An assessment of airborne lidar for forest growth studies

Accurate and up-to-date information on forest growth rates is important for management purposes. Recent studies indicate that airborne LiDAR offers a rapid and more cost-effective approach that challenges traditional methods of forest inventorying and may have the potential not only to revolutionise forest management but also to provide key data for assessing terrestrial carbon stocks. This study aims to assess the potential of LIDAR to estimate forest growth of the temperate Sitka spruce plantation forests using canopy height distribution models at Kielder Forest, Northumberland. LIDAR data from 2003 and 2006 provides an excellent opportunity to contribute to existing work which has so far been limited in focus, looking primarily at individual tree level growth in the less densely stocked, slow-growing, cold climate forests of Scandinavia. LIDAR point cloud data from the first and last pulse returns are filtered and classified. Ground returns are used to create digital elevation models (DEM), and first returns used to create digital canopy height models (DCHM). Processed LIDAR data from both years are compared to estimate forest growth. In continuation, LIDAR plot height and growth values are extracted. The results are compared with plot level ground-based data. Height correlations are strong and positive. Growth is detected at all plot locations but correlations with ground-based data are weak and mostly negative. Potential explanations for the lack of correlation are presented and discussed. Further study is necessary to quantify and eliminate systematic and random error within both the LiDAR and ground-based data before LIDAR may be used routinely for forest management purposes

Is soil organic carbon underestimated in the largest mangrove forest ecosystems? Evidence from the Bangladesh Sundarbans

Globally, mangroves sequester a large amount of carbon into the sediments, although spatial heterogeneity exists owing to a wide variety of local, regional, and global controls. Rapid environmental and climate change, including increasing sea-level rise, global warming, reduced upstream discharge and anthropogenic activities, are predicted to increase salinity in the mangroves, especially in the Bangladesh Sundarbans, thereby disrupting this blue carbon reservoir. Nevertheless, it remains unclear how salinity affects the belowground soil carbon despite the recognised effect on above ground productivity. To address this gap, research was undertaken in the Bangladesh Sundarbans to compare total soil organic carbon (SOC) across three salinity zones and to explore any potential predictive relationships with other physical, chemical properties and vegetation characteristics. Total SOC was significantly higher in the oligohaline zone (74.8 ± 14.9 Mg ha-1 23 ), followed by the mesohaline (59.3 ± 15.8 Mg ha-1), and polyhaline zone (48.3 ± 10.3 Mg ha-1 24 ) (ANOVA, F2, 500 = 118.9, p <0.001). At all sites, the topmost 10 cm of soil contained higher SOC density than the bottom depths (ANOVA, F3, 500= 30.1, p <0.001). On average, Bruguiera sp. stand holds the maximum SOC measured, followed by two pioneer species Sonneratia apetala and Avicennia sp. Multiple regression results indicated that soil salinity, organic C: N and tree diameter were the best predictor for the variability of the SOC in the Sundarbans (R2 = 0.62). Despite lower carbon in the soil, the study highlights that the conservation priorities and low deforestation have led to less CO2 emissions than most sediment carbon-rich mangroves in the world. The study also emphasised the importance of spatial conservation planning to safeguard the soil carbon-rich zones in the Bangladesh Sundarbans from anthropogenic tourism and development activities to support climate change adaptation and mitigation strategies

Using LIDAR to compare forest height estimates from IKONOS and Landsat ETM+ data in Sitka spruce plantation forests

This paper compares and contrasts predictions of forest height in Sitka spruce (Picea sitchensis) plantations based on medium‐resolution Landsat Enhanced Thematic Mapper Plus (ETM+), high‐resolution IKONOS satellite imagery and airborne Light Detection and Ranging (LiDAR) data. The relationship between field‐measured height and LiDAR height is linear and highly significant (R2 0.98). Despite the difference in spatial resolution and radiometry between Landsat ETM+ and IKONOS multi‐spectral data, the strength of the relationship between field height and predicted height using the green spectral band was very similar, with R2 values of 0.84 and 0.85, respectively. The inclusion of additional observations taken from the LiDAR data improved the strength of the relationship slightly for the Landsat ETM+ data (R2 = 0.87), but did not change the relationship for the IKONOS data (R2 = 0.84). Comparison of the height models derived from the satellite and LiDAR data shows that the optical models provide accurate predictions up to the point of forest canopy closure (10 m) in densely stocked plantations (>2000 stems ha−1); beyond this point, only the LiDAR model is able to provide a reliable estimate of forest height. The results demonstrate that the retrieval of forest structure information over the lifetime of a plantation forest is best achieved by the integration of satellite, airborne and ground‐based measurements. It is possible to use optical satellite data to identify forest stands that display unexpected growth characteristics, such as areas of high natural regeneration, poor or incomplete stocking

Development of an integrated geographical information system prototype for coastal habitat monitoring

