The ability to accurately assess liana (woody vine) infestation at the landscape level is essential to quantify their impact on carbon dynamics and help inform targeted forest management and conservation action. Remote sensing techniques provide potential solutions for assessing liana infestation at broader spatial scales. However, their use so far has been limited to seasonal forests, where there is a high spectral contrast between lianas and trees. Additionally, the ability to align the spatial units of remotely sensed data with canopy observations of liana infestation requires further attention. We combined airborne hyperspectral and LiDAR data with a neural network machine learning classification to assess the distribution of liana infestation at the landscape‐level across an aseasonal primary forest in Sabah, Malaysia. We tested whether an object‐based classification was more effective at predicting liana infestation when compared to a pixel‐based classification. We found a stronger relationship between predicted and observed liana infestation when using a pixel‐based approach (RMSD = 27.0% ± 0.80) in comparison to an object‐based approach (RMSD = 32.6% ± 4.84). However, there was no significant difference in accuracy for object‐ versus pixel‐based classifications when liana infestation was grouped into three classes; Low [0–30%], Medium [31–69%] and High [70–100%] (McNemar’s χ 2 = 0.211, P = 0.65). We demonstrate, for the first time, that remote sensing approaches are effective in accurately assessing liana infestation at a landscape scale in an aseasonal tropical forest. Our results indicate potential limitations in object‐based approaches which require refinement in order to accurately segment imagery across contiguous closed‐canopy forests. We conclude that the decision on whether to use a pixel‐ or object‐based approach may depend on the structure of the forest and the ultimate application of the resulting output. Both approaches will provide a valuable tool to inform effective conservation and forest management.
In a shadehouse experiment we tested the effects of light, nutrients and ectomycorrhizal fungi (EMF) on the growth of Vatica albiramis van Slooten (Diptero
Aim Conservation activities have increasingly focused on issues at the level of the landscape but are constrained by limited data and knowledge relating to biodiversity at this scale. Satellite remote sensing has considerable, but under‐exploited, potential as a source of information on biodiversity at the landscape level. Remote sensing has generally been used to assess biodiversity indirectly, using approaches that often fail to fully exploit the information content of the imagery and typically only with regard to the species richness component of biodiversity. The aim of this paper was to assess the potential of remote sensing as a source of information on the richness, evenness and composition of tree species in a tropical rain forest. Location The test site was a c. 225 km 2 region centred on the Danum Valley Field Centre, Borneo. This test site contained regions of undisturbed and differentially logged rain forest. Methods Data on tree biodiversity had been acquired for fifty‐two sample plots by standard field survey methods and were used to derive summary indices of biodiversity for seedlings, saplings and mature trees. Differences between logged and unlogged sites were evaluated by comparison of the indices and species accumulation curves. A Landsat Thematic Mapper (TM) image of the site acquired close to the date of the field survey was obtained and rigorously pre‐processed. Feedforward neural networks were used to derive predictions of biodiversity indices from the imagery. A Kohonen self organizing map neural network was used to ordinate the field data to derive classes of forest defined by relative similarity in species composition. The separability of the defined classes in the Landsat TM image was evaluated with a discriminant analysis. Results Analyses of the field data revealed considerable variation in the biodiversity of seedlings, saplings and trees at the site, associated, in part, with differences in logging activities. This variation in biodiversity was manifest in the remotely sensed data. The analyses indicated an ability to (1) predict biodiversity indices, with the highest correlation between predicted and actual index observed for evenness described by Shannon entropy ( r = 0.546), but especially to (2) classify nine forest classes defined on the basis of similarity in tree species composition (accuracy 95.8%). Main conclusions Logging activities impacted on biodiversity and the resulting variation in biodiversity was reflected in the remotely sensed imagery. Using methods that exploit more fully the information content of the imagery than those used in other previous studies, a richer representation of biodiversity may be derived. This representation includes estimates of key summary indices of biodiversity, notably richness and evenness, as well as information on species composition. The results indicate that remotely sensed data may be used as a source of information on biodiversity at the landscape scale that may be used to inform conservation science and management.
Tropical landscape regeneration affects hydrological ecosystem functioning by regulating the amount of water that reaches the soil surface and changing soil infiltration rates. This affects the recharge and storage of water in the soil and streamflow responses. Therefore, it is important to assess how the fraction of rainfall that reaches the forest floor changes as secondary forests mature, and how forest structure affects throughfall via changes in storage capacity and evapotranspiration. Therefore, we monitored throughfall for twelve regenerating, logged-over forest plots in Sabah, Malaysian Borneo over a 7-month period and tested if inclusion of measures of forest heterogeneity improved the prediction of throughfall as a fraction of precipitation. On average across all plots, throughfall was 84% of precipitation, but was lower (as low as 74%) in plots with a longer recovery time since logging. There was a significant relationship between throughfall and tree density and basal area, as well as the Shannon Diversity Index and the coefficient of variation of the diameter at breast height, although species and structural diversity measures (Shannon Index and the coefficient of variation) did not improve model performance substantially. The overall best performing model was a linear regression with tree density. There was no relation between LiDAR-derived Top of Canopy (TCH) and mean throughfall, suggesting that this remotely sensed proxy of canopy height is not needed to estimate throughfall and more in-depth analysis of other LiDAR-products such as point clouds may be required. Our results imply that estimating throughfall in this forest type can be reliably achieved using tree density, and that this is not substantially affected by species diversity or structural heterogeneity variables, at least in the context of logged and regenerating forests in Sabah. Graphical abstract Forest of a similar size or height can have a different structure. In this study we investigate if diversity also affects the amount of throughfall for plots across a disturbance gradient.
