Sub-canopy terrain modelling for archaeological prospecting in forested areas through multiple-echo discrete-pulse laser ranging: a case study from Chopwell Wood, Tyne & Wear

Airborne Light Detection and Ranging (LiDAR) technology is assessed for its effectiveness as a tool for measuring terrain under forest canopy. To evaluate the capability of multiple-return discrete-pulse airborne laser ranging for detecting and resolving sub-canopy archaeological features, LiDAR data were collected from a helicopter over a forest near Gateshead in July 2009. Coal mining and timber felling have characterised Chopwell Wood, a mixed coniferous and deciduous woodland of 360 hectares, since the Industrial Revolution. The state-of-the-art Optech ALTM 3100EA LiDAR system operated at 70,000 pulses per second and raw data were acquired over the study area at a point density of over 30 points per square metre. Reference terrain elevation data were acquired on-site to ‘train’ the progressive densification filtering algorithm of Axelsson (1999; 2000) to identify laser reflections from the terrain surface. A number of sites, offering a variety of tree species, variable terrain roughness & gradient and understorey vegetation cover of varying density, were identified in the wood to assess the accuracy of filtered LiDAR terrain data. Results showed that the laser scanner over-estimated the elevation of reference terrain data by 13±17 cm under deciduous canopy and 23±18 cm under coniferous canopy. Terrain point density was calculated as 4.1 and 2.4 points per square metre under deciduous and coniferous forest, respectively. Classified terrain points were modelled with the kriging interpolation technique and topographic archaeological features, such as coal tubways (transportation routes) and areas of subsidence over relic mine shafts, were identified in digital terrain models (DTMs) using advanced exaggeration and artificial illumination techniques. Airborne LiDAR is capable of recording high quality terrain data even under the most dense forest canopy, but the accuracy and density of terrain data are controlled by a combination of tree species, forest management practices and understorey vegetation

Multisource and Multitemporal Data Fusion in Remote Sensing

The sharp and recent increase in the availability of data captured by different sensors combined with their considerably heterogeneous natures poses a serious challenge for the effective and efficient processing of remotely sensed data. Such an increase in remote sensing and ancillary datasets, however, opens up the possibility of utilizing multimodal datasets in a joint manner to further improve the performance of the processing approaches with respect to the application at hand. Multisource data fusion has, therefore, received enormous attention from researchers worldwide for a wide variety of applications. Moreover, thanks to the revisit capability of several spaceborne sensors, the integration of the temporal information with the spatial and/or spectral/backscattering information of the remotely sensed data is possible and helps to move from a representation of 2D/3D data to 4D data structures, where the time variable adds new information as well as challenges for the information extraction algorithms. There are a huge number of research works dedicated to multisource and multitemporal data fusion, but the methods for the fusion of different modalities have expanded in different paths according to each research community. This paper brings together the advances of multisource and multitemporal data fusion approaches with respect to different research communities and provides a thorough and discipline-specific starting point for researchers at different levels (i.e., students, researchers, and senior researchers) willing to conduct novel investigations on this challenging topic by supplying sufficient detail and references

Algoritma penurasan data lidar untuk penjanaan model ketinggian digital bagi kawasan tropika

Filtering technique and the environmental factors are among the main factors, which affect Digital Elevation Model (DEM) accuracy obtained from the Light Detection and Ranging (LiDAR) data especially for steep area and covered by vegetation. Intensive research of LiDAR data filtering in tropical area is very limited and the improvement of the filtering technique using the environmental factor is very much needed. The purpose of this research is to improve the existing filtering techniques such as Progressive Morphology (PM) for DEM generation in the area covered by tropical vegetation. Initial test has been done by evaluating the filtering techniques such as PM, Adaptive Triangular Irregular Network (ATIN) and Elevation Threshold with Expand Window (ETEW) on the LiDAR data over Pekan, Pahang with slope between 0o and l0o. LiDAR DEM accuracy that was calculated based on ground reference point in mixed forest area shows that PM and ETEW filtering methods have produced minor RSME errors of A.226m and 0.192m compared to ATIN with 0.235m. Subsequent test was conducted for rubber area with slope value between 0o to 15o. The results show low RMSE error of 0.660m, 0.699m and A.717m for PM, ETEW and ATIN respectively. This shows that the slope parameter has an impact on the accuracy of the DEM, These results also demonstrate that the PM technique provides the highest accuracy. However the slope value in PM technique was based on constant value and applied to the entire LiDAR data. Compared to other filtering techniques, PM techniques provide more convenient way of improving the slope value. Improvement of PM filtering technique has been made by taking into account the actual slope value parameter and the revised method named AdapMorf algorithm. AdapMorf filtering technique was evaluated based on the slope gradient of the earth surface with the accuracy of the DEM error was evaluated for each area (i.e. mixed forest, rubber and oil palm) with slope between 0o and l5o. Three categories of assessments were carried out for each landcover and each category has a series of tests. DEM results were analyzed using RMSE error and the calculation of Type I and Type II errors. The best DEM's accuracy for AdapMorf by the types of landcover are 0.650m, 0.520m and 0.604m for mixed forests, rubber and oil palm respectively. The lowest results for Type I error are29.l7o/o,31.760/o and 35.l3Yo for rubber, mixed forest and oil palm respectively. The results for Type II error are 0.05oh,0.06% and 0.2lYs for rubber, mix forest and oil palm respectively. Due to the Type I error for AdapMorf was relatively high, the filtering technique was improved by introducing TyMof filtering technique. The tests were canied out and the results obtained show improvement in DEM's accuracy with RMSE for rubber and mixed forest are A.472m and 0.582m respectively. The Type I error for mixed forest and rubber are 28.90Yo and 19.29o/o respectively. This study shows that AdapMorf and TyMof filtering techniques were able to generate DEM with error smaller than the previous techniques for area with slope between 0o and 15". As a conclusion, AdapMorf and TyMof filtering techniques have shown that it can produce better quality of DEM for steep area and vegetated cover of tropical forest