Drones, Open-Source Tools, and Open Science for Participatory Land Administration in Namibia’s Informal Settlements

Interferometric synthetic aperture radar (InSAR) processing, if lacking high quality ground control points (GCPs), may produce large errors in the final DEM. Some kind of alignment or registration can reduce these errors. This paper evaluates the accuracy of InSAR processed digital elevation models (DEM), against a high resolution DEM. InSAR DEMs were aligned and least-squares registered with USGS, 1/3 arc second NED. The accuracy evaluations after registration show InSAR DEMs alignment and registration can eliminate DEM errors caused by lack of accurate GCPs. In some cases, this process improves the accuracy of the InSAR DEM even more efficiently than including GCPs into InSAR processing.

The Kenya government together with development partners has embarked on the development of informal settlements. Mombasa County has more than 70 informal settlements; information about the settlements and their relationship to the existing cadastral spatial layer covering the entire Mombasa County needs to be determined in order to support decision making for sustainable development. This study evaluates the Social Tenure Domain Model (STDM) land tool in the development of an informal Cadastre by capturing, storing, and manipulating social and spatial information in Kwarasi informal settlement in Mombasa. This case study is an example that can be replicated to the rest of the informal settlements in Mombasa forming a single informal cadastral database for the entire county. Participatory enumerations and the STDM tool were used in this research to present the situation of Kwarasi informal settlement. The housing structures were adopted as the spatial unit for the informal Cadastre over which the rights of the inhabitants were adjudicated, customized and uploaded into the STDM where social tenure relationships were created to form the Cadastre in the STDM system. The spatial component of Kwarasi informal Cadastre was overlaid on the Mombasa cadastral layer and their relationship determined. It has been demonstrated that the relationship would be useful for decision making to support sustainable development intervention, and ease land administration by maintaining a detail record of the informal settlements. Key words: Cadastre, global land tool network (GLTN), informal settlements, land administration domain model (LADM), social tenure domain model (STDM).

Abstract. Vendor-provided rational polynomial coefficients (RPCs) are commonly used to generate digital elevation models (DEMs) from high-resolution satellite images. This results in a level of accuracy that can be improved using ground control points (GCPs). It is well known that due to the inherent bias of sensor orientation the generated DEM is distorted. In the traditional way of working, the bias is corrected by integrating GCPs into the standard processing chain. This involves additional effort, since the provision of GCPs and the measurement of their image coordinates are required.In this paper, we examine whether and how the data recorded by NASA's ICESat (Ice, Cloud, and Land Elevation Satellite) mission can be used as GCPs without measuring image coordinates. The starting point are DEMs that are generated by image matching from KOMPSAT-3 satellite images with given RPCs. We developed a point-to-surface matching method that matches the ICESat points to the DEM in order to correct the DEM and improve its precision. For the experimental investigations a KOMPSAT 3 image pair is used that covers an area of 20 by 16 km2 in the Yangsan city regions. The generated DEM has a height accuracy of about 9 m. The point-to-surface algorithm with 505 ICESat points led to an improvement of the DEM height accuracy to about 2 m.

Digital surface models (DSMs) extracted from high-resolution Radarsat-2 stereo-images using different geometric modeling (deterministic, new hybrid, and empirical) are evaluated. The 3-D deterministic models are Toutin's and hybrid Toutin's models (TM and HTM) developed at the Canada Centre for Remote Sensing, and the empirical model is the rational function model (RFM). TM is computed with one and eight ground control points (GCPs), HTM without GCP and RFM supplied by MacDonald, Dettwiler and Associates Ltd. is postprocessed with 3-9 GCPs depending of degrees of 2-D polynomial functions. The DSMs are then generated and compared to 0.2-m accurate lidar elevation data. Because DSMs included the height of land covers, elevation linear errors with 68% and 90% confidence level (LE68 and LE90) are computed and compared over bare surfaces only. LE90 results are: TM with eight GCPs achieves the best results (6.3 m), then HTM with no GCP (7 m), TM with one GCP (8.6 m), and finally RFM the worst (9.7 m) whatever the polynomial degree and GCP number. HTM is the only modeling not using any GCP, which offers a strong advantage in operational environments.

One of the first essential steps in the analysis of satellite imagery is the orthorectification of the images. Orthorectification without ground control points (GCPs) using only the ephemeris and attitude data provided by the satellite operator provides an absolute accuracy of about 20 m to 1 km (depending on the satellite), which can be improved by measuring precise GCPs. In this paper, a method to obtain GCPs from an existing digital elevation model (DEM) is described and assessed. Since at least the SRTM DEM is available worldwide, DEMs could serve as a valuable additional source for the generation of GCPs. Furthermore, several planned and ongoing missions will increase the availability and accuracy of DEMs or stereo imagery respectively, e.g. ALOS, Tandem-X, etc.

