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In this research, the origin of the wildfire area is assessed by using the potential of burn severity and the WRF-SFIRE model. This research focuses on the mountainous savanna region, by taking the case of the Gililawa Darat wildfire event. The most accurate index among Sentinel-2B optical burn severity indices, i.e. dNBR, MIRBI, dMIRBI, CSI, dCSI, NDVI, dNDVI, EVI, and dEVI and among Landsat-8 OLI/TIRS thermal and mixed burn severity indices, i.e. LST, dLST, LST/EVI, and d(LST/EVI) was used to map the areas with low burn severity, an indication generally found at origin area. A series of fire spread simulation from these areas was conducted using WRF-SFIRE to assess the accuracy of each simulation in reproducing the burned area. The burn severity accuracy assessment showed that dNBR and dCSI indices had the highest value of Overall Accuracy and Kappa Hat Coefficient, i.e. 91.67% and 0.889 (almost perfect agreement). However, dNBR was the most suitable index for mapping burn severity in the region due to its goodness-of-fit measure for linear regression model with the R-squared value of 0.7856. The assessment of thermal and mixed burn severity indices based on Landsat-8 OLI/TIRS resulted in LST, LST/EVI, and d(LST/EVI) gained the same overall accuracy of 58.33% and Kappa Hat Coefficient of 0.444 indicating moderate agreement, whereas dLST performed poorer than these indices. However, it is not recommended to use these burn severity indices in the region due to the nonlinearity of severity level with the index value. According to WRF-SFIRE simulations result, it was found that fire ignition started from low burn severity area coordinates which have a distance of 0 to 334 metres from the origin area resulting in fire area witan h overall accuracy value range from 77.04% to 81.90% and Kappa Hat Coefficient value range from 0.536 to 0.626. The simulation from the origin area resulted in an overall accuracy of 81.57%, a Kappa hat coefficient of 0.613, underestimated burned area ratio of 0.08, overestimated burned area ratio of 0.23, and a backing fire perimeter difference ratio of 0.4 to the reference. Keywords: wildfire; Gililawa Darat; wildfire spread; burn severity; WRF-SFIRE, fire origin area

Air as an important environmental component in life that needs to be maintained and improved in quality. The presence of human activity causes the composition of clean air to undergo changes. Air pollution is caused by several factors. One of them is the industrial activities of power plants that use fossil fuels and natural gas. This research was conducted to monitor the distribution of emissions produced by power plants in PT. Indonesia Power Bali PGU uses Geographic Information System (GIS) technology and is validated using the paired t-test method. The result is that the direction of distribution of each emission parameter (SO2, NO2 and Dust (TSP)) follows the wind direction according to data obtained from BMKG with a downward trend according to the distance traveled. This is because emissions are propagated by wind with dispersion variables at any given distance. The degree of validity of the modeling analysis with the results of ambient air quality measurements at each monitoring point of the SO2 and NO2 parameters is no difference between the results obtained. However, for dust parameters (TSP) there are significant differences. This is due to atmospheric conditions at the time of sampling, the location of the monitoring point surrounded by buildings, 1 sample point being on the side of the highway, and community activities around the monitoring point location. Keywords: Power Plant Emission; Air Quaity Control; Geographic Information System (GIS); Gaussian Disperssion

Corn is a strategic agricultural commodity in Central Java Province because this plant is the second main food crop commodity after rice. Evaluation of agro-climate suitability aims to assess suitable areas based on climatic conditions and efforts to overcome climate-limiting factors in order to achieve optimal productivity in corn cultivation. Satellite imagery can be used to overcome the lack of density of climate stations, which are the source of data for climate suitability evaluation. This study aims to validate GSMaP satellite rainfall data and Terra MODIS satellite air temperature data against BMKG observation data, create a distribution map of climatic conditions, and create a distribution map of agro-climate suitability classes based on GIS for maize in Central Java Province. This study uses rainfall and temperature data from 30 BMKG observation points and satellite imagery data for 20 years (2001–2020). The correlation values, RMSE, and MBE were calculated to validate satellite rainfall and air temperature data with observed rainfall and air temperature. Satellite data correction calculations are used to fill in the data at points without observation. The validation results show that the satellite data is valid and that there is a pattern of conformity with the observational data. Central Java province has an annual rainfall of 1,500 mm/year to 6,000 mm/year, an average annual temperature of 24.9oC to 28.1oC, and three to nine months of wet months. In some areas, the agro-climate suitability class for maize plants was in the moderately suitable category (S2wa, tc) with an area of 156,302 ha, marginally suitable (S3wa) with an area of 1,277,813 ha, and not suitable (Nwa) with an area of 2,008,673 ha. Keywords: Corn; GSMaP; Terra MODIS; Climate Suitability; GIS

Global warming and biodiversity loss are critical issues, and forest retention and reforestation programs are crucial in mitigating climate change. However, discussions around these programs often exclude the role of urban vegetation in carbon sequestration. Preserving urban vegetation, such as trees, can also significantly reduce carbon emissions. Urban vegetation can be found in two main locations: Urban Green Open Spaces (UGS) and Road Landscapes (RL). In Denpasar Bali, Glodokan Tiang or Polyalthia longifolia trees are planted at those locations. Data management and carbon stock calculation mechanisms are required to demonstrate the contribution of urban vegetation in terms of carbon sequestration. The technology of Unmanned Aerial Vehicle (UAV) can be used as an alternative to efficiently calculate the estimated carbon stock. The calculation uses the Diameter Breast High (DBH) value approach using the canopy area and Canopy Height Model (CHM) obtained from UAV data processing using the Sfm method. UAV estimates show that the highest Above Ground Biomass (AGB) value at Bajra Sandhi Renon Field is 201.59 kg with a stored carbon content of 94.75 kg, while on I Gusti Ngurah Rai Bypass has the highest AGB value of 215.04 kg with a stored carbon content of 101.07 kg. These results have been validated by field observations, where the results of the regression analysis at the location of Bajra Sandhi Renon and I Gusti Ngurah Rai, show that between field observation data and estimation data with UAV there is no significant difference. While the results of the t-test: Paired Two Sample for Means at the Bajra Sandhi Renon Field and the Bypass I Gusti Ngurah Rai have a value above the significance level which proves that there is no significant difference between the carbon stock value from field observations and the carbon stock from the UAV approach. Keywords: Carbon Stock; Above Ground Biomass; Urban Vegetation; UAV-Sfm

