This PDF file contains the front matter associated with SPIE Proceedings Volume 11311 including the Title Page, Copyright information, Table of Contents, Introduction, and Conference Committee listing.

Food crops monitoring in developing countries such as Indonesia plays an essential role to support national goals in food security and self-sufficiency. One of the fundamental challenges is plant phase classification task which could help to estimate yield before harvest. In contrast to the conventional field survey method which required a large amount of human and capital resources, we explore a more scalable, inexpensive and real-time method using publicly available remote sensing data, i.e. Landsat-8 satellite. Landsat-8 provides rich spatiotemporal features which could support the detection of numerous vegetation and crop-related indices. However, to accurately classify the plant phase, the existing features require additional spectral pattern from different seasons. We found out the existence of temporal autocorrelation among features of food crops plant phase. We propose a supervised random forest method to make features engineering to select best multitemporal features. In this study, we focus on the rice plant phase classification in Banyuwangi Regency, Indonesia as a case study. The ground truth data are the monthly frame area sampling of average rice plant phase at the regency level which officially released by BPS-Statistics Indonesia. The experimental result shows the accuracy of 0.573 with one temporal feature. Furthermore, incorporating four consecutive temporal features gives higher accuracy gain to 0.727 which shows the temporal autocorrelation. Based on the extensive evaluations, our findings and contributions in this study include: (1) insight to capture the temporal autocorrelation to increase the model accuracy (2) a machine learning classification model which is not sensitive to multicollinearity. Our proposed method provides the potential benefit for the government and statistical agencies towards a more scalable agricultural survey.

Seagrass beds with various species are widespread in Indonesia, where one of them is on Parang Island. The important role of seagrass as a blue carbon sink makes the composition of species and carbon stocks of seagrass on Parang Island need to be mapped. PlanetScope image is one image that is expected to be able to map biophysical information on seagrass beds. The objectives of this study are (1) to map the distribution of composition of seagrass species and (2) to map the seagrass above-ground carbon stock (AGC) on Parang Island, Karimunjawa Islands using PlanetScope. The composition of seagrass species was obtained through multispectral classification (maximum likelihood, random forest, support vector machine) and AGC seagrass through empirical modeling. The class composition of seagrass species obtained was Cymodocea rotundata (Cr), Enhalus acoroides (Ea), Thalassia hemprichii (Th), and EaThCr, with the accuracy of 32.16%. Seagrass AGC empirical modeling has an R² 0.086. The DII23 water column corrected band has the highest accuracy for seagrass AGC mapping, which is 66.90% with a Standard Error of Estimate (SE) value of 4.78 gC/m² . The total estimated AGC of seagrass found on Parang Island is 50.15 t C.

Tin mining is one of the main sectors of the national economy where the Bangka Regency is the largest tin producer in Indonesia. However, this sector cannot be separated from the pros and cons for a long time. In a way, this sector can increase both national and regional income but on the other side, the adverse effects of it can threaten the survival of humans and the environment. Open tin mining activity has converted previously vegetated land cover become the nonvegetated land cover. Furthermore, the land cover changes to the mining area have a major impact on global warming which has become an international issue in the past few decades. This research aims to map and measuring land cover changes especially from vegetated to non-vegetated land cover related to tin mining activity in Bangka Regency. This research using multitemporal Landsat imagery data acquisition in the year 2004 (Landsat 5 TM) and 2017 (Landsat 8 OLI) through digital image processing using Maximum Likelihood Classifier method. Previously, the image as a classification input through relative radiometric normalization. The result shows that tin mining activity in Bangka regency for thirteen years causes an area reduction in vegetated land cover. These results are expected to be an important input in policymaking for local governments to support the action plan which leads to mitigation of climate change.