Background: Acclimation to light is a driver of tropical forest dynamics and key to understanding the coexistence of dipterocarps, and how their demographic rates and traits trade-off.Aims: We examined light niche divergence in six dipterocarp species and hypothesised that seedlings can be functionally grouped, and allocate resources to either growth or storage in response to light changes.Methods: A pot experiment was performed to measure size-specific growth rate, wood density and total non-structural carbohydrate (NSC) concentrations of dipterocarp seedlings exposed to a simulated gap opening.Results: Light-demanding species responded to a gap opening with increased growth and decreased wood density, whereas shade-tolerant species showed a greater relative increase in NSC concentration. Iditol – an alditol – was identified, and Dryobalanops lanceolata responded to a gap opening with a significantly smaller increase in alditol concentration compared to other species.Conclusions: We group light-demanding and shade-tolerant species based on their acclimation to light and show that a generalist species is unique based on its response of NSC concentration to a gap opening. Our findings emphasise that the ecology of these species needs to be further studied in the context of their physiology to support their effective use in large-scale forest restoration efforts.
NDVI, climate and river flow data
No abstract is provided for this article.
In order to account for the effects of debris cover in model scenarios of the response of glaciers to climate change and water resource planning, it is important to know the distribution and thickness of supraglacial debris and to monitor its change over time. Previous attempts to map surface debris thickness using thermal band remote sensing have relied upon time-specific empirical relationships between surface temperature and thickness, limiting their general applicability. In this paper, we develop a physically based model that utilizes Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) thermal band remotely sensed imagery and is based on a solution of the energy balance at the debris surface. The model is used to estimate debris thickness on Miage glacier, Italy, and is validated using field debris-thickness measurements and a previously published debris-thickness map. The temporal transferability of the model is demonstrated through successful application to a separate ASTER image from a different year using reanalysis meteorological input data. This model has the potential to be used for regional-scale supraglacial debris-thickness mapping and monitoring for debris up to at least 0.50 m thickness, but improved understanding of the spatial patterns of air temperature, aerodynamic roughness length and thermal properties across debris-covered glaciers is needed.
No abstract is provided for this article.
Muddy intertidal flats are important resources for coastal development activities, such as coastal shoal reclamation and mudflat cultivation. Understanding changes in intertidal flat topography is essential for intertidal zone development, management, and protection. As an essential topographic factor, the slope of an intertidal flat can effectively express profile morphology in the cross-shore direction, reflect topographic undulation in the long-shore direction, and be indicative of intertidal flat erosion and deposition. Previously, intertidal flat slopes have been estimated by the two-temporal average gradient (TTAG) method using two spatially separated waterlines derived from satellite remote sensing data and their relevant water elevations. However, this method does not adequately reflect the profile morphology of different coastline types, especially sinuous coastlines, and is highly sensitive to the selected waterlines. This study proposes an effective strategy for estimating slope considering not only the characteristics of the vertical terrain undulation but also the horizontal plane-form shape of the coastline. Using waterlines extracted from sequential satellite images and their corresponding tidal height information, the slopes were estimated utilizing a profile morphology discriminant inferential (PMDI) method on straight coasts and a digital elevation model (DEM) method on sinuous coasts. We obtained the following results: (a) for straight coasts, by judging the various shapes of the coastal profiles and curve-fitting separately, the PMDI method achieved significant improvements in accuracy and robustness of slope estimates compared to results obtained using the TTAG method; and (b) for sinuous coasts, the DEM-based method performed better for addressing the intersecting waterline issue and accurately retrieved the slope, although this accuracy is strongly dependent on the areal coverage of the DEM and the precision of the terrain inversion. Using this new paired methodology, we estimated that the average slope of the intertidal flats in the Mid-Jiangsu Province between the Sheyang Estuary, the Liangduo Estuary, and the Lianxing Port was 0.96‰. In general, the slopes from north to south in this coastal area exhibited a steep-gentle-steep pattern, which was consistent with in situ observed data. We conclude that stratifying the coastlines according to their plane-form shapes and applying different methods to straight and sinuous coastlines can result in a more robust estimation of coastal slope for muddy intertidal flats than previous TTAG methods. This work demonstrates the utility of satellite-based remote sensing for retrieving critical coastal geomorphological information in dynamic terrains where in situ data are difficult to obtain.
Human beings benefit from a wide range of goods and services from the natural environment that are collectively known as ecosystem services. However, rapid natural habitat loss, overexploitation and climate change is causing accelerating losses of populations and species, with largely unknown consequences on ecosystem functioning and the sustainable provision of ecosystem services. It is crucial, therefore, to develop a suite of indicators of the health and status of ecosystems, to monitor and quantify services delivery and to facilitate policy responses to stop and reverse negative trends. An effective framework to facilitate the development of suitable indicators is by using the SMART approach, which defines five criteria that could be applied to set monitoring and management goals, which are Specific, Measurable, Achievable, Realistic and Time-sensitive. Remote sensing provides a useful data source that can monitor ecosystems over multiple spatial and temporal scales. Although the development and application of landscape indicators (vegetation indices, for example) derived from remote sensing data are comparatively advanced, it is acknowledged that a number of organisms and ecosystem processes are not detectable by remote sensing. This paper explores several approaches to overcome this limitation, by examining the strong affinity of species with dominant habitat structures and through the coupling of remote sensing and ecosystem process models using examples drawn from a number of important ecosystems.
