Tropical Forest Canopy Research Articles

Leaf surface water, not plant water stress, drives diurnal variation in tropical forest canopy water content.

Variation in canopy water content (CWC) that can be detected from microwave remote sensing of vegetation optical depth (VOD) has been proposed as an important measure of vegetation water stress. However, the contribution of leaf surface water (LWs ), arising from dew formation and rainfall interception, to CWC is largely unknown, particularly in tropical forests and other high-humidity ecosystems. We compared VOD data from the Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E) and CWC predicted by a plant hydrodynamics model at four tropical sites in Brazil spanning a rainfall gradient. We assessed how LWs influenced the relationship between VOD and CWC. The analysis indicates that while CWC is strongly correlated with VOD (R2 =0.62 across all sites), LWs accounts for 61-76% of the diurnal variation in CWC despite being < 10% of CWC. Ignoring LWs weakens the near-linear relationship between CWC and VOD and reduces the consistency in diurnal variation. The contribution of LWs to CWC variation, however, decreases at longer, seasonal to inter-annual, time scales. Our results demonstrate that diurnal patterns of dew formation and rainfall interception can be an important driver of diurnal variation in CWC and VOD over tropical ecosystems and therefore should be accounted for when inferring plant diurnal water stress from VOD measurements.

Only sun-lit leaves of the uppermost canopy exceed both air temperature and photosynthetic thermal optima in a wet tropical forest

Tropical forests have evolved under relatively narrow temperature regimes, and therefore may be more susceptible to climatic change than forests in higher latitudes. Recent evidence shows that lowland tropical forest canopies may already be exceeding thermal maxima for photosynthesis. Height can strongly influence both the microclimate and physiology of forest canopy foliage, yet vertical trends in canopy micrometeorology are rarely examined in tropical forests. To improve our understanding of how climatological and micrometeorological conditions affect tropical tree function, we assessed vertical gradients of photosynthetic photon flux density, vapor pressure deficit, air temperature, leaf temperature, and the difference between leaf and air temperature (ΔT) in a Puerto Rican tropical wet forest. Both air temperature and vapor pressure deficit increased linearly with height. Leaf temperature, however, did not significantly differ across the shaded foliage from 0-16 m, while the uppermost layer (20 m) was up to 4°C hotter than the rest of the foliage and up to 5°C hotter than air temperature at the highest radiation intensity. As a result, leaf temperatures in the shaded middle canopy and understory showed nearly poikilothermic behavior (i.e., leaf temperatures = air temperature), while the uppermost canopy strata showed megathermic behavior (i.e., leaf temperatures greater than air temperature), revealing different thermoregulation strategies for sun-lit versus shaded foliage. In addition, the uppermost canopy was the only stratum to exceed mean photosynthetic temperature optima for this site (Topt = 30.2 ± 1.1°C). Because the upper canopy plays a disproportionately large role in whole-forest photosynthesis, continued warming could potentially weaken the tropics’ carbon sink capacity. However, the shaded leaves may be able increase carbon uptake with further warming because they appear to be able to maintain temperatures below photosynthetic optima, possibly with the help of radiation shielding provided by the uppermost canopy layer.

Latitude, paleo-history and forest size matter for Afromontane canopy beetle diversity in a world context

Tree canopies in forests host exceptional levels of biodiversity. However ecologists are still faced with vast regional gaps in need of fundamental information on the nature of tree canopy diversity. The southern Afrotemperate forests at the tip of Africa are ancient, existing today as a biogeographical string of natural remnants from long-past climate events. Their canopy arthropod fauna is scarcely known, but could provide insights into global canopy diversity patterns. As part of the broader Afromontane archipelago and southern hemispheric forests in general, they form part of the last and largest global gaps in canopy science. Here we ask how the canopy beetle diversity of these forests qualitatively and quantitatively compare with other forests around the world. We show that beetle species richness and richness per m2 are closer to temperate than tropical canopies. Estimates for beetle richness most closely matched the latitudinal equivalent Chilean temperate rainforests. However, feeding guild structure of beetles are closer in resemblance to tropical than temperate forest canopies, in line with expectations regarding the region’s paleo-history. The most diverse beetle families, Curculionidae, Staphylinidae and Chrysomelidae, were proportionately similar to both temperate and tropical forest canopies. We discuss the importance of latitude, paleo-history and forest size when considering canopy fauna in a global context.