In a modern urban society with a high demand for mobilization, location-based personal navigation service plays a fundamental role in daily activities. With the help of real-time and accessible GPS technology, online navigation service such as Google Map and Street View have become more important and increasingly popular. Being designed for simplicity and scalability, most of these current service will suggest routes based on the fastest travel time or the shortest distance to a destination. However, in developing countries such as Indonesia, which is still struggling with crime rate issue, the requirement for safety become an undoubtedly crucial factor for human mobility. In this paper, we propose an integrated web-based system using the crime hotspot area based on crime history from the local government agency, existing geotagged social media crime news and user reported data. The users could further involve and contribute by reporting their personal safety experience to increase the recommendation accuracy in the future. Build on the free and opensource GraphHopper Routing API, our proposed personalized user feature also include rerouting option and crime contour map. We focus on Jakarta area as our case study, which served as the heart of citizen activity in Indonesia. The key result of the proposed framework is a personal navigation map that recommends the safest route to the user which bypass potential crime-prone areas.

Studies of land use change have been increasingly carried out in recent years. Land use change is seen as one of the fundamental factor for the operation of environmental system from local to global scale. There are various methods to study land use change and cellular automata(CA)-based spatial simulation is a popular one. While this method (CA-based spatial simulation) is widely used in study of land use change, there are many aspects that need to be explored regarding its performance. Exploring the effect of spatial resolution on CA-based spatial simulation of land use change is the main objective of this research. Yogyakarta urban area was preferred as research area because of its interesting characteristic. Built up land is continuously increasing while agriculture land tend to decrease. Yogyakarta urban area consisted of the city of Yogyakarta and its suburban areas. Spatial simulation combined with experimental analysis were used as the main methods. CA-based spatial simulation were performed on different scenarios i.e. different spatial resolution of the data input. This study used three different spatial resolution that are 10 m, 50 m, and 75 m. Univariate statistical analysis against empirical data of land-use change was conducted to determine those spatial resolutions. Performance of CA-based spatial simulation was accessed using Kappa Index of Agreement (KIA) and two indices of spatial pattern, i.e. variance to mean ratio (VMR) and Moran’s I. This study shows that higher spatial resolution of data input tend to generate a more clustered spatial pattern on the simulated map. The minimum and average value of actual land use change area could be utilized as consideration for determining appropriate spatial resolution. Medium spatial resolution particularly for extended spatial simulation produce more “visually realistic” spatial pattern.

Map can show information needed by map users from various scientific fields, especially in Indonesia. Effective maps can help users understand. One of the factors that influence the effectiveness of map reading is the color symbol scheme used in symbolization. Effectiveness’ study of color symbol scheme applied on choropleth mapping. Choropleth map is using population density data in Special Region of Yogyakarta. The selection of the study area in the Special Province of Yogyakarta is because the Special Region of Yogyakarta is one of the provinces in Indonesia which has a fairly high population density in the area of 3,185.80 km² . In 2016, the population density of the Special Province of Yogyakarta ranked 4th in the Indonesian Statistics 2017 by the Central Bureau of Statistics, which amounted to 1,188 population per km2 . The effectiveness of color symbol schemes adapts the capabilities of each user. This study is expected to be able to study the effect of age group differences on maps with the best color symbol scheme. All scientific field that used choropleth map of population density consist of 2 age groups, those are 20-25 years old and >5 years old respondents. The purpose of this study was to observe age group influence for the most effective color symbol scheme for mapping population density in the Special Region of Yogyakarta. The result of this study shows the difference between age group based on the important aspects of conventional-eye tracking. The important aspects to consider are average answering duration, the accuracy of the answer and easiness level of symbolization readings. The first group (20-25 years) shows map 3 (diverging color scheme) as a map with the most effective color symbol scheme. While group 2 (>25 years) shows map 1 (ArcGIS 10.3 color scheme) as a map with the most effective color symbol scheme. This research is expected to be able to show the influence of age in determining the best color symbol scheme on population density maps so that its effectiveness can be adjusted specifically to map users.

