Integrating Remote Sensing and Machine Learning to Assess Forest Health and Susceptibility to Pest-induced Damage

Spruce budworm (Choristoneura fumiferana; SBW) outbreaks are cyclically occurring phenomena in the northeastern USA and neighboring Canadian provinces. These outbreaks are often of landscape level causing impaired growth and mortality of the host species namely spruce (Picea sp.) and balsam fir (Abies balsamea (L.) Mill.). Acknowledging the recent SBW outbreak in Canadian provinces like Quebec and New Brunswick neighboring the state of Maine, our study devised comprehensive techniques to assess the susceptibility of Maine forests to SBW attack. This study aims to harness the power of remote sensing data and machine learning algorithms to model and map the susceptibility of forest in terms of host species availability and abundance (basal area per hectare; BAPH, and leaf area index; LAI), their maturity and the defense mechanism prevalent. In terms of host species abundance mapping our study explores the integration of satellite remote sensing data to model BAPH and LAI of two economically vital SBW host species, red spruce (Picea rubens Sarg.) and balsam fir, in Maine USA. Combining Sentinel-1 synthetic aperture radar (SAR), Sentinel-2 multispectral, and site variables, we used Random Forest (RF) and Multi-Layer Perceptron (MLP) algorithms for modeling LAI and BAPH. The results demonstrated the superiority of RF over MLP, achieving smaller normalized root mean square error (nRMSE) by 0.01 and 0.06 for LAI and BAPH, respectively. Notably, Sentinel-2 variables, especially the red-edge spectral vegetation indices, played a significant role in both LAI and BAPH estimation, with the minor inclusion of site variables, particularly elevation. In addition, using various satellite remote sensing data such as Sentinel-1 C-band SAR, PALSAR L-band SAR and Sentinel-2 multispectral, along with site variables, the study developed large-scale SBW stand impact types and susceptibility maps for the entire state of Maine. The susceptibility of the forest was assessed based on the availability of SBW host species and their maturity. Integrating machine-learning algorithms, RF and MLP, the best model, utilizing site (elevation and aspect) and Sentinel-2 data achieved an overall accuracy of 83.4% to predict SBW host species. Furthermore, combining the host species data with age data from Land Change Monitoring, Assessment, and Projection (LCMAP) products we could produce the SBW susceptibility map based on stand impact types with an overall accuracy of 88.3%. Moreover, the work builds upon the assessment of susceptibility of SBW host species taking into account the concentration of several canopy traits using remote sensing and site data. The study focused on various foliar traits affecting insect herbivory, including nutritive such as nitrogen (N), phosphorous (P), potassium (K), and copper (Cu), non-nutritive such as iron (Fe) and calcium (Ca), and defensive parameters such as equivalent water thickness (EWT) and leaf mass per area (LMA). Using Sentinel-2 and site data, we developed trait estimation models using machine-learning algorithms like Random Forest (RF), Extreme Gradient Boosting (XGB), and Support Vector Machine (SVM). The accuracy of the developed model was evaluated based on the normalized root mean square error (nRMSE). Based on the model performances, we selected XGB algorithm to estimate Ca, EWT, Fe, and K whereas Cu, LMA, N, and P were estimated using RF algorithm. Regarding the variables used, almost all the best performing models included Sentinel-2 red-edge indices and depth to water table (DWT) as the most important variables. Ultimately, the study proposed a novel framework connecting the concentrations of foliar traits in SBW host foliage to tree susceptibility to the pest, enabling the assessment of host susceptibility on a landscape level. To sum up, this study highlights the advantages and effectiveness of integrating satellite remote sensing data for enhanced pest management, providing valuable insights into tree attributes and susceptibility to spruce budworm outbreaks in Northeast USA. The findings offer essential tools for forest stakeholders to improve management strategies and mitigate potential forthcoming SBW outbreaks in the region

Classification of Tropical Vegetation Using Multifrequency Partial SAR Polarimetry