The sub-alpine and alpine Sphagnum peatlands in Australia are geographically constrained to poorly drained areas c. 1000 m a.s.l. Sphagnum is an important contributor to the resilience of peatlands; however, it is also very sensitive to fire and often shows slow recovery after being damaged. Recovery is largely dependent on a sufficient water supply and impeded drainage. Monitoring the fragmented areas of Australia’s peatlands can be achieved by capturing ultra-high spatial resolution imagery from an unmanned aerial systems (UAS). High resolution digital surface models (DSMs) can be created from UAS imagery, from which hydrological models can be derived to monitor hydrological changes and assist with rehabilitation of damaged peatlands. One of the constraints of the use of UAS is the intensive fieldwork required. The need to distribute ground control points (GCPs) adds to fieldwork complexity. GCPs are often used for georeferencing of the UAS imagery, as well as for removal of artificial tilting and doming of the photogrammetric model created by camera distortions. In this study, Tasmania’s northern peatlands were mapped to test the viability of creating hydrological models. The case study was further used to test three different GCP scenarios to assess the effect on DSM quality. From the five scenarios, three required the use of all (16–20) GCPs to create accurate DSMs, whereas the two other sites provided accurate DSMs when only using four GCPs. Hydrological maps produced with the TauDEM tools software package showed high visual accuracy and a good potential for rehabilitation guidance, when using ground-controlled DSMs.

Unmanned aerial vehicle (UAV)-based snow depth is mapped as the difference between snow-on and snow-off digital surface models (DSMs), which are derived using the structure from motion (SfM) technique with ground control points (GCPs). In this study, we evaluated the impacts of the quality and deployment of GCPs on the accuracy of snow depth estimates. For 15 GCPs in our study area, we surveyed each of their coordinates using an ordinary global positioning system (GPS) and a differential GPS, producing two sets of GCP measurements (hereinafter, the low-accuracy and high-accuracy sets). The two sets of GCP measurements were then incorporated into SfM processing of UAV images by following two deployment strategies to create snow-off and snow-on DSMs and then to retrieve snow depth. In Strategy A, the same GCP measurements in each set were used to create both the snow-on and snow-off DSMs. In Strategy B, each set of GCP measurements was divided into two sub-groups, one sub-group for creating snow-on DSMs and the other sub-group for snow-off DSMs. The accuracy of snow depth estimates was evaluated in comparison to concurrent in-situ snow depth measurements. The results showed that Strategy A, using both the low-accuracy and high-accuracy sets, generated accurate snow depth estimates, while in Strategy B, only the high-accuracy set could generate reliable snow depth estimates. The results demonstrated that the deployment of GCPs had a significant influence on UAV-based SfM snow depth retrieval. When accurate GCP measurements cannot be guaranteed (e.g., in mountainous regions), Strategy A is the optimal option for producing reliable snow depth estimates. When highly accurate GCP measurements are available (e.g., collected by differential GPS in open space), both deployment strategies can produce accurate snow depth estimates.

The correct representation of the topography of terrain is an important requirement to generate photogrammetric products such as orthoimages and maps from high-resolution (HR) or very high-resolution (VHR) satellite datasets. The refining of the digital elevation model (DEM) for the generation of an orthoimage is a vital step with a direct effect on the final accuracy achieved in the orthoimages. The refined DEM has potential applications in various domains of earth sciences such as geomorphological analysis, flood inundation mapping, hydrological analysis, large-scale mapping in an urban environment, etc., impacting the resulting output accuracy. Manual editing is done in the presented study for the automatically generated DEM from IKONOS data consequent to the satellite triangulation with a root mean square error (RMSE) of 0.46, using the rational function model (RFM) and an optimal number of ground control points (GCPs). The RFM includes the rational polynomial coefficients (RPCs) to build the relation between image space and ground space. The automatically generated DEM initially represents the digital surface model (DSM), which is used to generate a digital terrain model (DTM) in this study for improving orthoimages for an area of approximately 100 km2. DSM frequently has errors due to mass points in hanging (floating) or digging, which need correction while generating DTM. The DTM assists in the removal of the geometric effects (errors) of ground relief present in the DEM (i.e., DSM here) while generating the orthoimages and thus improves the quality of orthoimages, especially in areas such as Dehradun that have highly undulating terrain with a large number of natural drainages. The difference image of reference, i.e., edited IKONOS DEM (now representing DTM) and automatically generated IKONOS DEM, i.e., DSM, has a mean difference of 1.421 m. The difference DEM (dDEM) for the reference IKONOS DEM and generated Cartosat-1 DEM at a 10 m posting interval (referred to as Carto10 DEM) results in a mean difference of 8.74 m.