In recent years, oil spill accidents very often occur due to the development of sea transportation and oil exploitation. Increased exploitation of oil resources is a concern for oil spills that are very harmful to marine ecosystems. On July 12 2019, there was a well kick in the well reactivation YYA-1 owned by PT. Pertamina Hulu Energi work area Offshore North West Java (ONWJ) in Karawang Sea. The oil spill were spread up to Banten and continued to spread widely. Remote sensing is one of the methods that can be used to detect and monitor the oil spill by quickly viewing the location and spill points, and the pace and direction of the oil that can be utilized for cleaning. This research aims to determine the detection of oil spill using Sentinel-1A imagery and to determine oil spill patterns in Karawang Sea before spills occur until the well YYA-1 was closed that was on October 7 2019. This detection is done with adaptive threshold algorithm through Sentinel applications platform (SNAP). The results showed that oil spill has a trajectory spread pattern at the beginning of the spill which occur on July 18 2019. After July 18th, the spread pattern turned random and interrupted the spreading process. The spread has an estimated spread area of 145.85 km2. The spread of oil spill moved towards the northwest to the west approaching the coast and further away from the source of the spill, to Pandjang Island, Banten.

The coastal area is an intersection between mainland and ocean. The tourism potential in the coastal area of Bulak District in Surabaya is expanded. It is shown by the construction of Surabaya Bridge in 2015. This construction will affect land use change. Remote sensing technology is one of the acquisitions to monitor land use change. This research focuses on identifying the land use change in the coastal area in Bulak District, Surabaya, in 2014 and 2020, as well as to determine the accuracy of classification method applied for mapping the land use change in 2020. The application of 2014 acquisitions data was used as the bridge construction plan, while the application of 2020 acquisitions data was used as the premise for the land classification system in the previous year. There are two methods used to classify land use in coastal areas, that is pixel-based classification (maximum likelihood algorithm) and object-based classification (nearest neighbor algorithm). The research shows that there are 6 land use classes in study area: built-up land, rice fields, forests, shrubs, non-built-up land, and ocean. By applying these two methods, the result shows different area changes. The conversion of the highest mainland by applying a pixel-based classification was found in built-up land (+23.03 ha) and rice fields (-24.84 ha), while the area changes by applying object-based classification method were found in built-up land (+32.75 ha) and rice fields (-26.91 ha), respectively. The accuracy by applying the pixel and object-based method is 89% and 92%, respectively, from the percentage indicates good interpretation.

Schmidt Ferguson map based on rainfall data is utilized to maximize agricultural potential by knowing the spatial pattern of agro-climatic zones. This study aims to produce Schmidt Ferguson agro-climatic zone by corrected IMERG Satellite rainfall data by station base data in Bali Province. The length of monthly rainfall data from each source is six years (2015 – 2016). Initially, IMERG Final Precipitation data level 3 is validated to 73 rainfall observation stations over Bali. Statistical methods used for this purpose are correlation coefficient (r), Root Mean Square Error (RMSE) and paired t-test. The validation results show a good correlation value of 0.62 – 0.93, RMSE value of 63.33 – 208.86 mm, the results of the paired t-test there are significant. Hereinafter, IMERG data are corrected by simple linear regression equation y = 0.905x + 4.0979 with an R2 value of 0.5878. The Schmidt Ferguson agro-climatic zone mapping from rain observation station data resulted in 5 climate zones, namely climate zone B, C, D, E, and F and from IMERG product data, 4 climate zones, namely climate zone B, C, D, and E. Keywords: Observation; IMERG;Data Validation; Schmidt Ferguson Agroclimate Zone

The study location of landslide is in Hokkaido, Japan which occurred due to the Iburi Earthquake 2018. In this study the landslide has been estimated by the fully Polarimetric SAR (Pol-SAR) technique based on ALOS-2 PALSAR-2 data using the Yamaguchi’s decomposition. The Yamaguchi's decomposition is proposed by Yoshio Yamaguchi et.al. The data has been analyzed using the deep learning process with SegNet architecture with color composite. In this research, the performance of SegNet is fast and efficient in memory usage. However, the result is not good, based on the Intersection over Union (IoU) evaluation obtained the lowest value is 0.0515 and the highest value is 0.1483. That is because of difficulty to make training datasets and of a small number of datasets. The greater difference between accuracy and loss graph along with higher epochs represents overfitting. The overfitting can be caused by the limited amount of training data and failure of the network to generalize the feature set over the training images.

Coastal areas in the Canggu and Seminyak areas located in Badung Regency, Bali Province are very attractive tourism. The development of tourism has an impact on coastal conditions. The coastal conditions analyzed are changes in coastline that occurred during 2015-2019 using remote sensing. The satellite image data used in the analysis is Sentinel 2A image data that can be accessed for free with a spatial resolution of 10 meters. Image data processing is divided into three stages, namely preprocessing, processing, and post processing using Sentinel Application Platform (SNAP) software. The preprocessing stage includes the resampling, masking, and subset areas. The processing stage includes digitizing the coastal area, digitizing accuracy analysis using the Support Vector Machine (SVM) method, and the post processing stage including correction of shoreline changes. Bands in image data used for detection of coastal areas are band 8 (NIR), 8A (narrow NIR), 11 (SWIR), and 12 (SWIR). Based on the results of the analysis of shoreline changes carried out during 2015-2019, it was found that the average shoreline changes were 1.42 m / year with erosion conditions in which the dominant wind direction originated from the southwest towards the northeast coast of the sea of ??Bali. The results of digitizing the coastal area using the Fine Gaussian SVM method with the greatest accuracy value is 87.8%. Keywords: Shoreline Change, Remote Sensing, Sentinel 2A, SVM, Wind Direction

