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Recent developments in space-based surveying methods of the Earth's topography, including the differential interferometric synthetic aperture radar (DInSAR), have increased the availability of options for monitoring land subsidence. However, DInSAR methods require expert knowledge and specialized software, and they are time-consuming. Here, we demonstrate that a land subsidence signal can be identified in the differences in the freely available global digital elevation models (e.g., SRTM and TanDEM-X) using a simple statistical method. This finding opens up a venue to develop a computer application to identify land subsidence or uplift of a few decimeters per at least a decade order. Such an application enables monitoring the effects of underground mining, seismic/tectonic movements, landslides, volcanic activities, and similar effects on the Earth's topography. It can also provide a valuable and cost-effective tool for studying land deformation.

The Mediterranean Ridge accretionary complex (MAC) is a product of the convergence of Africa–Europe–Aegean plates. As a result, the region exhibits a continuous mass change (horizontal/vertical movements) that generates earthquakes. Over the last 50 years, approximately 430 earthquakes with M ≥ 5, including 36 M ≥ 6 earthquakes, have been recorded in the region. This study aims to link the ocean bottom deformations manifested through ocean bottom pressure variations with the earthquakes’ time series. To this end, we investigated the time series of the ocean bottom pressure (OBP) anomalies derived from the Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-On (GRACE-FO) satellite missions. The OBP time series comprises a decreasing trend in addition to 1.02, 1.52, 4.27, and 10.66-year periodic components, which can be explained by atmosphere, oceans, and hydrosphere (AOH) processes, the Earth’s pole movement, solar activity, and core–mantle coupling. It can be inferred from the results that the OBP anomalies time series/mass change is linked to a rising trend and periods in the earthquakes’ energy time series. Based on this preliminary work, ocean-bottom pressure variation appears to be a promising lead for further research.

Mediterranean Ridge accretionary complex (MAC) is one of the most critical subduction zones in the world. It is known that the region exhibits a continuous mass change (horizontal/vertical movements). This process is associated with the devastating and tragic earthquakes shaking the MAC for centuries. Here, we investigate the ocean bottom pressure (OBP) anomalies in the MAC derived from the Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow On (GRACE-FO) satellite missions. The OBP time series for the MAC comprises a decreasing trend in addition to 1-, 1.53-, 2.36-, 3.67-, and 9.17-year periodic components partially explained by the atmosphere, oceans, and hydrosphere (AOH) processes, and Earth's pole movement. We noticed that the OBP anomalies appear to link to a rising trend and periods in earthquakes' power time series. This finding sheds new light on the mechanisms controlling the most destructive natural hazard.

Air temperature and light intensity in the tropical rainforest of Brunei Darussalam was recorded at a 20-minute interval from 2011 to 2017. Five units of the HOBO Pendant® data loggers were attached to tree trunks within a circle of 5 m radius at 2 m above the ground. Approximately once a year, data were downloaded, and the sensors serviced. The test site is in a peat swamp forest classified as a mixed dipterocarp forest. The redundancy of the sensors allows for the assessment of the relative precision of the datasets. The root means squared error (RMSE) for the temperature is 0.37°C, below the manufacturer's precision statement (0.53°C). The RMSE for the light intensity data was 569.5 lux. There was no detection of systematic errors in the data. The sensors were calibrated before deployment. The data sets were captured in 2017. Ancillary data were collected in 2018, consisting of RGB orthoimagery and Light Detection And Ranging (LiDAR) data collected from an airborne platform. The data is a unique source of information on the diurnal, intraannual periodic effects, as well as random weather-related and phenology-related phenomenons. Multiannual datasets in the same ecosystem will be published in the future, allowing for a multidisciplinary analysis of the data in the context of climate change and the impact on the tropical rainforest ecosystems. The raw and auxiliary datasets are hosted in the Mendeley repository [1] .

