The digital 3D documentation of architectural heritage using advanced 3D measurement technologies such as UAV photogrammetry and terrestrial LiDAR (TLS) becomes a potential and efficient method since it can produce 3D pointclouds in detail and high density of pointclouds levels. However, TLS is unable to scan the roof part of tall building, whereas UAV photogrammetry achieves high density of pointclouds at that area. In order to make a complete 3D pointclouds of heritage building, we merged and integrated the TLS and UAV pointclouds data by using Iterative Closest Point (ICP) algorithms into one reference system. In this study, we collected two architectural heritage building in Yogyakarta, Indonesia, i.e., "Vredeburg Fort Museum (VFM)" and "Kotagede Great Mosque (KGM)", the oldest mosque in Yogyakarta. For the data acquisition, we used Faro Focus X330 and GLS 2000 Laser Scanner. We produced three-dimensional point clouds from UAV imagery by using Structure from Motion and Multi View Stereo (SfM-MVS) technique through Photoscan software. In order to merging and integrating both of pointclouds data, Maptek I-Site Studio 6.1 with Educational License was used. Those data were successfully registered, and according to the registration report, we had observed 20.60 mm of RMS error. The 3D models and their textures in outdoor and indoor side were processed using Autodesk software. Modelling was carried out on the structure of building’s façade base on simple geometric primitive as planes, straight lines, circles, spheres and cylinder. For interactive visualization, a modern and widely accessible game engine technology (Unity3D) was used. The result was an interactive displaying 3D model of an architectural heritage building in LOD3 level with spatial function for measuring the size and dimension, as well as the area of object. Finally, we created the online version of interactive 3D viewer utilizing WebGL API and Mapbox Unity SDK.

High-accuracy peat maps are essential for peatland restoration management, but costly, labor-intensive, and require an extensive amount of peat drilling data. This study offers a new method to create an accurate peat depth map while reducing field drilling data up to 75%. Ordinary least square (OLS) adjustments were used to estimate the elevation of the mineral soil surface based on the surrounding soil parameters. Orthophoto and Digital Terrain Models (DTMs) from LiDAR data of Tebing Tinggi Island, Riau, were used to determine morphology, topography, and spatial position parameters to define the DTM and its coefficients. Peat depth prediction models involving 100%, 50%, and 25% of the field points were developed using the OLS computations, and compared against the field survey data. Raster operations in a GIS were used in processing the DTM, to produce peat depth estimations. The results show that the soil map produced from OLS provided peat depth estimations with no significant difference from the field depth data at a mean absolute error of ±1 meter. The use of LiDAR data and the OLS method provides a cost-effective methodology for estimating peat depth and mapping for the purpose of supporting peat restoration.

Malaysia has seen at least 40 cases of landslide due to slopes failures from 1961 to 2008 which have claimed at least 128 lives. This emphasizes the importance of monitoring such slopes for safety and maintenance purposes. However the monitoring of them could be both expensive and dangerous. Therefore this study provides alternative of monitoring the slope using a Digital Elevation Model (DEM) derived from close-range photogrammetric data. Mass movement on the slope area can be detected by comparison of DEMs derived from two or more epochs data. In this study the DEMs were generated from stereo photographs captured from two different times (13/05/2007 and 07/04/2008). This method used commercial pocket camera for its data capture, thus indicates its low cost and its suitability for monitoring inaccessible and risky areas remotely. Restitution of stereo model is performed by involving Ground Control Points (GCP) to yield camera parameters, positions and orientations of each photo. Having these parameters, positions of all sampling points on stereo photos can be transformed into ground coordinate system. Terrain data sampling points in this research were performed by regular sampling methods. To measure the quality of the generated DEM, they were compared with a set of data measured by reflector-less Total Station (TS). The result yield DEMs with centimeters accuracy in elevation and less than 0.5% in volume difference. The result shows that for the period of one year there were significant terrain changes on ground surface. It was indicated by distribution of gain and loss areas on the slope, and extreme changes in longitudinal and cross sections profiling.

