Liana canopy cover mapped throughout a tropical forest with high-fidelity imaging spectroscopy

Increasing size and abundance of lianas relative to trees are pervasive changes in Neotropical forests that may lead to reduced forest carbon stocks. Yet the liana growth form is chronically understudied in large-scale tropical forest censuses, resulting in few data on the scale, cause, and impact of increasing lianas. Satellite and airborne remote sensing provide potential tools to map and monitor lianas at much larger spatial and rapid temporal scales than are possible with plot-based forest censuses. We combined high-resolution airborne imaging spectroscopy and a ground-based tree canopy census to investigate whether tree canopies supporting lianas could be discriminated from tree canopies with no liana coverage. Using support vector machine algorithms, we achieved accuracies of nearly 90% in discriminating the presence–absence of lianas, and low error (15.7% RMSE) when predicting liana percent canopy cover. When applied to the full image of the study site, our model had a 4.1% false-positive error rate as validated against an independent plot-level dataset of liana canopy cover. Using the derived liana cover classification map, we show that 6.1%–10.2% of the 1823 ha study site has high-to-severe (50–100%) liana canopy cover. Given that levels of liana infestation are increasing in Neotropical forests and can result in high tree mortality, the extent of high-to-severe liana canopy cover across the landscape may have broad implications for ecosystem function and forest carbon storage. The ability to accurately map landscape-scale liana infestation is crucial to quantifying their effects on forest function and uncovering the mechanisms underlying their increase

Characterization of indicator tree species in neotropical environments and implications for geological mapping

FAPESP - FUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DE SÃO PAULOCNPQ - CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICOGeobotanical remote sensing (GbRS) in the strict sense is an indirect approach to obtain geological information in heavily vegetated areas for mineral prospecting and geological mapping. Using ultra- and hyperspectral technologies, the goals of this resea216385400FAPESP - FUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DE SÃO PAULOCNPQ - CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICOFAPESP - FUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DE SÃO PAULOCNPQ - CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO2010/51758-2, 2010/51718-0309712/2017-3, 302925/2015-

Applications of Remote Sensing to Alien Invasive Plant Studies

Biological invasions can affect ecosystems across a wide spectrum of bioclimatic conditions. Therefore, it is often important to systematically monitor the spread of species over a broad region. Remote sensing has been an important tool for large-scale ecological studies in the past three decades, but it was not commonly used to study alien invasive plants until the mid 1990s. We synthesize previous research efforts on remote sensing of invasive plants from spatial, temporal and spectral perspectives. We also highlight a recently developed state-of-the-art image fusion technique that integrates passive and active energies concurrently collected by an imaging spectrometer and a scanning-waveform light detection and ranging (LiDAR) system, respectively. This approach provides a means to detect the structure and functional properties of invasive plants of different canopy levels. Finally, we summarize regional studies of biological invasions using remote sensing, discuss the limitations of remote sensing approaches, and highlight current research needs and future directions

Discerning Oriental from European beech by leaf spectroscopy: Operational and physiological implications

European beech (Fagus sylvatica L.) forests have recently experienced severe diebacks that are expected to increase in future. Oriental beech (Fagus sylvatica spp. orientalis (Lipsky) Greut. & Burd) is a potential candidate for assisted migration (AM) in European forests due to its greater genetic diversity and potentially higher drought resistance. Yet AM entails not only benefits, but also risks, and it is therefore important to monitor the progression of introduced (sub)species. Here, we demonstrate the potential of leaf spectroscopy to replace resourceintensive genetic analysis and field phenotyping for the discrimination and characterization of these two beech subspecies. We studied two European beech forests, one in France and one in Switzerland, where Oriental beech from the Greater Caucasus was introduced over 100 years ago. During two summers (2021, 2022), we measured leaf spectral reflectance, leaf morphological and biochemical traits from genotyped adult trees. Subspecies prediction models were developed separately for top-of-canopy leaves (amenable to remote sensing) and bottom-of-canopy leaves (easier to harvest) using partial least squares discriminant analysis (PLS-DA) and different sets of spectral predictors. Morphological, biochemical and spectra-derived leaf traits indicated that Oriental beech trees at the sites studied were characterized by higher lignin and nitrogen per unit leaf area than European beech, suggesting more protein-rich leaves on a per-area basis. The model based on top-of-canopy leaf reflectance spectra in the short-wave-infrared region (SWIR I: 1450–1750 nm) most accurately distinguished Oriental from European beech (BA = 0.86 ± 0.08, k = 0.72 ± 0.15), closely followed by models based on SWIR II, and on spectra-derived traits (BA ≥ 0.84, k ≥ 0.67). This study provides a proof-of-principle for the development of spectroscopy-based approaches when monitoring introduced species, subspecies or provenances. Our findings hold promise for upscaling to large forest areas using airborne remote sensing

