Tropical Forest Disturbance Research Articles

Repeat GEDI footprints measure the effects of tropical forest disturbances

More of the Amazon rainforest is disturbed each year than completely deforested, but the impact of these disturbances on the carbon cycle remains poorly understood. Recent algorithmic advances using optical and radar remote sensing have improved detection of disturbances at fine spatiotemporal resolution, but quantifying changes in forest structure and biomass associated with these detected disturbances has proven challenging. The Global Ecosystem Dynamics Investigation (GEDI), a spaceborne lidar mission collecting data from 2019 to 2023, provides an opportunity to address this problem. GEDI captured billions of measurements of forest height, plant area, and understory structure within ∼25-m diameter footprints scattered across the tropics. Though the instrument had no guaranteed repeat cycle, it sometimes sampled nearby locations twice; some of these spatially near-coincident footprints happened to measure forest structure before and after a detected disturbance, providing information on how disturbance affected aboveground biomass. In this study, we developed an efficient general-purpose open-source pipeline for identifying spatially coincident footprints, which is a computationally complex task, and used the pipeline to find over 13,700 footprint pairs with intervening disturbance events across the Amazon biome. We also identified a set of ∼65,000 spatially near-coincident footprint pairs that lacked an intervening disturbance but came from regions with similar disturbance threat; these provided a control dataset to evaluate the effectiveness of estimating forest structure and biomass changes with nearby footprints. Analysis of this Amazon-wide dataset demonstrated that GEDI was able to measure statistically significant canopy height and biomass deficits following non-stand-replacing disturbances as small as 900 m2 (30 m × 30 m). GEDI's unique three-dimensional view of forest structure also reflected the effects of different intensities of fire disturbance, including 20% of burned areas where the upper canopy retained most of its height, but the understory suffered substantial foliage losses. Finally, we modeled the relationship between Landsat and Sentinel-1 disturbance detection parameters and GEDI-estimated percent biomass loss, showing that certain satellite-derived intensity metrics are correlated with increasing biomass loss and identifying temporal trends in biomass loss and recovery following disturbance. This work represents an important step towards the development of a pan-tropical, spatially explicit system for tracking carbon losses and structural changes arising from forest disturbance.

Pre-contact and post-colonial ecological legacies shape Surinamese rainforests.

Disturbances in tropical forests can have long-lasting ecological impacts, but their manifestations (ecological legacies) in modern forests are uncertain. Many Amazonian forests bear the mark of past soil modifications, species enrichments, and fire events, but the trajectories of ecological legacies from the pre-contact or post-colonial period remain relatively unexplored. We assessed the fire and vegetation history from 15 soil cores ranging from 0 to 10 km from a post-colonial Surinamese archaeological site. We show that (1) fires occurred from 96 bc to recent times and induced significant vegetation change, (2) persistent ecological legacies from pre-contact and post-colonial fire and deforestation practices were mainly within 1 km of the archaeological site, and (3) palm enrichment of Attalea, Oenocarpus and Astrocaryum occurred within 0, 1, and 8 km of the archaeological site, respectively. Our results challenge the notion of spatially extensive and persistent ecological legacies. Instead, our data indicate that the persistence and extent of ecological legacies are dependent on their timing, frequency, type, and intensity. Examining the mechanisms and manifestations of ecological legacies is crucial in assessing forest resilience and Indigenous and local land rights in the highly threatened Amazonian forests.

Recent advances and challenges in monitoring and modeling of disturbances in tropical moist forests

Tropical moist forests have been severely affected by natural and anthropogenic disturbances, leading to substantial changes in global carbon cycle and climate. These effects have received great attention in scientific research and debates. Here we review recent progress on drivers and ecological impacts of tropical moist forest disturbances, and their monitoring and modeling methods. Disturbances in tropical moist forests are primarily driven by clearcutting, selective logging, fire, extreme drought, and edge effects. Compound disturbances such as fire and edge effects aggravate degradation in the edge forests. Drought can result in terrestrial carbon loss via physiological impacts. These disturbances lead to direct carbon loss, biophysical warming and microclimate change. Remote sensing observations are promising for monitoring forest disturbances and revealing mechanisms, which will be useful for implementing disturbance processes in dynamic vegetation models. Yet, constrained spatiotemporal coverages and resolutions limit the application of these data in process-based models. It is also challenging to represent physical processes derived from fine-resolution remote sensing data in coarse-resolution models. We highlight the need to continuously integrate new datasets and physical processes in forest disturbance modeling to advance understanding of disturbance patterns and impacts. Interactions and impacts of climate change and anthropogenic activities should also be considered for modeling and assessing feedbacks of tropical moist forest disturbances.

