Improved ground and aerial system technologies enable mapping and monitoring forests and land to mitigate forest fires. UAV plays a role in monitoring by collecting forest area images from the air, which could be processed into 2D and 3D images. They can be analyzed to identify land cover types and objects in forest areas. This image data collection uses the DJI Phantom 4 Pro UAV controlled automatically with a flight plan made with Pix4D Capture, which is then processed using Agisoft. The result of the mapping has an average GSD of 2,03 cm/px. The mapping result shows that the 3D image produced can show objects in various land cover types. Weather related parameters were measured using ground sensors both in forest and plain area. We had successfully gathered forest and plain area images in addition to weather related parameters in Tangkuban Perahu Mountain area.

Indonesia has the highest number of active volcano all over the world. There were 63 eruptions with more than 70000 casualties from last year eruptions. To minimize the effect of this disaster, we have developed a volcano early warning system for Indonesian volcano. The main part of the system is volcano monitoring system. A system based on Internet of Things (IoT) has been developed to monitor some activities of a volcano. The physical parameters of these activities are indicated by the changing of temperature, gases (sulphur dioxide and carbon dioxide) concentration, vibration and landslides. The system consists of these parameter sensors, a microcontroller, a solar cell and a Long Range Radio (LoRa) for data transmission to the base station. The system has been tested in laboratory environment for the solar cell, sensors and radio communication. The solar cell could generate 18.81 Volt voltage and 0.23 Ampere current. The temperature sensor has been calibrated for until 51°C measurement (volcano normal condition), while the gas sensor could properly detect the gas until 8 ppm according to the calibration result. After Fast Fourier Transform (FFT) analysis the vibration sensor detected almost zero frequency with 0.168 m/s2, 0.0168 m/s2 and 1.125 m/s2 X, Y and Z axis zero offset respectively. In addition, the landslide had maximum about 4° degraded land slope error. Moreover, only 4% (maximum) percentage error rate (PER) shown by the radio for about 400 m data communication. Furthermore, the physical parameter data acquired then be real-time displayed using Node-red dashboard which can be accessed by the user or other parties who need the information.

Agricultural Micro-Small and Medium Enterprises (MSMEs) play a vital role in maintaining national food security. In terms of sources of economic growth, the agricultural sector is the largest contributor to West Java's economic growth in the first quarter of 2021. The agricultural business sector contributed as much as 0.94%, greater than its contribution in the fourth quarter of 2020 which amounted to 0.39%. MSMEs still have external and internal constraints, especially in terms of financing, product marketing, and lack of access to information. These constraints often hinder MSMEs in developing their business and expanding their market share. From various cases of marketing agricultural MSME products in West Java, it is necessary to implement technology to market agricultural commodities. One of them is a map application, which is useful in providing the location of agricultural MSMEs and how to reach that location from the customer's current location. So that sellers/farmers and buyers can do direct transactions. Therefore, map applications using Unmanned Aerial Vehicles (UAV) can be used as the alternative technology to solve that problem. While, this research only focuses on generating the map. UAV - DJI Phantom 4 Pro has been used in this research to take images at each location of the sample locations. To control the UAV automatically for each mission, it used Pix4D capture flight plans. The data were processed by Agisoft Metashape Professional software. The location of image data collection was carried out in various areas: a building, an open space area and a real small and medium agriculture enterprises location. Two-dimensional maps and 3D maps of these areas have been successfully created. The average RMS error is 0.17 (2.88 pixels) indicating under 1% of the average error.