Precision agriculture is an integrated farming system based on information and production, to increase the efficiency, productivity and profitability of agricultural production. The application of remote sensing for monitoring rice growth and calculating rice yields is considered more effective than conventional calculation methods. This study aims to review the application of remote sensing for mapping paddy fields and forecasting rice production. The review includes rice characteristics that can be sensed through remote sensing images, types of images, rice yield estimation models, and remote sensing analysis approaches. The study shows that optical and radar images are capable of mapping paddy fields and providing information on rice production by relying on the phenological of rice. Empirical, process-based, and semi-empirical estimation models provide information with different levels of accuracy and scale. The spatial ecological approach is able to examine the relationship between the suitability of paddy fields and production yields, while the spectral approach relies on the transformation of the vegetation index with production yields. Research on rice yield estimation is suggested to go to the field to test the accuracy of mapping paddy fields and interview farmers to obtain data on rice production.
The sea waters of Banyuasin have several estuaries. These conditions will affect the biophysics of the waters, especially chlorophyll-a as an indicator of water fertility and fishing ground habitat. This study aims to map the dynamics of the distribution of chlorophyll-a concentrations based on different seasons in the coastal water of Banyuasin Regency. The method used is the analysis of Landsat 8 OLI imagery. The images used consist of images recorded on September 19, 2019, December 30, 2019, April 20, 2020, and July 20, 2020. The images represent the transitional season II, western season, transitional season I, and eastern season, respectively. The results showed that the concentration of chlorophyll in the transitional season II was 0.502 mg/m3–2.514 mg/m3, the western season was 1.627 mg/m3–3.934 mg/m3, the transitional season I was 0.854 mg/m3–2.782 mg/m3, and the concentration of chlorophyll-a in the east monsoon was 0.801 mg/m3–2.904 mg/m3. The dynamics of Chlorophyll-a concentration in the study area varied according to the season, and its distribution pattern was seen to be higher in coastal areas, while its concentration decreased towards the sea.
The COVID-19 pandemic has had a huge impact on Indonesia, as many other nations throughout the globe, particularly on the travel and tourism industry. The most noticeable effect is the decline in tourist visitation, which fell by over 75% in 2020 compared to the prior year. Businesses and workers in the tourism industry have been significantly impacted by the fall in visitor numbers, particularly in Yogyakarta, one of the most well-liked tourist sites in the nation. This study intends to investigate the geographical effects of the COVID-19 pandemic on tourism-related activities. A strategy to determine changes in travel behaviour before and during the COVID-19 pandemic uses social media data, such as Flickr and Twitter. Both social media has been extensively used in tourism related studies in the past. Because December is the busiest month for tourism, Twitter data from that month was chosen as the sample. The selected sample ranges are for 2019, 2020, and 2021. While Flickr data covers from 2018 to 2023, to generate a different perspective than that of Twitter data. The study's findings demonstrate how limitations on community activities significantly influence the traditionally popular tourist attractions. Public spaces, dining establishments, and even hotels are preferred travel destinations by tourists.
KEYWORDS: Modeling, Data modeling, Visualization, Web 2.0 technologies, Statistical analysis, Java, Geography, Analytical research, Data conversion, Internet
Nowadays, Twitter data is significant to many studies since there is a shift in the data collection paradigm. As one of the contemporary social media with many active users, Twitter provides geotagging facilities to create a geotagged Tweet. Various spatial based studies use geotagged Tweet data. This paper aims to review the geo-temporal characteristics of geotagged Twitter data in nine major cities in Indonesia, namely five cities in the Greater Area of Jakarta, Surabaya, Bandung, Medan, and Makassar. Twitter data was collected by the streaming method for two years (January 2019- December 2020). The temporal analysis was carried out by graphing the number of Tweets with 30-minute intervals. Weekly Twitter activities were also visualized to get a specific understanding of when the optimum time to post a Tweet was. Density analysis was employed to Twitter data to find out the spatial patterns in the study area. Kernel Density Estimation (KDE) was used to determine the Tweets Density in the day and night. This study also used a simple framework of text analysis of topic modelling using Latent Semantic Indexing (LSI) to use the Twitter data better. Overall, Central Jakarta and South Jakarta have a significant number of Tweets compared to other cities. The study results show that, in general, big cities in Indonesia have almost the same temporal curve and the peak time for making geotagged tweets occurs from 4 pm to 8 pm. Our finding also points out that a high number of the population in a city does not always produce a high number of Tweets. The results of topic modelling in the Greater Area of Jakarta show that the themes of traffic jams/congestion, entertainment, and culinary tourism are widely mentioned by Twitter users, thus opening opportunities for research on these subjects.