Coleopterans (Hexapoda, Coleoptera) associated with canopies of Callisthene fasciculata (Spr.) Mart. (Vochysiaceae) in the Brazilian Pantanal

Abstract: The study of coleopteran assemblages associated with the canopies of tropical forests can help to expand the knowledge about species diversity and the ecological patterns related to the distribution of this diversity in tropical environments, including wetlands such as the Brazilian Pantanal. In this scenario, the present study examined the effect of temporal variation on the abundance, richness and distribution of the Coleoptera assemblage associated with canopies of Callisthene fasciculata (Spr.) Mart. (Vochysiaceae) throughout the dry and high-water seasonal periods in the Northern Pantanal of Mato Grosso, Brazil. Twelve specimens of C. fasciculata were sampled between 2010 and 2011, six per seasonal period, by thermal fogging with insecticide. A total of 1,663 coleopterans were collected, consisting of 1,572 adults and 91 larvae. The adults are distributed into 38 families and 251 species or morphospecies. Chrysomelidae, Curculionidae, Nitidulidae and Tenebrionidae predominated in terms of abundance and richness. Family distribution, abundance, species richness and trophic guilds varied between the dry and high-water periods, demonstrating a relationship with the phenological conditions of C. fasciculata in addition to seasonality effect. In conclusion, the seasonality imposed by the hydrological regime and its influence on the phenology of C. fasciculata affect the structure of the coleopteran assemblage associated with the canopy of this monodominant vegetation formation in the Pantanal of Mato Grosso.

Multisensor Data Fusion for Improved Segmentation of Individual Tree Crowns in Dense Tropical Forests

Automatic tree crown segmentation from remote sensing data is especially challenging in dense, diverse, and multilayered tropical forest canopies, and tracking mortality by this approach is even more difficult. Here, we examine the potential for combining airborne laser scanning (ALS) with multispectral and hyperspectral data to improve the accuracy of tree crown segmentation at a study site in French Guiana. We combined an ALS point cloud clustering method with a spectral deep learning model to achieve 83% accuracy at recognizing manually segmented reference crowns (with congruence >0.5). This method outperformed a two-step process that involved clustering the ALS point cloud and then using the logistic regression of hyperspectral distances to correct oversegmentation. We used this approach to map tree mortality from repeat surveys and show that the number of crowns identified in the first that intersected with height loss clusters was a good estimator of the number of dead trees in these areas. Our results demonstrate that multisensor data fusion improves the automatic segmentation of individual tree crowns and presents a promising avenue to study forest demography with repeated remote sensing acquisitions.

Tropical forest canopy height estimation from combined polarimetric SAR and LiDAR using machine-learning

Forest height is an important forest biophysical parameter which is used to derive important information about forest ecosystems, such as forest above ground biomass. In this paper, the potential of combining Polarimetric Synthetic Aperture Radar (PolSAR) variables with LiDAR measurements for forest height estimation is investigated. This will be conducted using different machine learning algorithms including Random Forest (RFs), Rotation Forest (RoFs), Canonical Correlation Forest (CCFs) and Support Vector Machine (SVMs). Various PolSAR parameters are required as input variables to ensure a successful height retrieval across different forest heights ranges. The algorithms are trained with 5000 LiDAR samples (less than 1% of the full scene) and different polarimetric variables. To examine the dependency of the algorithm on input training samples, three different subsets are identified which each includes different features: subset 1 is quiet diverse and includes non-vegetated region, short/sparse vegetation (0–20 m), vegetation with mid-range height (20–40 m) to tall/dense ones (40–60 m); subset 2 covers mostly the dense vegetated area with height ranges 40–60 m; and subset 3 mostly covers the non-vegetated to short/sparse vegetation (0–20 m) .The trained algorithms were used to estimate the height for the areas outside the identified subset. The results were validated with independent samples of LiDAR-derived height showing high accuracy (with the average R2 = 0.70 and RMSE = 10 m between all the algorithms and different training samples). The results confirm that it is possible to estimate forest canopy height using PolSAR parameters together with a small coverage of LiDAR height as training data.

