Multispectral terrestrial lidar : State of the Art and Challenges

The development of multispectral terrestrial laser scan-ning (TLS) is still at the very beginning, with only four instruments worldwide providing simultaneous three-dimensional (3D) point cloud and spectral measurement. Research on multiwavelength laser returns has been carried out by more groups, but there are still only about ten research instruments published and no commercial availability. This chapter summarizes the experiences from all these studies to provide an overview of the state of the art and future developments needed to bring the multispectral TLS technology into the next level. Alt-hough the current number of applications is sparse, they already show that multispectral lidar technology has po-tential to disrupt many fields of science and industry due to its robustness and the level of detail available

Recovery of forest canopy parameters by inversion of multispectral LiDAR data

We describe the use of Bayesian inference techniques, notably Markov chain Monte Carlo (MCMC) and reversible jump MCMC (RJMCMC) methods, to recover forest structural and biochemical parameters from multispectral LiDAR (Light Detection and Ranging) data. We use a variable dimension, multi-layered model to represent a forest canopy or tree, and discuss the recovery of structure and depth profiles that relate to photochemical properties. We first demonstrate how simple vegetation indices such as the Normalized Differential Vegetation Index (NDVI), which relates to canopy biomass and light absorption, and Photochemical Reflectance Index (PRI) which is a measure of vegetation light use efficiency, can be measured from multispectral data. We further describe and demonstrate our layered approach on single wavelength real data, and on simulated multispectral data derived from real, rather than simulated, data sets. This evaluation shows successful recovery of a subset of parameters, as the complete recovery problem is ill-posed with the available data. We conclude that the approach has promise, and suggest future developments to address the current difficulties in parameter inversion

Quantifying Dynamics in Tropical Peat Swamp Forest Biomass with Multi- Temporal LiDAR Datasets

Tropical peat swamp forests in Indonesia store huge amounts of carbon and are responsible for enormous carbon emissions every year due to forest degradation and deforestation. These forest areas are in the focus of REDD+ (reducing emissions from deforestation, forest degradation, and the role of conservation, sustainable management of forests and enhancement of forest carbon stocks) projects, which require an accurate monitoring of their carbon stocks or aboveground biomass (AGB). Our study objective was to evaluate multi-temporal LiDAR measurements of a tropical forested peatland area in Central Kalimantan on Borneo. Canopy height and AGB dynamics were quantified with a special focus on unaffected, selective logged and burned forests. More than 11,000 ha were surveyed with airborne LiDAR in 2007 and 2011. In a first step, the comparability of these datasets was examined and canopy height models were created. Novel AGB regression models were developed on the basis of field inventory measurements and LiDAR derived height histograms for 2007 (r(2) = 0.77, n = 79) and 2011 (r(2) = 0.81, n = 53), taking the different point densities into account. Changes in peat swamp forests were identified by analyzing multispectral imagery. Unaffected forests accumulated on average 20 t/ha AGB with a canopy height increase of 2.3 m over the four year time period. Selective logged forests experienced an average AGB loss of 55 t/ha within 30 m and 42 t/ha within 50 m of detected logging trails, although the mean canopy height increased by 0.5 m and 1.0 m, respectively. Burned forests lost 92% of the initial biomass. These results demonstrate the great potential of repetitive airborne LiDAR surveys to precisely quantify even small scale AGB and canopy height dynamics in remote tropical forests, thereby featuring the needs of REDD+

White paper – On the use of LiDAR data at AmeriFlux sites

Our aim is to inform the AmeriFlux community on existing and upcoming LiDAR technologies (atmospheric Doppler or Raman LiDAR often deployed at flux sites are not considered here), how it is currently used at flux sites, and how we believe it could, in the future, further contribute to the AmeriFlux vision. Heterogeneity in vegetation and ground properties at various spatial scales is omnipresent at flux sites, and 3D mapping of canopy, understory, and ground surface can help move the science forward

Assessing the role of EO in biodiversity monitoring: options for integrating in-situ observations with EO within the context of the EBONE concept