Tagging and Mapping of Mixed Dipterocarp Mountain Forest at Species Level using an Airborne Hyperspectral Imager

Forest inventories describe the quantity and quality of trees and other organisms of the forest and the characteristics of the land on which the forest grows. Thus, to manage the resource of the forest systematically, the forest has to be precisely identified and classified before implementing decisions. A study on the spatial distribution of mountain mixed hill dipterocarp forest tree species in Gunung Stong Forest Reserve, Kelantan, Malaysia was conducted using an airborne hyperspectral imaging technique to identify, tag and map tree distribution at species level for future sustainable harvest and management planning. The general objective of this study is to assess the capability and usefulness UPM-AISA airborne hyperspectral sensor in Pre-Felling forest inventory while the specific objectives are to identify, map and tag individual trees in Gunung Stong Forest Reserve, Kelantan. A Sobel filter was used to enhance the image followed by a Spectral Angle Mapper (SAM) analysis to classify the individual tree species within the study plot. A digital map of tree tagging was produced with the tree species identified were Kelat (Syzgium spp), Keledang (Artrocarpos spp), Mengkulang (Heritiera spp), Tempinis (Streblus elongatus), Keranji (Dialium spp), Tulang Daing (Callerya atropurea), Meranti Sarang Punai (Shorea parvifolia), Kembang Semangkuk Jantung (Scaphium macropodum), Bintangor (Calophyllum spp), Nyatoh Minyak (Sapotaceae spp), Mersawa (Anisoptera spp), Resak (Cotylelobium spp), Sepetir (Sindora spp), Temponek (Artocarpus rigidus) and Petaling. The mapping accuracy of 89.66% was attained in the 1 ha study plot. Tree tagging using airborne remote sensing has a great potential in the Pre-F inventory and should be integrated with a GIS database management for future decision in management, development and utilization of sustainable forest resource

Mapping Species Composition of Forests and Tree Plantations in Northeastern Costa Rica with an Integration of Hyperspectral and Multitemporal Landsat Imagery

An efficient means to map tree plantations is needed to detect tropical land use change and evaluate reforestation projects. To analyze recent tree plantation expansion in northeastern Costa Rica, we examined the potential of combining moderate-resolution hyperspectral imagery (2005 HyMap mosaic) with multitemporal, multispectral data (Landsat) to accurately classify (1) general forest types and (2) tree plantations by species composition. Following a linear discriminant analysis to reduce data dimensionality, we compared four Random Forest classification models: hyperspectral data (HD) alone; HD plus interannual spectral metrics; HD plus a multitemporal forest regrowth classification; and all three models combined. The fourth, combined model achieved overall accuracy of 88.5%. Adding multitemporal data significantly improved classification accuracy (p less than 0.0001) of all forest types, although the effect on tree plantation accuracy was modest. The hyperspectral data alone classified six species of tree plantations with 75% to 93% producer's accuracy; adding multitemporal spectral data increased accuracy only for two species with dense canopies. Non-native tree species had higher classification accuracy overall and made up the majority of tree plantations in this landscape. Our results indicate that combining occasionally acquired hyperspectral data with widely available multitemporal satellite imagery enhances mapping and monitoring of reforestation in tropical landscapes

A remote sensing approach to understanding patterns of secondary succession in tropical forest