The coastal area is a meeting space between land and sea that is easy to change temporally and spatially. The changes were triggered due to an increase in population and community activities such as industry, housing, ports, cultivation, transportation, farms, agriculture, tourism, and so on centered in the coastal area and become the center of Indonesia's economy. Remote sensing technology is one of the right ways for monitoring activities in the near term. This research aims to map the change of coastal land use in Rungkut district, Surabaya, in 2013 and 2019 using high-resolution satellite imagery of SPOT imagery. The method of classification of coastal land use two types of supervised classification, namely Minimum Distance and Maximum Likelihood. Land use class obtained in this study as many as six classes, namely mangrove, settlement, pond, green open space, the body of water, and industry. The results showed that using two different algorithms gave a difference in classification results. The largest land-use change from classification with Minimum Distance method is in mangrove and body of water with +231,80 and –230,89 ha, while the classification result with the method of Maximum Likelihood the change of the largest land use is in mangrove class and ponds respectively +202,41 and –210,89 ha. Accuracy test using error matrix obtained by 85,50% with kappa coefficient 0,78 on the classification result of coastal land use using Minimum Distance algorithm and for Maximum Likelihood algorithm obtained accuracy of 89% with Kappa coefficient is 0,84. It is demonstrated that by using the algorithm, Maximum Likelihood accuracy on the land use map is very good.

Integration of Remote Sensing Technology And Geographic Information Systems for Estimation of CO2 Updake and Emissions in Denpasar City Rapid economic growth in the Denpasar City has an impact on the rate of population growth. This will lead to increasing land requirements for settlements, infrastructure, and other supporting facilities. Meanwhile, the availability of land for green open space (RTH) will decrease. Several studies show that from year to year the area of ??vegetation cover decreases, and the air temperature in Denpasar City is increasing. Therefore, research to calculate CO2 uptake by urban plants and CO2 emissions from various activities in the city of Denpasar is needed to be done. Estimates of CO2 uptake by plants are carried out using remote sensing technology and GIS. Meanwhile, the calculation of CO2 emissions is carried out by an inventory of CO2 pollutant sources from point sources, areas sources, and mobile sources. The output of this study is a distribution map of CO2 absorption and emissions. From the map it can be seen whether the CO2 emissions of Denpasar City are higher than the ability of existing plants to absorb them. The results showed that the ability of plants in Denpasar as a green open space to absorb CO2 was 235,780.63 tCO2/year, while total emissions from pollutant sources were 862,955,856 tCO2/year. The sources of CO2 emissions include from point source 37,649 tons/year, from source area 95,310 tons/year, and from mobile sources at 862,955,856 tons/year. From the movable source the biggest contributor to CO2 emissions is light vehicles, which amounted to 540,355.88 tons/year (62.63%), then followed by motorcycles at 260,187.43 tons/year (30.16%). The amount of CO2 emissions in Denpasar City is 3.66 times greater than the ability of plants to absorb CO2 in 2015 and there is a tendency for this gap to be even greater in the future. To overcome this problem, regulations are needed such as reducing the number of motorized vehicles by increasing public transportation services. The use of vehicles using energy sources other than fuel such as electricity is another alternative to consider. Finally, the increase in the number and quality of green open spaces is a conventional method that needs to be done.

Change in sea surface temperature (SST) has a huge impact to rainfall intensity. This research analysed the trend of SST all over Bali island for 10 years (from 2009 to 2018) using MODIS satellite level 3, covered 7.8958oSouth -8.8958oSouth and 114.4791oEast - 115.6875oEast. The data for rainfall trend was collected from 12 rain observer stations of BMKG (Indonesian Meteorological, Climatological, and Geophhysical Agency). The statistical correlation between SST and rainfall intensity were analyzed using Microsoft Excel software. The result found that the SST in Bali ranged from 25.4oC until 31.5oC. The change of SST has a similar pattern to the monsoon cycle where the western monsoon is relatively warmer than the eastern monsoon. The rainfall trend in Bali is closely related to the monsoon cycle. During the western monsoon,the average monthly rainfall value in Bali is more than 150 mm, while the rainfall value on the eastern monsoon is slightly different, which is

The sustainable development process followed by high population growth is an important factor causing land conversion in coastal areas. Analysis of change and planning of land use is one form of coastal resource management to reduce land damage so that it can be used sustainably. This study aims to determine the types and changes in land use over 21-year period (1998-2018) in the coastal area of Gianyar Regency. This study also wants to examine the level of accuracy of Landsat imagery in land use classifications. The Landsat imagery used in this study was Landsat 7 ETM+ in 1998 and Landsat 8 Oli in 2018. The classification method chosen was the supervised classification with the Maximum Likelihood method. The software used is SAGA GIS to process images and QGIS to design map layouts. The results showed that the land use classes in the study area were water, bare land, settlements and buildings, shrubs/bushes, rice fields, moor and mixed gardens. In the period 1998-2018, four classes experienced declined in area, namely water (1.71 ha), bare land (54.99 ha), mixed gardens (28.08 ha), and rice fields (226.62 ha). Meanwhile, land use classes that have increased in area are shrubs/bushes (44.28 ha), moor (104.58 ha), and settlements and buildings (162.54 ha). The overall level of accuracy in land use classification is 85%.