Train is one of mass transportation that contains spatial and attribute data. Based on one of the development services of the 2030 national railway, the innovation of Indonesian Ministry of Transportation is focused on the ease of uses, namely the public, academics, and policy-makers to access data and information through web-based maps (WebGIS). At present, railways data in Indonesia have been publicly accessible. But general visualization of railways data, which is clear and easily understood by the public have not available. Therefore, this research aims to visualize the results of railways data which interactively combining spatial data and attribute data. The data used for this research was obtained from the Indonesian Ministry of Transportation. Visualization of the data requires a search for patterns, relationships and trends. The data variables used in this research include railways status, station status, station class, station group, the distance between stations, as well as the location of stations and railway lines. The results of the data processing are maps presented in a dashboard map view with visual analytics features. The data are processed with QGIS then are visualized by Operations Dashboard for ArcGIS. Visual analytics method is used because the results can show the relationship between variables. It can be used as a reference for decision making to develop transportation infrastructure, especially of rail transportation. Maps and graphics are visually interactive so change one attribute can affect the other attribute display. The result of this research is named Peta Jalur Kereta Api Jawa.

Yogyakarta urban area has grown throughout the year as the course of migration and its attraction to tourists and students resulting the high demand of living space which leads to the increment of built-up area such as hotels and other supportingtourism-activity accommodation, so-called urban sprawl. The increase of paved-surface causes the increase of land surface temperature (LST) which may impact to micro-climate in the urban area with adverse consequences for instance erratic rainfall and rainstorm in the urban area. Consequently, it triggers new future problems. This paper attempt to present the distribution of diurnal Land Surface Temperature (LST) in Yogyakarta urban area, extracted from remotely-sensed Landsat 8 image acquired from a two—year images. Prior the extraction, several variables are incorporated i.e Normalized Difference Vegetation Index (NDVI) to calculate emissivity, as well as atmospheric correction parameter transmissivity, upwelling and downwelling radiance. In order to obtain NDVI, the reflectance values are also corrected. Land surface temperature is extracted according to the procedures: conversion of digital number of Landsat image to radiance, correction of radiance value, conversion of the corrected radiance value to brightness temperature, then brightness temperature to land surface temperature. The extracted temperature map then presented into 10°C interval. Consecutively, the two-year of temperature maps are then analyzed to obtain the difference of its spatial distribution. The expected result is the expanding high temperature distribution in the urban area. The result shows there is an increase of the average land surface temperature by 3oC from two different image, 2014 and 2018. The majority value of temperature is between 30 – 40°C, dominated with built-up area. Two image shows that the respective area spread from 54% to 70%.

Land Surface Temperature (LST) is an important indicator of environment changes, especially related drought monitoring. It is necessary to accurately detect drought events using advanced technology proved information regarding the drought areas. Remote sensing images have proven to be efficient in detecting drought events. MODIS Terra and Landsat 7 ETM+ (Enhanced Thematic Mapper Plus) and Landsat 8 OLI/TIRS (The Operational Land Imager and the Thermal Infrared Scanner) represent remote imaging images with different spatial resolutions that enable us proved drought information. However, proper methods are needed to optimize these images for monitoring drought events. The purpose of this study is to find out the ability of multi-scale images proved information about drought monitoring using LST methods. The method used in LST is Temperature Condition Index (TCI), Crop Water Stress Index (CWSI), and Principal Component Analysis (PCA). All three equations are selected because they represent a modification of the method for LST input. The results suggest that the three equations used in multi-level imagery have a critical alignment of information regarding drought. The results show that drought pattern identified by MODIS Terra image was similar to the one detected by Landsat ETM+ and OLI/TIRS images. However, we found a temperature difference in dry season (especially in October) between Landsat ETM+ and OLI/TIRS. The degree of LST estimation accuracy between MODIS Terra and Landsat (ETM+ and OLI/TIRS) is indicated by the average difference between the results of those images, which was 1 degree Celsius (1°C). The use of these three equations for drought monitoring with multi-level imagery suggests that there is a positive relationship. This relationship manifests the same pattern, shape, and association that are produced, thus using a common equation for drought monitoring is more focused.

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 development of Jimbung Village, Klaten Regency, Central Java Province requires good spatial database to maximize its resources. However, GIS specialists who master GeoInformation Technology still need specific local knowledge so that the participation of local communities is extremely valuable. The objective of this research is to improve GIS products by incorporating local participation. We conducted aerial and surface observation within the village area in order to complete GIS database on village boundary delimitation, current potencies (industry, food and beverage, agriculture, tourism). Participatory Learning and Action was used to identify the future development of village. The results shows that Participatory Geoinformation Technology allows to clearly identify the industrial development, small medium enterprises on processed food, agricultural activity, and tourism in Jimbung village. Active participation from stakeholders can also help identify potential sectors that need to be developed in the future. This would be very important for local government while composing Middle Term Village Development Plan (RPJMDes).