Funding: E. Chraibi and J.-B. Féret acknowledge financial support from Agence Nationale de la Recherche (BioCop project—ANR-17-CE32-0001-01). A.E. Magurran acknowledges support from the Leverhulme Trust (RPG-2019-402).Biodiversity monitoring and understanding ecological processes on a global scale is a major challenge for biodiversity conservation. Field assessments commonly used to assess patterns of biodiversity and habitat condition are costly, challenging, and restricted to small spatial scales. As ecosystems face increasing anthropogenic pressures, it is important that we find ways to assess patterns of biodiversity more efficiently. Remote sensing has the potential to support understanding of landscape-level ecological processes. In this study, we considered cacao agroforests at different stages of secondary succession, and primary forest in the Northern Range of Trinidad, West Indies. We assessed changes in tree biodiversity over succession using both field data, and data derived from remote sensing. We then evaluated the strengths and limitations of each method, exploring the potential for expanding field data by using remote sensing techniques to investigate landscape-level patterns of forest condition and regeneration. Remote sensing and field data provided different insights into tree species compositional changes, and patterns of alpha- and beta-diversity. The results highlight the potential of remote sensing for detecting patterns of compositional change in forests, and for expanding on field data in order to better understand landscape-level patterns of forest diversity.Publisher PDFPeer reviewe

Remote detection of invasive alien species

The spread of invasive alien species (IAS) is recognized as the most severe threat to biodiversity outside of climate change and anthropogenic habitat destruction. IAS negatively impact ecosystems, local economies, and residents. They are especially problematic because once established, they give rise to positive feedbacks, increasing the likelihood of further invasions and spread. The integration of remote sensing (RS) to the study of invasion, in addition to contributing to our understanding of invasion processes and impacts to biodiversity, has enabled managers to monitor invasions and predict the spread of IAS, thus supporting biodiversity conservation and management action. This chapter focuses on RS capabilities to detect and monitor invasive plant species across terrestrial, riparian, aquatic, and human-modified ecosystems. All of these environments have unique species assemblages and their own optimal methodology for effective detection and mapping, which we discuss in detail

Canopy structural attributes derived from AVIRIS imaging spectroscopy data in a mixed broadleaf/conifer forest

There is a well-established need within the remote sensing community for improved estimation and understanding of canopy structure and its influence on the retrieval of leaf biochemical properties. The main goal of this research was to assess the potential of optical spectral information from NASA's Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) to discriminate different canopy structural types. In the first phase, we assessed the relationships between optical metrics and canopy structural parameters obtained from LiDAR in terms of different canopy structural attributes (biomass (i.e., area under Vegetation Vertical Profile, VVPint), canopy height and vegetation complexity). Secondly, we identified and classified different âcanopy structural typesâ by integrating several structural traits using Random Forests (RF). The study area is a heterogeneous forest in Sierra National Forest in California (USA). AVIRIS optical properties were analyzed by means of several sets of variables, including single narrow band reflectance and 1st derivative, sub-pixel cover fractions, narrow-band indices, spectral absorption features, optimized normalized difference indices and Principal Component Analysis (PCA) components. Our results demonstrate that optical data contain structural information that can be retrieved. The first principal component, used as a proxy for albedo, was the most strongly correlated optical metric with vegetation complexity, and it also correlated well with biomass (VVPint) and height. In conifer forests, the shade fraction was especially correlated to vegetation complexity, while water-sensitive optical metrics had high correlations with biomass (VVPint). Single spectral band analysis results showed that correlations differ in magnitude and in direction, across the spectrum and by vegetation type and structural variable. This research illustrates the potential of AVIRIS to analyze canopy structure and to distinguish several structural types in a heterogeneous forest. Furthermore, RF using optical metrics derived from AVIRIS proved to be a powerful technique to generate maps of structural attributes. The results emphasize the importance of using the whole optical spectrum, since all spectral regions contributed to canopy structure assessment