Rainfall is a weather element. Sea surface temperatures (SST) affects precipitation. SST and rainfall have a high variability which can be measured by satellite. At a regional scale, a research of the effect of SST on rainfall analyzed island rainfall, which means that there is still little research on rainfall in the waters. This study purposed to find out the variability and correlation between SST and rainfall in the Bali waters. It used satellite MODIS and TRMM for 10 years, started from 2010 to 2019. The data used was SST MODIS and rainfall TRMM level 3 with the geographic coordinates boundaries area 114.4281o East - 115.7145o East, and 7.8168o South 8.9868o South. The method of this study was correlation analyzed with time lag between of SST and rainfall. The variability of SST in the Bali waters were ranged from 25,2 oC to 31,6 oC. Furthermore, the variability of rainfall was ranged from 0 mm to 556,92 mm. The changes patterns of SST and precipitation in Bali water were related to the season in each month. The data showed that the SST was warmer in the rainy season compared to the SST in the dry season. Besides, the rainfall increases when entering the rainy season, and the decreases when entering the dry season. The correlation between SST and rainfall in this waters area ranged from weak to strong. Correlation formed in the rainy season is negative with a correlation coefficient between -0.34 to -0.74. However, in the dry season there was a positive correlation with a correlation coefficient ranging from 0.77 to 0.92.

Carbon absorption and storage, known as carbon sequestration, have reduced global warming through photosynthesis. Seagrass beds in coastal ecosystems have high plant productivity and are found to have the ability to reduce the effect of global warming due to the increase of CO2 in the atmosphere. Due to the anthropogenic effect, decreasing environmental quality, such as tourism, may affect seagrass diversity and growth, therefore its carbon contents. This study investigates the ability of seagrass species as carbon storage and their diversity in a common tourist destination area referred to as disturbed habitat, located at Sanur and an area that is less disturbed, referred to as undisturbed habitat. Samples were collected from 3 transects 50m in length that were placed on each site. Three plots, each measuring 0.5 x 0.5 m, were employed on each transect. The number of individuals of each species and the percent plant coverage were measured on each plot. Plant biomass and carbon content were extracted in the Lab and divided into below- and above-ground. The result shows that eight species were found at Sanur and only four from Samuh sites. On average, the percentage of plant coverage was higher at the Samuh site. Samuh site. The plant biomass and carbon storage were lower at Samuh than at the Sanur site. Species differences from each site affect the plant biomass, so the carbon content. Anthropogenic activities are not the only ones to determine the carbon storage of seagrass, but rather the species morphology.

Besides the second largest community in the coral reef ecosystem, the attractive shapes and colors of soft corals establish it an export commodity for the marine ornamental aquarium. The transplantation methods in soft coral are limited. Commonly method for soft coral transplantation is attached to the artificial substrate followed by placed at table frame. However, this method is easily covered by algae and costly. One of the alternative methods is vertically hanging that this method commonly applied in hard corals transplantation. This study aimed to examine the average differences of growth rates among different transplanted soft coral species, different location, and the presence of interaction between soft coral species and location towards the difference of growth rates. Moreover, it determined the average survival rates among different transplanted soft coral species at different location. There were three soft coral species, Lobophytum strictum, Sinularia polydactyla and S. asterolobata. They were fragmented at initial size ± 25 cm2, hanging vertically in 1.5 m length, and 0.03 m diameter followed by located inside and outside the Pegametan bay, Sumberkima village, Buleleng Regency. The increased size of each transplanted soft coral species and the number of survivals and environmental parameters were observed every two weeks for 12 weeks. The average in growth rates among species, location, and interaction was analysed using two ways Anova, whereas the survival rates were tested using Log-Rank. Results showed that the average growth rates of transplanted soft coral inside the bay were significantly higher (4.53 cm2) than outside the bay (1.64 cm2). Moreover, the average growth rates of transplanted S. polydactyla were significantly higher than others (5.22 cm2), (respectively; 2.35 cm2 and 1.7 cm2). It was also indicated that different location and species did not differentiate the transplanted soft corals' average growth rate. The survival rates of the transplanted soft corals in different location were not significantly different as well.

The water quality of Badung River in Pemogan village has exceeded the threshold limits.Therefore, It is needed to determinate the carrying capacity of Badung River. The determination of carrying capacity is very useful to control the disposal of waste into the Badung River. The objective of this study were to determine the sources of pollution, pollutant load and load capacity of Badung river at Pemogan village. Determination of pollutant source done with looked for land use and types of activities along the Badung river Pemogan village followed by field inspections. The pollution load was determined by multiplying the concentration of water quality and flow rate, while the carrying capacity was determined with the help of the program Qual2Kw ver 5.1. Determination of load capacity with Qualk2Kw ver 5.1 has several step such as data collect, the data entry process, calibration, verification and simulation according to the scenario. The result of this study showed sources of pollution that cause a decrease the water quality of Badung river in Pemogan village were non point source (agriculture and settlements) and point source (flow from upstream). The pollution load that flow into the Badung river in Pemogan village for BOD, COD and TSS respectively 419,97 kg/ day; 865,66 kg/day dan 160,70 kg/day. Load capacity of Badung river for COD and TSS were 1686.53 kg/day dan 4743.36 kg / day, Meanwhile, the load capacity of Badung River for BOD has overlimits.

Rainfall is among the most important climatic elements of the Maritime Continent. The Maritime Continent rainfall climate is uniquely located in the world’s most active convective area. Satellite data measured by the Tropical Rainfall Measuring Mission (TRMM) 3B43 based high-resolution rainfall products represent monthly Maritime Continent rainfall characteristics over 16 years. Several statistical scores were employed to analyse annual means, linear trends, seasonal means, and anomalous Maritime Continent rainfall characteristic percentages. The effects of land and topography on rainfall quantities were also studied and compared with the Global Precipitation Climatology Project (GPCP) gridded precipitation estimates which has low-resolution. Comparison also applied on linear correlation and partial correlation techniques to determine the relationship between rainfall and the El Nino Modoki and El Nino–Southern Oscillation (ENSO; hereafter conventional El Nino). The results show that north-south Maritime Continent precipitation is associated with and generated by the northwest and southeast monsoon patterns. In addition, the large-scale circulations are linked with heavy rainfall over this land-ocean region due to large-scale island-topography-induced convective organization. The rainfall responses to El Nino Modoki and conventional El Nino clearly indicated the times at which the conventional El Nino had a higher impact than El Nino Modoki, especially during northern winter and spring, and vice versa during northern fall, and similarly affect during northern summer. Furthermore, the dynamic movements of rainfall anomaly that are caused by El Nino Modoki and the conventional El Nino events spanned from the southwest during June-July-August (JJA) to throughout the northeast ending in March-April-May (MAM).