Impacts of bamboo dominance and die‐off on seedling dynamics in a tropical secondary forest (Brazil)

AbstractAulonemia aristulata is a tropical bamboo species of mixed rhizome type and monocarpic flowering that can become super‐dominant after anthropogenic disturbances in tropical forests. The synchronized reproductive events of flowering and die‐off of monocarpic bamboo systems provide a rare opportunity to evaluate the temporal effects of bamboo dominance on forest regeneration. To evaluate the effects of bamboo dominance on forest regeneration, we compared the structure (abundance, richness, composition) and dynamics (mortality, recruitment, survival and growth) of tree and shrub seedling communities from two non‐contiguous forests stands (bamboo‐dominated vs. non‐dominated sites) for 2 years, before and after a synchronized reproductive event. Bamboo dominated plots had higher abundance, floristic richness and recruitment of tree and shrub seedlings when compared with bamboo non‐dominated plots. Bamboo die‐off did not represent an opportunity to improve establishment and investment in the vertical growth (height) of seedlings in bamboo‐dominated plots. We postulate that the mode of rhizome extension seen in bamboos might be a consistent trait determining the spatial pattern of occupation and population structure and thereby playing a fundamental role in the pattern of native tree community establishment after bamboo die‐off. The formulation of a management approach is necessary in order to better understand the biological traits of bamboo species and their influence on the spatial pattern of occupation and population structure. Additionally, this influence may differently impact restoration and definition of management practices, including the need for direct intervention by manually controlling bamboo cover following die‐off to promote and maintain seedling establishment and diversity.

Use of high throughput DNA analysis to characterize the nodule-associated bacterial community from four ages of Inga punctata trees in a Costa Rican cloud forest.

Leguminous tree root nodule nitrogen-fixing bacteria are critical for recuperation of soil C and N cycle processes after disturbance in tropical forests, while other nodule-associated bacteria (NAB) may enhance nodule development and activity, and plant growth. However, little is known of these root nodule microbiomes. Through DNA analysis, we evaluated the bacterial taxa associated with the root nodules of the 1-year-old, 2-year-old, 13-year-old, and old growth Inga punctata trees in a cloud forest. Bradyrhizobium diazoefficiens was the dominant taxon found in all nodules at 63.16% to 85.71% mean percent sequences (MPS) of the total nodule bacterial DNA and was found in the youngest nodules examined (1 year old), suggesting that it is the primary nodular bacteria. There were 26 other NAB genera with collective MPS levels between 7.4% to 12.2%, while 15 of these genera were found in the Bulk Forest soils at collective MPS levels of 4.6%. These bacterial community compositions were different between the NAB and Bulk Forest soils, suggesting the NAB became concentrated within the root nodules, resulting in communities with different compositions from the Bulk Forest soils. Twenty-three of the 26 NAB genera were previously identified with the potential to perform 9 plant growth promoting (PGP) activities, suggesting their importance in root nodule development and plant growth. These NAB communities appeared to successionally develop over time into more complex taxonomic communities, which is consistent with the outcome of advanced microbial communities following succession. The presence of both B. diazoefficiens and the NAB communities in the nodules across all ages of tree roots, and the potential for PGP activities linked with most of the NAB genera, suggest the importance of B. diazoefficiens and the NAB community for nodule development and enhanced development and growth of I. punctata throughout its lifespan, and most critically in the younger plants.

Linking Anthropogenic Landscape Perturbation to Herbivory and Pathogen Leaf Damage in Tropical Tree Communities.