The green mussel cultivation by fishermen in Pasaran Island is influenced by nature and uses simple technology without regarding water conditions. In fact, site selection considering the water condition is one of the important factors in determining the success of quality green mussel cultivation. High market demand but not supported by modern technology, good marketing strategies, price stability, and appropriate cultivation site can reduce the production of green mussels. This research was conducted to determine the optimal location for the green mussel cultivation around Pasaran Island, in Lampung Bay and to formulate a management strategy based on the map. Modeling parameters measured on the field include depth, salinity, pH, temperature, current velocity, dissolved oxygen, water clarity, and chlorophyll-a. Data processing methods include inverse distance weighted (IDW) interpolations and fuzzy overlay. The study result in the form of raster-based physical water suitability maps for green mussel cultivation are intended to refine the uncertainties in the vector-based data presentation on water quality data so that it is expected to provide additional information to avoid a less optimal cultivation environment so it maintain the quality of green mussel products and support to accelerate aquaculture production raising program (minapolitan) in Lampung Bay.

Indonesia has many cultural heritages that attracts not only local tourist but also a foreigner. The most renowned site for cultural tourism is Borobudur, that attracts many tourists. It also included as one of the seven cultural wonders in the world. Tourism activity cannot be separated from photography since the visitors would want to have memories of the locations. Involuntary Geographic Information (iVGI) is one of the new sources of information that can be used to analyze the pattern of human activities spatially. This research explores Flickr data as an example of using photo-based iVGI data for hotspot analysis of human activities in cultural tourism objects. Each photo in Flickr’s database located in Borobudur can be assumed as an activity log since Flickr allowed the user to add geotagged photos. Though a data cleaning process must be done to filter irrelevant data. Point Density was employed in this study to explore photo distribution in the study area. Data density will act as an indicator that an area is more frequently visited by visitors. Besides, Zonation of Borobudur Region data was used to compare the density and the zone designated by the official document. The results of the study show that the peak of photography activity occurs at 6 am and area arround the Stupa has attracted visitor in undertaking photo shoot activity.

Data and disaster information is crucial to support disaster response, recovery, and relief process. Unfortunately, acquiring data during emergency situations is difficult. After a disaster occurred, information about the victim and the impact of the disaster must be collected immediately for aid distribution purposes. Collecting shelter location data with number of victims and their needs must be completed swiftly as a disaster response action. Furthermore, collecting information of damaged buildings is also essential to determine the amount of compensation for destroyed houses. Geographic Information System combined with remote sensing is practical and reliable to help all of these tasks. WorldView-3 high resolution imagery with 0.5 m spatial resolution provided by Digital Globe is used to interpret damaged building in Palu City study area. By comparing satellite imagery before and after disaster, damaged building information can be interpreted and extracted as a cartographic map. Cross validation with field data of damaged building demonstrated that accuracy of the interpretation is approximately 80.49 %. The result of the damaged building compared with shelter distribution data using service area accessibility analysis and then used to analyze optimum coverage of disaster mitigation shelters.

Data related to socio-economic activities in Indonesia mostly used statistical data. Statistics for large numbers of socioeconomics will make it difficult to interpret and analyze because it consists of many columns and rows with each value. Geo-visualization is a visualization of data represented in a geographic coordinate system. Socio-economic statistics can be visualized to facilitate the process of spatial analysis data that considers spatial surface of earth. Study area is in Special Region of Yogyakarta. This study aims to (1) Select, test and find out color symbol scheme most effective classification method for choropleth mapping of Demographic Map, (2) Mapping happiness profile of population using small area estimation method, (3) Analyzing tourist trends based on Instagram data using space time cube visualization. Secondary data used are population and happiness, while primary data uses social media data for tourist visualization. Geo-visualization of population and happiness used choropleth method. In social media geo-visualization for tourists using space time cube geo-visualization with hexagonal tessellation cells. The results obtained are population maps with best classification scheme, happiness maps at different scale levels, and tourist map using space time cube in Yogyakarta Special Region.