Vegetation Indices is one of the remote sensing parameters that can be used to estimate the mangrove forest density. The purpose of this study is to determine the vegetation index with the best accuracy to estimate the condition of mangrove density, as well as determine the spatial distribution of mangrove density in the TNBB area. This study uses Sentinel-2A satellite imagery data and five different vegetation indices, namely NDVI, NNIR, EVI, mRE-SR, and vegetation index developed in this study. The method of determining samples in the field uses stratified random and proportional sampling. Data collection of canopy density used hemispherical photography method, which is taking vertical photos with a 180o angle of view using a camera with a Fish Eye or Wide lens. Data analysis used in this study is regression analysis, coefficient of determination test, model validation test, and paired t test. From statistical tests conducted on several vegetation indices, the mRE-SR vegetation index value shows the best results on all the accuracy parameters tested. The R2 value was generated by the mRE-SR vegetation index from the relationship between mangrove density results from field measurements with the vegetation index value and the estimated density results shows that the highest values, namely 0.909 and 0.935. These results show that the mRE-SR vegetation index is the best vegetation index in explaining the variation of mangrove density in the field. The mRE-SR vegetation index also has the lowest deviation of the estimated value, with the resulting SE values in the two linear relationships of 1,592 and 0,999. In addition, the mRE-SR vegetation index has a P (T

The coastal zone is a unique region because the coastal area is a meeting place of land and sea. Coastal areas are vulnerable to various human activities that can affect the terrestrial and marine environment both directly and indirectly. This can result in changes in land use, especially in coastal areas. Rapid changes in land use in coastal areas due to the development of tourist areas and the high level of erosion and abrasion that occurs causes the need for planned, comprehensive and sustainable land management planning and regional development. the purpose of this study was to determine land use, land use changes and the level of accuracy of landsat imagery in coastal areas of Batu Layar District, West Lombok Regency. This study uses the Quantum GIS software to analize Landsat satellite imagery data in 2002 and 2019. The results showed that the land use change during of 17 years (2002 - 2019), namely the residential and building classes increased by 237.69 ha and while the bush class , forests, plantations, rice fields, open land and waters respectively decreased by 71.91 ha, 61.2 ha, 46.44 ha, 32.4 ha, 20.79 and 5.04 ha respectively. The Overall Accuracy of this study was 90%.

Tidal flood is a water inundation phenomenon happened on the coast of the mainland or the coast which is caused by the tides of the sea. Tidal flood causes inundation on the certain parts of the mainland due to land altitude is lower than sea level at high tides. Some beaches around Gianyar, Bali, potentially experience a tidal flood. There is no research about tidal flood in Gianyar regency yet gives impact to the information about areas that potentially experience a tidal flood. This study aimed to determine the distribution of flood-prone areas in Gianyar Regency. Remote sensing and other spatial data by using scoring methods were utilized to determine prone areas of tidal flood. The parameters used included land elevation, distance area from shoreline, distance area from river, slope, land cover, and soil types. Result of the study shows that the 1104 ha of the total research area 66,37 ha or 6,02% are vulnerable areas, 684,51 ha or 62,00% are quite vulnerable area, and 353,12 ha or 31,98% are classified as non-prone areas. Spatial distribution of tidal flood potential in Gianyar Regency includes Rangkan Beach, around Purnama Beach, Saba Beach, Keramas Beach, Pering Beach, Lebih Beach and the west side of Siyut Beach . Observations and interviews toward vulnerable areas were conducted as the validation of the result of the study.

This research was conducted in the northeastern slopes of Mount Agung in Karangasem regency, Bali province. The research area is dry land with land use patterns that have not demonstrated compliance with existing land potential. On the other hand, technological advances in the field of agriculture has been growing rapidly, one by analyzing the ability of the land to determine the potential of land resources and minimize the risk of failure of farmers. The results of the analysis of land capability is expected to serve as guidelines in a more optimal use of land in accordance with expectations. The purpose of this study was to determine the potential of land and recommends referral based land use conditions existing land capability in the area of research. The design used is descriptive design, with the main objective to provide a picture or description of land use capability and suitability of the study area. Guidelines for land capability analysis in this study refers to Arsyad (2006) and the Regulation of the Minister of Environment No. 17 Year 2009 on Guidelines for Determining Environmental Carrying Capacity in Regional Spatial Planning. The results showed that 1) land capability class research in areas ranging from class III to class VI. Land capability class III is spread on the land unit 1, land unit 2, land unit 3,and land unit 8. Capability class IV land located on land unit 4, land unit 7, land unit 9, land unit 10 and land unit 12.Land capability classes VI located on land unit 5, land unit 6, and land unit 11; 2) land use suitability value obtained was 92.85%. This shows that almost the entire study area (92.85%) is in conformity between the ability of the land and its use. The rest (7.15%) classified as not appropriate. Suitability of the land use study area into the high criteria; 3) Referral land use adapted to the existing conditions of land units. In class III-IV land that does not meet the user or not used optimally, land use options to do that seasonal crops, plantation crops. While on a class VI land use that is not appropriate or not used optimally, land use options to do that forest production and non-agricultural land use.