Leaf Vein Density of Tree Saplings Composing Lower Canopy in Tropical Forest Reflects Their Ecophysiological Characteristics

One factor affecting the survival of a species in a tropical ecosystem is its ability to respond to environmental conditions, which depend on their ecophysiological performances. Plants ability to transport water as a major environmental factor would determine their survival. The anatomy of xylem inside leaves and stem as water conductive tissue will dictate the rate of water transport through the plant stem and leaves. Leaf vein, which contains xylem vessels, dictates water transport through leaves and plant’s ability to control water loss through stomata. This research found that tree saplings composing a lower canopy of tropical forests have different ecophysiological attributes. Pioneer species, such as Cinnamomum sp., Diospyros macrophylla, Castanopsis costata, Elateriospermum tapos, and Ziziphus sp., have higher leaf vein density than primary species, such as a member of genus Garcinia, Shorea, Dipterocarpus, and Syzigium. It implies that pioneer species might have higher rates of water transport and consequently, higher rates of photosynthesis. If forest vegetation was more opened, then pioneer species may dominate the area as they are more tolerant of light. The Composition of forest vegetation with different ecophysiological characteristics may affect the forest dynamics and hydrological cycle.

Effect of forest disturbance on ant (Hymenoptera: Formicidae) diversity in a Mexican tropical dry forest canopy

Abstract1. Ants are the dominant insects in tropical forest canopies. The α and β diversities of ants change in response to forest disturbance. Understanding the response of ants to forest disturbance will help to guide ant conservation and forest management plans, especially in endangered ecosystems such as the tropical dry forest. 2. We analysed ant α and β diversities in 15 fragments (5 mature, 5 disturbed, and 5 secondary) of a tropical dry forest in central Mexico. We identified and measured all of the trees in two transects (10 × 50 m) established within each fragment. Pit‐fall traps were used to sample ants in all of the trees. 3. We identified 52 ant species (from a total of 11,524 individuals). The mature forest presented the greatest quantity of exclusive species and the highest α diversity. Ant composition differed among the mature forest but not between disturbed and secondary forest, and was related to tree size and richness. The β diversity was lower in the disturbed forest, reflecting the cost of biotic homogenization (15%). Among the β diversity components in all three forest types, this diversity was mainly influenced by turnover, while nestedness diminished in the disturbed forest. 4. In the tropical dry forest studied, anthropogenic disturbance homogenises the ant community, probably because the availability of resources is also homogenised, decreasing the α diversities of the canopy ants. Our results show the importance of the conservation of mature forests in terms of maintaining the diversity of canopy ants.

Seasonal and interannual variability of CO2 above the moist tropical forest of southern Vietnam

The paper presents the results of a study of seasonal and interannual variability of CO2 concentration above the moist tropical forest in southern Vietnam. Experimental data were collected during year-long observation of CO2 directly in the forest from 2012 to 2017. All data were obtained through the use of an air intake tube, placed on a metal tower at a height of 46 m and connected to a Li-Cor 820 gas analyzer (Li-Cor Inc., USA) located in a building at the foot of the tower. The values of the concentration were recorded with a resolution of 1 second; for further analysis, all data was averaged to 0.5 h values. Statistical processing based on the Fourier analysis allowed to evaluate the main characteristics of the annual distribution of CO2 concentration, such as the amplitude and phase, as well as to analyze their variability over the years. The results of the study showed a presence of a well-determined annual course of CO2 concentration above the canopy of the moist tropical forest.

Hot but Not Dry: Modest Changes in Water Relations for an Epiphytic Bromeliad in a Tropical Dry Deciduous Forest

Premise of research. Epiphytic bromeliads endure intense seasonal environmental changes in the canopy of dry tropical deciduous forests. The analysis of the physiological responses of these epiphyt...