Indonesia is one of the disaster-prone countries. Based on the Indonesian Disaster Information Data (DIBI) and the National Disaster Management Agency (BNPB) in the last five years from 2014 to April 2019 there have been 65 landslides in Purworejo. Landslide is one of the most common disaster that occur in Indonesia. Landslide is caused by meteorological and geomorphological factors. Purworejo is one of the potential area that could be experiencing landslides, because the geomorphological conditions which are included in Menoreh Hills are geographically sloping to very steep. Landslide susceptibility modeling in Purworejo Regency was carried out using three different methods, namely Information Value Model (IVM), Information Value Model-Analytical Hierarchy Process (IVM-AHP) and Information Value Model-Gray Clustering (IVM-GC). Each modeling is conducted using the Natural Breaks (Jenks) method to produce five classes, namely very low, low, medium, high and very high class based on the IVM value of each method. This research’s goal is to visualized 3 maps of modelling results. The visualization used is 3-dimensional mapping. This mapping is intended to make it easier to compare the map results of modeling that have been done before. The expected results of this study are accurate and reliable 3-dimensional visualization to study the advantages and disadvantages of each of the modeling methods used.

The goal of this study is to analyze the suitability of filtering method towards the generation of Digital Terrain Model (DTM) from Digital Surface Model (DSM). In this case DSM was produced by optical images sensor with stereoscopic viewing. There are 2 kind of optical image that used which are Very High Resolution Satellite (VHRS) image Worldview-3 and Aerial Photograph from Drone mission. Furthermore, there are three different DSM filtering methods are used which are Elevation Threshold with Expand Window (ETEW), 2D Morphological Square (Morph 2D Filter), and Adaptive TIN (ATin). The ALDPAT v.1.0 software has used to implement those algorithm. In addition, the visual interpretation from their correspondent images are used to evaluate the quality of the filtering process. A good filtering result will remove the point that not represent a terrain. The missing of non-ground point due to the filtering process than filling by implementation the same interpolation algorithm. The ETEW and ATin filter algorithm are suitable for residential areas. While ATin filter algorithm give the best results in the vegetation area. An iterative filtering process should be implemented to make a non-terrain point completely remove. Further, an interpolation process should be made for filling the missing non-terrain point.

Topographic correction over mountainous region is an essential preprocessing steps for landuse/landcover extraction from earth observation (EO) satellite data. Until the time of this paper writing, there has not been any publication regarding topographic correction on LAPAN-A3 multispectral data. Topographic correction mainly grouped into two categories: band ratio, and illumination modelling which required ancillary digital elevation model (DEM). This paper aim to evaluate three different DEM source used for topographic correction on LAPAN-A3. These DEMs are Shuttle Radar Topographic Mission (SRTM), ALOS World 3D (AW3D), and nation-wide DEMNAS. The topographic corrections were performed over a subset of forested mountainous region in South Sulawesi, Indonesia. Minnaert correction algorithm was used in all three DEMs and evaluate the results. Performance evaluation were based on visual assessment, as well as spectral homogeneity of the pixel value before and after correction. The spectral homogeneity were calculated based on coefficient variation changes before and after correction. The initial results showed that SRTM produced the best visual appearance, while DEMNAS performed the best in terms of highest reduction in coefficient variation.

Topographic feature is one of the several factors affecting the distortion of the real reflectance value of objects. Digital processing used the surface reflectance values of satellite imagery needs the corrected images with the most minimized disturbances, hence several topographic correction methods using digital elevation data have been developed. This study examined the different result of topographic correction from several available elevation data in Indonesia, including SRTM DEM, topographic map (RBI), and DEMNAS. Sun-Canopy-Sensor+C (SCS+C) correction was applied on Landsat-8 data over Menoreh Mountains, Indonesia. The results obtained showed that DEMNAS produced the most topographically normalized images based on statistical and visual analysis. The availability of DEMNAS throughout Indonesia is the advantage to be used as an input of this pre-processing method. However, it needs to be examined first since the quality is not surely similar to our study site.