Anthropogenic disturbance of tropical humid forests leads to habitat loss, biodiversity decline, landscape fragmentation, altered nutrient cycling and carbon sequestration, soil erosion, pest/pathogen outbreaks, among others. Nevertheless, the impact of these alterations in multitrophic interactions, including host-pathogen and vector-pathogen dynamics, is still not well understood in wild plants. This study aimed to provide insights into the main drivers for the incidence of herbivory and plant pathogen damage, specifically, into how vegetation traits at the local and landscape scale modulate such interactions. For this purpose, in the tropical forest of Calakmul (Campeche, Mexico), we characterised the foliar damage caused by herbivores and pathogens in woody vegetation of 13 sampling sites representing a gradient of forest disturbance and fragmentation in an anthropogenic landscape from well preserved to highly disturbed and fragmented areas. We also evaluated how the incidence of such damage was modulated by the vegetation and landscape attributes. We found that the incidence of damage caused by larger, mobile, generalist herbivores, was more sensitive to changes in landscape configuration, while the incidence of damage caused by small and specialised herbivores with low dispersal capacity was more influenced by vegetation and landscape composition. In relation to pathogen symptoms, the herbivore-induced foliar damage seems to be the main factor related to their incidence, indicating the enormous importance of herbivorous insects in the modulation of disease dynamics across tropical vegetation, as they could be acting as vectors and/or facilitating the entry of pathogens by breaking the foliar tissue and the plant defensive barriers. The incidence of pathogen damage also responded to vegetation structure and landscape configuration; the incidence of anthracnose, black spot, and chlorosis, for example, were favoured in sites surrounded by smaller patches and a higher edge density, as well as those with a greater aggregation of semi-evergreen forest patches. Fungal pathogens were shown to be an important cause of foliar damage for many woody species. Our results indicate that an increasing transformation and fragmentation of the tropical forest of southern Mexico could reduce the degree of specialisation in plant-herbivore interactions and enhance the proliferation of generalist herbivores (chewers and scrapers) and of mobile leaf suckers, and consequently, the proliferation of some symptoms associated with fungal pathogens such as fungus black spots and anthracnose. The symptoms associated with viral and bacterial diseases and to nutrient deficiency, such as chlorosis, could also increase in the vegetation in fragmented landscapes with important consequences in the health and productivity of wild and cultivated plant species. This is a pioneering study evaluating the effect of disturbances on multitrophic interactions, offering key insights on the main drivers of the changes in herbivory interactions and incidence of plant pathogens in tropical forests.

Near Real-Time Mapping of Tropical Forest Disturbance Using SAR and Semantic Segmentation in Google Earth Engine

Satellite-based forest alert systems are an important tool for ecosystem monitoring, planning conservation, and increasing public awareness of forest cover change. Continuous monitoring in tropical regions, such as those experiencing pronounced monsoon seasons, can be complicated by spatially extensive and persistent cloud cover. One solution is to use Synthetic Aperture Radar (SAR) imagery acquired by the European Space Agency’s Sentinel-1A and B satellites. The Sentinel 1A and B satellites acquire C-band radar data that penetrates cloud cover and can be acquired during the day or night. One challenge associated with operational use of radar imagery is that the speckle associated with the backscatter values can complicate traditional pixel-based analysis approaches. A potential solution is to use deep learning semantic segmentation models that can capture predictive features that are more robust to pixel-level noise. In this analysis, we present a prototype SAR-based forest alert system that utilizes deep learning classifiers, deployed using the Google Earth Engine cloud computing platform, to identify forest cover change with near real-time classification over two Cambodian wildlife sanctuaries. By leveraging a pre-existing forest cover change dataset derived from multispectral Landsat imagery, we present a method for efficiently developing a SAR-based semantic segmentation dataset. In practice, the proposed framework achieved good performance comparable to an existing forest alert system while offering more flexibility and ease of development from an operational standpoint.

