application of the CNBC topology is applicable to various classification problems no matter how the underlying data have been acquired, for example in case of remote sensing data. Moreover, the semi-supervised learning approach tackles the challenge of utilizing the unlabeled data. By combining these techniques for superior feature description and advanced machine-learning techniques exploiting classifier topologies and data, further contributions to polarimetric SAR image classification are made. According to the performance evaluations conducted including visual and numerical assessments, the proposed and investigated tech-niques showed valuable improvements and are able to aid the analysis and interpretation of PolSAR image data. Due to the generic nature of the developed techniques, their applications to other remote sensing data will require only minor adjustments

Dipterocarps protected by Jering local wisdom in Jering Menduyung Nature Recreational Park, Bangka Island, Indonesia

Apart of the oil palm plantation expansion, the Jering Menduyung Nature Recreational Park has relatively diverse plants. The 3,538 ha park is located at the north west of Bangka Island, Indonesia. The minimum species-area curve was 0.82 ha which is just below Dalil conservation forest that is 1.2 ha, but it is much higher than measurements of several secondary forests in the Island that are 0.2 ha. The plot is inhabited by more than 50 plant species. Of 22 tree species, there are 40 individual poles with the average diameter of 15.3 cm, and 64 individual trees with the average diameter of 48.9 cm. The density of Dipterocarpus grandiflorus (Blanco) Blanco or kruing, is 20.7 individual/ha with the diameter ranges of 12.1 – 212.7 cm or with the average diameter of 69.0 cm. The relatively intact park is supported by the local wisdom of Jering tribe, one of indigenous tribes in the island. People has regulated in cutting trees especially in the cape. The conservation agency designates the park as one of the kruing propagules sources in the province. The growing oil palm plantation and the less adoption of local wisdom among the youth is a challenge to forest conservation in the province where tin mining activities have been the economic driver for decades. More socialization from the conservation agency and the involvement of university students in raising environmental awareness is important to be done