The European Biodiversity Observation Network (EBONE) is a European contribution on terrestrial monitoring to GEO BON, the Group on Earth Observations Biodiversity Observation Network. EBONE’s aims are to develop a system of biodiversity observation at regional, national and European levels by assessing existing approaches in terms of their validity and applicability starting in Europe, then expanding to regions in Africa. The objective of EBONE is to deliver: 1. A sound scientific basis for the production of statistical estimates of stock and change of key indicators; 2. The development of a system for estimating past changes and forecasting and testing policy options and management strategies for threatened ecosystems and species; 3. A proposal for a cost-effective biodiversity monitoring system. There is a consensus that Earth Observation (EO) has a role to play in monitoring biodiversity. With its capacity to observe detailed spatial patterns and variability across large areas at regular intervals, our instinct suggests that EO could deliver the type of spatial and temporal coverage that is beyond reach with in-situ efforts. Furthermore, when considering the emerging networks of in-situ observations, the prospect of enhancing the quality of the information whilst reducing cost through integration is compelling. This report gives a realistic assessment of the role of EO in biodiversity monitoring and the options for integrating in-situ observations with EO within the context of the EBONE concept (cfr. EBONE-ID1.4). The assessment is mainly based on a set of targeted pilot studies. Building on this assessment, the report then presents a series of recommendations on the best options for using EO in an effective, consistent and sustainable biodiversity monitoring scheme. The issues that we faced were many: 1. Integration can be interpreted in different ways. One possible interpretation is: the combined use of independent data sets to deliver a different but improved data set; another is: the use of one data set to complement another dataset. 2. The targeted improvement will vary with stakeholder group: some will seek for more efficiency, others for more reliable estimates (accuracy and/or precision); others for more detail in space and/or time or more of everything. 3. Integration requires a link between the datasets (EO and in-situ). The strength of the link between reflected electromagnetic radiation and the habitats and their biodiversity observed in-situ is function of many variables, for example: the spatial scale of the observations; timing of the observations; the adopted nomenclature for classification; the complexity of the landscape in terms of composition, spatial structure and the physical environment; the habitat and land cover types under consideration. 4. The type of the EO data available varies (function of e.g. budget, size and location of region, cloudiness, national and/or international investment in airborne campaigns or space technology) which determines its capability to deliver the required output. EO and in-situ could be combined in different ways, depending on the type of integration we wanted to achieve and the targeted improvement. We aimed for an improvement in accuracy (i.e. the reduction in error of our indicator estimate calculated for an environmental zone). Furthermore, EO would also provide the spatial patterns for correlated in-situ data. EBONE in its initial development, focused on three main indicators covering: (i) the extent and change of habitats of European interest in the context of a general habitat assessment; (ii) abundance and distribution of selected species (birds, butterflies and plants); and (iii) fragmentation of natural and semi-natural areas. For habitat extent, we decided that it did not matter how in-situ was integrated with EO as long as we could demonstrate that acceptable accuracies could be achieved and the precision could consistently be improved. The nomenclature used to map habitats in-situ was the General Habitat Classification. We considered the following options where the EO and in-situ play different roles: using in-situ samples to re-calibrate a habitat map independently derived from EO; improving the accuracy of in-situ sampled habitat statistics, by post-stratification with correlated EO data; and using in-situ samples to train the classification of EO data into habitat types where the EO data delivers full coverage or a larger number of samples. For some of the above cases we also considered the impact that the sampling strategy employed to deliver the samples would have on the accuracy and precision achieved. Restricted access to European wide species data prevented work on the indicator ‘abundance and distribution of species’. With respect to the indicator ‘fragmentation’, we investigated ways of delivering EO derived measures of habitat patterns that are meaningful to sampled in-situ observations