Human civilization and culture on Borneo had generally developed in strong spatial relationship with rivers. Borneo rivers, almost constantly filled in by heavy tropical rains, were ideal topographic features for humans to build their dwellings over and along their banks. Also, as the rivers were full of fish and the banks rich in sago palms and animals, they provided a diversified livelihood for the population limiting agricultural activities. The rivers were also almost exclusive means of transportation to support the trade. These spatial constraints successfully harnessed by the Borneo population ensured the size of the human footprint remained small, thus maintaining the pristine forests with only a very limited space taken up by human settlements. Construction materials for houses were exclusively forest products, which tend to decay quickly because of the high humidity and air temperature. The only durable remains of abandoned human settlements are pottery, clay deposits, some stone and metal objects brought from the North, e.g., China. Therefore, the quest to uncover the cultural heritage of Brunei Darussalam is an extremely challenging one. In this contribution, we report on an attempt to use remote sensing and geoinformation to identify the most likely locations of long-abandoned human settlements. An initial data set is the location of cemeteries and mosques. Besides, topographic data, including LiDAR data, the location of rivers and ponds, abandoned rice fields, landslides, secondary forest plots, historical records, are utilized. Developed maps from this geodata will support possible subsequent archaeological investigations by helping to identify the location of sites of interests. The outcomes of this project may be of interest not only to government departments in charge of the historical and cultural heritage of Brunei Darussalam but also to ecologists documenting the interactions between human civilization and nature.

Delft University of Technology;et al.;FIG;University of Applied Sciences Stuttgart;University of South Wales;University of Twente, ISPRS TC IV Mid-Term Symposium on 3D Spatial Information Science - The Engine of Change -- 1 October 2018 through 5 October 2018 -- -- 140766, This paper discusses a current issue for several experimental science disciplines, which is the Big Data Problem (BDP). This research study focused on light intensity and ranging (LiDAR) datasets, which are collected for modelling spatial features found on the surface of the earth. Currently, LiDAR datasets are known to be extremely redundant for many applications. Using a formula that allows for calculating the variance of the target-induced error (so-called T-error) caused by the discretisation and quantisation of a 3D surface as a criterion for the quantitative assessment of the fidelity of a model, the use of a Q-tree-based split of the surface is proposed for cells of various sizes depending on the fidelity requirements. A LiDAR dataset representing a 1 km x 1 km terrain surface tile using approximately 12 x 106 points was used during the experiments. The initial LiDAR dataset was used to produce a digital terrain model (DTM) at a 0.5 m x 0.5 m resolution, which was used as a reference model. Subsequently, the initial LiDAR dataset was decimated at various rates, and the resulting DTMs were compared with the reference model. The Q-tree based data structure was utilised to illustrate that the Q-tree approach allows for the production of DTMs at a ‘controlled’ fidelity with a considerable reduction in data volume. © Authors 2018.

The digital elevation model (DEM) is one of the key geospatial datasets used in many fields of engineering and science for countless applications. In this contribution, we assess the vertical accuracy of the Advanced Land Observing Satellite (ALOS) World 3D-30m (AW3D30) DEM using the runway method (RWYM). The RWYM utilizes the longitudinal profiles of runways which are reliable and ubiquitous reference data. A reference dataset used in this project consists of 36 runways located at various points throughout the world. The same dataset was previously used to test the accuracy of WorldDEMTM. Our study indicates that AW3D30 has a remarkably high RMSE of 1.78 m (one σ). However, while analyzing the results, it has become apparent that it also contains a widespread elevation anomaly. We conclude that this anomaly is the result of uncompensated sensor noise and the data processing algorithm (downsampling of the higher resolution data). We believe that this issue should be communicated to the user community. Also, we would like to note that the traditional accuracy assessment of a DEM, e.g., statistical assessment of the elevation differences = model – reference, does not allow for identification of these type of anomalies in a DEM.

The Badas peat swamp forest, Brunei Darussalam, Borneo, is a large Baram river peatland formation facing pressures from natural and increasingly anthropogenic disturbances. Although the peat forest should regenerate naturally after disturbances, there is little quantitative information documenting this regeneration. Given the impenetrability and remoteness of peatland forests, remote sensing technology can fill the peatland forest regeneration knowledge gap. This paper reports on the findings of a study on the resilience of the Badas peat swamp forest, composed primarily of Shorea albida Sym. We identified the scars caused by logging and caterpillar infestation using photo interpretation and the forest inventory map. We employed the Shuttle Radar Topography Mission (SRTM), TanDEM-X 90 m (TDX) digital elevation products, and AW3D30 m (AW) digital surface model (DSM) to assess the regeneration of disturbed forest scars over 14 years. Through measurements of forest height and other indices, we found that the Badas peat swamp forest did not recover from these past disturbances and the depletion of scars progressed even further. Our findings support other authors' conclusions suggesting irrevocable degradation of Bornean peatland and peat forests. We also introduce an accuracy assessment of the forest biomass estimation relying on a comparison of DSMs and present a detailed morphology of the surface of the Badas peat dome.