This paper describes a prototype Integrated Geographical Information System (IGIS) developed for coastal habitat monitoring. It outlines the advantages and technical difficulties involved in combining specialist image processing functionality within the LaserScan IGIS software so that it could be used routinely by ecologists. The Coastal Habitat Information Monitoring System prototype set out to demonstrate the feasibility of using remotely sensed imagery for the routine mapping of important coastal habitats in the European Union. The prototype aims to provide an integrated software system to transform Earth Observation data into specialist map products. Normally such processing requires input from remote sensing scientists using specialist software and is not easily accessible to end-users. In principle, the methodology allows different satellite and airborne multi-spectral image data to be used to produce specialist coastal habitat maps. In this paper Landsat TM and IRS 1C LISS 3 data are evaluated and shown to be suitable for mapping the inter-tidal habitats of the Wash estuary on the East Coast of England. A strategic objective of study was to test the feasibility of incorporating all the processing functionality into a single integrated geographical information system. The project concluded satellite data from existing sensor systems can provide habitat data at a good level of detail but that to develop a fully operational processing capability would require considerable development of even the most advanced of the existing commercially available IGIS software package

High resolution elevation data derived from stereoscopic CORONA imagery with minimal ground control: an approach using IKONOS and SRTM data

The first space mission to provide stereoscopic imagery of the Earth’s surface was from the American CORONA spy satellite program from which it is possible to generate Digital Elevation Models (DEMs). CORONA imagery and derived DEMs are of most value in areas where conventional topographic maps are of poor quality, but the problem has been that until recently, it was difficult to assess their accuracy. This paper presents a methodology to create a high quality DEM from CORONA imagery using horizontal ground control derived from Ikonos space imagery and vertical ground control from map-based contour lines. Such DEMs can be produced without the need for field-based ground control measurements which is an advantage in many parts of world where ground surveying is difficult. Knowledge of CORONA image distortions, satellite geometry, ground resolution, and film scanning are important factors that can affect the DEM extraction process. A study area in Syria is used to demonstrate the method, and Shuttle Radar Topography Mission (SRTM) data is used to perform quantitative and qualitative accuracy assessment of the automatically extracted DEM. The SRTM data has enormous importance for validating the quality of CORONA DEMs, and so, unlocking the potential of a largely untapped part of the archive. We conclude that CORONA data can produce unbiased, high-resolution DEM data which may be valuable for researchers working in countries where topographic data is difficult to obtain

‘Looting marks’ in space-borne SAR imagery: Measuring rates of archaeological looting in Apamea (Syria) with TerraSAR-X Staring Spotlight

In archaeological remote sensing, space-borne Synthetic Aperture Radar (SAR) has not been used so far to monitor ‘looting’ (i.e. illegal excavations in heritage sites) mainly because of the spatial resolution of SAR images, typically not comparable to the ground dimensions of looting features. This paper explores the potential of the new TerraSAR-X beam mode Staring Spotlight (ST) to investigate looting within a workflow of radar backscattering change detection. A bespoke time series of five single polarisation, ascending mode, ST scenes with an unprecedented azimuth resolution of 0.24 m was acquired over the archaeological site of Apamea in western Syria, from October 2014 to June 2015 with a regular sampling of one image every two months. Formerly included in the Tentative List of UNESCO, the site has been heavily looted from at least early 2012 to May 2014, as confirmed by Google Earth Very High Resolution (VHR) optical imagery. Building upon the theory of SAR imaging, we develop a novel conceptual model of ‘looting marks’, identify marks due to occurrence of new looting and discriminate them from alteration (e.g. filling) of pre-existing looting holes. ‘Looting marks’ appear as distinctive patterns of shadow and layover which are visible in the ground-range reprojected ST image and generated by the morphology of the holes. The recognition of looting marks within ratio maps of radar backscatter (σ0) between consecutive ST scenes allows quantification of the magnitude, spatial distribution and rates of looting activities. In agreement with the estimates based on Google Earth imagery, the ST acquired in October 2014 shows that ~ 45% of the site was looted. In the following eight months new looting happened locally, with holes mainly dug along the margins of the already looted areas. Texture values of ~ 0.31 clearly distinguish these holes from the unaltered, bare ground nearby. Hot spots of change are identified based on the temporal variability of σ0, and colour composites indicate where repeated looting and alteration of existing holes occurred. Most looting marks are observed north of the two main Roman decumani. Looting intensified almost steadily from December 2014, with over 1500 new marks in February–April 2015. The estimated rates of looting increased from 214 looting marks/month in October–December 2014 to over 780 marks/month in April–June 2015, and numerically express the dynamic nature of the phenomenon to which Apamea is still exposed. The method of identifying looting marks in VHR radar images therefore proves a reliable opportunity for archaeologists and image analysts to measure remotely the scale of looting and monitor its temporal evolution