The total population of District Kuta Utara 2014 recorded 68.422 people with an average population density of 2,021 people/km2 while the number of tourism accommodation facilities (hotels, villas, restaurants) recorded 399 pieces. The rapid of population growth and the development of tourism accommodation facilities require ground water more increasing. Intake of large amounts of ground water potential to cause pollution/ sea water intrusion. This study aims to determine the quality of the groundwater, knowing distribution maps sea water intrusion and to predict the level of sea water intrusion coming years. This research method uses descriptive quantitative method. Furthermore, to determine the quantity and quality of ground water was measured groundwater levels and analysis of the parameters of temperature, total dissolved solid, pH, chloride, hardness and electrical conductivity of the 60 sample wells. Results of analysis of each of the key parameters that can be mapped and known overlay distribution maps sea water intruded area. The observation of the quantity and quality of groundwater in coastal areas of Kuta Utara subdistrict shows the range of the depth of the ground water level between -5.15 meters to -22.16 meters. Results of the analysis of ground water quality of the 60 groundwater samples showed that there has been a decline in the quality of ground water is as much as 14 samples total dissolved solid (TDS) the highest reaches 1,122 mg/ l, the highest electrical conductivity reached 1,677 ìMhos / cm and the highest chloride reached 532, 5 mg/l. Distribution maps sea water intruded area shows that in coastal areas troubled district of Kuta Utara indicated seawater intrusion, especially in the area of Petitenget and Batubelig, Kerobokan Kelod Village area of 78.44 Ha. Prediction of sea water intrusion in 2022 was highest in the area of tourism accommodation caused by ground water extraction enough much, kind of gray-brown soil regosol has a rough texture and high permeability characteristics so it is easy to pass groundwater.

The need for bananas in Bali far exceeds the production. To obtain optimal production according to their genetic potential, the development of banana cultivation should be preceded by a land suitability evaluation study. This study aims to evaluate the land suitability based on agroecological parameters such as rainfall, altitude, dry moon, slope, and considering current land use.The results showed that 257.467 ha or 46.16% of the area of Bali Province has the potential to be planted with bananas.Buleleng Regency has the widest area for the development of banana plants, followed by Karangasem, Tabanan, Jembrana and Bangli. Denpasar town has the smallest suitable area.Based on the observed agroclimate parameters, slope is the most severe limiting factor in banana cultivation, while rainfall, altitude, and dry months are not significant limiting factors. Recommended land use for the development of banana plants is garden, grass, rain-fed rice field, scrub, bare land, and moor.

Giao Thuy located a coastal area, which has fully the characteristed of microclimate Red River delta. This study was conducted to detect the land cover changes from 2000 to 2015 and the causes. The changes are evaluated using Landsat data by method of land use classification combined with interviews. The results showed that most portion of the land cover class was crops land and land cover change was crops land decreases, forest land rises, aquaculture ponds increase, surface water reduces and unuse flat land area increases. The causes were policy issuses, market, economic, salinity intrusion, climate change and deposits alluvial.

Existing methods for rice field classification have some limitations due to the large variety of land covers attributed to rice fields. This study used temporal variance analysis of daily Moderate Resolution Imaging Spectroradiometer MODIS satellite images to discriminate rice fields from other land uses. The classification result was then compared with the reference data. Regression analysis showed that regency and district comparisons produced coefficients of determination R 2 of 0.97490 and 0.92298, whereas the root mean square errors RMSEs were 1570.70 and 551.36 ha, respectively. The overall accuracy of the method in this study was 87.91%, with commission and omission errors of 35.45% and 17.68%, respectively. Kappa analysis showed strong agreement between the results of the analysis of the MODIS data using the method developed in this study and the reference data, with a kappa coefficient value of 0.8371. The results of this study indicated that the algorithm for variance analysis of multitemporal MODIS images could potentially be applied for rice field mapping.

First, we analysed alabcore catch data based on time, positions, and layer alabcore caught and ROMS result data monthly climatology data for temperature, salinity. current velocity, and sea surface height for 2005–2008. Then, we analyzed the relationship between catch data and ROMS data by combining the statistical method of regression trogh origin (RTO) and geographic information system (GIS). Three model RTO were generated with the abundance of albacore tuna as a response variable, and temperature, salinity. current velocity, and sea surface height as predictor variables. All of the predictors of temperature, salinity. current velocity, and sea surface height were highly significant (P < 0.001) to the number of albacore tuna. Values of temperature, salinity. current velocity, and sea surface height in albacore tuna preferences ranged from 220 to 230 C, 34.79 to 34.84 Psu, 0.01 to 0.03 m/s and 0.66 to 0.70 m, respectively. Validation of the predicted number ofalbacore tuna with the observed value was significant (P < 0.05, r2 = 0.60). sea surface height was the most important environmental variable to the number of albacore tuna caught, followed by temperature, salinity and current velocity.

A single paragraph of about 250 words maximum. For research articles, abstracts should give a pertinent overview of the work. We strongly encourage authors to use the following style of structured abstracts, but without headings: 1) Background: Place the question addressed in a broad context and highlight the purpose of the study; 2) Methods: Describe briefly the main methods or treatments applied; 3) Results: Summarize the article's main findings; and 4) Conclusion: Indicate the main conclusions or interpretations. The abstract should be an objective representation of the article, it must not contain results which are not presented and substantiated in the main text and should not exaggerate the main conclusions.

The Use of Landsat 8 Imagery to Estimate Chlorophyl Content and Yield of Rice Plant.Predicting rice yield before harvest time is important to supporting planners and decision makers topredict the amount of rice that should be imported or exported and to enable governments to put inplace strategic contingency plans for the redistribution of food during times of famine. This study usedLandsat 8 that has better both spectral and radiometric resolution compared with the Landsat 7. Theresult of this study shows that using several Landsat 8 band as vegetation index provided better relationshipwith rice chlorophyll compared usind single band. Normalized Difference Vegetation Index (NDVI)has the best relationship with the chlorophyll content of rice plant compared with other vegetation indexand single band of Landsat 8 with the R2 of 0.936 . The form of relationship is linear with equation of y= 7.1043x + 0.0661, where y and x are chlorophyll content of rice plant and NDVI, respectively.Rice yield can be estimated in rice age approximatelly of 2 months with the equation of y = 30.495x2 -36.884x + 19.334, where y and x are rice yield in ton/ha and NDVI, respectively. The R² of thisestimation is 0.893 with the standard error of 0.372.

