No abstract is provided for this article.
The full realization of the potential of remote sensing as a source of environmental information requires an ability to generalize in space and time. Here, the ability to generalize in space was investigated through an analysis of the transferability of predictive relations for the estimation of tropical forest biomass from Landsat TM data between sites in Brazil, Malaysia and Thailand. The data sets for each test site were acquired and processed in a similar fashion to facilitate the analyses. Three types of predictive relation, based on vegetation indices, multiple regression and feedforward neural networks, were developed for biomass estimation at each site. For each site, the strongest relationships between the biomass predicted and that measured from field survey was obtained with a neural network developed specifically for the site (r>0.71, significant at the 99% level of confidence). However, with each type of approach problems in transferring a relation to another site were observed. In particular, it was apparent that the accuracy of prediction, as indicated by the correlation coefficient between predicted and measured biomass, declined when a relation was transferred to a site other than that upon which it was developed. Part of this problem lies with the observed variation in the relative contribution of the different spectral wavebands to predictive relations for biomass estimation between sites. It was, for example, apparent that the spectral composition of the vegetation indices most strongly related to biomass differed greatly between the sites. Consequently, the relationship between predicted and measured biomass derived from vegetation indices differed markedly in both strength and direction between sites. Although the incorporation of test site location information into an analysis resulted in an increase in the strength of the relationship between predicted and actual biomass, considerable further research is required on the problems associated with transferring predictive relations.
Liana cutting is a management practice currently applied to encourage seedling regeneration and tree growth in some logged tropical forests. However, there is limited empirical evidence of its effects on forest demographic rates in Southeast Asia. We used 22 four‐hectare plots in the Sabah Biodiversity Experiment (a reduced impact logging site) enrichment line planted with 16 dipterocarp species to assess the effects of complete liana cutting on tree growth and survival. We compared plots where lianas were only cut along planting lines (standard enrichment line planting) with those with one (2014) or two rounds (2011 and 2014) of complete liana cutting. We found increased seedling growth following the first complete liana cut in 2011 relative to the enrichment line planting, consistent with previous studies. The response after 3 years to the cutting in 2014 depended on whether lianas had been previously cut or not: in twice‐cut plots, seedling growth was not significantly different from the standard enrichment planting controls, whereas growth in plots with only one complete cut in 2014 was significantly slower. Seedling survival decreased through time for both once‐ and twice‐cut liana treatments but remained stable in controls. Sapling growth after the 2014 liana cutting showed a similar pattern to seedling growth, while tree growth following the 2014 liana cutting was significantly lower than controls regardless of whether lianas were cut twice (2011 and 2014) or once (2014). Differences in response between the two rounds of liana cutting were likely due to changes in precipitation—2011 was followed by consistent rainfall while 2014 was followed by two severe droughts within 2 years. Synthesis and applications . Our results generally support the widely reported positive effects of liana cutting on tree growth and survival. However, reduced growth and survival after the 2015/2016 El Niño suggests that drought may temporarily undermine the benefits of liana cutting in logged tropical forests. Managers of similar areas in SE Asia should consider halting liana cutting during El Niño events. In other tropical areas, seedling survival should be monitored to assess to what extent results from SE Asia are transferable.
Regional botanical surveys supported by field experiments suggest that atmospheric nitrogen deposition threatens the balance between species and causes los
Forest carbon stocks in rapidly developing tropical regions are highly heterogeneous, which challenges efforts to develop spatially-explicit conservation actions. In addition to field-based biodiversity information, mapping of carbon stocks can greatly accelerate the identification, protection and recovery of forests deemed to be of high conservation value (HCV). We combined airborne Light Detection and Ranging (LiDAR) with satellite imaging and other geospatial data to map forest aboveground carbon density at 30 m (0.09 ha) resolution throughout the Malaysian state of Sabah on the island of Borneo. We used the mapping results to assess how carbon stocks vary spatially based on forest use, deforestation, regrowth, and current forest protections. We found that unlogged, intact forests contain aboveground carbon densities averaging over 200 Mg C ha−1, with peaks of 500 Mg C ha−1. Critically, more than 40% of the highest carbon stock forests were discovered outside of areas designated for maximum protection. Previously logged forests have suppressed, but still high, carbon densities of 60–140 Mg C ha−1. Our mapped distributions of forest carbon stock suggest that the state of Sabah could double its total aboveground carbon storage if previously logged forests are allowed to recover in the future. Our results guide ongoing efforts to identify HCV forests and to determine new areas for forest protection in Borneo.
