Structured Light-Based 3D Reconstruction System for Plants.

Camera-based 3D reconstruction of physical objects is one of the most popular computer vision trends in recent years. Many systems have been built to model different real-world subjects, but there is lack of a completely robust system for plants. This paper presents a full 3D reconstruction system that incorporates both hardware structures (including the proposed structured light system to enhance textures on object surfaces) and software algorithms (including the proposed 3D point cloud registration and plant feature measurement). This paper demonstrates the ability to produce 3D models of whole plants created from multiple pairs of stereo images taken at different viewing angles, without the need to destructively cut away any parts of a plant. The ability to accurately predict phenotyping features, such as the number of leaves, plant height, leaf size and internode distances, is also demonstrated. Experimental results show that, for plants having a range of leaf sizes and a distance between leaves appropriate for the hardware design, the algorithms successfully predict phenotyping features in the target crops, with a recall of 0.97 and a precision of 0.89 for leaf detection and less than a 13-mm error for plant size, leaf size and internode distance

The use of remotely sensed data for forest biomass monitoring : a case of forest sites in north-eastern Armenia

Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial TechnologiesIn recent years there has been an increasing interest in the use of synthetic aperture radar (SAR) data and geospatial technologies for environmental monitoring․ Particularly, forest biomass evaluation was of high importance, as forests have a crucial role in global carbon emission. Within this study we evaluate the use of Sentinel 1 C-band multitemporal SAR data with combination of Alos Palsar L-band SAR and Sentinel 2 multispectral remote sensing (RS) data for mapping forest aboveground biomass (AGB) of dry subtropical forests in mountainous areas. Field observation from National Forest Inventory was used as a ground truth data. As the SAR data suffers greatly by the complex topography, a simple approach of aspect and slope information as forestry ancillary data was implemented directly in the regression model for the first time to mitigate the topography effect on radar backscattering value․ Dense time-series analysis allowed us to overcome the SAR saturation by the forest phenology and select the optimal C-band scene. Image texture measures of SAR data has been strongly related to the biomass distribution and has robustly contributed to the prediction․ Multilinear Stepwise Regression allowed to select and evaluate the most relevant variables for AGB. The prediction model combining RS with ancillary data explained the 62 % of variance with root-mean-square error of 56.6 t ha¯¹. The study also reveals that C-band SAR data on forest biomass prediction is limited due to their short wavelength. Further, the mountainous condition is a major constraint for AGB estimation. Additionally, this research demonstrates a positive outcome in forest AGB prediction with freely accessible RS data

Complex land cover classifications and physical properties retrieval of tropical forests using multi-source remote sensing