Over the past decades, upwelling research has intensified in the South China Sea resulting in interesting findings about the phenomenon and its drivers especially at seasonal, intra-seasonal and annual timescales. However, whether upwelling variability follows well defined and coherent cycles on a multi-temporal basis, remains a least examined area of research. This is understandable because previous studies have been largely based on various proxy variables which limits the predictability of upwelling intensity. Consequently, a recently developed time-series of upwelling index (1967–2014),generated across northern South China Sea (NSCS): off Fujian, Guangdong, Hanoi and S.W. Taiwan, using NOAA-ERD model has been analyzed using a number of complementary statistical methods. The results reveal four distinct temporal modes of upwelling/downwelling variability including: seasonal, annual, inter-annual and Decadal patterns. A summary of the outcomes of this study is as follows: • Off Hanoi, Fujian and Guangdong, upwelling is found to be a dominant feature in summer (S.W. Monsoon) while off S.W. Taiwan, upwelling occurs all year round but with greater intensity in winter (N.E. Monsoon); • The trend of upwelling peaked between 1967 and 1980 off S.W. Taiwan and declined gradually in subsequent years and decades; the general trend of upwelling off Fujian and Hanoi was positive, while the trend of upwelling off Guangdong was relatively flat during the study period; • In contrast, downwelling intensity weakened considerably off Hanoi, Fujian and Guangdong, and intensified off S.W. Taiwan; • A further breakdown of the long-term trends reveals distinct multi-annual and decadal patterns; • The dominant temporal variability patterns of upwelling/downwelling in all four zones are found to be annual and inter-annual, showing a strong correspondence with ENSO cycles; Finally, further analysis is recommended in order to enhance understanding of mechanisms of the multiple temporal patterns of upwelling/downwelling variability as well as uncover its impacts on regional marine biogeochemistry.

The air temperature and light intensity were recorded in a tropical rainforest of Brunei Darussalam at 20-minute interval in 2017. HOBO Pendant® data loggers were attached to tree trunks approximately 2 m above ground. The obtained data can be used to study various microclimatic, biological and ecological characteristics of tropical rainforests in Brunei. The long term observations can also be used to study the impact of global climate change on the canopy of tropical rainforest. DOI: https://doi.org/10.17632/5vzp6svhwh.3 (Becek, 2018).

23rd International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences Congress, ISPRS 2016 -- 12 July 2016 through 19 July 2016 -- -- 122460, We examined the dependency of the pixel reflectance of hyperspectral imaging spectrometer data (HISD) on a normalized total insolation index (NTII). The NTII was estimated using a light detection and ranging (LiDAR)-derived digital surface model (DSM). The NTII and the pixel reflectance were dependent, to various degrees, on the band considered, and on the properties of the objects. The findings could be used to improve land cover (LC)/land use (LU) classification, using indices constructed from the spectral bands of imaging spectrometer data (ISD). To study this possibility, we investigated the normalized difference vegetation index (NDVI) at various NTII levels. The results also suggest that the dependency of the pixel reflectance and NTII could be used to mitigate the shadows in ISD. This project was carried out using data provided by the Hyperspectral Image Analysis Group and the NSF-funded Centre for Airborne Laser Mapping (NCALM), University of Houston, for the purpose of organizing the 2013 Data Fusion Contest (IEEE 2014). This contest was organized by the IEEE GRSS Data Fusion Technical Committee.

In this case study, an active runway of a civilian airport in Zonguldak, Turkey was used to assess the suitability of spaceborne digital elevation models (DEMs) to model an anthropogenic structure. The tested DEMs include the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), the Advanced Land Observing Satellite (ALOS) World 3D 30 m (AW3D30), the Shuttle Radar Topography Mission (SRTM)-1&rdquo, the SRTM-3&rdquo, the SRTM-X, the TanDEM-3&rdquo, and the WorldDEMTM. A photogrammetric high accuracy DEM was also available for the tests. As a reference dataset, a line-leveling survey of the runway using a Leica Sprinter 150/150M instrument was performed. The selection of a runway as a testbed for this type of investigation is justified by its unique characteristics, including its flat surface, homogenous surface material, and availability for a ground survey. These characteristics are significant because DEMs over similar structures are free from environment- and target-induced error sources. For our test area, the most accurate DEM was the WorldDEMTM followed by the SRTM-3&rdquo, and TanDEM-3&rdquo, with vertical errors (LE90) equal to 1.291 m, 1.542 m, and 1.56 m, respectively. This investigation uses a method, known as the runway method, for identifying the vertical errors in DEMs.