QUANTIFYING GRASSLAND NON-PHOTOSYNTHETIC VEGETATION BIOMASS USING REMOTE SENSING DATA

Non-photosynthetic vegetation (NPV) refers to vegetation that cannot perform a photosynthetic function. NPV, including standing dead vegetation and surface plant litter, plays a vital role in maintaining ecosystem function through controlling carbon, water and nutrient uptake as well as natural fire frequency and intensity in diverse ecosystems such as forest, savannah, wetland, cropland, and grassland. Due to its ecological importance, NPV has been selected as an indicator of grassland ecosystem health by the Alberta Public Lands Administration in Canada. The ecological importance of NPV has driven considerable research on quantifying NPV biomass with remote sensing approaches in various ecosystems. Although remote images, especially hyperspectral images, have demonstrated potential for use in NPV estimation, there has not been a way to quantify NPV biomass in semiarid grasslands where NPV biomass is affected by green vegetation (PV), bare soil and biological soil crust (BSC). The purpose of this research is to find a solution to quantitatively estimate NPV biomass with remote sensing approaches in semiarid mixed grasslands. Research was conducted in Grasslands National Park (GNP), a parcel of semiarid mixed prairie grassland in southern Saskatchewan, Canada. Multispectral images, including newly operational Landsat 8 Operational Land Imager (OLI) and Sentinel-2A Multi-spectral Instrument (MSIs) images and fine Quad-pol Radarsat-2 images were used for estimating NPV biomass in early, middle, and peak growing seasons via a simple linear regression approach. The results indicate that multispectral Landsat 8 OLI and Sentinel-2A MSIs have potential to quantify NPV biomass in peak and early senescence growing seasons. Radarsat-2 can also provide a solution for NPV biomass estimation. However, the performance of Radarsat-2 images is greatly affected by incidence angle of the image acquisition. This research filled a critical gap in applying remote sensing approaches to quantify NPV biomass in grassland ecosystems. NPV biomass estimates and approaches for estimating NPV biomass will contribute to grassland ecosystem health assessment (EHA) and natural resource (i.e. land, soil, water, plant, and animal) management

Big Earth Data for Cultural Heritage in the Copernicus Era

Digital data is stepping in its golden age characterized by an increasing growth of both classical and emerging big earth data along with trans- and multidisciplinary methodological approaches and services addressed to the study, preservation and sustainable exploitation of cultural heritage (CH). The availability of new digital technologies has opened new possibilities, unthinkable only a few years ago for cultural heritage. The currently available digital data, tools and services with particular reference to Copernicus initiatives make possible to characterize and understand the state of conservation of CH for preventive restoration and opened up a frontier of possibilities for the discovery of archaeological sites from above and also for supporting their excavation, monitoring and preservation. The different areas of intervention require the availability and integration of rigorous information from different sources for improving knowledge and interpretation, risk assessment and management in order to make more successful all the actions oriented to the preservation of cultural properties. One of the biggest challenges is to fully involve the citizen also from an emotional point of view connecting “pixels with people” and “bridging” remote sensing and social sensing

Remote sensing for the Spanish forests in the 21st century: a review of advances, needs, and opportunities