Geographic Object-Based Image Analysis (GEOBIA) is an emerging approach in remote sensing image analysis and classification which relies on segments or objects created by a group of pixels on the image. GEOBIA has been utilized for many remote sensing applications with various degree of success. However, from the literature, its application for landform analysis and classification is still rare. This study aims to test GEOBIA interpretation capabilities to identify landform in part of Opak Watershed (Central Java, Indonesia) using Landsat 8 OLI and DEMNAS imagery (30 and 8- meters pixel size, respectively) and evaluate the result. Both image data were fused to create an image with high spectral and spatial resolution and contains elevation data, as an input for the segmentation process. GEOBIA interpretation process was performed gradually; first, initial Multiresolution Segmentation Algorithm was conducted to identify the variation of slope found in the study site. Then, the slope segments/objects were used to identify landform using Ruleset-Based Classification considering the image object information including object values, pattern, shape, and other parameters. The accuracy of the result was evaluated based on the percentage accuracy of the landform classification. From this study, we found that fusion-image and GEOBIA are capable of distinguishing landform elements very well with the percentage of overall accuracy is 88%. This result shows that GEOBIA has potential in identifying and classifying landform objects.
The Gunungsewu area is one of karstic regions in the southern part of the island of Java whose a variety of archaeological remains. Archaeological data were scattered around the Gunungsewu region starting from remains of humans fossil and animals, bone artifacts, clamshell artifacts, Pacitanian cultural stone artifacts, and prehistoric caves that show evidence of occupation caves as well as sustain of prehistoric human communities. This research used the model MaxEnt as a method for estimating prehistoric occupation cave sites in the karst area of Gunung Sewu, Gunung Kidul. The objectives of this research were: (1) assessed the ability of DEM Alos Palsar, Sentinel-2a images and GIS data to extract environmental parameters related to prehistoric occupation cave sites. (2) prepared a spatial model for estimating prehistoric occupation cave sites using DEM Alos Palsar image, Sentinel-2a imagery and GIS data for input model MaxEnt (maximum entropy). (3) test accuracy of model MaxEnt to estimated the location of prehistoric occupation caves. This research used 68 location cave as attendance data input in the model MaxEnt. Environmental variables extracted from the 12.5-meter resolution DEM Alos Palsar, Sentinel- 2A images with 10 meters resolution, and GIS data. There were 8 environmental variables used in this study, there are: OBIA valley-hill classification map, distance map of valley base, elevation map, slope map, aspect map, distance map of lineament, lineament density map and map distance from water sources. Modeling using location data input as many as 68 prehistoric occupations pointed caves with 8 environmental variables resulted in modeling performance with an AUC value of 0. 715 with good performance. Modeling produces the results of the jackknife test, analyzes the response curve of the environment variable and probability map in the researched area. Based on the probability map produced, this studied obtained prehistoric cave location data. Therefore, this modeling shows that MaxEnt could be used as a method for estimating archeological sites.
The main problem faced by farmers in Indonesia is the diminishing area of agricultural land due to land conversion. Therefore, farmers must be able to choose the most productive types of agricultural plants based on their capability and land suitability in their agricultural areas. This study aims to develop an agroecosystem zone in Temanggung Regency based on remote sensing image processing and geographic information systems (GIS) for evaluating land suitability. The stages of this research are: (1) inventory of land biophysical conditions using primary data (interpretation of multispectral satellite images) and secondary (maps, statistics, results of previous studies); (2) study of agroecosystem components to map agroecosystem zone in the research area; and (3) evaluation of land suitability to determine the most productive types of agricultural crops for each agroecosystem zone. This research uses quantitative empirical methods based on landscape ecological approaches. The field sample was determined by stratified random sampling. Data used include: Sentinel-2 image, geological map, soil map, slope map, rainfall data, and land suitability tables for various types of agricultural crops. Image processing techniques begin with the process of rectification, sharpening, and multispectral classification to produce landform maps and land uses. Agroecosystem zones were produced from the overlay process of the agroecosystem components resulting from image and map analysis. Field surveys on predetermined samples were used for obtaining information about the bio-geophysical conditions of the land and the existing conditions of agricultural crops and their productivity, as material for preparing the land suitability map of the research area.
