Estimation of Canopy Height Using an Airborne Ku-Band Frequency-Modulated Continuous Waveform Profiling Radar

An airborne Ku-band frequency-modulated continuous waveform (FMCW) profiling radar terms as Tomoradar provides a distance-resolved measure of microwave radiation backscattered from the canopy surface and the underlying ground. The Tomoradar waveform data are acquired in the southern Boreal Forest Zone with Scots pine, Norway spruce, and birch as major species in Finland. A weighted filtering algorithm based on statistical properties of noise is designed to process the original waveform. In addition, another algorithm of estimating canopy height for the processed waveform is developed by extracting the canopy top and ground position. A higher-precision reference data from a Velodyne VLP-16 laser scanner and a digital terrain model are introduced to validate the accuracy of extracted canopy height. According to the processed results from 127 765 copolarization measurements in 32 stripes of Tomoradar field test, the mean error of canopy height varies from-0.04 to 1.53 m, and the root-mean-square error approximates 1 m. Moreover, the estimated canopy heights highly correlate with the reference data in view of that the correlation coefficients maintain from 0.86 to 0.99 with an average value of 0.96. All these results demonstrate that Tomoradar presents an important approach in estimating the canopy height with several meters footprint and is feasible of being a validation instrument for satellite LiDAR with large footprint in the forest inventory.</p

Monitoring the ecological environment of open-pit coalfields in cold zone of Northeast China using Landsat time series images of 2000-2015

Procjena pogoršanja ekološkog okruženja i vegetacije rudnika u Kini zbog prekomjernog vađenja ugljena važna je zbog osjetljivog ekološkog okruženja i niske temperature u hladnim i sušnim područjima. U ovom se istraživanju kao primjeri uzimaju rudnici Haizhou, Gulianhe i Huolinhe s otvorenim jamskim oknom te se predlaže metoda za procjenu njihovog ekološkog okruženja primjenom Landsat vremenske serije slika na temelju varijacija Normalized Difference Vegetation Index-a (NDVI) rudnika s otvorenim jamskim oknom u hladnim i sušnim područjima. Prosječna NDVI vrijednost rudarskog područja izračunavala se svakog mjeseca primjenom podataka Landsat serije slika od 2000 do 2015. Područje nalazišta ugljena pod vegetacijom određeno je u skladu s graničnim vrijednostima NDVI pa su tako izrađeni grafikoni godišnjeg maksimalnog NDVI i područja pod vegetacijom. Prilagodili smo liniju trenda varijacije maksimalne vrijednosti NDVI i područja pod vegetacijom kako bi se smanjio učinak meteoroloških čimbenika na NDVI vrijednosti. Rezultati pokazuju da se poslije zatvaranja jame i čišćenja područja odlaganja, naglo, tijekom zadnjeg desetljeća, povećao NDVI rudnika s otvorenim jamskim otvorom i područja pod vegetacijom, a ekološko okruženje tih rudnika se očito poboljšalo. Rudarske aktivnosti su dovele do naglog opadanja godišnjeg maksimalnog NDVI i područja pod vegetacijom s trajno smrznutim slojem tla, a ekološko okruženje rudnika se nastavlja pogoršavati. Premda četverogodišnji prosječni NDVI ostaje nepromijenjen u dijelovima nalazišta ugljena koji se eksploatiraju, a nemaju trajno smrznuti sloj tla, područje ugljenokopa pod vegetacijom se linearno smanjuje, ukazujući na činjenicu da se ekološko okruženje ugljenokopa pogoršava. Sa stajališta zaštite ekološkog okruženja, rezultati ovog istraživanja čine osnovu za donošenje odluke o otvaranju velikih rudnika s otvorenim jamskim oknom u hladnim i sušnim područjima.Evaluating the deterioration of ecological environment and vegetation of coalfields caused by China’s large-scale coal mining activities is important because of the fragile ecological environment and low temperature in cold and arid areas. This study takes the open coal pits of Haizhou, Gulianhe, and Huolinhe as examples and proposes a method for evaluating their ecological environment using Landsat time series images based on the Normalized Difference Vegetation Index (NDVI) variations of open-pit coalfields in cold and arid zones. The average NDVI value of the mining area each month was calculated using Landsat image data from 2000 to 2015. The vegetation cover area in the coalfields was extracted according to the NDVI threshold, and the scatter plots of the annual maximum NDVI and vegetation cover area were drawn. We fitted the variation trend line of maximum NDVI value and vegetation cover area to reduce the effect of meteorological factors on NDVI values. Results show that after the closure of open pit and reclamation of dump area, the NDVI of open-pit coalfields and vegetation cover area have been increasing rapidly over the last decade, and the ecological environment of these coalfields has obviously improved. The coal mining activities have led to the rapid decline of annual maximum NDVI and vegetation cover area of the coalfields in permafrost zones, and the ecological environment of coalfields continues to deteriorate. Although the quarterly average NDVI remains unchanged in non-permafrost mining coalfields under coal exploitation, the vegetation cover area in the coalfields decreases linearly, indicating that the ecological environment of the coalfields tends to deteriorate. From an ecological environment protection perspective, the results of this study provide a basis for decision making in constructing large-scale open pits in cold and arid zones