Climate change induced alterations to rainfall patterns have the potential to affect the regeneration dynamics of plant species, especially in historically everwet tropical rainforest. Differential species response to infrequent rainfall may influence seed germination and seedling establishment in turn affecting species distributions. We tested the role of watering frequency intervals (from daily to six-day watering) on the germination and the early growth of Dipterocarpaceae seedlings in Borneo. We used seeds that ranged in size from 500 to 20,000 mg in order to test the role of seed mass in mediating the effects of infrequent watering. With frequent rainfall, germination and seedling development traits bore no relationship to seed mass, but all metrics of seedling growth increased with increasing seed mass. Cumulative germination declined by 39.4% on average for all species when plants were watered at six-day intervals, and days to germination increased by 76.5% on average for all species from daily to six-day intervals. Final height and biomass declined on average in the six-day interval by 16% and 30%, respectively, but the percentage decrease in final size was greater for large-seeded species. Rooting depth per leaf area also significantly declined with seed mass indicating large-seeded species allocate relatively more biomass for leaf production. This difference in allocation provided an establishment advantage to large-seeded species when water was non-limiting but inhibited their growth under infrequent rainfall. The observed reduction in the growth of large-seeded species under infrequent rainfall would likely restrict their establishment in drier microsites associated with coarse sandy soils and ridge tops. In total, these species differences in germination and initial seedling growth indicates a possible niche axis that may help explain both current species distributions and future responses to climate change.
No abstract is provided for this article.
Responses of alpine tree line ecosystems to increasing atmospheric CO2 concentrations and global warming are poorly understood. We used an experiment at the Swiss tree line to investigate changes in vegetation biomass after 9 years of free air CO2 enrichment (+200 ppm; 2001-2009) and 6 years of soil warming (+4 °C; 2007-2012). The study contained two key tree line species, Larix decidua and Pinus uncinata, both approximately 40 years old, growing in heath vegetation dominated by dwarf shrubs. In 2012, we harvested and measured biomass of all trees (including root systems), above-ground understorey vegetation and fine roots. Overall, soil warming had clearer effects on plant biomass than CO2 enrichment, and there were no interactive effects between treatments. Total plant biomass increased in warmed plots containing Pinus but not in those with Larix. This response was driven by changes in tree mass (+50%), which contributed an average of 84% (5.7 kg m(-2) ) of total plant mass. Pinus coarse root mass was especially enhanced by warming (+100%), yielding an increased root mass fraction. Elevated CO2 led to an increased relative growth rate of Larix stem basal area but no change in the final biomass of either tree species. Total understorey above-ground mass was not altered by soil warming or elevated CO2 . However, Vaccinium myrtillus mass increased with both treatments, graminoid mass declined with warming, and forb and nonvascular plant (moss and lichen) mass decreased with both treatments. Fine roots showed a substantial reduction under soil warming (-40% for all roots <2 mm in diameter at 0-20 cm soil depth) but no change with CO2 enrichment. Our findings suggest that enhanced overall productivity and shifts in biomass allocation will occur at the tree line, particularly with global warming. However, individual species and functional groups will respond differently to these environmental changes, with consequences for ecosystem structure and functioning.
No abstract is provided for this article.