Estimating Time Since the Last Stand-Replacing Disturbance (TSD) from Spaceborne Simulated GEDI Data: A Feasibility Study

Stand-level maps of past forest disturbances (expressed as time since disturbance, TSD) are needed to model forest ecosystem processes, but the conventional approaches based on remotely sensed satellite data can only extend as far back as the first available satellite observations. Stand-level analysis of airborne LiDAR data has been demonstrated to accurately estimate long-term TSD (~100 years), but large-scale coverage of airborne LiDAR remains costly. NASA’s spaceborne LiDAR Global Ecosystem Dynamics Investigation (GEDI) instrument, launched in December 2018, is providing billions of measurements of tropical and temperate forest canopies around the globe. GEDI is a spatial sampling instrument and, as such, does not provide wall-to-wall data. GEDI’s lasers illuminate ground footprints, which are separated by ~600 m across-track and ~60 m along-track, so new approaches are needed to generate wall-to-wall maps from the discrete measurements. In this paper, we studied the feasibility of a data fusion approach between GEDI and Landsat for wall-to-wall mapping of TSD. We tested the methodology on a ~52,500-ha area located in central Idaho (USA), where an extensive record of stand-replacing disturbances is available, starting in 1870. GEDI data were simulated over the nominal two-year planned mission lifetime from airborne LiDAR data and used for TSD estimation using a random forest (RF) classifier. Image segmentation was performed on Landsat-8 data, obtaining image-objects representing forest stands needed for the spatial extrapolation of estimated TSD from the discrete GEDI locations. We quantified the influence of (1) the forest stand map delineation, (2) the sample size of the training dataset, and (3) the number of GEDI footprints per stand on the accuracy of estimated TSD. The results show that GEDI-Landsat data fusion would allow for TSD estimation in stands covering ~95% of the study area, having the potential to reconstruct the long-term disturbance history of temperate even-aged forests with accuracy (median root mean square deviation = 22.14 years, median BIAS = 1.70 years, 60.13% of stands classified within 10 years of the reference disturbance date) comparable to the results obtained in the same study area with airborne LiDAR.

Comparable canopy and soil free-living nitrogen fixation rates in a lowland tropical forest

Biological nitrogen fixation (BNF) is a fundamental part of nitrogen cycling in tropical forests, yet little is known about the contribution made by free-living nitrogen fixers inhabiting the often-extensive forest canopy.We used the acetylene reduction assay, calibrated with 15N2, to measure free-living BNF on forest canopy leaves, vascular epiphytes, bryophytes and canopy soil, as well as on the forest floor in leaf litter and soil. We used a combination of calculated and published component densities to upscale free-living BNF rates to the forest level.We found that bryophytes and leaves situated in the canopy in particular displayed high mass-based rates of free-living BNF. Additionally, we calculated that nearly 2 kg of nitrogen enters the forest ecosystem through free-living BNF every year, 40% of which was fixed by the various canopy components.Our results reveal that in the studied tropical lowland forest a large part of the nitrogen input through free-living BNF stems from the canopy, but also that the total nitrogen inputs by free-living BNF are lower than previously thought and comparable to the inputs of reactive nitrogen by atmospheric deposition.

Isotopic and morphologic proxies for reconstructing light environment and leaf function of fossil leaves: a modern calibration in the Daintree Rainforest, Australia.