How textural features can improve SAR-based tropical forest disturbance mapping

How textural features can improve SAR-based tropical forest disturbance mapping

Deep learning and automatic reference label harvesting for Sentinel-1 SAR-based rapid tropical dry forest disturbance mapping

The advent of temporally dense radar data such as the Sentinel-1 SAR have opened the door for rapid forest disturbance detection in the humid tropics. Tropical dry forest disturbance detection, however, were challenged by seasonality and more open canopy characteristics. In this manuscript, we proposed a Sentinel-1 SAR and deep learning based rapid forest disturbance detection approach for tropical dry forests. We demonstrated a weakly supervised method for reference label harvesting based on medium resolution globally available forest and forest disturbance maps. We trained a deep neural network model to derive forest and forest disturbance probabilities from Sentinel-1 images in the first step. We then implemented a probabilistic disturbance detection and refinement method to map forest disturbances in near real-time in two test regions in Paraguay and Mozambique. We mapped new forest disturbances in an emulated near real-time scenario for 2020 and 2021 and evaluated the spatial accuracy of the disturbance alerts by generating area adjusted precision, recall and F-1 score. We also evaluated the improvement in timeliness of disturbance detection by estimating mean time difference of disturbance events detection with that of Landsat-based GLAD alerts. The generated alerts in Paraguay and Mozambique achieved a precision, recall and F-1 score of 0.99, 0.61, 0.75 and 0.97, 0.51, 0.66, respectively. The proposed method detected disturbances with a mean of 21 days (± 18 days) earlier in Paraguay and 18 days (± 18 days) earlier in Mozambique than the Landsat-based GLAD alerts. These results demonstrated the efficacy of the proposed method and its viability to be used in an operational setting to generate large area rapid near real-time disturbance alerts in the dry tropics.

Species Composition and Diversity of Plants along Human-Induced Disturbances in Tropical Moist Sal Forests of Eastern Ghats, India

Plant biodiversity data are prerequisites for the sustainable management of a forest. We used quantitative ecological tools to determine the species composition, diversity (richness and evenness), population structure, distribution patterns, and regeneration status of trees in a Tropical Moist Sal Forest of Eastern Ghats, India. For this purpose, a field inventory was conducted during 2020–2022 in sixteen 1.0 ha forest stands along a human-induced disturbance gradient. A total of 161 species (61 trees, 40 shrubs, 60 herbs) belonging to 77 families and 143 genera were recorded in the 16.0 ha of forest area. The results revealed a significant (p < 0.01) decrease in the tree and shrub species density, basal area, species richness, and diversity along the gradient of disturbance. However, in the case of herbs, the Shannon–Weiner diversity index increased significantly (p < 0.01) with increasing disturbance levels. Irrespective of forest types and disturbance levels, the tree diameter class (10–30 cm) accounted for the highest stem density in the forest. A mixed trend was found in the case of the basal area; the >41 cm diameter class constituted the highest basal area in the Pure Sal Forest while the 10–30 cm class was in the Moist Deciduous Forest without Sal. Tree species richness was found higher in lower diameter classes. Disturbances impacted the distribution pattern of trees; in the Pure Sal Forest, the contagious distribution of trees were 61.54%, 40%, and 12.5% in undisturbed, low-disturbed, and moderately disturbed sites, respectively. The percent of trees showing random and regular distribution increased with the increased level of disturbance in all forest types. The number of tree species having good regeneration decreased with the increased disturbance intensity in all forest types. Frequent grazing, repeated forest fires, and poor soil seed banks at the Moderately Disturbed site were the main reasons for the poor/no regeneration of Pterocarpus marsupium, Adina cordifolia, Terminalia bellerica, and some other economical species. Significant changes in structural attributes of the tree community revealed the impact of human-induced disturbances in the Moist Sal Forests of Eastern Ghats. The disturbance mosaics promoted the growth of many invasive weed species and lianas, depleting the number of valuable species in the forest. This study suggests the adaption of sustainable biodiversity conservation approaches through the active participation of the tribal so that the remnants the Moist Sal Forests of Eastern Ghats can be controlled to prevent further degradation.

