Publications

Airborne survey of debris-covered glaciers in the western Italian Alps

No abstract is provided for this article.

Modelling climate change impact on water resources of the Upper Indus Basin

Climate change has implications for water resources by increasing temperature, shifting precipitation patterns and altering the timing of snowfall and glacier melt, leading to shifts in the seasonality of river flows. Here, the Soil & Water Assessment Tool was run using downscaled precipitation and temperature projections from five global climate models (GCMs) and their multi-model mean to estimate the potential impact of climate change on water balance components in sub-basins of the Upper Indus Basin (UIB) under two emission (RCP4.5 and RCP8.5) and future (2020–2050 and 2070–2100) scenarios. Warming of above 6 °C relative to baseline (1974–2004) is projected for the UIB by the end of the century (2070–2100), but the spread of annual precipitation projections among GCMs is large (+16 to −28%), and even larger for seasonal precipitation (+91 to −48%). Compared to the baseline, an increase in summer precipitation (RCP8.5: +36.7%) and a decrease in winter precipitation were projected (RCP8.5: −16.9%), with an increase in average annual water yield from the nival–glacial regime and river flow peaking 1 month earlier. We conclude that predicted warming during winter and spring could substantially affect the seasonal river flows, with important implications for water supplies.

The World's Tallest Tropical Tree in Three Dimensions

OPINION article Front. For. Glob. Change, 18 June 2019Sec. Tropical Forests Volume 2 - 2019 | https://doi.org/10.3389/ffgc.2019.00032

Optimal feature selection for estimating biomass using a genetic algorithm

Why can we detect lianas from space?

Lianas, woody vines acting as structural parasites of trees, have profound effects on the composition and structure of tropical forests, impacting tree growth, mortality, and forest succession. Remote sensing offers a powerful tool for quantifying the scale of liana infestation, provided the availability of robust detection methods. We analyze the consistency and global specificity of spectral signals from liana-infested tree crowns and forest stands, examining the underlying mechanisms. We compiled a database, including leaf reflectance spectra from 5424 leaves, fine-scale airborne reflectance data from 999 liana-infested canopies, and coarse-scale satellite reflectance data covering hectares of liana-infested forest stands. To unravel the mechanisms of the liana spectral signal, we applied mechanistic radiative transfer models across scales, corroborated by field data on liana leaf chemistry and canopy structure. We find a consistent liana spectral signature at canopy and stand scales across sites. This signature mainly arises at the canopy level due to direct effects of leaf angles, resulting in a larger apparent leaf area, and indirect effects from increased light scattering in the NIR and SWIR regions, linked to lianas’ less costly leaf construction compared to trees. The existence of a consistent global spectral signal for lianas suggests that large-scale quantification of liana infestation is feasible. However, because the traits identified are not exclusive to lianas, accurate large-scale detection requires rigorously validated remote sensing methods. Our models highlight challenges in automated detection, such as potential misidentification due to leaf phenology, tree life-history, topography, and climate, especially where the scale of liana infestation is less than a single remote sensing pixel. The observed cross-site patterns also prompt ecological questions about lianas’ adaptive similarities across environments, indicating possible convergent evolution due to shared constraints on leaf biochemical and structural traits. Open data statement Of the 17 datasets used, 10 are published and publicly accessible, with links provided in this submission (Appendix S1: Section S1). Upon acceptance, remaining seven datasets will be provided via Smithsonian’s Dspace. The open-source model code is available as R-package ccrtm ( https://cran.r-project.org/web/packages/ccrtm/index.html ) and on github ( https://github.com/MarcoDVisser/ccrtm ). Code will be archived in Zenodo should the manuscript be accepted for publication

Death of a Glacier:Glacier d'Estelette

No abstract is provided for this article.