Mangrove ecosystem is one of coastal resources that have many benefits for coastal communities. The mangrove ecosystem has very high economic and ecological functions if it is developed and preserved properly. Nowadays, many mangrove ecosystems are threatened by human activities. It is necessary to preserve and to develop the mangrove ecosystems to avoid the impact of human activity and to increase their usefulness. In the process of developing the mangrove ecosystems, detailed data are required to provide a comprehensive overview of the environmental and physical conditions of the mangrove ecosystems. The study aims at identifying and making the inventory of the existing condition of the mangrove ecosystems related to mangrove cover and biodiversity. The data are collected using aerial photography and UAVs, observation and field measurement. The data inventory making is the first step in the process of developing and preserving the mangrove ecosystems. It finds that the use of the UAVs for the mangrove ecosystem data inventory making can give high accuracy data. The mangrove cover can easily be identified using image segmentation or onscreen digitization analysis. Finally, the UAVs can be a promising technology in the management and the monitoring of the mangrove ecosystems.

Karst area has a fascinating tourist attraction especially special interest tourism such as cave tubing in Kalisuci turism area, Gunungkidul, Yogyakarta. The presence of visitor restrictions has an impact on tourism waiting times. However, it also spurs the development potential besides cave exploration. Planning and development of tourist sites that include locations and consider the existing conditions of the region can only be done optimally with data and spatial analysis. On the other hand, the specific terrain condition, steeply hilly karst, is a great challenge in planning and developing karst area. This research aims to built basic spatial data in the Kalisuci Tourism area and provide a reference about the role of spatial data produced for planning special interest tourism in Kalisuci karst area, Gunungkidul. Basic spatial data is built from the results of data acquisition using Unmanned Aerial Vehicle (UAV) technology. This data is very important in the planning inputs for the development of the Kalisuci special interest tourism area in supporting the planning of tourist areas in accordance with terrain conditions. In addition, the data obtained from acquisition with UAVs are expected to provide detail spatial data collection, which is currently very limited. The resulting data are small format aerial photographs, aerial photo imagery (orthorectification results), and digital elevation models in the form of Digital Surface Model (DSM). This paper discusses how far the data generated can contribute to overall field observation and assist in planning special interest tourism in karst area.

Southern region of Banjarnegara Regency, Central Java, Indonesia have been experiencing water scarcity throughout dry season every year due to meteorological and geological condition. Meteorological drought in dry season have been recorded since 1984. About 85,000 people are affected. Local authorities were forced to send clean water aid routinely. This study aim to delineate groundwater potential zones using remote sensing, Geographical Information System (GIS), and Analytic Hierarchy Process (AHP). This study evaluate groundwater potential zones using 5 factors involving lineament, lithology, slope, drainage, and rainfall. Digital Elevation Model (DEM) from DEMNAS (published by Indonesian Geospatial Agency) was used to generate lineament delineation and slope map. Hydrography data provided by Indonesian Geospatial Agency was used to generate drainage density. Geological maps which were generated from remote sensing interpretation were provided from Geological Survey Center of Indonesia. Rainfall data were provided by BPS-Statistics of Banjarnegara. 52 springs and 2 bore wells data were used for result validation. All 5 thematic layers were prepared in GIS. All factors and its classes were assigned weights using AHP techniques and normalization of weights was conducted through the AHP. Groundwater potential zones map were generated, the results was classified into five zones as very high, high, moderate, low, and very low. The zones covered of 1.02 km² (1.18%), 14.49 km² (16.80%), 33.65 km² (38.99%), 37.12 km² (43.02%), and 1529 m² (0.00%) of study area respectively. Result validation by comparing the AHP map values with discharge of springs and bore wells showed promising result.