As secondary tropical forests grow, their canopy structure and density change. This affects the canopy storage and aerodynamic roughness, and thus the amount of water that is lost to interception. Because interception is a considerable part of total evapotranspiration, it is important to assess how interception changes as secondary forests mature, and how this is affected by forest structure. However, the effect of tropical forest regeneration, and in particular changes in forest structure, on mean throughfall are so far poorly studies. This hampers the estimation of the interception loss, and thus the water balance, for regenerating forests. Therefore, we monitored throughfall for twelve regenerating, logged-over forest plots in Sabah, Malaysian Borneo over a 7-month period to determine the effect of forest regeneration on mean throughfall and tested if inclusion of measures of forest heterogeneity improved the prediction of mean throughfall compared to estimates based on tree height or density alone. Mean throughfall varied between 74% and 89% (average: 84%) of precipitation and was lowest in regenerating forest plots with a longer time since logging. There was a significant negative relationship between mean throughfall and tree density or basal area, as well as variables reflecting forest heterogeneity (i.e., the Shannon Diversity Index and the coefficient of variation of the diameter at breast height). Nevertheless, the inclusion of these indicators of heterogeneity did not improve model performance substantially; the best model was a linear relation with tree density alone. These results suggest that in the context of logged and regenerating forests in Sabah, mean throughfall depends mainly on tree density and is not substantially affected by species diversity or structural heterogeneity. To see if mean throughfall could be estimated over larger spatial scales based on LiDAR data, we also tested the relation between mean throughfall and LiDAR-derived Top of Canopy (TCH) but this relation was not significant. A more in-depth analysis of LiDAR-products, such as point clouds, may be needed to estimate mean throughfall over large areas in tropical rainforests.
Permanent forest plot data from Sabah, Malaysia. Danum Valley Conservation Area. Here we provide a brief description of the permanent plot data collected following the same protocols utilized within the Kuamut forest reserve. Twenty circular field plots with a 30 m radius that were established across the protected area as part of a collaboration between SEARRP and the Carnegie Airborne Observatory (CAO) in 2017. These plots were surveyed following the same protocols as those described by the Kuamut conservation project. The field protocol is provided. Field plot locations where selected using airborne LiDAR to specifically target both high Aboveground Carbon Density (ACD) areas, and also areas with different ACD predictions based on a draft set of carbon mapping models. See Jucker et al. (2018) and Asner et al. (2017) for further details. Note subsequent measurements (in 2020) include more plots. Subsequent measurements allowed further data checking and corrections, so some minor updates to the 2017 dataset have been made since the previous publications. Please see additional Zenodo data entries for the 2020 measure. Use of these data require citation of this dataset and citation of the two original journal articles that delivered and explain the work, and that you inform us of the use of the data. We would also appreciate the opportunity to be involved in work making use of this data. The required citations are as follows: Asner, Gregory P., Philip G. Brodrick, Christopher Philipson, Nicolas R. Vaughn, Roberta E. Martin, David E. Knapp, Joseph Heckler, et al. 2017. “Mapped Aboveground Carbon Stocks to Advance Forest Conservation and Recovery in Malaysian Borneo.” <em>Biological Conservation</em> 217 (June 2017): 289–310. https://doi.org/10.1016/j.biocon.2017.10.020 Jucker, Tommaso, Gregory P. Asner, Michele Dalponte, Philip G. Brodrick, Christopher D. Philipson, Nicholas R. Vaughn, Yit Arn Teh, et al. 2018. “Estimating Aboveground Carbon Density and Its Uncertainty in Borneo’s Structurally Complex Tropical Forests Using Airborne Laser Scanning.” <em>Biogeosciences</em> 15 (12): 3811–30. https://doi.org/10.5194/bg-15-3811-2018 This work would not be possible without the incredibly hard work and dedication of the entire SEARRP field team, including Philip Ak Ulok; Hii Siew Yee; Remmy Bin Murus; Alexander Karolus; Andy Brian Karolus; Frederica Karolus; Zidey Fulgentius; Welday Bin Girang; Mohamad Taufiq Bin Sumin, Mohd Fadil Bin Abd Karim, Joulu Rasion, and Japin Bin Rasion. We are very grateful to the help of all the field staff not specifically mentioned here.