A regional model for estimating the aboveground carbon density of Borneo's tropical forests from airborne laser scanning

Borneo contains some of the world's most biodiverse and carbon dense tropical forest, but this 750,000-km2 island has lost 62% of its old-growth forests within the last 40 years. Efforts to protect and restore the remaining forests of Borneo hinge on recognising the ecosystem services they provide, including their ability to store and sequester carbon. Airborne Laser Scanning (ALS) is a remote sensing technology that allows forest structural properties to be captured in great detail across vast geographic areas. In recent years ALS has been integrated into state-wide assessment of forest carbon in Neotropical and African regions, but not yet in Asia. For this to happen new regional models need to be developed for estimating carbon stocks from ALS in tropical Asia, as the forests of this region are structurally and compositionally distinct from those found elsewhere in the tropics. By combining ALS imagery with data from 173 permanent forest plots spanning the lowland rain forests of Sabah, on the island of Borneo, we develop a simple-yet-general model for estimating forest carbon stocks using ALS-derived canopy height and canopy cover as input metrics. An advanced feature of this new model is the propagation of uncertainty in both ALS- and ground-based data, allowing uncertainty in hectare-scale estimates of carbon stocks to be quantified robustly. We show that the model effectively captures variation in aboveground carbons stocks across extreme disturbance gradients spanning tall dipterocarp forests and heavily logged regions, and clearly outperforms existing ALS-based models calibrated for the tropics, as well as currently available satellite-derived products. Our model provides a simple, generalised and effective approach for mapping forest carbon stocks in Borneo, providing a key tool to support the protection and restoration of its tropical forests.

Developing ground and remotely sensed indicators of the sustainability of tropical rainforest exploitation systems in South East Asia

No abstract is provided for this article.

Spatiotemporal dynamics of heat stress and cold stress on UK rapeseed cropping over 1961–2020

Most temperature stress research in oil crops has focused on either heat or cold stress with analyses of the effects of both in combination rare. For the UK, neither the spatiotemporal hot spots of temperature stressed arable areas nor the comparative trends of heat and cold stresses for rapeseed cropping under climate change are understood. This study investigated the spatiotemporal heat and cold stresses for UK rapeseed over 1961–2020, and quantified the normalized rapeseed production loss index (fRPL) induced by heat stress during flowering. Stress indices including a literature derived heat stress index (fHS), cold degree days (CDD), with historical land cover and crop productivity data were used to comparatively study both stresses and to estimate fRPL. Results showed increasing fHS, particularly during flowering (April to May) and main yield-forming reproductive stages (spanning flowering through pod and seed development from April to July) over the study period, with fHS being negatively correlated with latitude. The decreasing values of CDD and frequency of cold stress suggest cold stress decreased during the flowering, vegetative (September to November) and reproductive stages. Notably, this study observed that annually at the UK level heat stress was increasing at a faster rate than cold stress was decreasing during flowering. The increasing values of fRPL, with significant differences between decades and regions, suggested an increasing production loss. These results presented a potentially trend of increasing heat stress impacts on future rapeseed production and further work is required to understand the specific impacts and mitigation strategies for addressing UK food security.

SAR imagery for flood monitoring and assessment

Exploring temporality in socio-ecological resilience through experiences of the 2015–16 El Niño across the Tropics

In a context of both long-term climatic changes and short-term climatic shocks, temporal dynamics profoundly influence ecosystems and societies. In low income contexts in the Tropics, where both exposure and vulnerability to climatic fluctuations is high, the frequency, duration, and trends in these fluctuations are important determinants of socio-ecological resilience. In this paper, the dynamics of six diverse socio-ecological systems (SES) across the Tropics – ranging from agricultural and horticultural systems in Africa and Oceania to managed forests in South East Asia and coastal systems in South America – are examined in relation to the 2015–16 El Niño, and the longer context of climatic variability in which this short-term 'event' occurred. In each case, details of the socio-ecological characteristics of the systems and the climate phenomena experienced during the El Niño event are described and reflections on the observed impacts of, and responses to it are presented. Drawing on these cases, we argue that SES resilience (or lack of) is, in part, a product of both long-term historical trends, as well as short-term shocks within this history. Political and economic lock-ins and dependencies, and the memory and social learning that originates from past experience, all contribute to contemporary system resilience. We propose that the experiences of climate shocks can provide a window of insight into future ecosystem responses and, when combined with historical perspectives and learning from multiple contexts and cases, can be an important foundation for efforts to build appropriate long-term resilience strategies to mediate impacts of changing and uncertain climates.