The Study of Land Resource Potentials at Manggis District, Karangasem Regency forDevelopment of Horticulture Crops.The objective of the research was to study the potential ofland resources for growing horticulture crops at Manggis District, Karangasem Regency. Soil surveyand laboratory analysis were used in this research. The parameters of soil characteristics observedwere soil morphology in the fields, and physical and chemical properties. The results showed that thelands of Manggis District classified into ‘suitable enough’ until ‘very suitable’ for growing horticulturecrops. The limited factors were root medium; nutrient retention and terrains

The rate of growth and development of Denpasar city increases the demand for land to supply the needs of urban facilities and infrastructure. This affected to the increase of conversion of vegetation coverage into built-up land cover. The conversion of the vegetation coverage impacts on urban environmental quality which is increase the rate of air temperature. The purpose of this study is to examine the impact of vegetation coverage change on the air temperature change in Denpasar city in year of 2003, 2009 and 2015. Remote sensing and regression statistic method were used in this study. The results showed that the decrease of vegetation coverage influenced the increase of air temperature in Denpasar city. Statistically, the correlation can be projected on the equation y = 31,295-0,078x, where x and y are vegetaton coverege and air temperature respectively. This equation shows that decresing of 1% vegetation coverege will increase 0,078 oC of air temperature. The effort to conceder in responding to the temperature rise that occurred in Denpasar is increasing the number of green open space. Based on the classification value of vegetation coverage and air temperature of Denpasar City, there are three categories of priority areas for green open space development that is high, medium and low priority. The development of green open space in each priority area can be adjusted to the characteristics of land use.

Remote sensing technology provides an opportunity for effective and efficient bathymetry mapping, especially in areas which level of depth changes quickly. Bathymetry information is very useful for hydrographic and shipping safety. Landsat medium resolution satellite imagery can be used for the extraction of bathymetry information. This study aims to extract information from the Landsat bathymetry by using Van Hengel and Spitzer rotation algorithm transformation (1991) in the water of Menjangan Island, Bali. This study shows that Van Hengel and Spitzer rotation algorithm transformation (1991) can be used to extract information on the bathymetry of Menjangan Island. Extraction of bathymetric information generated from Landsat TM imagery data in March 19, 1997 had shown the depth interval of (-0.6) m to (-12.3) m and R2 value of 0.671. While Data LANDSAT ETM + dated June 23, 2000 resulted in depth interval of 0 m to (-19.1) m and R2 value of 0.796. Furthermore, data LANDSAT ETM + dated March 12, 2003 resulted in depth interval of 0 m to (-22.5) m and R2 value of 0.931.

Forecasting rice yield before harvest time is important to supporting planners and decision makers to predict the amount of rice that should be imported or exported and to enable governments to put in place strategic contingency plans for the redistribution of food during times of famine. This study used the Normalized Difference Vegetation Index (NDVI) of Landsat Enhanced Thematic Mapper plus (ETM+) images of rice plants to estimate rice yield based on field observation. The result showed that the rice yield could be estimated using the exponential equation of y = 0.3419e 4.1587x , where y and x are rice yield and NDVI, respectively. The R 2 and SE of the estimation were 0.852 and 0.077 ton/ha, respectively. An accuracy assessment of rice yield estimation using Landsat images was performed by comparing the rice yields from the estimation result and the reference data. The results show that the linear relationship with the R

The standard image classification method typically uses multispectral imagery on one acquisition date as an input for classification. Rice fields exhibit high variability in land cover states, which influences their reflectance. Using the existing standard method for rice field classification may increase errors of commission and omission, thereby reducing classification accuracy. This study utilised temporal variance in a vegetation index as a modified input image for rice field classification. The results showed that classification of rice fields using modified input images provided a better result. Using the modified classification input improved the correspondence between rice field area obtained from the classification result and reference data (R2 increased from 0.2557 to 0.9656 for regencylevel comparisons and from 0.5045 to 0.8698 for district-level comparisons). The classification accuracy and the estimated Kappa value also increased when using the modified classification input compared to the standard method, from 66.33 to 83.73 and from 0.49 to 0.77, respectively. The commission error, omission error, and Kappa variance decreased from 68.11 to 42.36, 28.48 to 27.97, and 0.00159 to 0.00039, respectively, when using modified input images compared to the standard method. The Kappa analysis concluded that there are significant differences between the procedure developed in this study and the standard method for rice field classification. Consequently, the modified classification method developed here is significant improvement over the standard procedure.

Information on the distribution of paddy rice fields is essential for food security and water resource management. Remote sensing is a technique that can be used to obtain rice production information from space. In the present study, Moderate Resolution Imaging Spectroradiometer (MODIS) images were used to evaluate the relationship between the rice spectral (normalized difference vegetation index (NDVI)) of Landsat images and rice yield. The results indicated that the relationship between the NDVI and rice age was best described by a quadratic equation, and R 2 values ranging from 0.916 to 0.973 were obtained. Three growth variables were evaluated in the present study, and the total NDVI (∑NDVI) showed the strongest exponential relationship to rice yield (R 2 = 0.9203)

The visible band of Landsat ETM+ (Band 1, Band 2, and Band 3) showed a weak exponential relationship to rice age; however, the reflective infrared band of Landsat ETM+ (Band 4 and B5) and the entire vegetation index showed a strong exponential relationship to rice age. Rice Growth Vegetation Index (RGVI) developed in this study is the best vegetation index compared to existing vegetation indices. The relationship between rice age and RGVI has shown the highest determination coefficient (R 2 ) of 0.9045. The RGVI then used to develop a model to rice plant mapping. Quantitative comparison of the rice plant area between analysis results and reference data showed a linear relationship, with the equation y = 0.920x - 3.841 and R² = 0.971, where y is the rice plant area of reference data, and x is the rice plant area of the analysis results of the Landsat ETM+. The standard error of this estimation was 43.04 ha.