Noise in SAR imagery was produced due to different backscatter response from the objects in the earth surface. This resulted in a grainy image, usually known as “salt and pepper” noise, which reducing the capability to identify object from radar imagery. Therefore, speckle filtering was conducted to decrease this noise from SAR imagery. This study aims to assess the performance of different types of speckle filters especially when used to construct forest aboveground biomass (AGB) model from Sentinel-1 data in Barru Regency, South Sulawesi. There were 4 filters used in this study i.e. Frost, Gamma-MAP, Median, and Refined Lee. AGB model was constructed by using dual polarization C-band SAR of Sentinel1 data and ground inventory plots. 23 plots were collected in the field and the allometric equation was used to calculate the biomass value of the field survey data then cross validation models were generated by using biomass value and backscatter data from VV and VH polarization. Quality control was performed by comparing the coefficient of determination (R2 ) of those filters. The result shows that Frost filter especially on VH polarization was chosen as the bestfit model to estimate the AGB based on higher value of R² (0.3464158) and RMSE (33.5231). The result demonstrated Frost filter as the best filter for retaining and/or enhancing the backscatter signal in Sentinel-1 data to be used in vegetation bio-physical modelling.

Water is the resource and determinant factor that determines the performance of the agricultural sector, although the role is very strategic, the water management is still far from expected so that the water that should be a farmer's turn turned into a cause of disaster for farmers. Small farm reservoir is built to accommodate excess rainwater during the rainy season. The water collected is then used as a source of supplementary irrigation for the cultivation of high-value economic commodities in the dry season. This research aims to survey, inventory, and study the potential of small farm reservoir development, and to plan potential site locations to be developed into small farm reservoir based on the results of analysis of physical and socio-economic data and drought potential using advanced remote sensing technology in Jombang Regency. Research planning of small farm reservoir location in Jombang Regency is designed in four stages of activity that is data gathering, mapping, and compilation of database, analysis, planning of location, and recommendation of the small farm reservoir location. The results of this research state that the appropriate area built small farm reservoir based on the parameters used are Ngoro, Mojoagung, Kesamben, and Kabuh village.

Image segmentation is the most important stage on Geographic Object Based Image Analysis (GEOBIA). The result of segmentation affects the final accuracy of classification. One of the applications of image segmentation operations is to delineate vegetation objects. Further analysis of vegetation could be used for inventory of natural resources, agricultural, land cover, land use, etc. However, applying image segmentation for separating vegetation types is challenging due to their irregular shapes and various patterns and colors. This study aims to determine the optimum parameters of image segmentation for delineating vegetation types using a pan-sharpened WorldView-2 image (0.5 m pixel size) which was acquired on August 2018. Combinations of scale parameter and composition of homogeneity criterion (shape and compactness) were systematically simulated to obtain the best segmentation parameters. The result of segmentation was assessed quantitatively based on visually interpreted image map as a reference. This study found that application of shape and compactness simultaneously for vegetation extraction would produce rough segmentation result. The optimum parameters for segmenting vegetation types using WorldView-2 were using scale parameter of 5, shape of 0 and compactness of 0.5.

Remote sensing can present the latest land cover information in an efficient manner, but the land cover mapping that has been widely carried out still uses optical imagery. Indonesia is a tropical country, so it is likely to be disrupted by cloud cover throughout the year. To solve this problem can use radar imagery. Radar images use microwaves that can penetrate clouds. But behind these advantages, radar images have noise in the form of black and white spots or salt and papper which can affect the results of processing when done on pixel basis. Therefore, it is necessary to extract information on radar images that do not only consider pixel values, namely object-based classification. This study aims to determine the best segmentation to map land cover. The second objective is to know the accuracy value of the segment produced. This research was conducted using a radar image, namely Citra Sentinel-1A with 10mx10m resolution. The segmentation process carried out using a multiresolution segmentation algorithm. Based on the results of the study, the best segmentation has an input channel parameter weight of 1, 0.5, 1, output parameter weight 25, shape parameter weight 0.3 and compactness parameter weight 0.9. The value of segmentation accuracy produced by considering five parameters in the shape of oversegmentation (OSeg), undersegmentation (USeg), root mean square error (D), area fit index (AFI), and quality rate (Qr) is 57%. Low accuracy value because radar images focus on object morphology in the shape of altitude and surface conditions. Whereas in a land cover the object's morphology can vary and surface roughness can vary.

