A Method for the Precise Coordinate Determination of an Inaccessible Location.

Surveyors are occasionally tasked to with determining the coordinates of inaccessible locations or points in civil engineering applications, ground control points for photogrammetry or LiDAR data acquisition, among others. The present work outlines and investigates a novel method for estimating the GNSS coordinates of an inaccessible location where a surveying instrument cannot be set up. The procedure is based on the well-known surveying intersection method and data extracted from an Earth Gravity Model (e.g., EGM 2008). The location's coordinates are obtained from the least-squares adjustment of the angles and distances measured from at least two sites to the unknown point using a total station, within the framework of the Gauss-Helmert method. Field tests confirmed that the accuracy of the determined coordinates of the inaccessible point is at the level of 1 cm. The proposed method bypasses standard coordinate transformation steps performed with the traditional approach, directly producing geocentric coordinates of the unknown points.

Brunei Darussalam rainforest temperature and light intensity data recorded in 2017.

Air temperature and light intensity in the tropical rainforest of Brunei Darussalam was recorded at a 20-min 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].

Landslide susceptibility mapping in an area of underground mining using the multicriteria decision analysis method.

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.

Modeling land use/land cover change using remote sensing and geographic information systems: case study of the Seyhan Basin, Turkey.

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.