Within closed-canopy forests, vertical gradients of light and atmospheric CO2 drive variations in leaf carbon isotope ratios, leaf mass per area (LMA), and the micromorphology of leaf epidermal cells. Variations in traits observed in preserved or fossilized leaves could enable inferences of past forest canopy closure and leaf function and thereby habitat of individual taxa. However, as yet no calibration study has examined how isotopic, micro- and macromorphological traits, in combination, reflect position within a modern closed-canopy forest or how these could be applied to the fossil record. Leaves were sampled from throughout the vertical profile of the tropical forest canopy using the 48.5 m crane at the Daintree Rainforest Observatory, Queensland, Australia. Carbon isotope ratios, LMA, petiole metric (i.e., petiole-width2 /leaf area, a proposed proxy for LMA that can be measured from fossil leaves), and leaf micromorphology (i.e., undulation index and cell area) were compared within species across a range of canopy positions, as quantified by leaf area index (LAI). Individually, cell area, δ13 C, and petiole metric all correlated with both LAI and LMA, but the use of a combined model provided significantly greater predictive power. Using the observed relationships with leaf carbon isotope ratio and morphology to estimate the range of LAI in fossil floras can provide a measure of canopy closure in ancient forests. Similarly, estimates of LAI and LMA for individual taxa can provide comparative measures of light environment and growth strategy of fossil taxa from within a flora.

Understanding the 2500 yr BP rainforest crisis in West and Central Africa in the framework of the Late Holocene: Pluridisciplinary analysis and multi-archive reconstruction

Abstract Numerous palaeoclimatic and prehistoric reconstructions have been reported at both the local scale and across the very large area of tropical Central Africa, clearly highlighting the Late Holocene Rainforest Crisis (LHRC) which mainly developed from ca. 2500 to 2000 yr BP. The broad synchroneity of this interval is striking and has been revealed in many different deep lakes from humid or montane forest, lakes or swamps marginal to tropical forest, and swamp forests where the oscillations of the surface water table produce temporary emersions. In parallel, a chronological review is presented of the Bantu arrival in these areas, including indicators for burning, metallurgy and land clearance. Nevertheless, evidence of human occupation, such as artefacts or deposits containing charcoal is exceptional and generally absent in most areas. Although the archaeological data exhibit a gradual southward densification of human occupation throughout Central Africa, the increase of settlements clearly began after 2350 yr BP, not before 2500 BP. However, some authors have interpreted the geochemical signal of increased erosion in the Congo Basin or the opening up of the forest around Lake Barombi Mbo in Cameroon as being attributed to agricultural clearance, or even the supply of charcoal required for metallurgy. In short, these early Bantu settlers (in such modest densities) may have been responsible for some local landscape degradation (clearance, fire, metallurgy), but these same settlers could not, under any circumstances and across all Central Africa, be held responsible directly or indirectly for the synchronous changes of lake levels, draining vast swamps and opening up of the tropical forest canopy, which was due to an increased dry season, while the recovery with the recolonization of light demanding species, including oil palms (Elaeis guineensis) assisted by dispersers such as chimpanzees, was likewise due to natural processes. Therefore, it can be concluded that no data available validate the hypothesis that the major erosion or vegetation destruction ca. 2500 yr BP, was the result of large population movements. Indeed, the evolution of the environments of Central Africa are linked to the natural responses induced by general palaeoclimatic processes, observed synchronously not only in northern and eastern Africa, but globally. The natural recovery and resilience of these forests until the last centuries contrasts with the situation currently being faced.

Comparison of Smartphone and Drone Lidar Methods for Characterizing Spatial Variation in PAI in a Tropical Forest

Estimating leaf area index (LAI) and assessing spatial variation in LAI across a landscape is crucial to many ecological studies. Several direct and indirect methods of LAI estimation have been developed and compared; however, many of these methods are prohibitively expensive and/or time consuming. Here, we examine the feasibility of using the free image processing software CAN-EYE to estimate effective plant area index (PAIeff) from hemispherical canopy images taken with an extremely inexpensive smartphone clip-on fisheye lens. We evaluate the effectiveness of this inexpensive method by comparing CAN-EYE smartphone PAIeff estimates to those from drone lidar over a lowland tropical forest at La Selva Biological Station, Costa Rica. We estimated PAIeff from drone lidar using a method based in radiative transfer theory that has been previously validated using simulated data; we consider this a conservative test of smartphone PAIeff reliability because above-canopy lidar estimates share few assumptions with understory image methods. Smartphone PAIeff varied from 0.1 to 4.4 throughout our study area and we found a significant correlation (r = 0.62, n = 42, p &lt; 0.001) between smartphone and lidar PAIeff, which was robust to image processing analytical options and smartphone model. When old growth and secondary forests are assumed to have different leaf angle distributions for the lidar PAIeff algorithm (spherical and planophile, respectively) this relationship is further improved (r = 0.77, n = 42, p &lt; 0.001). However, we found deviations in the magnitude of the PAIeff estimations depending on image analytical options. Our results suggest that smartphone images can be used to characterize spatial variation in PAIeff in a complex, heterogenous tropical forest canopy, with only small reductions in explanatory power compared to true digital hemispherical photography.