Geometric correction is an important step in image pre-processing, because it determines the the positional accuracy of the data. However, the geometric correction also includes pixel values interpolation in their new position, so that it may change original values. This study objectives were (a) to provide information on the effect of geometric correction models on the accuracy of land-cover classification, especially using per-pixel classification with maximum likelihood algorithm; and (b) to assess the effect of image resampling methods on the accuracy of the multispectral classification results. This study made use of Landsat 8 OLI Level 1G imagery covering Kulon Progo Area, Yogyakarta, so that several ground control points (GCPs) were needed to suppress geometric errors. Non-systematic geometric correction was undertaken using first, second and third order polynomial transformations. After that, several resampling processes were applied to the geometrically corrected image, i.e. Nearest Neighbour, Bilinear and Cubic Convolution interpolations. It was found that the affine transformation using six GCPs distributed over the edges of the image, delivered an RMSE value of 0.355539. In addition, the second order polynomial with 10 GCPs scattered around the edges of the image gave an RMSE value of 0.178053. While the third order polynomial transformation with 17 GCPs that were evenly distributed in the image produced an RMSE value of 0.100343. The resampling process produced new images with new pixel values, which were then tested with respect to their classification accuracies based on maximum likelihood algorithm. Samples for accuracy assessment were taken using stratified random sampling strategy. Samples were taken in terms of polygons whose size was determined by considering the pixels’ displacement as the results of geometric corrections. This study also found that resampling with nearest neighbour interpolation using third order polynomial equation produced the best overall accuracy of 75.46%, with a Kappa of 0.7032.

Multispectral classification is one of the main methods in the analysis and processing of digital remotely sensed imagery, which until now is still widely used to generate land-cover/ land-use information. Technically, pixel-based classification methods rely on conventional approaches, as compared to GeoBIA, and it can be implemented using either supervised or unsupervised methods. The classification methods are supported by the rapid development of various image processing software, which provide a wide variety of algorithm options, so that the classification process can be carried out easily. Although relatively simple, an appropriate selection of multispectral classification algorithm may provide highly accurate land-cover maps. However, the highly accurate land-cover/land-use maps may also be influenced by image types and classification schemes that are used in the study. This study aimed to compare the results of the multispectral classification using maximum likelihood algorithm, for generating land-cover maps based on Landsat-8 OLI images (30 meters) and Pleiades imagery (2 meters). The classification referred to two different classification schemes relating to spectral and spatial dimensions. The results showed that the multispectral classification with spectral-related classification scheme applied to Pleiades imagery gave higher overall accuracy as compared to that of Landsat-8 OLI. It was also found that the highest overall accuracy achieved in this study was 81.7%, obtained using Pleiades imagery and referring to spectral dimension classification scheme. On the other hand, the lowest overall accuracy was obtained by the same imagery applied using spatial-related dimension. The relatively similar values of low overall accuracy for spatial-related dimension was also gained by Landsat-8 OLI imagery, proving that multispectral classification does not work well for spatial-related land cover classification scheme.

Development of spatial databases for systematic thematic mapping is a relatively complex activity, as compared to mapping a small area with arbitrary boundary. This research was conducted in the provinces of Riau Islands, Bangka Belitung Islands, Jambi, and Bengkulu, southern Sumatera. The stages of spatial databases development involved remote sensing, GIS, and cartographic activities. A synoptic overview of the landscape was carried out prior to the spatial database development. The landscape complexity was assessed using landscape-ecological approach, which was implemented in the delineation and classification. The remote sensing process started from spatial data collection of various images with various spatial resolutions, image pre-processing, followed by image analysis and interpretation. Pan-sharpened Landsat-8 images (15 meters) were used as main data, supported by SPOT 6/7 imagery (6 meters), Sentinel-2A imagery (10 meters) and DEMNAS digital elevation model (8 meters) for particular areas. This stage gave consequences to the multi-scale analysis in the process of land-cover delineation. The GIS process comprised the stages of compiling the interpretation results to form a seamless mosaic, topology construction, coding into Indonesian Geographic Element Catalog (KUGI) standard, metadata development, followed by topology checking. Those processes aimed to achieve a single map of Sumatera at 1: 50,000 scale. The cartographic layout design was the final stage of the spatial database development, where the land-cover classes symbol was also carried out in accordance with the established standards. Some problems and solutions in the whole processes were also discussed in this paper.