The application of remote sensing technology and precision agriculture

No abstract

Estimation of aboveground carbon stock on stands vegetation, especially in green open space, has become an urgent issue in the effort to calculate, monitor, manage, and evaluate carbon stocks, especially in a massive urban area such as Samarinda City, Kalimantan Timur Province, Indonesia. The use of Sentinel-1 imagery was maximised to accommodate the weaknesses in its optical imagery, and combined with its ability to produce cloud-free imagery and minimal atmospheric influence. The study aims to test the accuracy of the estimated model of above-ground carbon stocks, to ascertain the total carbon stock, and to map the spatial distribution of carbon stocks on stands vegetation in Samarinda City. The methods used included empirical modelling of carbon stocks and statistical analysis comparing backscatter values and actual carbon stocks in the field using VV and VH polarisation. Model accuracy tests were performed using the standard error of estimate in independent accuracy test samples. The results show that Samarinda Utara subdistrict had the highest carbon stock of 3,765,255.9 tons in the VH exponential model. Total carbon stocks in the exponential VH models were 6,489,478.1 tons, with the highest maximum accuracy of 87.6 %, and an estimated error of 0.57 tons/pixel.

The spread of mosquito-borne diseases in Southeast Asia has dramatically increased in the latest decades. These infections include dengue, chikungunya and Japanese Encephalitis (JE), high-burden viruses sharing overlapping disease manifestation and vector distribution. The use of Geographical Information Systems (GIS) to monitor the dynamics of disease and vector distribution can assist in disease epidemic prediction and public health interventions, particularly in Southeast Asia where sustained high temperatures drive the epidemic spread of these mosquito-borne viruses. Due to lack of accurate data, the spatial and temporal dynamics of these mosquito-borne viral disease transmission countries are poorly understood, which has limited disease control effort. By following studies carried out on these three viruses across the region in a specific time period revealing general patterns of research activities and characteristics, this review finds the need to improve decision-support by disease mapping and management. The results presented, based on a publication search with respect to diseases due to arboviruses, specifically dengue, chikungunya and Japanese encephalitis, should improve opportunities for future studies on the implementation of GIS in the control of mosquito-borne viral diseases in Southeast Asia.

In this research, Airborne broadband (maximum 400 MHz bandwidth) C band circularly polarized synthetic aperture radar (SAR) is proposed and developed for further study on airborne compact polarized synthetic aperture radar (CP SAR) system using circular polarization. This paper explains the scattering characteristic of circular polarization as the concept of Circularly Polarized SAR, system configuration, RF system and antenna, ground test, and flight test of Circularly Polarized SAR in Hinotori-C2 (Firebird-C2) mission onboard CN235MPA aircraft on 14-15 March 2018 at South Celebes, Indonesia. The result of the flight test depicts multipath full polarimetric circularly polarized images that show good performance of Circularly Polarized SAR and matched well to the result of the ground test of a multi polarized single pulse. The Circularly Polarized scattering clarification using trihedral, cylindrical, and Omni corner reflectors (TCR, CCR, and OCR), assessment of Circularly Polarized image analysis, and image classification using the conventional axial ratio (AR), ellipticity (), and polarization ratio () is discussed. The proposed Circularly Polarized SAR will enrich the existing CP SAR systems and could be employed in further study of CP SAR calibration technique, also applications development for the environment and disaster monitoring using CP SAR.

No abstract

The decreased rainfall in Indonesia is mainly influenced by the east monsoon so air pressure from the southern hemisphere which is dry will flow through Indonesia. In a relatively long time, this may cause drought condition on agricultural land in Indonesia in general and in Temanggung Regency in particular. In addition, ENSO (El Nino Southern Oscillation) contributed to the decreased rainfall in Indonesia. This phenomenon will be more intensive and extreme with the existence of global warming. The identification of vulnerability of agricultural drought is an effort to mitigate disasters. This study aims to determine the distribution of agricultural drought and determine the factors that influence agricultural drought in Temangung Regency. The research method used the Analytical Hierarchy Process (AHP) to build a model of agricultural drought vulnerability by considering several factors. The results showed that the area of agricultural land which is vulnerable and very vulnerable  to drought is 86,2 km2 and 74,14 km2, while agricultural land with moderate vulnerability is 208,21 km2, and agricultural land which is not vulnerable and very not vulnerable to drought is 128,15 km2 and 267,33 km2. The main factor as a determinant of agricultural drought in Temanggung Regency is rainfall. Meanwhile, the next factor is the respective land cover and soil texture. This research concludes that the effect of slope is not a big impact on agricultural drought in Temanggung Regency.

Ganoderma boninense is a major devastating disease for oil palm. The severity level identification of Ganoderma boninense on oil palm plantation is important to support the decision making on managerial activities. There have been researches conducted about the usage of unmanned aerial photograph (UAV) on oil palm plantation, nonetheless, the utilization of digital data on the visible aerial photograph has not optimally used. This study aims to obtain alternative methods to identify the severity level of Ganoderma boninense infection with visible spectral index from a visible aerial photograph (RGB). Visible aerial photograph (RGB-aerial photograph) is adopted on this research and carried out at Dusun Ulu plantation with various visible spectral-index methods. The visible spectral-index methods are the excess green index (ExG), the excess red index (ExR), the excess green minus excess red index, and the colour of vegetation extraction (CIVE). The results of four visible spectral-index methods are able to differentiate the severity level of Ganoderma boninense infection on each individual of oil palm.