Landslides are geomorphological phenomena that affect anthropogenic and natural features on the Earth’s surface. Many previous studies have identified several factors that have contributed to landslides. Among these factors are physical characteristics, such as slope, aspect, and land cover, of Earth’s surface. Moreover, landslides can be triggered by human activities such as underground mining. This study aims to identify landslide susceptibility areas by analyzing landslide-related factors, including land subsidence triggered by underground mining. The area of interest was Kozlu, Turkey, where underground mining has been in progress for the past 100 years. Thus, to identify landslide risk zones, the multicriteria decision analysis method, together with the analytical hierarchy method, was used. The datasets included were topography, land cover, geological settings, and mining-induced land subsidence. The spatial extent of land subsidence was estimated using a previously published model. A landslide susceptibility map (LSM) was developed using a purposely developed GIS-based software. The results were compared with a terrain deformation map, which was developed in a separate study using the differential synthetic aperture radar interferometry (DInSAR) technique. The results showed a substantial correlation between the LSM and DInSAR map. Furthermore, it was found that ~ 88% of the very high and high landslide risk areas coincided with location of the past landslide events. These facts suggest that the algorithm and data sources used were sufficient to produce a sufficiently accurate LSM, which may be used for various purposes such as urban planning. Electronic supplementary material The online version of this article (10.1007/s10661-018-7085-5) contains supplementary material, which is available to authorized users.

PubMedID: 30066225 Land use and land cover (LULC) changes affect several natural environmental factors, including soil erosion, hydrological balance, biodiversity, and the climate, which ultimately impact societal well-being. Therefore, LULC changes are an important aspect of land management. One method used to analyze LULC changes is the mathematical modeling approach. In this study, Cellular Automata and Markov Chain (CA-MC) models were used to predict the LULC changes in the Seyhan Basin in Turkey that are likely to occur by 2036. Satellite multispectral imagery acquired in the years 1995, 2006, and 2016 were classified using the object-based classification method and used as the input data for the CA-MC model. Subsequently, the post-classification comparison technique was used to determine the parameters of the model to be simulated. The Markov Chain analyses and the multi-criteria evaluation (MCE) method were used to produce a transition probability matrix and land suitability maps, respectively. The model was validated using the Kappa index, which reached an overall level of 77%. Finally, the LULC changes were mapped for the year 2036 based on transition rules and a transition area matrix. The LULC prediction for the year 2036 showed a 50% increase in the built-up area class and a 7% decrease in the open spaces class compared to the LULC status of the reference year 2016. About an 8% increase in agricultural land is also likely to occur in 2036. About a 4% increase in shrub land and a 5% decrease in forest areas are also predicted. © 2018, Springer Nature Switzerland AG.

Wyznaczenie przebiegu nowej geoidy na obszarze Brunei zostało zrealizowane z wykorzystaniem lądowych, lotniczych i altimetrycznych danych grawimetrycznych oraz modelu geopotencjału EGM08 metodą kolokacji. Obliczenia zostały przeprowadzone z wykorzystaniem techniki „remove-restore”. W celu uzyskania lepszego wglądu, w jakość danych wejściowych oszacowano dokładność danych grawimetrycznych i geometrycznych odstępów geoidy od elipsoidy na punkach sieci GPS wykorzystując do tego celu model geopotencjalu EGM08. Z przyprowadzonych oszacowań wynika przede wszystkim niska dokładność danych GPS/niwelacja. Wynikiem przeprowadzonych obliczeń jest grawimetryczna geoida dla obszaru Brunei, obliczona metodą kolokacji, której dokładność szacuje się poniżej ±0.3 m.