Scale challenges in inventory of forests aided by remote sensing

The impact of changing the scale of observation on information derived from forest inventories is the basis of scale-related research in forest inventory and analysis (FIA). Interactions between the scale of observation and observed heterogeneity in studied variables highlight a dependence on scale that affects measurements, estimates, and relationships between inventory data from terrestrial and remote sensing surveys. This doctoral research defines "scale" as the divisions of continuous space over which measurements are made, or hierarchies of discrete units of study/analysis in space. Therefore, the "scale of observation" (also known as support) refers to that integral of space over which statistics are computed and forest inventory variables regionalized. Given the ubiquitous nature of scale issues, a case study approach was undertaken in this research (Articles I-IV) with the goal to provide fundamental understanding of responses to the scale of observation for specific FIA variables. The studied forest inventory variables are; forest stand structural heterogeneity, forest cover proportion and tree species identities. Forest cover proportion (or simply forest area) and tree species are traditional and fundamental forest inventory variables commonly assessed over large areas using both terrestrial samples and remote sensing data whereas, forest stand structural heterogeneity is a contemporary FIA variable that is increasingly demanded in multi-resource inventories to inform management and conservation efforts as it is linked to biodiversity, productivity, ecosystem functioning and productivity, and used as auxiliary data in forest inventory. This research has two overall aims: 1. To improve the understanding of the association between the scale of observation and observed heterogeneity in inventory of forest stand structural heterogeneity, forest-cover proportions, and identification of tree species from a combination of terrestrial samples and remote sensing data. 2. To contribute knowledge to the estimation of scale-dependence in inventory of forest stand structural heterogeneity, forest-cover proportions, and identification of tree species from a combination of terrestrial samples and remote sensing data. Different scales of observation were considered across the four case studies encompassing individual leaf, crown-part or branch, single-tree crown, forest stand, landscape and global levels of analysis. Terrestrial and remote sensing data sets from a variety of temperate forests in Germany and France were utilized across case studies. In cases where no inventory data were available, synthetic data was simulated at different scales of observation. Heterogeneity in FIA variable estimates was monitored across scales of observation using estimators of variance and associated precision. As too much heterogeneity is hardly interpreted due to a low signal to noise ratio, object-based image analysis (OBIA) methods were used to manage heterogeneity in high resolution remote sensing data before evaluating scale dependence or scaling across observed scales. Similarly, ensemble classification techniques were applied to address methodological heterogeneity across classifiers in a case study on classification of two physically and spectrally similar Pinus species. Across case studies, a dependence on the scale of observation was determined by linking estimates of heterogeneity to their respective scales of observation using linear regression and a combination of geo-statistics and Monte-Carlo approaches. In order to address scale-dependence, thresholds to scale domains were identified so as to enable efficient observation of studied FIA variables and scaling approaches proposed to bridge observations across scales. For scaling, this research evaluated the potential of different regression techniques to map forest stand structural heterogeneity and tree species wall-to-wall from remote sensing data. In addition, radiative transfer modelling was evaluated in the transfer between leaf and crown hyperspectra, and a global sampling grid framework proposed to efficiently link different stages of survey sampling. This research shows that the scale of observation affected all studied FIA variables albeit to varying degrees, conditioned on the spatial structure and aggregation properties of the assessed FIA variable (i.e. whether the variable is extensive, intensive or scale-specific) and the method used in aggregation on support (e.g. mean, variance, quantile etc.). The scale of observation affected measurements or estimates of the studied FIA variables as well as relationships between spatially structured FIA variables. The scale of observation determined observed heterogeneity in FIA variables, affected parameter retrieval from radiative transfer models, and affected variable selection and performance of models linking terrestrial and remote sensing data. On the other hand, this research shows that it is possible to determine domains of scale dependence within which to efficiently observe the studied FIA variables and to bridge between scales of observation using various scaling methods. The findings of this doctoral research are relevant for the general understanding of scale issues in FIA. Research in Article I, for example, informs optimization of plot sizes for efficient inventory and mapping of forest structural heterogeneity, as well as for the design of natural resource inventories. Similarly, research in Article II is applicable in large area forest (or general land) cover monitoring from sampling by both visual interpretation of high resolution remote sensing imagery and terrestrial surveys. This research is also useful to determine observation design for efficient inventory of land cover. Research in Article III contributes in many contexts of remote sensing assisted inventory of forests especially in management and conservation planning, pest and diseases control and in the estimation of biomass. Lastly, research in Article IV highlights scale-related effects in passive optical remote sensing of forests currently understudied and can ultimately contribute to sensor calibration and modelling approaches