International audienceThis letter presents a case study addressing the comparison between different synthetic aperture radar (SAR) partial polarimetric options for tropical-vegetation cartography. These options include compact polarization (CP), dual polarization (DP), and alternating polarization (AP). They are all derived from fully polarimetric (FP) SAR data acquired by the airborne SAR (AIRSAR) sensor over the French Polynesian Tubuai Island. The classification approach is based on the support vector machine algorithm and is further validated by several ground surveys. For a single frequency band, FP data give significantly better results than any other partial polarimetric configuration. Among the partial polarimetric architectures, the CP mode performs best. In addition, the DP mode shows better performance than the AP mode, highlighting the value of the polarimetric differential phase. The combination of different frequency bands (P-, L-, and C-bands) holds the most significant improvement: The multifrequency diversity adds generally more information than the multipolarization diversity. A noticeable result is the major contribution of the C-band at VV polarization (the only polarization available at C-band with the AIRSAR data set used in this letter) to the classification performance, due to its ability to discriminate between Pinus and Falcata

The integration of freely available medium resolution optical sensors with Synthetic Aperture Radar (SAR) imagery capabilities for American bramble (Rubus cuneifolius) invasion detection and mapping.

Doctoral Degree. University of KwaZulu- Natal, Pietermaritzburg.The emergence of American bramble (Rubus cuneifolius) across South Africa has caused severe ecological and economic damage. To date, most of the efforts to mitigate its effects have been largely unsuccessful due to its prolific growth and widespread distribution. Accurate and timeous detection and mapping of Bramble is therefore critical to the development of effective eradication management plans. Hence, this study sought to determine the potential of freely available, new generation medium spatial resolution satellite imagery for the detection and mapping of American Bramble infestations within the UNESCO world heritage site of the uKhahlamba Drakensberg Park (UDP). The first part of the thesis determined the potential of conventional freely available remote sensing imagery for the detection and mapping of Bramble. Utilizing the Support Vector Machine (SVM) learning algorithm, it was established that Bramble could be detected with limited users (45%) and reasonable producers (80%) accuracies. Much of the confusion occurred between the grassland land cover class and Bramble. The second part of the study focused on fusing the new age optical imagery and Synthetic Aperture Radar (SAR) imagery for Bramble detection and mapping. The synergistic potential of fused imagery was evaluated using multiclass SVM classification algorithm. Feature level image fusion of optical imagery and SAR resulted in an overall classification accuracy of 76%, with increased users and producers’ accuracies for Bramble. These positive results offered an opportunity to explore the polarization variables associated with SAR imagery for improved classification accuracies. The final section of the study dwelt on the use of Vegetation Indices (VIs) derived from new age satellite imagery, in concert with SAR to improve Bramble classification accuracies. Whereas improvement in classification accuracies were minimal, the potential of stand-alone VIs to detect and map Bramble (80%) was noteworthy. Lastly, dual-polarized SAR was fused with new age optical imagery to determine the synergistic potential of dual-polarized SAR to increase Bramble mapping accuracies. Results indicated a marked increase in overall Bramble classification accuracy (85%), suggesting improved potential of dual-polarized SAR and optical imagery in invasive species detection and mapping. Overall, this study provides sufficient evidence of the complimentary and synergistic potential of active and passive remote sensing imagery for invasive alien species detection and mapping. Results of this study are important for supporting contemporary decision making relating to invasive species management and eradication in order to safeguard ecological biodiversity and pristine status of nationally protected areas

