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Satellite and UAV Platforms, Remote Sensing for Geographic Information Systems

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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Remote Sensors".

Deadline for manuscript submissions: closed (22 April 2022) | Viewed by 34927

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Prof. Dr. Alfred Colpaert

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Department of Geographical and Historical Studies, University of Eastern Finland, Yliopistokatu 7, 80100 Joensuu, Finland
Interests: geoinformatics; spatial analysis; remote sensing
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Special Issue Information

Dear Colleagues,

Satellite and UAV (Unmanned Aerial Vehicle) imagery has become an important source of data for Geographic Information Systems (GISs). Remote Sensing and GISs are part of the broader concept of Geoinformatics. Satellite imagery in a wide range of spatial, spectral, and temporal resolutions provides the scientific community with rapidly available global data to be used as an integral part of spatial data structures and analyses. Remote sensing platforms, such as Modis and Landsat, have a unique historical record of providing tens of years of uninterrupted global data.

For local applications, the rapid evolution of unmanned aerial vehicles and lightweight sensors has provided the scientific community with a tool for acquiring extremely high-resolution data covering areas that vary from several hectares to hundreds of square kilometres in size.

The Special Issue intends to highlight advances in satellite and UAV data applications and the use of these to expand and improve data integration in Geoinformatics. UAVs and drones are often cost-effective when compared to the use of manned helicopters or fixed-wing aircraft.

Possible topics include, but are not limited to:

ecosystem monitoring;
vegetation monitoring;
forest inventory;
animal habitat analysis;
land use/land cover analysis and monitoring;
hyperspectral and three-dimensional (3D) mapping;
urban mapping and planning;
mapping;
open source software for unmanned aerial vehicle (UAV) mosaicking and 3D solutions.

Prof. Dr. Alfred Colpaert
Guest Editor

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Keywords

hyperspectral data
multispectral data
monochromatic data
radar data applications
data integration and fusion
new UAV platforms and instruments
automated mapping and updating
real-time applications.

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Published Papers (11 papers)

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Editorial

Jump to: Research

2 pages, 157 KiB

Open AccessEditorial

Satellite and UAV Platforms, Remote Sensing for Geographic Information Systems

by Alfred Colpaert

Sensors 2022, 22(12), 4564; https://doi.org/10.3390/s22124564 - 17 Jun 2022

Cited by 7 | Viewed by 1862

Abstract

Satellite and UAV (unmanned aerial vehicle) imagery has become an important source of data for Geographic Information Systems (GISs) [...] Full article

(This article belongs to the Special Issue Satellite and UAV Platforms, Remote Sensing for Geographic Information Systems)

Research

Jump to: Editorial

18 pages, 9186 KiB

Open AccessArticle

Assessing the Impact of Wildlife on Vegetation Cover Change, Northeast Namibia, Based on MODIS Satellite Imagery (2002–2021)

by Augustine-Moses Gaavwase Gbagir, Colgar Sisamu Sikopo, Kenneth Kamwi Matengu and Alfred Colpaert

Sensors 2022, 22(11), 4006; https://doi.org/10.3390/s22114006 - 25 May 2022

Cited by 6 | Viewed by 3282

Abstract

Human–wildlife conflict in the Zambezi region of northeast Namibia is well documented, but the impact of wildlife (e.g., elephants) on vegetation cover change has not been adequately addressed. Here, we assessed human–wildlife interaction and impact on vegetation cover change. We analyzed the 250 m MODIS and ERA5 0.25° × 0.25° drone and GPS-collar datasets. We used Time Series Segmented Residual Trends (TSS-RESTREND), Mann–Kendall Test Statistics, Sen’s Slope, ensemble, Kernel Density Estimation (KDE), and Pearson correlation methods. Our results revealed (i) widespread vegetation browning along elephant migration routes and within National Parks, (ii) Pearson correlation (p-value = 5.5 × 10⁻⁸) showed that vegetation browning areas do not sustain high population densities of elephants. Currently, the Zambezi has about 12,008 elephants while these numbers were 1468, 7950, and 5242 in 1989, 1994, and 2005, respectively, (iii) settlements and artificial barriers have a negative impact on wildlife movement, driving vegetation browning, and (iv) vegetation greening was found mostly within communal areas where intensive farming and cattle grazing is a common practice. The findings of this study will serve as a reference for policy and decision makers. Future studies should consider integrating higher resolution multi-platform datasets for detailed micro analysis and mapping of vegetation cover change. Full article

(This article belongs to the Special Issue Satellite and UAV Platforms, Remote Sensing for Geographic Information Systems)