[EN] Forest ecosystems provide a host of services and societal benefits, including carbon storage, habitat for fauna, recreation, and provision of wood or non-wood products. In a context of complex demands on forest resources, identifying priorities for biodiversity and carbon budgets require accurate tools with sufficient temporal frequency. Moreover, understanding long term forest dynamics is necessary for sustainable planning and management. Remote sensing (RS) is a powerful means for analysis, synthesis, and report, providing insights and contributing to inform decisions upon forest ecosystems. In this communication we review current applications of RS techniques in Spanish forests, examining possible trends, needs, and opportunities offered by RS in a forestry context. Currently, wall-to-wall optical and LiDAR data are extensively used for a wide range of applications-many times in combination-whilst radar or hyperspectral data are rarely used in the analysis of Spanish forests. Unmanned Aerial Vehicles (UAVs) carrying visible and infrared sensors are gaining ground in acquisition of data locally and at small scale, particularly for health assessments. Forest fire identification and characterization are prevalent applications at the landscape scale, whereas structural assessments are the most widespread analyses carried out at limited extents. Unparalleled opportunities are offered by the availability of diverse RS data like those provided by the European Copernicus programme and recent satellite LiDAR launches, processing capacity, and synergies with other ancillary sources to produce information of our forests. Overall, we live in times of unprecedented opportunities for monitoring forest ecosystems with a growing support from RS technologies.Part of this work was funded by the Spanish Ministry of Science, innovation and University through the project AGL2016-76769-C2-1-R "Influence of natural disturbance regimes and management on forests dynamics. structure and carbon balance (FORESTCHANGE)".Gómez, C.; Alejandro, P.; Hermosilla, T.; Montes, F.; Pascual, C.; Ruiz Fernández, LÁ.; Álvarez-Taboada, F.... (2019). Remote sensing for the Spanish forests in the 21st century: a review of advances, needs, and opportunities. Forest Systems. 28(1):1-33. https://doi.org/10.5424/fs/2019281-14221S133281Ungar S, Pearlman J, Mendenhall J, Reuter D, 2003. Overview of the Earth Observing-1 (EO-1) mission. IEEE T Geosci Remote 41: 1149−1159.Valbuena R, Mauro F, Arjonilla FJ, Manzanera JA, 2011. Comparing Airborne Laser Scanning-Imagery Fusion Methods Based on Geometric Accuracy in Forested Areas. Remote Sens Environ 115(8): 1942-1956.Valbuena R, Mauro F, Rodríguez-Solano R, Manzanera JA, 2012. Partial Least Squares for Discriminating Variance Components in GNSS Accuracy Obtained Under Scots Pine Canopies. Forest Sci 58(2): 139-153.Valbuena R, De Blas A, Martín Fernández S, Maltamo M, Nabuurs GJ, Manzanera JA, 2013a. Within-Species Benefits of Back-projecting Laser Scanner and Multispectral Sensors in Monospecific P. sylvestris Forests. Eur J Remote Sens 46: 401-416.Valbuena R, Maltamo M, Martín-Fernández S, Packalen P, Pascual C, Nabuurs G-J, 2013b. Patterns of covariance between airborne laser scanning metrics and Lorenz curve descriptors of tree size inequality. Can J Remote Sens 39(1): 18-31.Valbuena R, Packalen P, García-Abril A, Mehtätalo L, Maltamo M, 2013c. Characterizing Forest Structural Types and Shelterwood Dynamics from Lorenz-based Indicators Predicted by Airborne Laser Scanning. Can J For Res 43: 1063-1074.Valbuena R, Maltamo M, Packalen P, 2016a. Classification of Multi-Layered Forest Development Classes from Low-Density National Airborne LiDAR Datasets. Forestry 89: 392-341.Valbuena R, Maltamo M, Packalen P, 2016b. Classification of Forest Development Stages from National Low-Density LiDAR Datasets: a Comparison of Machine Learning Methods. Revista de Teledetección 45: 15-25.Valbuena R, Hernando A, Manzanera JA, Martínez-Falero E, García-Abril A, Mola-Yudego B, 2017a. Most Similar Neighbour Imputation of Forest Attributes Using Metrics Derived from Combined Airborne LIDAR and Multispectral Sensors. Int J Digit Earth 11 (12): 1205-1218.Valbuena R, Hernando A, Manzanera JA, Görgens EB, Almeida DRA, Mauro F, García-Abril A, Coomes DA, 2017b. Enhancing of accuracy assessment for forest above-ground biomass estimates obtained from remote sensing via hypothesis testing and overfitting evaluation. Eco Mod 622: 15-26.Valbuena-Rabadán M, Santamaría-Pe-a J, Sanz-Adán F, 2016. Estimation