The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) aboard the Terra satellite was designed to generate along‐track stereo images. The data are available at low cost, providing a feasible opportunity for generating digital elevation models (DEMs) in areas where little or no elevation data are yet available. This study evaluates the accuracy of DEMs extracted from ASTER data covering mountainous terrain. For an assessment of the achieved accuracies in the Andean study site, comparisons were made to similar topographical conditions in Switzerland, where reference data were available. All raw DEMs were filtered and interpolated by the post‐processing tools included with PCI Geomatica, the software package used. After carefully checking the DEM quality, further post‐processing was undertaken to eliminate obvious artefacts such as peaks and sinks. Accuracy was tested by comparing the DEMs in the Swiss Alps to three reference models. The achieved results of the generated DEMs are promising, considering the extreme terrain. Given accurate and well‐distributed ground control points (GCPs), it is possible to generate DEMs with a root mean square (RMS) error between 15 m and 20 m in hilly terrain and about 30 m in mountainous terrain. The DEMs are very accurate in nearly flat regions and on smooth slopes with southern expositions: errors are generally within ±10 m in those cases. Larger errors do appear in forested, snow covered or shady areas and at steep cliffs and deep valleys with extreme errors of a few hundred metres. The evaluation showed that the quality of the DEMs is sufficient for enabling atmospheric, topographic and geometric correction to various satellite datasets and for deriving additional products.

A method for ground control point (GCP) acquisition using a simulated synthetic aperture radar (SAR) image derived from a digital elevation model (DEM) is proposed. Also proposed is a method for the evaluation of the accuracy of GCP matching with texture features from a reference GCP chip image. Results from experiments with simulated GCP chip images as reference images and geometrically distorted GCP chip images, derived using simulated SAR images as current images, show good coincidence with GCP matching accuracy in terms of pixel distances between matched GCP points in reference and current chip images and texture features. In particular, the correlation coefficient between the angular second moment (ASM) and the matching accuracy is 0.757, followed by Ent (entropy), Horn (homoginiety), Dis (dissimilarity), Con (contrast), Var (variability coefficient) and Chi (Chi-square), for the skewed images while for the rotated images, Dis shows the highest correlation of 0.628, followed by Horn, Asm, Co...

Abstract. In this paper we describe how recent high-resolution digital elevation models (DEMs) can be used to extract glacier surface DEMs from old aerial photographs and to evaluate the uncertainty of the mass balance record derived from the DEMs. We present a case study for Drangajökull ice cap, NW Iceland. This ice cap covered an area of 144 km2 when it was surveyed with airborne lidar in 2011. Aerial photographs spanning all or most of the ice cap are available from survey flights in 1946, 1960, 1975, 1985, 1994 and 2005. All ground control points used to constrain the orientation of the aerial photographs were obtained from the high-resolution lidar DEM. The lidar DEM was also used to estimate errors of the extracted photogrammetric DEMs in ice- and snow-free areas, at nunataks and outside the glacier margin. The derived errors of each DEM were used to constrain a spherical semivariogram model, which along with the derived errors in ice- and snow-free areas were used as inputs into 1000 sequential Gaussian simulations (SGSims). The simulations were used to estimate the possible bias in the entire glaciated part of the DEM and the 95 % confidence level of this bias. This results in bias correction varying in magnitude between 0.03 m (in 1975) and 1.66 m (in 1946) and uncertainty values between ±0.21 m (in 2005) and ±1.58 m (in 1946). Error estimation methods based on more simple proxies would typically yield 2–4 times larger error estimates. The aerial photographs used were acquired between late June and early October. An additional seasonal bias correction was therefore estimated using a degree-day model to obtain the volume change between the start of 2 glaciological years (1 October). This correction was largest for the 1960 DEM, corresponding to an average elevation change of −3.5 m or approx. three-quarters of the volume change between the 1960 and the 1975 DEMs. The total uncertainty of the derived mass balance record is dominated by uncertainty in the volume changes caused by uncertainties of the SGSim bias correction, the seasonal bias correction and the interpolation of glacier surface where data are lacking. The record shows a glacier-wide mass balance rate of Ḃ = −0.26 ± 0.04 m w.e. a−1 for the entire study period (1946–2011). We observe significant decadal variability including periods of mass gain, peaking in 1985–1994 with Ḃ = 0.27 ± 0.11 m w.e. a−1. There is a striking difference when Ḃ is calculated separately for the western and eastern halves of Drangajökull, with a reduction of eastern part on average ∼ 3 times faster than the western part. Our study emphasizes the need for applying rigorous geostatistical methods for obtaining uncertainty estimates of geodetic mass balance, the importance of seasonal corrections of DEMs from glaciers with high mass turnover and the risk of extrapolating mass balance record from one glacier to another even over short distances.