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23 pages, 3270 KiB

Open AccessArticle

Determination of Motion Parameters of Selected Major Tectonic Plates Based on GNSS Station Positions and Velocities in the ITRF2014

by Marcin Jagoda

Sensors 2021, 21(16), 5342; https://doi.org/10.3390/s21165342 - 7 Aug 2021

Cited by 8 | Viewed by 3114

Abstract

Current knowledge about tectonic plate movement is widely applied in numerous scientific fields; however, questions still remain to be answered. In this study, the focus is on the determination and analysis of the parameters that describe tectonic plate movement, i.e., the position (Φ and Λ) of the rotation pole and angular rotation speed (ω). The study was based on observational material, namely the positions and velocities of the GNSS stations in the International Terrestrial Reference Frame 2014 (ITRF2014), and based on these data, the motion parameters of five major tectonic plates were determined. All calculations were performed using software based on a least squares adjustment procedure that was developed by the author. The following results were obtained: for the African plate, Φ = 49.15 ± 0.10°, Λ = −80.82 ± 0.30°, and ω = 0.267 ± 0.001°/Ma; for the Australian plate, Φ = 32.94 ± 0.05°, Λ = 37.70 ± 0.12°, and ω = 0.624 ± 0.001°/Ma; for the South American plate, Φ = –19.03 ± 0.20°, Λ = −119.78 ± 0.39°, and ω = 0.117 ± 0.001°/Ma; for the Pacific plate, Φ = −62.45 ± 0.07°, Λ = 111.01 ± 0.14°, and ω = 0.667 ± 0.001°/Ma; and for the Antarctic plate, Φ = 61.54 ± 0.30°, Λ = −123.01 ± 0.49°, and ω = 0.241 ± 0.003°/Ma. Then, the results were compared with the geological plate motion model NNR-MORVEL56 and the geodetic model ITRF2014 PMM, with good agreement. In the study, a new approach is proposed for determining plate motion parameters, namely the sequential method. This method allows one to optimize the data by determining the minimum number of stations required for a stable solution and by identifying the stations that negatively affect the quality of the solution and increase the formal errors of the determined parameters. It was found that the stability of the solutions of the Φ, Λ, and ω parameters varied depending on the parameters and the individual tectonic plates. Full article

(This article belongs to the Special Issue Satellite and UAV Platforms, Remote Sensing for Geographic Information Systems)

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36 pages, 4913 KiB

Open AccessArticle

Hierarchical Mission Planning with a GA-Optimizer for Unmanned High Altitude Pseudo-Satellites

by Jane Jean Kiam, Eva Besada-Portas and Axel Schulte

Sensors 2021, 21(5), 1630; https://doi.org/10.3390/s21051630 - 26 Feb 2021

Cited by 3 | Viewed by 2771

Abstract

Unmanned Aerial Vehicles (UAVs) are gaining preference for mapping and monitoring ground activities, partially due to the cost efficiency and availability of lightweight high-resolution imaging sensors. Recent advances in solar-powered High Altitude Pseudo-Satellites (HAPSs) widen the future use of multiple UAVs of this sort for long-endurance remote sensing, from the lower stratosphere of vast ground areas. However, to increase mission success and safety, the effect of the wind on the platform dynamics and of the cloud coverage on the quality of the images must be considered during mission planning. For this reason, this article presents a new planner that, considering the weather conditions, determines the temporal hierarchical decomposition of the tasks of several HAPSs. This planner is supported by a Multiple Objective Evolutionary Algorithm (MOEA) that determines the best Pareto front of feasible high-level plans according to different objectives carefully defined to consider the uncertainties imposed by the time-varying conditions of the environment. Meanwhile, the feasibility of the plans is assured by integrating constraints handling techniques in the MOEA. Leveraging historical weather data and realistic mission settings, we analyze the performance of the planner for different scenarios and conclude that it is capable of determining overall good solutions under different conditions. Full article

(This article belongs to the Special Issue Satellite and UAV Platforms, Remote Sensing for Geographic Information Systems)

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24 pages, 5271 KiB

Open AccessArticle

Analysis of UAV-Acquired Wetland Orthomosaics Using GIS, Computer Vision, Computational Topology and Deep Learning

by Sarah Kentsch, Mariano Cabezas, Luca Tomhave, Jens Groß, Benjamin Burkhard, Maximo Larry Lopez Caceres, Katsushi Waki and Yago Diez