Democratic Republic of the Congo Tropical Forest Canopy Height and Aboveground Biomass Estimation with Landsat-8 Operational Land Imager (OLI) and Airborne LiDAR Data: The Effect of Seasonal Landsat Image Selection

Inventories of tropical forest aboveground biomass (AGB) are often imprecise and sparse. Increasingly, airborne Light Detection And Ranging (LiDAR) and satellite optical wavelength sensor data are used to map tree height and to estimate AGB. In the tropics, cloud cover is particularly prevalent and so several years of satellite observations must be considered. This may reduce mapping accuracy because of seasonal and inter-annual changes in the forest reflectance. In this paper, the sensitivity of airborne LiDAR and Landsat-8 Operational Land Imager (OLI) based dominant canopy height and AGB 30 m mapping is assessed with respect to the season of Landsat acquisition for a ~10,000 Km2 tropical forest area in the Democratic Republic of the Congo. A random forest regression estimator is used to predict and assess the 30 m dominant canopy height using LiDAR derived test and training data. The AGB is mapped using an allometric model parameterized with the dominant canopy height and is assessed by comparison with field based 30 m AGB estimates. Experiments are undertaken independently using (i) only a wet season Landsat-8 image, (ii) only a dry season Landsat-8 image, and (iii) both Landsat-8 images. At the study area level there is little reported sensitivity to the season of Landsat image used. The mean dominant canopy height and AGB values are similar between seasons, within 0.19 m and 5 Mg ha−1, respectively. The mapping results are improved when both Landsat-8 images are used with Root Mean Square Error (RMSE) values that correspond to 18.8% of the mean study area mapped tree height (20.4 m) and to 41% of the mean study area mapped AGB (204 Mg ha−1). The mean study area mapped AGB is similar to that reported in other Congo Basin forest studies. The results of this detailed study are illustrated and the implications for tropical forest tree height and AGB mapping are discussed.

Challenges in Estimating Tropical Forest Canopy Height from Planet Dove Imagery

Monitoring tropical forests using spaceborne and airborne remote sensing capabilities is important for informing environmental policies and conservation actions. Developing large-scale machine learning estimation models of forest structure is instrumental in bridging the gap between retrospective analysis and near-real-time monitoring. However, most approaches use moderate spatial resolution satellite data with limited capabilities of frequent updating. Here, we take advantage of the high spatial and temporal resolutions of Planet Dove images and aim to automatically estimate top-of-canopy height (TCH) for the biologically diverse country of Peru from satellite imagery at 1 ha spatial resolution by building a model that associates Planet Dove textural features with airborne light detection and ranging (LiDAR) measurements of TCH. We use and modify features derived from Fourier textural ordination (FOTO) of Planet Dove images using spectral projection and train a gradient boosted regression for TCH estimation. We discuss the technical and scientific challenges involved in the generation of reliable mechanisms for estimating TCH from Planet Dove satellite image spectral and textural features. Our developed software toolchain is a robust and generalizable regression model that provides a root mean square error (RMSE) of 4.36 m for Peru. This represents a helpful advancement towards better monitoring of tropical forests and improves efforts in reducing emissions from deforestation and forest degradation (REDD+), an important climate change mitigation approach.