The work presented in this thesis mainly focuses on two subjects related to the application of remote sensing data: (1) for land cover classification combining optical sensor, texture features generated from spectral information and synthetic aperture radar (SAR) features, and (2) to develop a non-destructive approach for above ground biomass (AGB) and forest attributes estimation employing multi-source remote sensing data (i.e. optical data, SAR backscatter) combined with in-situ data. Information provided by reliable land cover map is useful for management of forest resources to support sustainable forest management, whereas the generation of the non-destructive approach to model forest biophysical properties (e.g. AGB and stem volume) is required to assess the forest resources more efficiently and cost-effective, and coupled with remote sensing data the model can be applied over large forest areas. This work considers study sites over tropical rain forest landscape in Indonesia characterized by different successional stages and complex vegetation structure including tropical peatland forests. The thesis begins with a brief introduction and the state of the art explaining recent trends on monitoring and modeling of forest resources using remote sensing data and approach. The research works on the integration of spectral information and texture features for forest cover mapping is presented subsequently, followed by development of a non-destructive approach for AGB and forest parameters predictions and modeling. Ultimately, this work evaluates the potential of mosaic SAR data for AGB modeling and the fusion of optical and SAR data for peatlands discrimination. The results show that the inclusion of geostatistics texture features improved the classification accuracy of optical Landsat ETM data. Moreover, the fusion of SAR and optical data enhanced the peatlands discrimination over tropical peat swamp forest. For forest stand parameters modeling, neural networks method resulted in lower error estimate than standard multi-linear regression technique, and the combination of non-destructive measurement (i.e. stem number) and remote sensing data improved the model accuracy. The up scaling of stem volume and biomass estimates using Kriging method and bi-temporal ETM image also provide favorable estimate results upon comparison with the land cover map.Die in dieser Dissertation präsentierten Ergebnisse konzentrieren sich hauptsächlich auf zwei Themen mit Bezug zur angewandten Fernerkundung: 1) Der Klassifizierung von Oberflächenbedeckung basierend auf der Verknüpfung von optischen Sensoren, Textureigenschaften erzeugt durch Spektraldaten und Synthetic-Aperture-Radar (SAR) features und 2) die Entwicklung eines nichtdestruktiven Verfahrens zur Bestimmung oberirdischer Biomasse (AGB) und weiterer Waldeigenschaften mittels multi-source Fernerkundungsdaten (optische Daten, SAR Rückstreuung) sowie in-situ Daten. Eine zuverlässige Karte der Landbedeckung dient der Unterstützung von nachhaltigem Waldmanagement, während eine nichtdestruktive Herangehensweise zur Modellierung von biophysikalischen Waldeigenschaften (z.B. AGB und Stammvolumen) für eine effiziente und kostengünstige Beurteilung der Waldressourcen notwendig ist. Durch die Kopplung mit Fernerkundungsdaten kann das Modell auf große Waldflächen übertragen werden. Die vorliegende Arbeit berücksichtigt Untersuchungsgebiete im tropischen Regenwald Indonesiens, welche durch verschiedene Regenerations- und Sukzessionsstadien sowie komplexe Vegetationsstrukturen, inklusive tropischer Torfwälder, gekennzeichnet sind. Am Anfang der Arbeit werden in einer kurzen Einleitung der Stand der Forschung und die neuesten Forschungstrends in der Überwachung und Modellierung von Waldressourcen mithilfe von Fernerkundungsdaten dargestellt. Anschließend werden die Forschungsergebnisse der Kombination von Spektraleigenschaften und Textureigenschaften zur Waldbedeckungskartierung erläutert. Desweiteren folgen Ergebnisse zur Entwicklung eines nichtdestruktiven Ansatzes zur Vorhersage und Modellierung von AGB und Waldeigenschaften, zur Auswertung von Mosaik- SAR Daten für die Modellierung von AGB, sowie zur Fusion optischer mit SAR Daten für die Identifizierung von Torfwäldern. Die Ergebnisse zeigen, dass die Einbeziehung von geostatistischen Textureigenschaften die Genauigkeit der Klassifikation von optischen Landsat ETM Daten gesteigert hat. Desweiteren führte die Fusion von SAR und optischen Daten zu einer Verbesserung der Unterscheidung zwischen Torfwäldern und tropischen Sumpfwäldern. Bei der Modellierung der Waldparameter führte die Neural-Network-Methode zu niedrigeren Fehlerschätzungen als die multiple Regressions. Die Kombination von nichtdestruktiven Messungen (z.B. Stammzahl) und Fernerkundungsdaten führte zu einer Steigerung der Modellgenauigkeit. Die Hochskalierung des Stammvolumens und Schätzungen der Biomasse mithilfe von Kriging und bi-temporalen ETM Daten lieferten positive Schätzergebnisse im Vergleich zur Landbedeckungskarte

Optical remote sensing for biomass estimation in the tropics: the case study of Uganda

This study investigates the capabilities and limitations of freely available optical satellite data at medium resolution to estimate aboveground biomass density of vegetation at national scales in the tropics, and compares this approach with existing methodologies to understand and quantify the sources of variability in the estimations. Uganda was chosen as a case-study because it presents a reliable national biomass reference dataset. As a result of this thesis, aboveground woody biomass for the year circa-2000 was mapped at national scale in Uganda at 30-m spatial resolution on the basis of Landsat ETM+ images, a national land cover dataset and field data using an object-oriented approach. A regression tree-based model (Random Forest) produced good results (cross-validated R² 0.81, RMSE 13 Mg/ha) when trained with a sufficient number of field plots representative of the vegetation variability. This study demonstrated that in certain contexts Landsat data can effectively spatialize field biomass measurements and produce accurate and detailed estimates of biomass distribution at national scale. This approach tended to provide conservative biomass estimates and its limitations were mainly related to the saturation of the optical signal at high biomass density and to the cloud cover. When compared with the Uganda national biomass dataset, the map produced in this study presented higher agreement than other five regional/global biomass maps. The comparative analysis showed strong disagreement between the products, with estimates of total biomass of Uganda ranging from 343 to 2201 Tg and different spatial distribution patterns. Maps based on biome-average biomass values, such as the Intergovernmental Panel on Climate Change default values, and global land cover datasets strongly overestimated biomass stocks, while maps based on satellite data provided conservative estimates. The comparison of the maps predictions with field data confirmed the above findings

Assesment of biomass and carbon dynamics in pine forests of the Spanish central range: A remote sensing approach

Forests play a dynamic role in the terrestrial carbon (C) budget, by means of the biomass stock and C fluxes involved in photosynthesis and respiration. Remote sensing in combination with data analysis constitute a practical means for evaluation of forest implications in the carbon cycle, providing spatially explicit estimations of the amount, quality, and spatio-temporal dynamics of biomass and C stocks. Medium and high spatial resolution optical data from satellite-borne sensors were employed, supported by field measures, to investigate the carbon role of Mediterranean pines in the Central Range of Spain during a 25 year period (1984-2009). The location, extent, and distribution of pine forests were characterized, and spatial changes occurred in three sub-periods were evaluated. Capitalizing on temporal series of spectral data from Landsat sensors, novel techniques for processing and data analysis were developed to identify successional processes at the landscape level, and to characterize carbon stocking condition locally, enabling simultaneous characterization of trends and patterns of change. High spatial resolution data captured by the commercial satellite QuickBird-2 were employed to model structural attributes at the stand level, and to explore forest structural diversity