Monitoring monthly tropical humid forest disturbances with Planet NICFI images in Cameroon

Selective logging and smallholder clearing are the dominant drivers of tropical forest disturbances in Cameroon (CAM). However, they are difficult to monitor accurately by satellite remote sensing because openings in the canopy can be very small, the vegetation is generally fast-growing, and cloud cover is common. Norway's International Climate and Forest Initiative (NICFI) provides access to monthly and biannual collections of 5 m Planet images in the tropics, creating a great opportunity for mapping tropical forest disturbances. In this paper, we develop a method to monitor monthly small-scale tropical humid forest disturbances using 2021 Planet NICFI images. First, a cloud mask for each of the monthly Planet NICFI images was predicted by integrating a cloud cover possibility map with a haze optimal transformation (HOT) index image. Second, possible monthly forest disturbances were mapped from a self-referenced Hue_forest (rHue_forest) index image. Finally, an adjusted monthly forest disturbance map was produced by eliminating many false positives with a spatio-temporal filter. Results in CAM demonstrated that the method applied to monthly Planet NICFI images was effective in identifying numerous small-scale tropical forest disturbances that were short-lived, lasting only a few months. After filtering out new possible forest disturbances in 2021 which did not meet a temporal permanence criterion based on the monthly images, the adjusted user´s and producer´s accuracies for CAM were 84.7 ± 2.9% and 61.5 ± 46.4%, respectively (±95% confidence intervals). Our results provide much greater spatial detail than forest disturbance methods based on Sentinel-1 and Landsat images. The adjusted disturbed area of humid forests in CAM was estimated as 1,168 ± 882 km2 in 2021. The proposed method for monthly mapping of forest disturbance using Planet NICFI images has great potential to complement existing forest cover change products and monitor hitherto neglected tropical forest disturbances due to small-scale clearing.

ReCuSum: A polyvalent method to monitor tropical forest disturbances

Change detection methods based on Earth Observations are increasingly used to monitor rainforest in the intertropical band. Until recently, deforestation monitoring was mainly based on remotely sensed optical images which often face limitations in humid tropical areas due to frequent cloud coverage. This leads to late detections of disturbance events. Since the launch of Sentinel-1 acquiring images with a revisit time of 12 days and a spatial resolution of 5 × 20 m in Brazil, Synthetic Aperture Radar (SAR) images have been increasingly used to monitor deforestation. In this study, we propose a multitemporal version of the change detection method we previously applied to timeseries of Sentinel-1 SAR images, to monitor deforestation/degradation in the Congo rainforest. Our approach is based on a Cumulative Sum (CuSum) method combined with a spatial recombination of Critical Thresholds (CuSum cross-Tc). The newly developed multitemporal CuSum method (ReCuSum) was applied to a time-series of 82 dual polarization (VH, VV) Ground Range Detected (GRD) Sentinel-1 images acquired in the Para State, in the Brazilian Amazonia, from 29/09/2016 to 01/07/2019. The ReCuSum method consists of iteratively applying the CuSum cross-Tc to monitor multiple changes in a time-series by splitting the time-series at each date of detected change and by independently iterating over the time periods resulting from the splits. The number of changes in the time-series was then analysed according to the vegetation type (Forest, non-forest vegetation) determined by visual inspection of optical Sentinel-2 image and PlanetScope monthly mosaic. This showed a difference between non-forest vegetation and forested areas. A threshold based on the number of changes (Tnbc) was then developed to differentiate forest from non-forest disturbances. The ability to monitor non-forest vegetation was analysed: the CuSum cross-Tc detected up to 90% of the total non-forest vegetation area over the study region in the past period. After removing past disturbances and past non-forest vegetation, then removing the pixels covered with non-forest vegetations based on Tnbc, the application of the ReCuSum led to a precision of 81%, a recall of 68%, a kappa coefficient of 0.72 and a F1- score of 0.74 in forest disturbance monitoring. According to these results, ReCuSum applied to Sentinel-1 time-series of images can be used for efficient forest disturbance monitoring and for generating a forest / non-forest map after the application of newly developed post-processing steps. Sentinel-1 imagery can be used for both Forest / Non-forest mapping and for forest disturbance detection. ReCuSum was released as an open-source GIT project available at: https://forgemia.inra.fr/bertrand.ygorra/cusum-deforestation_monitoring.

