Abstract Satellite remote sensing offers the potential to accurately identify and monitor spectrally separable land‐cover classes at a range of spatial and temporal scales. This paper investigates the separability of despoiled land in imagery acquired by sensors carried by the Landsat satellites. These systems offer the opportunity to map areas at large to medium scales at a relatively high temporal frequency and so provide information on environmental quality necessary for many monitoring and planning activities. From an investigation using imagery of South Wales it was found that despoiled land was separable from other classes in the Landsat TM imagery, with an accuracy of over 90 per cent. Furthermore, with Landsat TM data three spectral wavebands were found to provide a level of separability similar to that based on all wavebands available, illustrating potential savings to the analyst. Despoiled land cover was classified from Landsat TM and MSS data and these classifications were evaluated against a manually produced map of despoiled land cover derived from the interpretation of aerial photographs. Estimates of the extent of despoiled land cover in administrative units derived from the Landsat TM data were significantly correlated ( r = 0.81) with the map based estimates, although a weaker correlation was observed with Landsat MSS data.
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.
No abstract is provided for this article.
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.
No abstract is provided for this article.
No abstract is provided for this article.
No abstract is provided for this article.
Due to challenging conditions of field survey techniques, it is difficult to measure the topography of tidal flats, an important parameter to understanding the evolution and dynamics of the constantly changing zone. This study used remotely sensed sediment moisture estimates to retrieve tidal flat elevation. The method is based on the observation that the intertidal zone is gradually exposed from land to sea at low tide, meaning that higher elevations contain less moisture. Here, we investigate the nature of the relationship between reflectance and moisture content from Landsat Enhanced Thematic Mapper Plus images and the study areas as a proxy for mapping the elevation of an exposed tidal flat surface. Statistical analysis confirmed a negative correlation between moisture and elevation; however, the correlation coefficient was relatively weak, and the slope of the intersecting tidal creek was found to be a crucial factor affecting this relationship. After segmenting the slope to correspond to areas of tidal flat and nontidal flat surfaces, the correlation coefficient of the moisture and elevation increased significantly. A retrieval model was then developed to generate the tidal flat elevations of different slope grades. After verification, the retrieval accuracy of the model was up to 17.3 cm. This research study demonstrated that the remotely sensed moisture method is suitable for monitoring the surface elevation of tidal flats.
Over 50% of the world's population live within 3 km of rivers and lakes highlighting the on-going importance of freshwater resources to human health and societal well-being. Whilst covering c. 3.5% of the Earth's non-glaciated land mass, trends in the environmental quality of the world's standing waters (natural lakes and reservoirs) are poorly understood, at least in comparison with rivers, and so evaluation of their current condition and sensitivity to change are global priorities. Here it is argued that a geospatial approach harnessing existing global datasets, along with new generation remote sensing products, offers the basis to characterise trajectories of change in lake properties e.g., water quality, physical structure, hydrological regime and ecological behaviour. This approach furthermore provides the evidence base to understand the relative importance of climatic forcing and/or changing catchment processes, e.g. land cover and soil moisture data, which coupled with climate data provide the basis to model regional water balance and runoff estimates over time. Using examples derived primarily from the Danube Basin but also other parts of the World, we demonstrate the power of the approach and its utility to assess the sensitivity of lake systems to environmental change, and hence better manage these key resources in the future.
The understanding and management of biodiversity is often limited by a lack of data. Remote sensing has considerable potential as a source of data on biodiversity at spatial and temporal scales appropriate for biodiversity management. To-date, most remote sensing studies have focused on only one aspect of biodiversity, species richness, and have generally used conventional image analysis techniques that may not fully exploit the data's information content. Here, we report on a study that aimed to estimate biodiversity more fully from remotely sensed data with the aid of neural networks. Two neural network models, feedforward networks to estimate basic indices of biodiversity and Kohonen networks to provide information on species composition, were used. Biodiversity indices of species richness and evenness derived from the remotely sensed data were strongly correlated with those derived from field survey. For example, the predicted tree species richness was significantly correlated with that observed in the field (r =0.69, significant at the 95% level of confidence). In addition, there was a high degree of correspondence (∼83%) between the partitioning of the outputs from Kohonen networks applied to tree species and remotely sensed data sets that indicated the potential to map species composition. Combining the outputs of the two sets of neural network based analyses enabled a map of biodiversity to be produced.
One of the main environmental threats in the tropics is selective logging, which has degraded large areas of forest. In southeast Asia, enrichment planting with seedlings of the dominant group of dipterocarp tree species aims to accelerate restoration of forest structure and functioning. The role of tree diversity in forest restoration is still unclear, but the 'insurance hypothesis' predicts that in temporally and spatially varying environments planting mixtures may stabilize functioning owing to differences in species traits and ecologies. To test for potential insurance effects, we analyse the patterns of seedling mortality and growth in monoculture and mixture plots over the first decade of the Sabah biodiversity experiment. Our results reveal the species differences required for potential insurance effects including a trade-off in which species with denser wood have lower growth rates but higher survival. This trade-off was consistent over time during the first decade, but growth and mortality varied spatially across our 500 ha experiment with species responding to changing conditions in different ways. Overall, average survival rates were extreme in monocultures than mixtures consistent with a potential insurance effect in which monocultures of poorly surviving species risk recruitment failure, whereas monocultures of species with high survival have rates of self-thinning that are potentially wasteful when seedling stocks are limited. Longer-term monitoring as species interactions strengthen will be needed to more comprehensively test to what degree mixtures of species spread risk and use limited seedling stocks more efficiently to increase diversity and restore ecosystem structure and functioning.