Impacts of Four Decades of Forest Loss on Vertebrate Functional Habitat on Borneo

Tropical forests are undergoing drastic transformations, putting at risk the species that rely on them. On the island of Borneo, between 1973 and 2015, 50% of the forest has been lost, much of this to oil palm and other industries. We explore the impacts of these four decades of forest loss on the functionally connected habitat of 245 forest birds and mammals. First, we map potential suitable habitat in 1973 and 2015 by refining reported ranges by elevation, forest cover and patch size. We find that, on average, these species have lost 28% of habitat within their ranges. Then we use graph-theory connectivity models to calculate the amount of area that is functionally connected for each species, according to their natal dispersal abilities. This analysis shows a mean loss of 35% of their connected habitat and reveals the larger, and often hidden, impacts from deforestation. Losses in functionally connected habitat are largely driven by area of habitat loss, yet maximum elevational range limit also explain some of the differences modelled across species with lowland species being the most affected. We present a vulnerability index of threat arising from functionally connected habitat loss. The spatial aggregation of this vulnerability index serves as a tool for setting conservation priorities for forest remnants on Borneo, given that most of the ranges of these species are not protected. We make recommendations for the use of connectivity models to prioritize resources for research and conservation on Borneo and other biodiversity hotspots.

Modelling the humification of exposed blanket peat with HyMap data

Estimating aboveground carbon density and its uncertainty in Borneo's structurally complex tropical forests using airborne laser scanning

. Borneo contains some of the world's most biodiverse and carbon-dense tropical forest, but this 750 000 km2 island has lost 62 % of its old-growth forests within the last 40 years. Efforts to protect and restore the remaining forests of Borneo hinge on recognizing the ecosystem services they provide, including their ability to store and sequester carbon. Airborne laser scanning (ALS) is a remote sensing technology that allows forest structural properties to be captured in great detail across vast geographic areas. In recent years ALS has been integrated into statewide assessments of forest carbon in Neotropical and African regions, but not yet in Asia. For this to happen new regional models need to be developed for estimating carbon stocks from ALS in tropical Asia, as the forests of this region are structurally and compositionally distinct from those found elsewhere in the tropics. By combining ALS imagery with data from 173 permanent forest plots spanning the lowland rainforests of Sabah on the island of Borneo, we develop a simple yet general model for estimating forest carbon stocks using ALS-derived canopy height and canopy cover as input metrics. An advanced feature of this new model is the propagation of uncertainty in both ALS- and ground-based data, allowing uncertainty in hectare-scale estimates of carbon stocks to be quantified robustly. We show that the model effectively captures variation in aboveground carbon stocks across extreme disturbance gradients spanning tall dipterocarp forests and heavily logged regions and clearly outperforms existing ALS-based models calibrated for the tropics, as well as currently available satellite-derived products. Our model provides a simple, generalized and effective approach for mapping forest carbon stocks in Borneo and underpins ongoing efforts to safeguard and facilitate the restoration of its unique tropical forests.

Impact Assessment of Flood Extent on Agricultural Land and Communities Using Google Earth Engine and SAR Data: A Case Study of Indus River Basin