Toward utilizing multitemporal multispectral airborne laser scanning, Sentinel-2, and mobile laser scanning in map updating

The rapid development of remote sensing technologies pro-vides interesting possibilities for the further development of nationwide mapping procedures that are currently based mainly on passive aerial images. In particular, we assume that there is a large undiscovered potential in multitemporal airborne laser scanning (ALS) for topographic mapping. In this study, automated change detection from multitemporal multispectral ALS data was tested for the first time. The results showed that direct comparisons between height and intensity data from different dates reveal even small chang-es related to the development of a suburban area. A major challenge in future work is to link the changes with objects that are interesting in map production. In order to effectively utilize multisource remotely sensed data in mapping in the future, we also investigated the potential of satellite images and ground-based data to complement multispectral ALS. A method for continuous change monitoring from a time series of Sentinel-2 satellite images was developed and tested. Finally, a high-density point cloud was acquired with terres-trial mobile laser scanning and automatically classified into four classes. The results were compared with the ALS data, and the possible roles of the different data sources in a fu-ture map updating process were discussed

Fast Registration of Terrestrial LiDAR Point Clouds Based on Gaussian-Weighting Projected Image Matching

Terrestrial point cloud registration plays an important role in 3D reconstruction, heritage restoration and topographic mapping, etc. Unfortunately, current research studies heavily rely on matching the 3D features of overlapped areas between point clouds, which is error-prone and time-consuming. To this end, we propose an automatic point cloud registration method based on Gaussian-weighting projected image matching, which can quickly and robustly register multi-station terrestrial point clouds. Firstly, the point cloud is regularized into a 2D grid, and the point density of each cell in the grid is normalized by our Gaussian-weighting function. A grayscale image is subsequently generated by shifting and scaling the x-y coordinates of the grid to the image coordinates. Secondly, the scale-invariant features (SIFT) algorithm is used to perform image matching, and a line segment endpoint verification method is proposed to filter out negative matches. Thirdly, the transformation matrix between point clouds from two adjacent stations is calculated based on reliable image matching. Finally, a global least-square optimization is conducted to align multi-station point clouds and then obtain a complete model. To test the performance of our framework, we carry out the experiment on six datasets. Compared to previous work, our method achieves the state-of-the-art performance on both efficiency and accuracy. In terms of efficiency, our method is comparable to an existing projection-based methods and 4 times faster on the indoor datasets and 10 times faster on the outdoor datasets than 4PCS-based methods. In terms of accuracy, our framework is ~2 times better than the existing projection-based method and 6 times better than 4PCS-based methods

Pole-Like Road Furniture Detection and Decomposition in Mobile Laser Scanning Data Based on Spatial Relations

