CRC for Spatial information

Abstract: Nutrient-use efficiency is a key issue for grazing systems in Australia. Spatial variability in soil pH and nutrients at the sub-paddock scale may affect the efficiency of utilisation of, and provide an opportunity for, site-specific management (SSM) of fertiliser and soil ameliorants. However, there has been little research exploring the potential for SSM in grazing systems. This study examines the spatial variability of soil test pH, phosphorus (P), potassium (K) and sulfur (S) in two typical pasture fields (a native and an improved) on the Northern Tablelands of New South Wales and evaluates the potential for SSM based on a comparison with critical values. In both fields, the overall paddock mean from a grid survey containing >80 samples for pH, P, K and S (0–10 cm) exceeded the critical values, suggesting that the addition of fertiliser or lime was not required. However, considerable sub-paddock-scale variability was observed, with CV ranging from 35% to 66% for the key nutrients (P, K and S). The Sprengel–Liebig Law of the Minimum was applied to evaluate the proportion of each field constrained by one or more soil characteristics. Up to 55% of the improved paddock and 78% of the native pasture was potentially responsive to amendments. The results of this study suggest that SSM of fertilisers and ameliorants could provide substantial improvements in productivity and possibly reductions in fertiliser use. The development and application of appropriate systems and tools to effectively quantify this spatial variability remain a challenge, coupled with management strategies that optimise the placement of amendments and account for the variability in other production limiting factors.

Abstract: This paper presents a new segmentation technique for LIDAR point cloud data for automatic extraction of building roof planes. Using the ground height from a DEM (Digital Elevation Model), the raw LIDAR points are separated into two groups: ground and nonground points. The ground points are used to generate a "building mask" in which the black areas represent the ground where there are no laser returns below a certain height. The non-ground points are segmented to extract the planar roof segments. First, the building mask is divided into small grid cells. The cells containing the black pixels are clustered such that each cluster represents an individual building or tree. Second, the non-ground points within a cluster are segmented based on their coplanarity and neighbourhood relations. Third, the planar segments are refined using a rule-based procedure that assigns the common points among the planar segments to the appropriate segments. Finally, another rule-based procedure is applied to remove tree planes which are small in size and randomly oriented. Experimental results on the Vaihingen data set show that the proposed method offers high building detection and roof plane extraction rates.

Abstract: This letter evaluates the biomass-retrieval error in pine-dominated stands when using high-spatial-resolution airborne measurements from fully polarimetric L-band radar and airborne laser scanning sensors. Information on total above-ground biomass was estimated through allometric relationships from plot-level field measurements. Multiple-linear-regression models were developed to model relationships between biomass and radar/lidar data. Overall, lidar data provided lower estimation errors (17.2 t·ha-1, 28% relative) when compared with radar data (30.3 t·ha-1, 61% relative). However, for the 30-100 t·ha-1 biomass range, the relative error from radar-based models was only 9% higher than that from lidar-based models. This suggests that high-spatial-resolution radar data could provide fundamentally similar results to lidar for some biomass intervals. This is an important finding for large-scale biomass estimation that needs to rely upon satellite data, as there are no lidar satellites planned for the foreseeable future.

Abstract: Successful implementation of integer ambiguity resolution enabled precise point positioning (aka PPP-RTK) algorithms is inextricably linked to the ability of a user to perform near real-time positioning by quickly and reliably resolving the integer carrier-phase ambiguities. In the PPP-RTK technique, a major barrier to successful ambiguity resolution is the unmodelled impact of the ionosphere. We present a 4D ionospheric tomographic model that computes in real time the ionospheric electron density as a linear combination of basis functions, namely B-splines. The results show that when the ionospheric estimates are provided as atmospheric corrections for a PPP-RTK end-user, the time to fix its horizontal position below 10 cm is around 20 epochs (the sample rate is 30 s) at the 90 % of the cumulative distribution function (CDF), as opposed to the time it takes when no external corrections are provided, which is around 80 epochs at 90 % of the CDF.