The Indus River Basin has undergone significant hydrological changes due to climate change, leading to increased flood frequency, posing a risk to agriculture-based food security. This study focused on rain-induced flood events from August to September 2022 across the provinces of Punjab, Sindh, and Baluchistan using Google Earth Engine, Sentinel-1A Synthetic Aperture Radar (SAR) data, and Landcover datasets. Flooding caused considerable damage to agricultural land and communities, affecting 49,602.92 km2 of land. In Sindh, the total land inundated is 2,042.1 km2 with 915.9 km2 of agricultural land and 609.8 km2 of built-up areas affected. Hence, district-level damage assessment includes Sukkur (497.1 km2), Sanghar (565.2 km2), and Khairpur (979.9 km2). In Baluchistan province, the flooded area was 10,733.4 km2. The agricultural land affected was 674.8 km2, and 47.8 km2 of built-up land. Heavy rain further intensified flooding affected 1002.2 km2 in Jhal Magsi, 7,266.5 km2 in Khuzdar, and 2,464.7 km2 in Lasbella. In Punjab, 4001.3 km2 of land flooded including 297.6 km2 of built-up areas, and 776.9 km2 affected agricultural land. At the District-level affected areas were D.G. Khan (1,871.3 km2), Muzaffargarh (620 km2), and Rajanpur (1,509.7 km2). integration of remote sensing and GEE provided crucial flood insights and climate risk reduction strategies, especially in data-scarce regions.

Globally, functional traits are weak predictors of juvenile tree growth, and we do not know why

1. Plant functional traits, in particular specific leaf area (SLA), wood density and seed mass, are often good predictors of individual tree growth rates within communities. Individuals and species with high SLA, low wood density and small seeds tend to have faster growth rates. 2. If community-level relationships between traits and growth have general predictive value, then similar relationships should also be observed in analyses that integrate across taxa, biogeographic regions and environments. Such global consistency would imply that traits could serve as valuable proxies for the complex suite of factors that determine growth rate, and, therefore, could underpin anew generation of robust dynamic vegetation models. Alternatively, growth rates may depend more strongly on the local environment or growth-trait relationships may vary along environmental gradients. 3. We tested these alternative hypotheses using data on 27 352 juvenile trees, representing 278 species from 27 sites on all forested continents, and extensive functional trait data, 38% of which were obtained at the same sites at which growth was assessed. Data on potential evapotranspiration (PET), which summarizes the joint ecological effects of temperature and precipitation, were obtained from a global data base. 4. We estimated size-standardized relative height growth rates (SGR) for all species, then related them to functional traits and PET using mixed-effect models for the fastest growing species and for all species together. 5. Both the mean and 95th percentile SGR were more strongly associated with functional traits than with PET. PET was unrelated to SGR at the global scale. SGR increased with increasing SLA and decreased with increasing wood density and seed mass, but these traits explained only 3.1% of the variation in SGR. SGR-trait relationships were consistently weak across families and biogeographic zones, and over a range of tree statures. Thus, the most widely studied functional traits in plant ecology were poor predictors of tree growth over large scales. 6. Synthesis.We conclude that these functional traits alone may be unsuitable for predicting growth of trees over broad scales. Determining the functional traits that predict vital rates under specific environmental conditions may generate more insight than a monolithic global relationship can offer.

The topographic and spectral expression of mining subsidence

No abstract is provided for this article.

The value of logged tropical forests: A study of ecosystem services in Sabah, Borneo

Combatting the global threats to climate and biodiversity require further efforts in conserving earth's key ecosystems such as tropical forests. Today, worldwide conservation strategies have largely focused on protecting old-growth forests. Yet logged forests are more widespread than old-growth forests and harbour huge amounts of biodiversity and carbon stores, providing many ecosystem services and functions. In Borneo, logged forests support the livelihood of half a million indigenous people. Empowering these communities to manage logged forests can result in ecological, social and economic benefits. This study took place in Sabah, Malaysian Borneo, and focused on logged forests. We investigated how community members value ecosystem services (ES) in terms of importance for their lives, and explored factors influencing their valuation. We combined a qualitative analysis of semi-structured interviews with a statistical analysis of respondents' preferences for ES. We present three key findings: 1. Clean water, clean air, regulation of temperature, flood and erosion are the ES ranked as the most important by all community members regardless of their spatial and socio-demographic backgrounds, 2. Prioritization for other ES varied according to members' age, gender, ethnicity, dependence on forest resources and distance to forest, 3. Community members' priorities for ES align with the ecological literature that these services are retained by logged forest. Our results emphasize the need to promote people-centred approaches to design sustainable conservation policies of logged forests in Sabah.