Road furniture plays an important role in road safety. To enhance road safety, policies that encourage the road furniture inventory are prevalent in many countries. Such an inventory can be remarkably facilitated by the automatic recognition of road furniture. Current studies typically detect and classify road furniture as one single above-ground component only, which is inadequate for road furniture with multiple functions such as a streetlight with a traffic sign attached. Due to the recent developments in mobile laser scanners, more accurate data is available that allows for the segmentation of road furniture at a detailed level. In this paper, we propose an automatic framework to decompose road furniture into different components based on their spatial relations in a three-step procedure: first, pole-like road furniture are initially detected by removing ground points and an initial classification. Then, the road furniture is decomposed into poles and attachments. The result of the decomposition is taken as a feedback to remove spurious pole-like road furniture as a third step. If there are no poles extracted in the decomposition stage, these incorrectly detected pole-like road furniture—such as the pillars of buildings—will be removed from the detection list. We further propose a method to evaluate the results of the decomposition. Compared with our previous work, the performance of decomposition has been much improved. In our test sites, the correctness of detection is higher than 90% and the completeness is approximately 95%, showing that our procedure is competitive to state of the art methods in the field of pole-like road furniture detection. Compared to our previous work, the optimized decomposition improves the correctness by 7.3% and 18.4% in the respective test areas. In conclusion, we demonstrate that our method decomposes pole-like road furniture into poles and attachments with respect to their spatial relations, which is crucial for road furniture interpretation

Tridimensional Reconstruction Applied to Cultural Heritage with the Use of Camera-Equipped UAV and Terrestrial Laser Scanner

No single sensor can acquire complete information by applying one or several multi-surveys to cultural object reconstruction. For instance, a terrestrial laser scanner (TLS) usually obtains information on building facades, whereas aerial photogrammetry is capable of providing the perspective for building roofs. In this study, a camera-equipped unmanned aerial vehicle system (UAV) and a TLS were used in an integrated design to capture 3D point clouds and thus facilitate the acquisition of whole information on an object of interest for cultural heritage. A camera network is proposed to modify the image-based 3D reconstruction or structure from motion (SfM) method by taking full advantage of the flight control data acquired by the UAV platform. The camera network improves SfM performances in terms of image matching efficiency and the reduction of mismatches. Thus, this camera network modified SfM is employed to process the overlapping UAV image sets and to recover the scene geometry. The SfM output covers most information on building roofs, but has sparse resolution. The dense multi-view 3D reconstruction algorithm is then applied to improve in-depth detail. The two groups of point clouds from image reconstruction and TLS scanning are registered from coarse to fine with the use of an iterative method. This methodology has been tested on one historical monument in Fujian Province, China. Results show a final point cloud with complete coverage and in-depth details. Moreover, findings demonstrate that these two platforms, which integrate the scanning principle and image reconstruction methods, can supplement each other in terms of coverage, sensing resolution, and model accuracy to create high-quality 3D recordings and presentations

The comparison of canopy height profiles extracted from Ku-band profile radar waveforms and LiDAR data

An airborne Ku-band frequency-modulated continuous waveform (FM-CW) profiling radar, Tomoradar, records the backscatter signal from the canopy surface and the underlying ground in the southern boreal forest zone of Finland. The recorded waveforms are transformed into canopy height profiles (CHP) with a similar methodology utilized in large-footprint light detection and ranging (LiDAR). The point cloud data simultaneously collected by a Velodyne® VLP-16 LiDAR on-board the same platform represent the frequency of discrete returns, which are also applied to the extraction of the CHP by calculating the gap probability and incremental distribution. To thoroughly explore the relationships of the CHP derived from Tomoradar waveforms and LiDAR data we utilized the effective waveforms of one-stripe field measurements and comparison them with four indicators, including the correlation coefficient, the root-mean-square error (RMSE) of the difference, and the coefficient of determination and the RMSE of residuals of linear regression. By setting the Tomoradar footprint as 20 degrees to contain over 95% of the transmitting energy of the main lobe, the results show that 88.17% of the CHPs derived from Tomoradar waveforms correlated well with those from the LiDAR data; 98% of the RMSEs of the difference ranged between 0.002 and 0.01; 79.89% of the coefficients of determination were larger than 0.5; and 98.89% of the RMSEs of the residuals ranged from 0.001 to 0.01. Based on the investigations, we discovered that the locations of the greatest CHP derived from the Tomoradar were obviously deeper than those from the LiDAR, which indicated that the Tomoradar microwave signal had a stronger penetration capability than the LiDAR signal. Meanwhile, there are smaller differences (the average RMSEs of differences is only 0.0042 when the total canopy closure is less than 0.5) and better linear regression results in an area with a relatively open canopy than with a denser canopy