ON VOLUME DATA REDUCTION FOR LIDAR DATASETS

This paper discusses a current issue for several experimental science disciplines, which is the Big Data Problem (BDP). This research study focused on light intensity and ranging (LiDAR) datasets, which are collected for modelling spatial features found on the surface of the earth. Currently, LiDAR datasets are known to be extremely redundant for many applications. Using a formula that allows for calculating the variance of the target-induced error (so-called T-error) caused by the discretisation and quantisation of a 3D surface as a criterion for the quantitative assessment of the fidelity of a model, the use of a Q-tree-based split of the surface is proposed for cells of various sizes depending on the fidelity requirements. A LiDAR dataset representing a 1 km x 1 km terrain surface tile using approximately 12 x 106 points was used during the experiments. The initial LiDAR dataset was used to produce a digital terrain model (DTM) at a 0.5 m x 0.5 m resolution, which was used as a reference model. Subsequently, the initial LiDAR dataset was decimated at various rates, and the resulting DTMs were compared with the reference model. The Q-tree based data structure was utilised to illustrate that the Q-tree approach allows for the production of DTMs at a ‘controlled’ fidelity with a considerable reduction in data volume

IDENTIFICATION AND ASSESSEMENT OF FACTORS AFFECTING FOREST DEPLETION IN BRUNEI DARUSSALAM

In this paper we attempt to isolate spatially those factors responsible for forest degradation in Brunei Darussalam. While human activities are one of the deforestation forces, the degradation of forest far from development corridors may implicate extra-human factors in its explanation. In the absence of apparent sources of pollution, mean temperature rise is a good candidate factor as heat stress is capable of reducing photosynthesis, which can manifest itself in a drop in canopy density. Considering that leaves are the most active scattering kernels of electromagnetic waves within tree canopies, especially in the C-band (λ=5.3cm), one can expect that the shuttle radar topography mission elevation data product (SRTM) developed for that band should exhibit a variable bias over forest depending on its density. This relationship, e.g., vegetation density versus elevation bias, is used to measure forest depletion. Based on data from a forest map, SRTM and a reference DTM we calculated a typical bias for seven forest types. A linear relationship was established between elevation bias and forest depletion level. The typical bias means zero depletion, and bias equal to zero means 100 % depletion. A map of forest depletion was developed using that relationship. By excluding pixels most likely to be affected by human activities (2.5km buffer around settlements and 0.5km buffer around sealed roads), depletion levels for all remaining pixels were categorized by forest type. It was found that forest plots potentially free of direct human activities are also depleted to various degrees. It would indicate the presence of a forest depleting force, possibly an increase in the mean monthly average temperature, which has been observed in Brunei over the last 30 years. 1

Horizontal positional accuracy of Google Earth's imagery over rural areas: a study case in Tamaulipas, Mexico

Due to the popularity of Google Earth (GE), users commonly assume that it is a credible and accurate source of information. Consequently, GE's imagery is frequently used in scientific and others projects. However, Google states that data available in their geographic products are only approximations and, therefore, their accuracy is not officially documented. In this paper, the horizontal positional accuracy of GE's imagery is assessed by means of comparing coordinates extracted from a rural cadastral database against coordinates extracted from well-defined and inferred check points in GE's imagery. The results suggest that if a large number of well-defined points are extracted from areas of high resolution imagery, GE's imagery over rural areas meets the horizontal accuracy requirements of the ASPRS for the production of "Class 1" 1:20,000 maps. Nonetheless, the results also show that georegistration and large horizontal errors occur in GE's imagery. Consequently, despite its overall horizontal positional accuracy, coordinates extracted from GE's imagery should be used with caution

Accuracy evaluation of the SRTM topographic data product over selected sites in Australia and Brunei Darussalam

The paper presents the results of a study on the accuracy of the SRTM band C topography data product (defined in this paper as DTM.C, pronounced as dtm-dot-c), conducted over two selected sites in Australia and Brunei Darussalam. The DTM.C was compared against accurate DTMs developed from 1m (Australian site) and 10m (Brunei site) contours. Land cover data derived from aerial photographs and forestry maps were also used in this study. The discrepancy between DTM.C and DTM allowed the development of an accuracy statement which takes into an account "vegetation" noise caused by the vegetation impenetrability of the band C electromagnetic waves. It was found that the site specific accuracy of the DTM.C could be characterised by a vertical, positive shift equal to 9.8m and 8.3m, and the height error: :ł 11.5m and :ł 18.7m (at 90% confidence level), for the Australian and Brunei sites, respectively. These values agree with the performance requirements for the SRTM data products [4], and could be accepted as a realistic accuracy statement, if DTM. C is used as a surrogate for a DTM over an area of interests