Sensors 2021, 21(2), 471; https://doi.org/10.3390/s21020471 - 11 Jan 2021

Cited by 21 | Viewed by 5159

Abstract

Invasive blueberry species endanger the sensitive environment of wetlands and protection laws call for management measures. Therefore, methods are needed to identify blueberry bushes, locate them, and characterise their distribution and properties with a minimum of disturbance. UAVs (Unmanned Aerial Vehicles) and image analysis have become important tools for classification and detection approaches. In this study, techniques, such as GIS (Geographical Information Systems) and deep learning, were combined in order to detect invasive blueberry species in wetland environments. Images that were collected by UAV were used to produce orthomosaics, which were analysed to produce maps of blueberry location, distribution, and spread in each study site, as well as bush height and area information. Deep learning networks were used with transfer learning and unfrozen weights in order to automatically detect blueberry bushes reaching True Positive Values (TPV) of 93.83% and an Overall Accuracy (OA) of 98.83%. A refinement of the result masks reached a Dice of 0.624. This study provides an efficient and effective methodology to study wetlands while using different techniques. Full article

(This article belongs to the Special Issue Satellite and UAV Platforms, Remote Sensing for Geographic Information Systems)

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16 pages, 11262 KiB

Open AccessArticle

Assessing the Trend of the Trophic State of Lake Ladoga Based on Multi-Year (1997–2019) CMEMS GlobColour-Merged CHL-OC5 Satellite Observations

by Augustine-Moses Gaavwase Gbagir and Alfred Colpaert

Sensors 2020, 20(23), 6881; https://doi.org/10.3390/s20236881 - 1 Dec 2020

Cited by 11 | Viewed by 2934

Abstract

The trophic state of Lake Ladoga was studied during the period 1997–2019, using the Copernicus Marine Environmental Monitoring Service (CMEMS) GlobColour-merged chlorophyll-a OC5 algorithm (GlobColour CHL-OC5) satellite observations. Lake Ladoga, in general, is mesotrophic but certain parts of the lake have been eutrophic since the 1960s due to the discharge of wastewater from industrial, urban, and agricultural sources. Since then, many ecological assessments of the Lake’s state have been made. These studies have indicated that various changes are taking place in the lake and continuous monitoring of the lake is essential to update the current knowledge of its state. The aim of this study was to assess the long-term trend in chl-a in Lake Ladoga. The results showed a gradual reduction in chl-a concentration, indicating a moderate improvement. Chl-a concentrations (minimum-maximum values) varied spatially. The shallow southern shores did not show any improvement while the situation in the north is much better. The shore areas around the functioning paper mill at Pitkäranta and city of Sortavala still show high chl-a values. These findings provide a general reference on the current trophic state of Lake Ladoga that could contribute to improve policy and management strategies. It is assumed that the present warming trend of surface water may result in phytoplankton growth increase, thus partly offsetting a decrease in nutrient load. Precipitation is thought to be increasing, but the influence on water quality is less clear. Future studies could assess the current chemical composition to determine the state of water quality of Lake Ladoga. Full article

(This article belongs to the Special Issue Satellite and UAV Platforms, Remote Sensing for Geographic Information Systems)

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14 pages, 1720 KiB

Open AccessArticle

Satellite Laser Ranging for Retrieval of the Local Values of the Love h₂ and Shida l₂ Numbers for the Australian ILRS Stations

by Marcin Jagoda, Miłosława Rutkowska, Paweł Lejba, Jacek Katzer, Romuald Obuchovski and Dominykas Šlikas

Sensors 2020, 20(23), 6851; https://doi.org/10.3390/s20236851 - 30 Nov 2020

Cited by 4 | Viewed by 2206

Abstract

This paper deals with the analysis of local Love and Shida numbers (parameters h₂ and l₂) values of the Australian Yarragadee and Mount Stromlo satellite laser ranging (SLR) stations. The research was conducted based on data from the Medium Earth Orbit (MEO) satellites, LAGEOS-1 and LAGEOS-2, and Low Earth Orbit (LEO) satellites, STELLA and STARLETTE. Data from a 60-month time interval, from 01.01.2014 to 01.01.2019, was used. In the first research stage, the Love and Shida numbers values were determined separately from observations of each satellite; the obtained values of h₂, l₂ exhibit a high degree of compliance, and the differences do not exceed formal error values. At this stage, we found that it was not possible to determine l₂ from the data of STELLA and STARLETTE. In the second research stage, we combined the satellite observations of MEO (LAGEOS-1+LAGEOS-2) and LEO (STELLA+STARLETTE) and redefined the h₂, l₂ parameters. The final values were adopted, and further analyses were made based on the values obtained from the combined observations. For the Yarragadee station, local h₂ = 0.5756 ± 0.0005 and l₂ = 0.0751 ± 0.0002 values were obtained from LAGEOS-1 + LAGEOS-2 and h₂ = 0.5742 ± 0.0015 were obtained from STELLA+STARLETTE data. For the Mount Stromlo station, we obtained the local h₂ = 0.5601 ± 0.0006 and l₂ = 0.0637 ± 0.0003 values from LAGEOS-1+LAGEOS-2 and h₂ = 0.5618 ± 0.0017 from STELLA + STARLETTE. We found discrepancies between the local parameters determined for the Yarragadee and Mount Stromlo stations and the commonly used values of the h₂, l₂ parameters averaged for the whole Earth (so-called global nominal parameters). The sequential equalization method was used for the analysis, which allowed to determine the minimum time interval necessary to obtain stable h₂, l₂ values. It turned out to be about 50 months. Additionally, we investigated the impact of the use of local values of the Love/Shida numbers on the determination of the Yarragadee and Mount Stromlo station coordinates. We proposed to determine the stations (X, Y, Z) coordinates in International Terrestrial Reference Frame 2014 (ITRF2014) in two computational versions: using global nominal h₂, l₂ values and local h₂, l₂ values calculated during this research. We found that the use of the local values of the h₂, l₂ parameters in the process of determining the stations coordinates influences the result. Full article

