Applied Geomatics最新文献

Quantitative structure models (QSMs) are topological ordered cylinder models of trees which cover the complete branching structure from the stem’s base up to all tips. But the thin branches appear too large in the input point clouds. This leads to a well known problem, the overestimation of the QSM cylinders’ volumes and radii in thin branches. We present here a solution to this problem by introducing two QSM filters correcting the radii of such cylinders. The filters itself are build upon the theoretical fundamentals of allometric scaling theories. For validation we use QSMs produced from an open point cloud data set of tree clouds with the SimpleForest software. We compare the QSM volume against the harvested reference data for 65 felled trees. We also found QSM data of TreeQSM, a competitive and broadly accepted QSM modeling tool utilizing a different filter method. Our method performed more accurate on three different error measures. We quantify the error of our method with a RMSE of 127 (mathtt {dm^{3}}), a (mathtt {r^{2}_{adj.}}) of 0.96 and a CCC of 0.97. With those filters the accuracy of estimating total or partial volume of trees does significantly increase.

Hyperspectral remote sensing enables a detailed spectral description of the object’s surface, but it also introduces high redundancy because the narrow contiguous spectral bands are highly correlated. This has two consequences, the Hughes phenomenon and increased processing effort due to the amount of data. In the present study, it is introduced a model that integrates stacked-autoencoders and convolutional neural networks to solve the spectral redundancy problem based on the feature selection approach. Feature selection has a great advantage over feature extraction in that it does not perform any transformation on the original data and avoids the loss of information in such a transformation. The proposed model used a convolutional stacked-autoencoder to learn to represent the input data into an optimized set of high-level features. Once the SAE is learned to represent the optimal features, the decoder part is replaced with regular layers of neurons for reduce redundancy. The advantage of the proposed model is that it allows the automatic selection and extraction of representative features from a dataset preserving the meaningful information of the original bands to improve the thematic classification of hyperspectral images. Several experiments were performed using two hyperspectral data sets (Indian Pines and Salinas) belonging to the AVIRIS (Airborne Visible/Infrared Imaging Spectrometer) sensor to evaluate the performance of the proposed method. The analysis of the results showed precision and effectiveness in the proposed model when compared with other feature selection approaches for dimensionality reduction. This model can therefore be used as an alternative for dimensionality reduction.

This study aims to delineate groundwater potential zones using an integrated approach of remote sensing (RS), geographical information system (GIS), and analytical hierarchy process (AHP) method in the middle and high Cheliff basin, Algeria. Multiple data such as lithology, lineament density, geomorphology, slope, soil, rainfall, drainage density, and land use/land cover were considered for delineating the groundwater potential zones. Spatially distributed maps/thematic layers of all the aforementioned parameters were created using remotely sensed data as well as ground data in a GIS environment. The assigned weights of the thematic layers and their features were then normalized by using the AHP technique. The delineated groundwater potential zones in this study area were categorized as very good, good, moderate, and poor, respectively. The results showed that the area along the Chlef River which is approximately 6% of the total study area was delineated as an area having “very good” potential for groundwater. The “good zone” delineated encompassed approximately 31% of the study area and was found in the pediment-pediplain complex zone. The moderate zones encompassed approximately 58% of the area. The “poor zones” encompassed approximately 5% of the area which included the cities of Ramka, El Hadjadj, Moussadek, and certain parts of Mekhatria. The groundwater potential zones map was compared with the actual discharge data from various wells within the study area and was found reasonable. Overall, this study provides a convenient approach of delineating the potential of groundwater availability which ultimately will aid in better planning and managing of groundwater resources.

Graphical abstract

Currently, the real-time kinematic (RTK) method is common to be used in the global navigation satellite system (GNSS) positioning solutions, whereas it was primarily used for cadastral measurements, especially measurements of land parcels in Indonesia. In addition, the real-time precise point positioning (RTPPP) method is currently used extensively in Indonesia for positioning applications. Indonesia’s position located in the Asia-Pacific region makes it possible to observe a huge number of multi-GNSS satellite signals from GPS, GLONASS, Galileo, and Beidou which are very favorable for such measures. One particular problem in point positioning in Indonesia is that the measurements are often made in harsh environments covered by vegetation or buildings. This research is aimed at determining the quality of measurement data in static, RTK, and RTPPP methods in harsh environments and determining the contribution of multi-satellite constellations to the measurement of the three methods in harsh areas. Data acquisition of the methods was conducted in various locations covered by vegetation and building obstruction in the baseline distance scheme of 2.5 km, 5 km, 10 km, 20 km, and 50 km. In addition, an analysis of the level of accuracy and precision of static, RTK, and RTPPP measurement methods was conducted. In harsh environments, the accuracy and precision results of the static and RTK methods using multi-satellite constellations may provide solutions that meet the standards of land parcel measurement. Results obtained on a 50-km baseline are still good. However, the results of the baseline distance scheme show that the longer the baseline, the greater tendency for accuracy to decrease. The RTPPP method is not capable of generating data with a fixed solution for all satellite constellation schemes.

Digital elevation models (DEMs) are used for many geosciences studies; hence, their accuracy is essential. Throughout the world, there are many small islands of various sizes and densities; hence, it is important to assess the DEM accuracy on very small islands since DEMs serve as the major data source for many investigations, particularly in geomorphology, land-use planning, and disaster management. Therefore, this paper aims to validate the accuracy of an open-source Indonesian DEM (DEMNAS) in the very small islands of Karimunjawa–Indonesia. Validation was conducted by comparing elevation values from DEMNAS to the true elevation values in four very small islands in Karimunjawa, namely Cemara Besar, Cemara Kecil, Menjangan Besar, and Menjangan Kecil. The true elevation values were obtained by orthorectification of aerial imagery using a DJI Mavic Air-2 Unmanned Aerial Vehicle (UAV). The orthorectification came from ground control points (GCP) from the geodetic Global Positioning System (GPS). In the study area, fourteen GCP were erected; for more significant coverage, they were placed along the edges of the very small islands. After that, Agisoft software analyzed the images to produce a DEM using GCP orthorectification. Based on 280 sampling points, we applied a root-mean-square error (RMSE) to calculate elevation errors, and we performed the linear error 90% (LE90) calculation to judge the average errors with the 90% threshold of absolute values of discrepancies. The DEMNAS RMSE and LE90 calculation results in the Karimunjawa archipelago were 6.33 m and 10.45 m, respectively. Citing Regulation Number 15 of the Head of the Indonesian Geospatial Information Agency of 2014 concerning Technical Guidelines for Basic Map Accuracy, DEMNAS with 10.45 m LE90 can be utilized for producing geomorphological maps with scales of 1:25,000 or smaller. However, detailed geomorphological mapping of a very small island (less than 100 km²) needs better DEM data that is usually produced using aerial photogrammetry. Using UAVs for DEMs creation may benefit small island developing states (SIDS) worldwide.