A STUDY OF THE IMPACT OF INSOLATION ON REMOTE SENSING-BASED LANDCOVER AND LANDUSE DATA EXTRACTION

We examined the dependency of the pixel reflectance of hyperspectral imaging spectrometer data (HISD) on a normalized total insolation index (NTII). The NTII was estimated using a light detection and ranging (LiDAR)-derived digital surface model (DSM). The NTII and the pixel reflectance were dependent, to various degrees, on the band considered, and on the properties of the objects. The findings could be used to improve land cover (LC)/land use (LU) classification, using indices constructed from the spectral bands of imaging spectrometer data (ISD). To study this possibility, we investigated the normalized difference vegetation index (NDVI) at various NTII levels. The results also suggest that the dependency of the pixel reflectance and NTII could be used to mitigate the shadows in ISD. This project was carried out using data provided by the Hyperspectral Image Analysis Group and the NSF-funded Centre for Airborne Laser Mapping (NCALM), University of Houston, for the purpose of organizing the 2013 Data Fusion Contest (IEEE 2014). This contest was organized by the IEEE GRSS Data Fusion Technical Committee

Air temperature and light intensity in tropical rainforest of Brunei Darussalam in 2017

The air temperature and light intensity were recorded in tropical rainforest of Brunei Darussalam, at 20’ interval in 2017. HOBO Pendant® data loggers were attached to tree trunks approx. 2 m above ground. The data can be used to study various microclimatic and ecological characteristics of tropical rainforests in Brunei and other locations

Spaceborne Digital Elevation Data of Runway at Zonguldak Airport, Turkey

This data set contains elevation of 475 points located on the surface of an active runway of Zonguldak airport, Turkey. The elevation of points were interpolated from the spaceborne DEMs, including ASTER GDEM v.3, AW3D30 m, SRTM-1", SRTM-3", SRTM-X, TanDEM-3", WorldDEM, Photogrammetry and line levelling. This data set may be used to study the vertical accuracy of Digital Elevation Models - existing and future. Because of a specific features of runways (flatness and homogeneous surface - concrete) this data set allows for investigation of the instrument-induced error sources of digital elevation data

Air temperature and light intensity in tropical rainforest of Brunei Darussalam in 2017

The air temperature and light intensity were recorded in tropical rainforest of Brunei Darussalam, at 20’ interval in 2017. HOBO Pendant® data loggers were attached to tree trunks approx. 2 m above ground. The data can be used to study various microclimatic and ecological characteristics of tropical rainforests in Brunei and other locations

SOURCES OF ARTEFACTS IN SYNTHETIC APERTURE RADAR INTERFEROMETRY DATA SETS

In recent years, much attention has been devoted to digital elevation models (DEMs) produced using Synthetic Aperture Radar Interferometry (InSAR). This has been triggered by the relative novelty of the InSAR method and its world-famous product—the Shuttle Radar Topography Mission (SRTM) DEM. However, much less attention, if at all, has been paid to sources of artefacts in SRTM. In this work, we focus not on the missing pixels (null pixels) due to shadows or the layover effect, but rather on outliers that were undetected by the SRTM validation process. The aim of this study is to identify some of the causes of the elevation outliers in SRTM. Such knowledge may be helpful to mitigate similar problems in future InSAR DEMs, notably the ones currently being developed from data acquired by the TanDEM-X mission. We analysed many cross-sections derived from SRTM. These cross-sections were extracted over the elevation test areas, which are available from the Global Elevation Data Testing Facility (GEDTF) whose database contains about 8,500 runways with known vertical profiles. Whenever a significant discrepancy between the known runway profile and the SRTM cross-section was detected, a visual interpretation of the high-resolution satellite image was carried out to identify the objects causing the irregularities. A distance and a bearing from the outlier to the object were recorded. Moreover, we considered the SRTM look direction parameter. A comprehensive analysis of the acquired data allows us to establish that large metallic structures, such as hangars or car parking lots, are causing the outliers. Water areas or plain wet terrains may also cause an InSAR outlier. The look direction and the depression angle of the InSAR system in relation to the suspected objects influence the magnitude of the outliers. We hope that these findings will be helpful in designing the error detection routines of future InSAR or, in fact, any microwave aerial- or space-based survey. The presence of outliers in SRTM was first reported in Becek, K. (2008). Investigating error structure of shuttle radar topography mission elevation data product, Geophys. Res. Lett., 35, L15403