(This article belongs to the Special Issue Satellite and UAV Platforms, Remote Sensing for Geographic Information Systems)

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18 pages, 3896 KiB

Open AccessArticle

Soil Salinity Inversion of Winter Wheat Areas Based on Satellite-Unmanned Aerial Vehicle-Ground Collaborative System in Coastal of the Yellow River Delta

by Guanghui Qi, Gengxing Zhao and Xue Xi

Sensors 2020, 20(22), 6521; https://doi.org/10.3390/s20226521 - 14 Nov 2020

Cited by 16 | Viewed by 2872

Abstract

Soil salinization is an important factor affecting winter wheat growth in coastal areas. The rapid, accurate and efficient estimation of soil salt content is of great significance for agricultural production. The Kenli area in the Yellow River Delta was taken as the research area. Three machine learning inversion models, namely, BP neural network (BPNN), support vector machine (SVM) and random forest (RF) were constructed using ground-measured data and UAV images, and the optimal model is applied to UAV images to obtain the salinity inversion result, which is used as the true salt value of the Sentinel-2A image to establish BPNN, SVM and RF collaborative inversion models, and apply the optimal model to the study area. The results showed that the RF collaborative inversion model is optimal, R² = 0.885. The inversion results are verified by using the measured soil salt data in the study area, which is significantly better than the directly satellite remote sensing inversion method. This study integrates the advantages of multi-scale data and proposes an effective “Satellite-UAV-Ground” collaborative inversion method for soil salinity, so as to obtain more accurate soil information, and provide more effective technical support for agricultural production. Full article

(This article belongs to the Special Issue Satellite and UAV Platforms, Remote Sensing for Geographic Information Systems)

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19 pages, 4344 KiB

Open AccessArticle

Generating Time-Series LAI Estimates of Maize Using Combined Methods Based on Multispectral UAV Observations and WOFOST Model

by Zhiqiang Cheng, Jihua Meng, Jiali Shang, Jiangui Liu, Jianxi Huang, Yanyou Qiao, Budong Qian, Qi Jing, Taifeng Dong and Lihong Yu

Sensors 2020, 20(21), 6006; https://doi.org/10.3390/s20216006 - 23 Oct 2020

Cited by 13 | Viewed by 3288

Abstract

Green leaf area index (LAI) is an important variable related to crop growth. Accurate and timely information on LAI is essential for developing suitable field management strategies to mitigate risk and boost yield. Several remote sensing (RS) based methods have been recently developed to estimate LAI at the regional scale. However, the performance of these methods tends to be affected by the quality of RS data, especially when time-series LAI are required. For crop LAI estimation, supplementary growth information from crop model is helpful to address this issue. In this study, we focus on the regional-scale LAI estimations of spring maize for the entire growth season. Using time-series multispectral RS data acquired by an unmanned aerial vehicle (UAV) and the World Food Studies (WOFOST) crop model, three methods were applied at different crop growth stages: empirical method using vegetation index (VI), data assimilation method and hybrid method. The VI-based method and assimilation method were used to generate time-series LAI estimations for the whole crop growth season. Then, a hybrid method specially for the late-stage LAI retrieval was developed by integrating WOFOST model and data assimilation. Using field-collected LAI data in Hongxing Farm in 2014, the performances of these three methods were evaluated. At the early stage, the VI-based method (R² = 0.63, RMSE = 0.16, n = 36) achieved higher accuracy than the assimilation method (R² = 0.54, RMSE = 0.52, n = 36), whereas at the mid stage, the assimilation method (R² = 0.63, RMSE = 0.46, n = 28) showed higher accuracy than the VI-based method (R² = 0.41, RMSE = 0.51, n = 28). At the late stage, the hybrid method yielded the highest accuracy (R² = 0.63, RMSE = 0.46, n = 29), compared with the VI-based method (R² = 0.19, RMSE = 0.43, n = 28) and the assimilation method (R² = 0.20, RMSE = 0.44, n = 29). Based on the results above, we considered a combination of the three methods, i.e., the VI-based method for the early stage, the assimilation method for the mid stage, and the hybrid method for the late stage, as an ideal strategy for spring-maize LAI estimation for the entire growth season of 2014 in Hongxing Farm, and the accuracy of the combined method over the whole growth season is higher than that of any single method. Full article