The biomass and biomass dynamics of forests are major uncertainties in our understanding of tropical environments. Remote sensing is often the only practical means of acquiring information on forest biomass but has not always been used successfully. Here the conventional approaches to the estimation of forest biomass from remotely sensed data were evaluated relative to techniques based on the application of artificial neural networks. Together these approaches were used to estimate and map the biomass of tropical forests in north‐eastern Borneo from Landsat TM data. The neural networks were found to be particularly suited to the application. A basic multi‐layer perceptron network, for example, provided estimates of biomass that were strongly correlated with those measured in the field ( r = 0.80). Moreover, these estimates were more strongly correlated with biomass than those derived from 230 conventional vegetation indices, including the widely used normalized difference vegetation index (NDVI).
No abstract is provided for this article.
Abstract Continued deglaciation in the Bolivian Andes threatens regional water security and may result in increased exposure to geohazards. We analyse high spatial resolution (∼3–5 m) satellite imagery to constrain annual glacier and glacial lake evolution across the Bolivian Andes between 2016 and 2022. The total glaciated area of the region decreased by 9.1%, from 316.6 ± 3.2 km 2 to 287.8 ± 2.9 km 2 ; a rate of loss of 4.8 km 2 a −1 . Concurrently, the number (total surface area) of glacial lakes increased by 2.6% (1.9%), from 704 (37.1 ± 0.7 km 2 ) to 770 (37.8 ± 0.8 km 2 ). A comprehensive glacial lake outburst flood susceptibility analysis was undertaken for the 2022 lake inventory, with eleven lakes identified as ‘high susceptibility’. Subglacial topographic analysis was undertaken to predict potential future sites for lake formation. We identified 55 such sites given continued deglaciation. The model was tested by applying it to areas where glaciers retreated between 2000 and 2022. Of the 22 potentially susceptible lakes which formed during this period, 14 (64%) did so in overdeepenings identified by the model. This is the first time that an inventory of potential future lake sites has been produced for the region.
Upland vegetation represents an important resource that requires frequent monitoring. However, the heterogeneous nature of upland vegetation and lack of ground data require classification techniques that have a high degree of generalization ability. This study investigates the use of artificial neural networks as a means of mapping upland vegetation from remotely sensed data. First, the optimum size of support to map upland vegetation was estimated as being less than 4 m, which suggested that soft classification techniques and high spatial resolution IKONOS imagery were required. The use of high spatial resolution imagery for regional‐scale areas has introduced new challenges to the remote sensing community, such as using limited ground data and mapping land‐cover dynamics and variation over large areas. This work then investigated the utility of artificial neural networks (ANN) for regional‐scale upland vegetation from IKONOS imagery using limited ground data and to map unseen data from remote geographical locations. A Multiple Layer Perceptron was trained with pixels from an IKONOS image using early stopping; however, despite high classification accuracies when calculated for pixels from an area where training pixels were extracted, the networks did not produce high accuracies when applied to unseen data from a remote area.
Uganda is one of the four top refugee-hosting countries in the world and the largest in Africa, a product of the surrounding geopolitical context and Uganda's progressive refugee laws and policy. Refugees in Uganda are afforded freedom of movement, the right to work, the provision of social services, and are allocated land for residential and agricultural use in settlements. High dependence on natural resources to meet needs for shelter, food, fuel and income generation has caused environmental change and degradation in and around refugee settlements. Increasing demand for fuelwood and timber amongst growing populations puts strain on forest resources, threatening biodiversity and the provision of ecosystem services critical to livelihoods. Yet these dynamics differ depending on socio-cultural, political-economic and ecological factors specific to local settlement contexts. This report generates a nuanced view of environment–livelihood interactions, informing recommendations for protracted refugee contexts. The research aims to: 'Explore how displacement impacts on environmental change and the subsequent development of sustainable livelihoods' through the following objectives:• Examine the nature and extent of environmental change in different settlements using satellite remote sensing and field-based observations.• Understand the various ways in which refugees and host communities, living in or around new and long-term refugee settlements, interact with the environment and ecosystem services.• Explore the variety of knowledges and values of refugee and host households for understanding how the environment is used.• Offer recommendations for the management of increasing pressure on land resources within sustainable livelihood practices for development and policy programming.