A test of community assembly rules using foliar endophytes from a tropical forest canopy

Abstract Community assembly theory assumes that ecological communities are spatially delimited into patches. Within these patches, coexistence results from environmental filtering, competition and immigration. Truly delineated communities exist in laboratory studies of microbial cultures in Petri dishes, yet empirical tests conducted in continuous environments often use patches defined by convention as opposed to realized boundaries. Here we perform a test of ecological community assembly rules using foliar endophyte communities from a tropical rainforest, where leaves are considered as patches for both fungal and bacterial communities. We determined the diversity of fungal and bacterial endophytes using environmental DNA sequencing of 365 top‐canopy leaves, collected from 38 host trees belonging to 22 different species across a 4‐ha research plot. Three leaves were collected from three or more branches within each tree crown. We tested the effect of host tree species and their level of phylogenetic relatedness on community composition as well as the contribution of geographic distance between leaves to endophyte community diversity. Endophyte diversity significantly differed across host tree species, as did community composition. Within certain endophytic orders (Xylariales, Rhizobiales) species assemblages significantly differed across host tree species, but this trend was weaker or non‐existent in other orders known to contain pathogens and saprotrophs (Polyporales, Solirubrobacterales). Phylogenetically related host tree species displayed more similar endophyte communities than expected by chance, but geographically close trees did not. Consistent with the finding of host‐specificity, nearby leaves tended to host more similar communities than distantly positioned ones. These findings demonstrate that foliar endophytes are structured by dispersal across small spatial scales, but at the scale of the canopy they display patterns of neutral filtering, with only a small part of variation described by host tree differences. Endophyte communities thus act as a model system in evoking the rules predicted by theoretical community ecology.

The importance of forests as microclimate refuges for mammals in Sumatra

The forests of Aceh are key habitat for several iconic, but critically endangered Sumatran mammals. Preserving these forests is vital to ensuring the continued survival of these species. In addition to the immediate threats of habitat loss and degradation, hunting and human-wildlife conflict, climate change will present further challenges to mammal conservation. A major focus of ecological research is to predict species ranges under future climate change using species distribution models, which correlate existing species distributions with environmental data. This enables us to identify and prioritise the most vulnerable habitats and species for protection, restoration or relocation and ensure the best use of limited resources. Producing these models in Sumatra is challenging due to a lack of species data and accurate environmental data. Furthermore, climatic conditions under tropical forest canopy are significantly cooler and less variable than background macroclimate conditions and can shield mammals from climatic extremes. This decoupling effect has been observed in both primary and secondary forests, although the full extent to which human activities is not known. Identifying microclimate refuges and characteristics which promote thermal buffering of forests will favour long-term mammal conservation under climate change by identifying key locations for habitat protection and restoration to protect climate sensitive species from climate extremes.

Corrections to “A Machine-Learning Approach to PolInSAR and LiDAR Data Fusion for Improved Tropical Forest Canopy Height Estimation Using NASA AfriSAR Campaign Data” [Oct 19 3453-3463

This paper investigates the benefits of integrating multibaseline polarimetric interferometric SAR (PolInSAR) data with LiDAR measurements using a machine-learning approach in order to obtain improved forest canopy height estimates. Multiple interferometric baselines are required to ensure consistent height retrieval performance across a broad range of tree heights. Previous studies have proposed multibaseline merging strategies using metrics extracted from PolInSAR measurements. Here, we introduce the multibaseline merging using a support vector machine trained by sparse LiDAR samples. The novelty of this method lies in the new way of combining the two datasets. Its advantage is that it does not require a complete LiDAR coverage, but only sparse LiDAR samples distributed over the PolInSAR image. LiDAR samples are not used to obtain the best height among a set of height stacks, but rather to train the retrieval algorithm in selecting the best height using the variables derived through PolInSAR processing. This enables more accurate height estimation for a wider scene covered by the SAR with only partial LiDAR coverage. We test our approach on NASA AfriSAR data acquired over tropical forests by the L-band UAVSAR and the LVIS LiDAR instruments. The estimated height from this approach has a higher accuracy (r2 = 0.81, RMSE = 7.1 m) than previously introduced multibaseline merging approach (r2 = 0.67, RMSE = 9.2 m). This method is beneficial to future spaceborne missions, such as GEDI and BIOMASS, which will provide a wealth of near-contemporaneous LiDAR samples and PolInSAR measurements for mapping forest structure at global scale.