of diameter and height of individual trees for Pinus sylvestris L. based on the individualising of crowns using airborne LiDAR and the National Forest Inventory data. For Sys 25(1): e046Varo-Martínez MA, Navarro-Cerrillo RM, Hernández-Clemente R, Duque-Lazo J, 2017. Semi-automated stand delineation in Mediterranean Pinus sylvestris plantations through segmentation of LiDAR data: The influence of pulse density. Int J Appl Earth Obs 56: 54-64.Vázquez de la Cueva A, 2008. Structural attributes of three forest types in central Spain and Landsat ETM+ information evaluated with redundancy analysis. Int J Remote Sens 29: 5657-5676.Verdú F, Salas J, 2010. Cartografía de áreas quemadas mediante análisis visual de imágenes de satélite en la Espa-a peninsular para el periodo 1991–2005. Geofocus 10: 54–81.Viana-Soto A, Aguado I, Martínez S, 2017. Assessment of post-fire vegetation recovery using fire severity and geographical data in the Mediterranean region (Spain). Environments 4: 90.Vicente-Serrano SG, Pérez-Cabello F, Lasanta T, 2011. Pinus halepensis regeneration after a wildfire in a semiarid environment: assessment using multitemporal Landsat images. Int J Wildland Fire 20Ñ 195-208.Viedma O, Quesada J, Torres I, De Santis A, Moreno JM, 2015. Fire severity in a large fire in a Pinus pinaster forest is highly predictable from burning conditions, stand structure, and topography. Ecosystems 18: 237-250.Yebra M, Chuvieco E, 2009. Generation of a species-specific look-up table for fuel moisture content assessment. IEEE J Selected topics in applied earth observation and RS 2 (1): 21-26.White JC, Wulder MA, Varhola A, Vastaranta M, Coops NC, Cook BD, Pitt D, Woods M, 2013. A best practices guide for generating forest inventory attributes from airborne laser scanning data using an area-based approach. Natural Resources Canada, Canadian Forest Service, Canadian Wood Fibre Centre, Victoria, BC. Information Report FI-X-010, 39 pp.White JC, Wulder MA, Hobart GW, Luther JE, Hermosilla T, Griffiths P, Coops NC, Hall RJ, Hostert P, Dyk A, Guindon L, 2014. Pixel-based image compositing for large-area dense time series applications and science. Can J Remote Sens 40 (3): 192-212.White JC, Coops NC, Wulder MA, Vastaranta M, Hilker T, Tompalski P, 2016. Remote sensing technologies for enhancing forest inventories: a review. Can J Remote Sens 42: 619-641.White JC, Wulder MA, Hermosilla T, Coops NC, Hobart GW, 2017. A nationwide characterization of 25 years of forest disturbance and recovery for Canada using Landsat time series. Remote Sens Environ 194: 303-321.Wulder MA, 1998. Optical remote-sensing techniques for the assessment of forest inventory and biophysical parameters. Progr Phys Geog 22 (4): 449-476.Wulder MA, Dymond CC, 2004. Remote sensing in survey of Mountain Pine impacts: review and recommendations. MPBI Report. Canadian Forest Service. Natural Resources Canada, Victoria, BC, Canada. 89 pp.Wulder MA, Masek JG, Cohen WB, Loveland TR, Woodcock CE, 2012. Opening the archive: how free data has enabled the science and monitoring promise of Landsat. Remote Sens Environ 122: 2-10.Wulder MA, Hilker T, White JC, Coops NC, Masek JG, Pflugmacher D, Crevier Y, 2015. Virtual constellations for global terrestrial monitoring. Remote Sens Environ 170: 62-76.Wulder MA, White JC, Loveland TR, Woodcock CE, Belward AS, Cohen WB, Fosnight EA, Shaw J, Masek JG, Roy DP, 2016. The global Landsat archive: Status, consolidation, and direction. Remote Sens Environ 185: 271-283.Xie Q, Zhu J, Wang Ch, Fu H, López-Sánchez JM, Ballester-Berman JD, 2017. A modified dual-baseline PolInSAR method for forest height estimation. Remote Sens-Basel 9 (8): 819.Xie Y, Sha Z, Yu M, 2008. Remote sensing imagery in vegetation mapping: a review. J Plant Ecol 1 (1): 9-23.Zald HSJ, Wulder MA, White JC, Hilker T, Hermosilla T, Hobart GW, Coops NC, 2016. Integrating Landsat pixel composites and change metrics with LiDAR plots to predictively map forest structure and aboveground biomass in Saskatchewan, Canada. Remote Sens Environ 176: 188-201.Zarco-Tejada PJ, Diaz-Varela R, Angileri V, Loudjani P, 2014. Tree height quantification using very high resolution imagery acquired from an unmanned aerial vehicle (UAV) and automatic 3D photo-reconstruction methods. Eur J Agron 55: 89-99.Zarco-Tejada PJ, Hornero A, Hernández-Clemente R, Beck PSA, 2018. Understanding the temporal dimension of the red-edge spectral region for forest decline detection using high-resolution hyperspectral and Sentinel-2A imagery. ISPRS J Photogramm 137: 134-148