ASSESSMENT OF VOLUME AND ABOVE-GROUND BIOMASS IN ARAUCARIA FOREST THROUGH SATELLITE IMAGES, COMPARING DIFFERENT METHODS IN THE SOUTH OF CHILE

Abstract. Initial results of biomass estimation in the La Fusta area from existing equations found in literature are presented. As expected, accuracy of general equations suffer from the equation coefficients being obtained from fitting training data from different sites. It is also clear from the results that there is a high variance between different methods, in particular when complex data mixture is applied. Biomass is difficult to assess for dense forests, as pixels are saturated. This must be considered when planning field-data collection, with more samples in dense forest to provide more robust estimators from the training phase. The SAR-only (PALSAR) method from eq. 4 provided the most bias in results, overestimating with respect to the other methods

Temporal changes in mediterranean pine forest biomass using synergy models of ALOS PALSAR-Sentinel 1-Landsat 8 Sensors

Currently, climate change requires the quantification of carbon stored in forest biomass. Synthetic aperture radar (SAR) data offers a significant advantage over other remote detection measurement methods in providing structural and biomass-related information about ecosystems. This study aimed to develop non-parametric Random Forest regression models to assess the changes in the aboveground forest biomass (AGB), basal area (G), and tree density (N) of Mediterranean pine forests by integrating ALOS-PALSAR, Sentinel 1, and Landsat 8 data. Variables selected from the Random Forest models were related to NDVI and optical textural variables. For 2015, the biomass models with the highest performance integrated ALS-ALOS2-Sentinel 1-Landsat 8 data (R2 = 0.59) by following the model using ALS data (R2 = 0.56), and ALOS2-Sentinel 1-Landsat 8 (R2 = 0.50). The validation set showed that R2 values vary from 0.55 (ALOS2-Sentinel 1-Landsat 8) to 0.60 (ALS-ALOS2-Sentinel 1-Landsat 8 model) with RMSE below 20 Mg ha−1. It is noteworthy that the individual Sentinel 1 (R2 = 0.49). and Landsat 8 (R2 = 0.47) models yielded equivalent results. For 2020, the AGB model ALOS2-Sentinel 1-Landsat 8 had a performance of R2 = 0.55 (validation R2 = 0.70) and a RMSE of 9.93 Mg ha−1. For the 2015 forest structural variables, Random Forest models, including ALOS PAL-SAR 2-Sentinel 1 Landsat 8 explained between 30% and 55% of the total variance, and for the 2020 models, they explained between 25% and 55%. Maps of the forests’ structural variables were generated for 2015 and 2020 to assess the changes during this period using the ALOS PALSAR 2-Sentinel 1-Landsat 8 model. Aboveground biomass (AGB), diameter at breast height (dbh), and dominant height (Ho) maps were consistent throughout the entire study area. However, the Random Forest models underestimated higher biomass levels (>100 Mg ha−1) and overestimated moderate biomass levels (30–45 Mg ha−1). The AGB change map showed values ranging from gains of 43.3 Mg ha−1 to losses of −68.8 Mg ha−1 during the study period. The integration of open-access satellite optical and SAR data can significantly enhance AGB estimates to achieve consistent and long-term monitoring of forest carbon dynamics

Utilization of bistatic TanDEM-X data to derive land cover information

Forests have significance as carbon sink in climate change. Therefore, it is of high importance to track land use changes as well as to estimate the state as carbon sink. This is useful for sustainable forest management, land use planning, carbon modelling, and support to implement international initiatives like REDD+ (Reducing Emissions from Deforestation and Degradation). A combination of field measurements and remote sensing seems most suitable to monitor forests. Radar sensors are considered as high potential due to the weather and daytime independence. TanDEM-X is a interferometric SAR (synthetic aperture radar) mission in space and can be used for land use monitoring as well as estimation of biophysical parameters. TanDEM-X is a X-band system resulting in low penetration depth into the forest canopy. Interferometric information can be useful, whereas the low penetration can be considered as an advantage. The interferometric height is assumable as canopy height, which is correlated with forest biomass. Furthermore, the interferometric coherence is mainly governed by volume decorrelation, whereas temporal decorrelation is minimized. This information can be valuable for quantitative estimations and land use monitoring. The interferometric coherence improved results in comparison to land use classifications without coherence of about 10% (75% vs. 85%). Especially the differentiation between forest classes profited from coherence. The coherence correlated with aboveground biomass in a R² of about 0.5 and resulted in a root mean square error (RSME) of 14%. The interferometric height achieved an even higher correlation with the biomass (R²=0.68) resulting in cross-validated RMSE of 7.5%. These results indicated that TanDEM-X can be considered as valuable and consistent data source for forest monitoring. Especially interferometric information seemed suitable for biomass estimation