Due to the large number of total population and its high population growth, food needs become the most important issue in Indonesia. This study aims to (1) map the agro-ecosystem zones based on the analysis of remote sensing images; (2) estimate the food production (rice and cassava); and (3) analyze the food security in the study area based on the mapping of the agro-ecosystem zones. Gunung Kidul Regency was selected as the study area because it is one of areas with food insecurity in D.I. Yogyakarta. This research used Landsat 8 OLI recorded on 14 April 2014 and 27 June 2013 and assisted by other spatial data such as the RBI map, soil map and slope map using Geographic Information System (GIS). The data of population statistics was also used to calculate the amount of food needs in the study area. Field survey was conducted to determine the productivity of the land in each agroecosystem zone, and to test the accuracy of the results of remote sensing images processing. The results of this study are: (1) Gunung Kidul Regency can be divided into seven agroecosystem zones, each of which has a different productivity for rice and cassava; (2) Gunung Kidul Regency is included in areas experiencing food insecurity when only taking into account the production of rice, with a shortage of 13,134.05 t; and (3) If the production of rice and cassava are taken into account, Gunung Kidul Regency is not categorized as foodinsecure areas because it has a food surplus of 435,192.20 t.
Mangrove species inventory and mapping is very important as an effort to preserve the ecosystem and biodiversity of mangrove forests. One way of efficient mangrove species inventory and mapping is to use remote sensing imagery, especially through the analysis of its spectral reflectance pattern. This study aims to map the fourteen mangrove species on Karimunjawa Island, Central Java, Indonesia by: (1) measuring the mangrove species spectral reflectance pattern in the field, (2) characteristic analysis of the mangrove species reflectance pattern, and (3) mapping the dominant mangrove species distribution. The spectral reflectance measurement of mangrove species objects in the field was done by using JAZ EL-350 VIS-NIR (ranges from 300 to 1100 nm). The JAZ field spectrometer was pointed at a distance of 2 cm from the target objects with 10 reading repetitions for each species. Field measurements results were then taken to the laboratory for analysis of spectral reflectance and absorbance patterns, which served as key object recognition in this study. To combine the field and image spectral reflectance patterns, the field reflectance patterns were resampled to the spectral resolution of WorldView-2 image (8 bands, 2 m pixel size). The spectral angle mapper (SAM) method was the used to locate and map the distribution of each targeted mangrove species. As expected, the results showed that the largest difference of spectral curves between species was at the NIR wavelength spectrum (700-900nm). Hence, it is potential to be used as the basis for identification of species mangrove from remote sensing imagery. However, the result of this mapping approach only showed a low accuracy of 62%. The low value of map accuracy was attributed to the inaccuracy in defining threshold in SAM for each class. This study provides a basic understanding of the use of spectral reflectance for mangrove species mapping from remote sensing imagery.
Food security is one of the most important issue for Indonesia. The huge population number and high population growing
rate has made the food security a critical issue. This paper describe the application of remote sensing data to (1) map agroecosystem
zones in Bantul District, Special Region of Yogyakarta, Indonesia in 2012 and (2) analyze the food security in the
study area based on the resulting agro-ecosystem map. Bantul District is selected as the pilot area because this area is among
the highest food crop production area in the Province. ALOS AVNIR-2 image accquired on 15 June 2010 was integrated with
Indonesian Surface map (RBI map), soil types map, and slope steepness map. Population statistics data was also used to
calculate the food needs. Field survey was conducted to obtain the crop field productivity information on each agro-ecosystem
zone and assess the accuracy of the model. This research indicates that (1) Bantul District can be divided into three agroecosystem
zones, where each zone has unique topograhic configuration and soil types composition, and (2) Bantul Distict
is categorized as food secure area since the rice production in 2012 managed to cover the food needs of the people with
the surplus of 33,208.6 tonnes of rice. However, when the analysis was conducted at sub-district level, there are four subdistrict
with food insecurity where the food needs surpass the rice production. These sub-district are Kasihan Sub-district
(-5,598.4 t), Banguntapan Sub-district (-2,483.4 t), Pajangan Sub-district (-1,039.6 t) and Dlingo Sub-district (-798.7 t).
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