Carbon stock dynamics in a disturbed tropical forest ecosystem of Central India: Strategies for achieving carbon neutrality

Anthropogenic land use changes in the vicinity of forest catchments are explicitly responsible for the deforestation and degradation of tropical forest thereby altering the carbon (C) balance under rising atmospheric carbon dioxide (CO2). Thus, strategic mitigation measures are required to achieve climate neutrality by 2030. It becomes imperative to understand the impact of land cover conversion on the functioning of tropical forests and to determine the carbon dynamics. The present study examined the land cover changes (LUC) in dry tropical ecosystem of Central India during the last two decades and assessed its influence on the forest biomass, litter crop, and C stocks. Geospatial techniques coupled with ground measurements were employed in the study. The total biomass, vegetative C stocks and soil C were estimated as 64 to 338.3 Mg ha−1, 26.4 to 131.1 Mg C/ha and 24.6 to 50.2 Mg C/ha respectively. In the past decades (2000–2020), about 1851.8 ha of forest constituting 15.1% of the total area under study was lost (mainly to Agriculture) under open mixed forest, moderately mixed forest and mixed teak forest. The study demonstrated that forests of the studied region behave as a potential C source, which accounted for a net loss of 59 gigagram carbon (Gg C) equivalent to 216 Gg CO2eq. Besides, discussed three viable scenarios of restoration to offset C emissions and to achieve C neutral landscape by 2030. The conservation and restoration of 26–85% degraded forests (open mixed forest and moderately mixed dense forests), use of agroforestry on 1/5th of existing agricultural lands, or implementation of regenerative agricultural practices in 30% of the area will compensate for net losses, while attaining C neutrality. Further, the finding of this study helps to understand the climatic extremes as per the commitments of Paris Agreement, 2015 and Glasgow Pact, 2021.

Spatial variation of soil seed banks along a gradient of anthropogenic disturbances in tropical forests on coral islands

Poor regeneration of natural vegetation is a major factor contributing to the degradation of tropical coral islands. Soil seed banks (SSB) are important for maintaining the resilience of plant communities. However, the community characteristics and spatial distribution of SSBs and the controlling factors along human disturbance on coral islands are unclear. To fill this gap, we measured the community structure and spatial distributions of forest SSBs on three coral islands in the South China Sea, with varying degrees of human disturbance. The results showed that strong human disturbance increased the diversity, richness, and density of SSBs, as well as increased the richness of invasive species. With increased human disturbance, the heterogeneity pattern of SSBs spatial distribution changed from difference between forest east and west to forest center and edge. The similarity between the SSBs and above-ground vegetation also increased, and the distribution of invasive species extended from the edge to the central area of the forests, demonstrating that human disturbance limited the outward dispersal of seeds of resident species but increased the inward dispersal of seeds of invasive species. Interaction between soil properties, plant characteristics, and human disturbance explained 23–45% of the spatial variation of forest SSBs on the coral islands. However, human disturbance reduced the correlations of plant communities and spatial distribution of SSBs with soil factors (i.e., available phosphorus and total nitrogen) and increased the correlations of the community characteristics of SSB with landscape heterogeneity index, road distance, and shrub and litter cover. Resident seed dispersal on tropical coral islands might be enhanced by reducing building height, constructing buildings in down-wind locations, and preserving corridors that support animal movement among forest fragments.

Monitoring direct drivers of small-scale tropical forest disturbance in near real-time with Sentinel-1 and -2 data