In order to improve the method for prediction of tuna fishing ground, the modification of the analysis about satellite altimeter data was made as trial. In this study, we focused on the satellite altimeter, TOPEX/POSEIDON series, to improve the method of fishing ground prediction. Fishery data were supplied as hook rate by local fishing information around Indonesia and hearing infromation. The gradient of sea surface height is calculated between the neighbor grid which has the maximum gradient. Result showed that the fishery data with hook rate over 0.8 are grouped in a zone from 1.0E-06 of sea prediction of fishing ground quantitatively, but also reasonable accuracy as shown in the change in the standard deviation. This method can be utilized for the effective fishing plan with the resource protection and the economy in the fishing operation in near future. Keywords: sea surface altimeter, sea surface gradient, remote sensing, fishing ground search, hook rate, fishery resource management.

Change in the sea surface height anomaly derived from satellite altimeter was examined and applied to evaluate the possibility of tsunami prediction before the occurance. Sea surface height anomaly was composed period during earthquake and tsunami occurance. Daily variability in the sea surface height anomaly was traced about the location of hypocenter, aftershock, and the end of erthquakes from satellite altimetry. Results shows that there are the locations where the sea surface height anomaly suddenly increased or decreased before tsunami event at least. This result can be utilized and applied for the development in not only the stunami monitoting system as the disaster monitoring, but also for the effective tsunami prediction system in the near future. Keywords: tsunami, earthquake, bottom topography, sea surface height, satellite altimetry, altimeter, disaster prevention.

Princeton Ocean Model (POM) was used to calculate the tidal current and M2-residual current in Benoa Bay using barotropic model (mode 2). The model was forced by tidal elevation, which was given along the open boundary condition using tide data prediction from Hydro-Oceanography Division-Indonesian Navy (DISHIDROS TNI-AL). The computed tidal current and residual current have been compared with both data in Benoa Bay, that are data of the open boundary of Benoa Bay and condition of Benoa Bay after developed a port and reclamation of Serangan Island. The maximum velocity of tidal current for open boundary conditions at flood tide is 0.71 m/sec, whereas at ebb tide is 0.65 m/sec and the maximum velocity after developed a port and reclamation of Serangan Island, at flood tide, is 0.69 m/sec. The simulation of residual current with particular emphasis on predominant constituent of M2 after developed a port and reclamation of Serangan Island shows a strong flow at the western part of Tanjung Benoa and Benoa Harbor and also at bay mouth between Serangan Island and Tanjung Benoa. Maximum velocity of M2-residual current is 0.0585 m/sec by the simulation and showed that the current which was produced forming two eddies in the bay of which one eddy is in the mouth of bay in southern part. The residual current for open boundary condition of bay shows four eddies circulation, one big eddies and the others small. The anticlockwise circulation occurs in the inner part of the bay. Key words: model, simulation, tidal current, residual current

An algorithm of estimating Vertical distribution of Chlorophyll-a (Chl-a) was evaluated based on Artificial Neural Networks (ANN) method in Hokkaido field in the northwest of Pacific Ocean. The algorithm applied to the data of SeaWiFS on OrbView-2 and AVHRR on NOAA off Hokkaido, has been applied on September 24, 1998 and September 28, 2001. Ocean color sensor provides the information of the photosynthetic pigment concentration for the upper 22% of the euphotic zone. In order to model a primary production in the water column derived from satellite, it is important to obtain the vertical profile of Chl-a distribution, because the maximum value of Chl-a concentration used to lie in the subsurface region. A shifted Gaussian model has been proposed to describe the variation of the chlorophyll-a (Chl-a) profile which consists of four parameters, i.e. background biomass (B0), maximum depth of Chl-a (zm), total biomass in the peak (h), and a measurement of the thickness or vertical scale of the peak (cr). However, these parameters are not easy to be determined directly from satellite data. Therefore, in the present study, an ANN methodology is used. Using in-situ data from 1974 to 1994 around Japan Islands, the above four parameters are calculated to derive the Chl-a concentration, sea surface temperature, mixed layer depth, latitude, longitude, and Julian days. The total of 6983 profiles of Chl-a and temperature are used for ANN. The correlation coefficients of these parameters are 0.79 (B0), 0.73 (h), 0.76 (cr) and 0.79 (zm) respectively. A site called A-linc off Hokkaido is used to evaluate Chl-a concentration in each depth. After comparing with in-situ data and ANN model, the results show good agreement relatively. Therefore, the ANN method is applicable and available tool to estimate primary production and fish resources from the space. Keywords : Ocean color, Chlorophyll-a (Chl-a), Vertical structure, Artificial Neural Networks (ANN).

The preliminary study using Landsat ETM+ to estimate the rice production in Regency of Tabanan, Bali Province was conducted. The objectives of this study were to know spectral characteristic of rice plant in three importance growth periods of rice, and to develop a model to identify the distribution of rice. Landsat ETM+ in two acquisition dates (March 21st, 2003 and May 24*, 2003) were used in this study. Characteristics of rice were analyzed using radiance value of Landsat ETM+ obtained from converting digital number of Landsat data. Multi-variable linear regression analysis was developed to classify the rice in its growth period. The result showed that the rice plant has different reflectance in seedling-development period, ear differentiation period and maturation period. It is expressed by the radiance value of Landsat ETM+. However, spectral characteristic of rice in each band of Landsat ETM+ is similar to the green vegetations in general, except in blue band (Bl). Based on statistical analysis, the classification of rice in each its growth period can be classified. Key words: Rice field, Landsat ETM+, Spectral Characteristic, Multi-temporal.