Degraded peatland is caused by forest clearing and the construction of artificial water networks. When water management is not implemented across land uses in the entire peatland landscape, then it will be a big issue that causes a water deficit and leads to increasing droughts and fires. Effective restoration must first identify the part of Peatland Hydrological system Units (PHUs) with insufficient water storage and resources. This study used intercorrelated factors of water balance, deficit months, NDMI-NDVI indices, dry periods, recurrent fires, peat depth, and water loss conditions, as the evaluation parameters, within individual sub-PHUs to determine the most degraded areas that require intervention and restoration. Sub-PHU was determined based on the peat hydrological unity concept by identifying streamline, outlet channels, peat-depth, slopes, and network connectivity. Global hydrological data using TerraClimate and CHIRPS, combined with field observations, were used to validate and calculate each sub-PHU’s water balance and dry periods. Soil moisture (NDMI), vegetation density (NDVI), and fire frequency were extracted from multispectral satellite images (e.g., Landsat 8, MODIS-Terra, and MODIS-Aqua). Each parameter was ranked by the score for each sub-PHU. The parameters that can be ranked are only the ordinal type of number. The lowest ranks indicated the most degraded sub-PHUs requiring peat rewetting interventions.

Batas administrasi desa memiliki peran yang sangat penting dalam berbagai kegiatan pemerintahan. Batas desa di Kabupaten Gunungkidul didelineasi ulang pada tahun 2018 melalui kegiatan yang difasilitasi oleh Dinas Pertanahan dan Tata Ruang Provinsi Daerah Istimewa Yogyakarta. Acuan utama untuk membuat Peta Kerja Batas adalah Peta Desa Lama skala 1:5.000 yang dibuat antara tahun 1932-1938. Batas desa pada Peta Desa Lama tersebut diinterpretasi dan didigitasi di Citra Tegak Resolusi Tinggi dari Badan Informasi Geospasial. Penelitian ini menganalisis perbedaan batas dalam hal karaksteristik segmen batas, pergeseran segmen batas, dan perubahan luas wilayah. Terdapat perubahan karakteristik segmen batas yang berupa titik temu, segmen berbatasan dan segmen tidak berbatasan. Pergeseran posisi segmen batas yang terjadi sampai 1.773 m pada Peta RBI, dan 997 m pada hasil identifikasi peta desa lama. Luas wilayah Kabupaten Gunungkidul mengalami penurunan dibandingkan dengan luas menurut Peta RBI sebesar 287,79 hektar, dan 269,22 hektar jika dibandingkan dengan data BPS. Dalam hal luas wilayah desa terdapat 71 desa mengalami penambahan luas wilayah dibandingkan dengan Peta RBI dan 67 desa jika dibandingkan dengan data BPS. Perbedaan sumber data, skala, dan metode pembuatan batas di Peta RBI dan hasil kesepakatan menghasilkan perbedaan karakteristik batas, posisi garis batas, dan luas wilayah. Berdasarkan hasil ini, batas desa definitif perlu disegerakan penyediaannya untuk menggantikan jenis batas lain yang terpaksa digunakan.

Sermo dam has operated for 20 years. This dam has a great benefit, especially as a water storage for water supply, irrigation and flood prevention. For the purposes of Sermo dam deformation monitoring, the control network was built around the Sermo dam in 2010. Then, this network was developed in 2015 by adding 5 new control points. For the time being, the network consists of 8 existing control points (micro) and 5 new control points (macro). The aim of the study is to monitor the control network displacement in the period of 2015 to 2016 based on GNSS observation data.GNSS measurement were conducted at 9 to10 of May 2015 and 12 to 15 of May 2016. The data observation was processed using GAMIT/GBLOK scientific software referenced to IGS station to determine the coordinates and its standard deviation. Further, horizontal and vertical displacement was analyzed by comparing with 2.5 times the standard deviation. The results showed that within a year, the coordinate difference varied from 0.12 to 1.68 cm on the N component, 0.21 to 0.65 cm on the E component, while the U component (the vertical displacement) have greater one, varied from 2.71 to 16.34 cm. Six control points (MAK2, MAK3, BMB2, BMB5, BMS2 and BMS5) have significant different in the horizontal displacement and six control points have significant different in vertical displacement (MAK5, BMB1, BMB2, BMS1, BMS2 and BMS3). In general, the horizontal displacement tends toward southeast. However, for obtaining more accurate and realistic results, further evaluation is required in the processing strategy such as the use of local control point as reference.