(This article belongs to the Special Issue Satellite and UAV Platforms, Remote Sensing for Geographic Information Systems)

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16 pages, 1433 KiB

Open AccessArticle

Monitoring Winter Stress Vulnerability of High-Latitude Understory Vegetation Using Intraspecific Trait Variability and Remote Sensing Approaches

by Elmar Ritz, Jarle W. Bjerke and Hans Tømmervik

Sensors 2020, 20(7), 2102; https://doi.org/10.3390/s20072102 - 8 Apr 2020

Cited by 4 | Viewed by 2950

Abstract

In this study, we focused on three species that have proven to be vulnerable to winter stress: Empetrum nigrum, Vaccinium vitis-idaea and Hylocomium splendens. Our objective was to determine plant traits suitable for monitoring plant stress as well as trait shifts during spring. To this end, we used a combination of active and passive handheld normalized difference vegetation index (NDVI) sensors, RGB indices derived from ordinary cameras, an optical chlorophyll and flavonol sensor (Dualex), and common plant traits that are sensitive to winter stress, i.e. height, specific leaf area (SLA). Our results indicate that NDVI is a good predictor for plant stress, as it correlates well with height (r = 0.70, p < 0.001) and chlorophyll content (r = 0.63, p < 0.001). NDVI is also related to soil depth (r = 0.45, p < 0.001) as well as to plant stress levels based on observations in the field (r = −0.60, p < 0.001). Flavonol content and SLA remained relatively stable during spring. Our results confirm a multi-method approach using NDVI data from the Sentinel-2 satellite and active near-remote sensing devices to determine the contribution of understory vegetation to the total ecosystem greenness. We identified low soil depth to be the major stressor for understory vegetation in the studied plots. The RGB indices were good proxies to detect plant stress (e.g. Channel G%: r = −0.77, p < 0.001) and showed high correlation with NDVI (r = 0.75, p < 0.001). Ordinary cameras and modified cameras with the infrared filter removed were found to perform equally well. Full article

(This article belongs to the Special Issue Satellite and UAV Platforms, Remote Sensing for Geographic Information Systems)

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23 pages, 14294 KiB

Open AccessArticle

Automatic Seamline Determination for Urban Image Mosaicking Based on Road Probability Map from the D-LinkNet Neural Network

by Shenggu Yuan, Ke Yang, Xin Li and Hongyue Cai

Sensors 2020, 20(7), 1832; https://doi.org/10.3390/s20071832 - 26 Mar 2020

Cited by 16 | Viewed by 2893

Abstract

Image mosaicking which is a process of constructing multiple orthoimages into a single seamless composite orthoimage, is one of the key steps for the production of large-scale digital orthophoto maps (DOM). Seamline determination is one of the most difficult technologies in the automatic mosaicking of orthoimages. The seamlines that follow the centerlines of roads where no significant differences exist are beneficial to improve the quality of image mosaicking. Based on this idea, this paper proposes a novel method of seamline determination based on road probability map from the D-LinkNet neural network for urban image mosaicking. This method optimizes the seamlines at both the semantic and pixel level as follows. First, the road probability map is obtained with the D-LinkNet neural network and related post processing. Second, the preferred road areas (PRAs) are determined by binarizing the road probability map of the overlapping area in the left and right image. The PRAs are the priority areas in which the seamlines cross. Finally, the final seamlines are determined by Dijkstra’s shortest path algorithm implemented with binary min-heap at the pixel level. The experimental results of three group data sets show the advantages of the proposed method. Compared with two previous methods, the seamlines obtained by the proposed method pass through the less obvious objects and mainly follow the roads. In terms of the computational efficiency, the proposed method also has a high efficiency. Full article

(This article belongs to the Special Issue Satellite and UAV Platforms, Remote Sensing for Geographic Information Systems)

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