Classification of Compact Polarimetric Synthetic Aperture Radar Images

The RADARSAT Constellation Mission (RCM) was launched in June 2019. RCM, in addition to dual-polarization (DP) and fully quad-polarimetric (QP) imaging modes, provides compact polarimetric (CP) mode data. A CP synthetic aperture radar (SAR) is a coherent DP system in which a single circular polarization is transmitted followed by the reception in two orthogonal linear polarizations. A CP SAR fully characterizes the backscattered field using the Stokes parameters, or equivalently, the complex coherence matrix. This is the main advantage of a CP SAR over the traditional (non-coherent) DP SAR. Therefore, designing scene segmentation and classification methods using CP complex coherence matrix data is advocated in this thesis. Scene classification of remotely captured images is an important task in monitoring the Earth's surface. The high-resolution RCM CP SAR data can be used for land cover classification as well as sea-ice mapping. Mapping sea ice formed in ocean bodies is important for ship navigation and climate change modeling. The Canadian Ice Service (CIS) has expert ice analysts who manually generate sea-ice maps of Arctic areas on a daily basis. An automated sea-ice mapping process that can provide detailed yet reliable maps of ice types and water is desirable for CIS. In addition to linear DP SAR data in ScanSAR mode (500km), RCM wide-swath CP data (350km) can also be used in operational sea-ice mapping of the vast expanses in the Arctic areas. The smaller swath coverage of QP SAR data (50km) is the reason why the use of QP SAR data is limited for sea-ice mapping. This thesis involves the design and development of CP classification methods that consist of two steps: an unsupervised segmentation of CP data to identify homogeneous regions (superpixels) and a labeling step where a ground truth label is assigned to each super-pixel. An unsupervised segmentation algorithm is developed based on the existing Iterative Region Growing using Semantics (IRGS) for CP data and is called CP-IRGS. The constituents of feature model and spatial context model energy terms in CP-IRGS are developed based on the statistical properties of CP complex coherence matrix data. The superpixels generated by CP-IRGS are then used in a graph-based labeling method that incorporates the global spatial correlation among super-pixels in CP data. The classifications of sea-ice and land cover types using test scenes indicate that (a) CP scenes provide improved sea-ice classification than the linear DP scenes, (b) CP-IRGS performs more accurate segmentation than that using only CP channel intensity images, and (c) using global spatial information (provided by a graph-based labeling approach) provides an improvement in classification accuracy values over methods that do not exploit global spatial correlation

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

Advanced machine learning algorithms for Canadian wetland mapping using polarimetric synthetic aperture radar (PolSAR) and optical imagery

Wetlands are complex land cover ecosystems that represent a wide range of biophysical conditions. They are one of the most productive ecosystems and provide several important environmental functionalities. As such, wetland mapping and monitoring using cost- and time-efficient approaches are of great interest for sustainable management and resource assessment. In this regard, satellite remote sensing data are greatly beneficial, as they capture a synoptic and multi-temporal view of landscapes. The ability to extract useful information from satellite imagery greatly affects the accuracy and reliability of the final products. This is of particular concern for mapping complex land cover ecosystems, such as wetlands, where complex, heterogeneous, and fragmented landscape results in similar backscatter/spectral signatures of land cover classes in satellite images. Accordingly, the overarching purpose of this thesis is to contribute to existing methodologies of wetland classification by proposing and developing several new techniques based on advanced remote sensing tools and optical and Synthetic Aperture Radar (SAR) imagery. Specifically, the importance of employing an efficient speckle reduction method for polarimetric SAR (PolSAR) image processing is discussed and a new speckle reduction technique is proposed. Two novel techniques are also introduced for improving the accuracy of wetland classification. In particular, a new hierarchical classification algorithm using multi-frequency SAR data is proposed that discriminates wetland classes in three steps depending on their complexity and similarity. The experimental results reveal that the proposed method is advantageous for mapping complex land cover ecosystems compared to single stream classification approaches, which have been extensively used in the literature. Furthermore, a new feature weighting approach is proposed based on the statistical and physical characteristics of PolSAR data to improve the discrimination capability of input features prior to incorporating them into the classification scheme. This study also demonstrates the transferability of existing classification algorithms, which have been developed based on RADARSAT-2 imagery, to compact polarimetry SAR data that will be collected by the upcoming RADARSAT Constellation Mission (RCM). The capability of several well-known deep Convolutional Neural Network (CNN) architectures currently employed in computer vision is first introduced in this thesis for classification of wetland complexes using multispectral remote sensing data. Finally, this research results in the first provincial-scale wetland inventory maps of Newfoundland and Labrador using the Google Earth Engine (GEE) cloud computing resources and open access Earth Observation (EO) collected by the Copernicus Sentinel missions. Overall, the methodologies proposed in this thesis address fundamental limitations/challenges of wetland mapping using remote sensing data, which have been ignored in the literature. These challenges include the backscattering/spectrally similar signature of wetland classes, insufficient classification accuracy of wetland classes, and limitations of wetland mapping on large scales. In addition to the capabilities of the proposed methods for mapping wetland complexes, the use of these developed techniques for classifying other complex land cover types beyond wetlands, such as sea ice and crop ecosystems, offers a potential avenue for further research