Abstract. Digital Elevation Models (DEMs) are fundamental for ensuring the geolocation accuracy of Synthetic Aperture Radar (SAR) imagery, as vertical errors in elevation data propagate into horizontal displacements. This study examines the impact of DEM accuracy on the geolocation performance of Sentinel-1 Ground Range Detected (GRD) products over an urban area in Ankara, Türkiye. A comparative analysis was conducted using four global DEMs (SRTM 1″, SRTM 3″, AW3D30, Copernicus 30 m), a high- resolution national DEM (YÜKPAF), and two interferometrically derived DEMs generated from Sentinel-1 Single Look Complex (SLC) pairs. Vertical accuracy was validated against ICESat-2 ATL08 reference elevations, with Copernicus (2.2 m RMSE) and YÜKPAF (3.2 m RMSE) providing the highest reliability, while the InSAR-derived DEMs showed larger errors (>12 m) due to coherence loss and phase unwrapping inconsistencies. Horizontal accuracy was evaluated using Ground Control Points (GCPs) obtained from HGM-Küre. The results demonstrated that high-quality DEMs, particularly YÜKPAF, achieved the lowest horizontal RMSE (8.2 m), whereas InSAR-based DEMs produced the largest errors, approaching 15 m. Despite these variations, all orthorectified outputs remained below 15 m geolocation error, owing to the precise orbital information of Sentinel-1 and the flat topography of the study area. Overall, the study confirms that DEM quality is a decisive factor for SAR geolocation accuracy and offers practical guidance for dataset selection in operational SAR orthorectification workflows.

The major global pressures of rapid urbanization and urban growth are being compounded by climate impacts, resulting in increased vulnerability for urban dwellers, with these vulnerabilities exacerbated during the COVID-19 pandemic. Much of this is concentrated in urban and peri-urban areas where urban development spreads into hazard-prone areas. Often, this development is dominated by poor-quality homes in informal settlements or slums with poor tenure security. Lessons from a resilience-building project in the Pacific shows that a fit-for-purpose (FFP) approach to land administration can provide solutions by increasing the number of households with security of tenure, and consequently, improving resilience outcomes as informal settlements grow. This paper specifically discusses the influence of FFP land administration on reducing vulnerabilities to external shocks, such as climate change and COVID-19. It proposes ways to be better manage urban growth through the responsible governance of land tenure rights and more effective land-use planning to improve resilience to multiple shocks and stresses, hence, delivering improved access to safe land and shelter. Land administration systems can contribute to enhanced resilience to the shocks of climate extremes and pandemics by improving tenure security and enhancing land-use planning controls. It is argued that climate change adaptation and disaster risk reduction need to be better mainstreamed into two major elements of land governance: (i) securing and safeguarding of land rights, and (ii) planning and control of land use.

Research has shown the importance of measuring topography for surface change detection and that the use of remote sensing methods is ideal for this application. A prerequisite for measuring change is a historical data‐set that covers the time period of interest. Many remote methods of topographic data collection such as lidar are relatively recent developments and therefore cannot provide records longer than about a decade. Alternatively, aerial photography has been in common use since the early 20th century and archives upwards of 50 years are therefore not uncommon. While photogrammetry is dependent on well distributed, high quality ground control points (GCPs), such data has been successfully applied retroactively. Therefore, contemporary lidar data, which requires little ground‐truth data, should be an ideal source of GCPs for controlling historical aerial photographs. This study aims to evaluate the success with which GCPs can be extracted from a high‐resolution lidar data‐set for controlling aerial photography for digital elevation model (DEM) production. Using a data‐set collected in Upper Wharfedale, northern England, GCPs were measured both by means of a lidar data‐set and by using traditional field‐based methods. The results showed that while the use of lidar‐derived ground control produced a DEM of inferior quality, increasing the number of GCPs used in the model produced results comparable to the GPS‐controlled DEM. These results are significant especially for surface change detection research in remote areas where high quality ground control is difficult to secure.

In this study, we generated Digital Elevation Model (DEM) from Unmanned Aerial Vehicle (UAV) to confirm the suitability of UAV to the tidal flat study. The DEMs were generated from aerial triangulation method using rotary-wing UAV. For the accurate generation of mosaic images and DEM, the distorted images occurred by interior and exterior orientation were corrected using camera calibration. In addition, we set up a Ground Control Points (GCPs) in order to correct of the UAV position error. Therefore, the DEM was obtained with geometric error less than 30 cm. The height of generated DEM by UAV was compared with the levelled elevation by RTK-GPS and TanDEM-X radar satellite DEM. From this study, we could confirm that accurate DEM of the tidal flat can be generated using UAV and these detailed spatial information about tidal flat will be widely used for tidal flat management.