Diurnal microclimate profile in tropical coastal built environment (case: Universitas Islam Sultan Agung campus area, Semarang)

Microclimate is caused by the interaction between atmosphere and earth surfaces in local areas. Built environment has pay attention in microclimate since it considerably affects the thermal earth surfaces. This research is located in tropical coastal area, specifically at Universitas Islam Sultan Agung Semarang campus area as a part of built environment where close to the Java sea. The aim of this paper is to investigate the diurnal pattern of microclimate in research location before modelled to the wider area. A numerical modelling method is applied in this research to simulate the diurnal thermal pattern. The research shows that microclimate is influenced by the earth surface objects and following the sun movement respectively.

A tool for analysis of the influence of the Earth surface soil layer temperature on the inhomogeneity of grain crops development by the Earth remote sensing data

The work is devoted to the analysis of the influence of the earth surface temperature on the inhomogeneity of the agricultural crops development. The aim of the work is to expand the object-relational model for describing the inhomogeneous spatial structure of a spatial object by including surface temperature as one of the key features that allow determining the cause of vegetation heterogeneity, along with relief features, differences in the soil chemical composition and other significant characteristics. Experimental studies are carried out at sites located in Sukhobuzimsky district of Krasnoyarsk Territory, for which agricultural crops (grains) and the their sowing dates are known a priori, which allows stating any facts of the vegetation development deviation from the normative trajectory with reference to the sequence and timing norms of phenological phase changing. Landsat-8 OLI (Operational Land Imager) TIRS (Thermal Infrared Sensor) data are used as initial data for temperature measurements. Objects of research are presented in the form of a polygon map in SHP format. The temperature values are calculated using the algorithm for estimating the earth temperature developed by Weng Q., Lu D. and Schubring J. The surface reflectance values are the NDVI vegetation index values also obtained from the Landsat-8 OLI data that underwent atmospheric correction by the DOS method. The research results are implemented in the form of a software module and integrated into the Earth remote monitoring (ERM) system of SFU Space and Information Technologies Institute (SITI). The results are used within the concept of object-oriented monitoring of spatial objects developed by the team of authors, and represent index images of the surface temperature of objects, as well as vector schematic maps.

Calculation of vertical displacement of the earth surface in the “Vostochny” open pit using radar data

The paper deals with the issues of assessment of the condition and changes in the land surface on the territory of the Vostochny open pit (Kemerovo region). The application of the multi-pass series of Sentinel-1 satellite radar data using the Small Baseline Subset (SBaS) method to determine the Earth surface displacement dynamics using constructed vertical displacement maps is demonstrated.

Gravity Field Theory

Gravity keep all things on the earth surface on the ground. Gravity method is one of the oldest geophysical methods. It is used to solve many geological problems. This method can be integrated with the other geophysical methods to prepare more accepted geophysical model. Understanding the theory and the principles concepts considered as an important step to improve the method. Chapter one attempt to discuss Newton’s law, potential and attraction gravitational field, Geoid, Spheroid and geodetically figure of the earth, the gravity difference between equator and poles of the earth and some facts about gravity field.

Human-in-the-Loop Segmentation of Earth Surface Imagery

Segmentation, or the classification of pixels (grid cells) in imagery, is ubiquitously applied in the natural sciences. Manual methods are often prohibitively time-consuming, especially those images consisting of small objects and/or significant spatial heterogeneity of colors or textures. Labeling complicated regions of transition that in Earth surface imagery are represented by collections of mixed-pixels, -textures, and -spectral signatures, can be especially error-prone because it is difficult to reliably unmix, identify and delineate consistently. However, the success of supervised machine learning (ML) approaches is entirely dependent on good label data. We describe a fast, semi-automated, method for interactive segmentation of N-dimensional (x,y,N) images into two-dimensional (x,y) label images. It uses human-in-the-loop ML to achieve consensus between the labeler and a model in an iterative workflow. The technique is reproducible; the sequence of decisions made by human labeler and ML algorithms can be encoded to file, so the entire process can be played back and new outputs generated with alternative decisions and/or algorithms. We illustrate the scientific potential of segmentation of imagery of diverse settings and image types using six case studies from river, estuarine, and open coast environments. These photographic and non-photographic imagery consist of 1- and 3-bands on regular and irregular grids ranging from centimeters to tens of meters. We demonstrate high levels of agreement in label images generated by several labelers on the same imagery, and make suggestions to achieve consensus and measure uncertainty, ideal for widespread application in training supervised ML for image segmentation.