The sea-surface distribution of four phytoplankton functional groups in the South China Sea (SCS) namely Coccolithophores (Coc), Diatoms (Dia), Cyanobacteria (Cya) and Chlorophytes (Chlo) was studied across spatial and temporal scales. The time-series datasets derived from the National Aeronautic and Space Administration (NASA) Ocean Biogeochemical Model and retrieved via the Giovanni Portal were analysed statistically with well-known methods including Pearson’s correlation coefficient, ordinary least square regression (OLR), Maximum Entropy Spectral Analysis (MESA) and Principal Components Analysis (PCA). The data were analysed for relative abundance, seasonality, cyclicity, long-term trends, spatial variability, inter-relationships, and relative contributions of individual phytoplankton groups to primary production (PP, using Chl-a as a proxy). The results reveal that the numerically dominant phytoplankton group in terms of relative abundance is Cya, comprising about 57% of the planktonic biomass, followed closely by Coc (about 40%), while Dia constitutes almost 3% of the total abundance. Dia was found to have the strongest annual cycle, and hence displayed the highest rate of seasonal variability. Clear spatial segregation patterns have also been uncovered; Coc and Dia are strongly correlated in the Northern SCS (NSCS) while Cya dominate in Southern SCS (SSCS). Coc and Dia display a strong positive correlation with PP, whereas the seasonal relationships between Cya and Chlo with PP are non-linear, and hence appear statistically non-significant. The rise in the trend of Cya and decrease in Coc, since 2004, may be indicative of changes in successional patterns of phytoplankton functional groups in the SCS. This study, therefore, sets the precedence for more robust research to uncover the immediate and remote causes of the observed spatiotemporal changes in marine phytoplankton dynamics and to develop predictive models of marine primary productivity (MPP) and ecological health.

Examining the actual state of canopy gaps in forests is critically important for evaluating forest community structure and dynamics, and for comparing gap disturbance regimes in different forests. We, therefore, re-examined our own previously published data from three studies of three different gap survey methods – the Belt transect method, the canopy coverage census method, and an aerial photography method – in order to ascertain how gap parameters (area, size and density of gaps, and related gap characteristics) obtained from these methods differ from each other. We found that the Belt transect method is easy to apply and practical in the field, and the method used for gap measurement is simple. Mean gap size and size distribution obtained using the Belt transect method were not different statistically from those of the other gap survey methods, and quantified gap parameters were in the range recorded for other forests of the world. For preliminary studies of gap dynamics in different forest types, an easy, speedy and workable gap survey method is needed. In addition, surveying a large number of gaps and a large area improve the accuracy of the method. We conclude that the Belt transect method is most suitable method for preliminary gap surveys in the field.

The Internet of Things (IoT) is an emerging technology that was conceived in 1999. The key components of the IoT are intelligent sensors, which represent objects of interest. The adjective ‘intelligent’ is used here in the information gathering sense, not the psychological sense. Some 30 billion sensors that ‘know’ the current status of objects they represent are already connected to the Internet. Various studies indicate that the number of installed sensors will reach 212 billion by 2020. Various scenarios of IoT projects show sensors being able to exchange data with the network as well as between themselves. In this contribution, we discuss the possibility of deploying the IoT in cartography for real-time mapping. A real-time map is prepared using data harvested through querying sensors representing geographical objects, and the concept of a virtual sensor for abstract objects, such as a land parcel, is presented. A virtual sensor may exist as a data record in the cloud. Sensors are identified by an Internet Protocol address (IP address), which implies that geographical objects through their sensors would also have an IP address. This contribution is an updated version of a conference paper presented by the author during the International Federation of Surveyors 2014 Congress in Kuala Lumpur. The author hopes that the use of the IoT for real-time mapping will be considered by the mapmaking community.

[1] An attempt was made to experimentally assess the instrumental component of error of the C-band SRTM (SRTM). This was achieved by comparing elevation data of 302 runways from airports all over the world with the shuttle radar topography mission data product (SRTM). It was found that the rms of the instrumental error is about ±1.55 m. Modeling of the remaining SRTM error sources, including terrain relief and pixel size, shows that downsampling from 30 m to 90 m (1 to 3 arc-sec pixels) worsened SRTM vertical accuracy threefold. It is suspected that the proximity of large metallic objects is a source of large SRTM errors. The achieved error estimates allow a pixel-based accuracy assessment of the SRTM elevation data product to be constructed. Vegetation-induced errors were not considered in this work.