Advancements in satellite-based forest monitoring increasingly enable the near real-time detection of small-scale tropical forest disturbances. However, there is an urgent need to enhance such monitoring methods with automated direct driver attributions to detected disturbances. This would provide important additional information to make forest disturbance alerts more actionable and useful for uptake by different stakeholders. In this study, we demonstrate spatially explicit and near real-time methods to monitor direct drivers of small-scale tropical forest disturbance across a range of tropical forest conditions in Suriname, the Republic of the Congo and the Democratic Republic of the Congo. We trained a convolutional neural network with Sentinel-1 and Sentinel-2 data to continuously classify newly detected RAdar for Detecting Deforestation (RADD) alerts as smallholder agriculture, road development, selective logging, mining or other. Different monitoring scenarios were evaluated based on varying sensor combinations, post-disturbance time periods and confidence levels. In general, the use of Sentinel-2 data was found to be most accurate for driver classifications, especially with data composited over a period of 4 to 6 months after the disturbance detection. Sentinel-1 data showed to be valuable for more rapid classifications of specific drivers, especially in areas with persistent cloud cover. Throughout all monitoring scenarios, smallholder agriculture was classified most accurately, while road development, selective logging and mining were more challenging to distinguish. An accuracy assessment throughout the full extent of our study regions revealed a Macro-F1 score of 0.861 and an Overall Accuracy of 0.897 for the best performing model, based on the use of 6-month post-disturbance Sentinel-2 composites. Finally, we addressed three specific monitoring use cases that relate to rapid law enforcement against illegal activities, ecological impact assessments and timely carbon emission reporting, by optimizing the trade-off in classification timeliness and confidence to reach required accuracies. Our findings demonstrate the strong capacities of high spatiotemporal resolution satellite data for monitoring direct drivers of small-scale forest disturbance, considering different user interests. The produced forest disturbance driver maps can be accessed via: https://bartslagter94.users.earthengine.app/view/forest-disturbance-drivers.

Fear or food? Prey availability is more important than predation risk in determining aerial insectivorous bat responses across a disturbed tropical forest landscape

Fear or food? Prey availability is more important than predation risk in determining aerial insectivorous bat responses across a disturbed tropical forest landscape

A common framework to model recovery in disturbed tropical forests

1. Despite their exceptional biodiversity and carbon stocks, more than 80% of tropical forests are disturbed. However, a lot of interrogations remain around the ability of vegetation attributes in tropical forests to recover from the various anthropogenic disturbances coexisting in many tropical landscapes. While these different disturbances are usually studied separately, this work provides, for the first time, a common modelling framework of vegetation attribute recovery in differently disturbed forests.2. We develop an original Bayesian hierarchical model of recovery trajectories, considering disturbed forests in a common framework, through a disturbance intensity gradient. As a case study, we test our modelling approach on data from two long-term experiments, Tirimbina (Costa Rica) and Paracou (French Guiana), where forest permanent sample plots have been set up following selective logging (63.25 ha), agriculture (4 ha), and clearcutting+fire (6.25 ha).3. We build a modelling framework that stands out by: (i) its interpretability, with model parameters having a clear ecological meaning; (ii) its robustness, allowing to compare parameter values amongst ecological systems to ensure that predictions are ecologically sound; (iii) its versatility to consider disturbance intensity through post-disturbance changes in a structural variable, either as input data, or as a transformed parameter without requiring pre-disturbance monitoring; (iv) its flexibility to explicitly consider, and test, the effects on forest recovery of various disturbance types, along an intensity gradient, in a single integrative model.4. First conclusions drawn from our common framework underline the strongest above-ground biomass and diversity recovery rate offered by selective logging, compared to agriculture and clearcutting+fire, as well as the strong effect of disturbance intensity on taxonomic composition recovery.5. Considering disturbed forests in a common framework might help managers to know which disturbance types need to be firmly avoided, which intensity range makes human activities sustainable in forested environments, and where inexpensive natural regeneration should be favoured over active restoration, such as tree planting. Testing this framework with various monitoring tools to estimate disturbance intensity and model vegetation attribute recovery, i.e., with forest monitoring or remote sensing data, will be the next step to make it widely applicable.

Near real-time monitoring of tropical forest disturbance by fusion of Landsat, Sentinel-2, and Sentinel-1 data

The ability to detect forest disturbance rapidly relies heavily on the frequency of usable satellite observations. Combining optical and SAR data can provide the most usable observations in the tropics and hence has the potential to achieve more timely detection of forest disturbance than using any individual sensor system. A new algorithm (Fusion Near Real-Time, or FNRT) for near real-time monitoring of tropical forest disturbance by fusion of Landsat, Sentinel-2, and Sentinel-1 data fits a time series model to the data of each individual sensor system and produces a change score for each clear observation during the monitoring period. The change scores from the three different kinds of sensors are then combined for disturbance monitoring. Applying FNRT using all three data sources in eight selected sites in the Amazon Basin detected 69.8 ± 5% and 84.6 ± 4.1% of the forest disturbances within the first 30 and 60 days, respectively. FNRT using all three sensors reached a peak producer's accuracy of 91.6 ± 3.2% at approximately a 100-day lag time, compared to 86.4 ± 3.8% using the two optical sensors combined, and 71.7 ± 4.7% using Landsat only (the best-performing single-sensor). The user's accuracy of FNRT using all three sensors increased to 90.9 ± 3.3% when excluding false detections in non-forest areas. Overall, the model result using all three data sources had the highest detection rate with shorter lag times. Although modest, including Sentinel-1 data in the multi-sensor fusion approach improved the detection speed of some disturbance events. Given the abundance of optical data during the dry season in our study region, Sentinel-1 data would be more valuable during the wet season or in persistently cloudy regions such as Madagascar or coastal West Africa.

Developing a more complete understanding of tropical montane forest disturbance ecology through landslide research

Landslides are a central component of tropical montane forest disturbance regimes, including in the tropical Andes biodiversity hotspot, one of the most biodiverse ecosystems in the world. Technological developments in remote sensing have made landscape-scale landslide studies possible, unlocking new avenues for understanding montane biodiversity, ecosystem functioning, and the future effects of climate change. Here, we outline three axes of inquiry for future landslide ecology research in Andean tropical montane forest. We focus exclusively on the Andes due to the vast floral diversity and high endemicity of the tropical Andes biodiversity hotspot, and its importance for global biodiversity and regional ecosystem service provisioning; the broad elevational, latitudinal, and topographic gradients across which landslide dynamics play out; and the existence of long-term plot networks that provide the necessary baseline data on mature forest structure, composition, and functioning to contextualize disturbance impacts. The three lines of study we outline, which draw heavily on remote sensing data and techniques, will deepen scientific understanding of tropical montane forest biodiversity and ecosystem functioning, and the potential impacts of climate change on both. They are: (1) tracking landslide biodiversity dynamics across time and space with high spatial and temporal resolution satellite and unoccupied aerial vehicle imagery; (2) assessing the ecological influence of landslides through the lens of plant functional diversity with imaging spectroscopy; and (3) understanding current and predicting future landslide regimes at scale by building a living landslide inventory spanning the tropical Andes. The research findings from these three axes of inquiry will shed light on the role of landslides and the process of forest recovery from them in both the Andes and worldwide.

Towards the use of satellite-based tropical forest disturbance alerts to assess selective logging intensities

Illegal logging is an important driver of tropical forest loss. A wide range of organizations and interested parties wish to track selective logging activities and verify logging intensities as reported by timber companies. Recently, free availability of 10 m scale optical and radar Sentinel data has resulted in several satellite-based alert systems that can detect increasingly small-scale forest disturbances in near-real time. This paper provides insight in the usability of satellite-based forest disturbance alerts to track selective logging in tropical forests. We derive the area of tree cover loss from expert interpretations of monthly PlanetScope mosaics and assess the relationship with the RAdar for Detecting Deforestation (RADD) alerts across 50 logging sites in the Congo Basin. We do this separately for various aggregation levels, and for tree cover loss from felling and skidding, and logging roads. A strong linear relationship between the alerts and visually identified tree cover loss indicates that with dense time series satellite data at 10 m scale, the area of tree cover loss in logging concessions can be accurately estimated. We demonstrate how the observed relationship can be used to improve near-real time tree cover loss estimates based on the RADD alerts. However, users should be aware that the reliability of estimations is relatively low in areas with few disturbances. In addition, a trade-off between aggregation level and accuracy requires careful consideration. An important challenge regarding remote verification of logging activities remains: as opposed to tree cover loss area, logging volumes cannot yet be directly observed by satellites. We discuss ways forward towards satellite-based assessment of logging volumes at high spatial and temporal detail, which would allow for better remote sensing based verification of reported logging intensities and tracking of illegal activities.