This PDF file contains the front matter associated with SPIE Proceedings Volume 12082 including the Title Page, Copyright information, and Table of Contents.

Currently, the Geospatial Information Agency of Indonesia has provided a relatively complete Base Maps RBI (Rupa Bumi Indonesia) on the map scale of 1/25,000 to 1/50,000. This Base maps data that can be downloaded for free via the https://tanahair.indonesia.go.id/. However, some of the RBI Maps were produced in 2001. Therefore, it is necessary to update the Maps. The existence of access to medium-resolution image data such as the Sentinel-2 satellite imagery can be a low-cost solution. However, the Sentinel-2 imagery should be enhanced for better visual interpretation. This research tries to sharpen Sentinel-2 imagery. The results can be used to update the existing themes in the RBI Map. This study takes the case of the Sleman district in the province of Yogyakarta area which is developing quite rapidly. The Sentinel-2 level 2A data from 2 epochs was used for sharpening. The process of sharpening is carried out with the concept of upscaling and superposition operator using two-epoch images. The procedure has succeeded in sharpening imagery for visible interpretation of selected band. After sharpening imagery, the thematic object's shape can be seen clearly so that visual interpretation is more reliable to use. Furthermore, band combination operators and the color composites formation are carried out to enhance the selected thematic objects more clearly. The process of updating the RBI map is done by visual interpretation. The road network theme data on the RBI map is used as a geometric and updated reference. So that the updating process is carried out in one block area that does not cross the road line. Sharpening of Sentinel-2 imagery has opened up opportunities to apply visual interpretation to update thematic objects on RBI maps at a 1/25,000 scale.

Topographic Correction (TC) is one of the essential pre-processing methods to reduce the topographic effect in remote sensing data. It is one of the main factors affecting the reflectance value of objects in remote sensing imagery in the rugged topographic area and contributes to quantitative analysis. High-resolution satellite imagery also requires high spatial resolution of the Digital Elevation Model (DEM) as an important requirement in applying TC methods. This study evaluated the performance of five different TC algorithms (i.e., Statistical-Empirical (SE), C-Correction, Minnaert, Gamma, and Sun Canopy Sensor+C (SCS+C) over four hilly to the undulating area with different land-cover characteristics on SPOT-6/7 Multispectral imagery in Sulawesi Island and used the nation-wide DEMNAS as DEM. Visual and statistical evaluations were used to examine the surface reflectance value before and after correction by calculating linear regression and Pearson Correlation (R) between illumination (IL) and reflectance value, and the difference between mean reflectance value of lit and shadowed for vegetated slope. The results showed that the Minnaert, SCS+C, and C-Correction, perform better than other methods. However, Minnaert and SCS+C statistically and visually performed better in all topographic conditions, and C-Correction showed moderate performance. The Gamma method tends to be under-correction but is visually suitable for favorable topographic conditions and poorly illuminated areas in shaded slopes area. In contrast, SE tends to overcorrect all SPOT-6/7.

The multi-sensor approach has been widely implemented for earth monitoring in many applications to enable regular space-based information. Spectral comparison between sensors is an essential preliminary step before conducting a data fusion for such applications. Comparison of medium spatial resolution data, e.g., Landsat-8 and Sentinel-2 were mostly conducted in subtropical regions, nevertheless, limited in equatorial tropical environments. This study utilised the same day of Landsat-8 and Sentinel-2 data to investigate the spectral changes over relatively unchanged environment conditions during 3-10 minutes of differences acquisitions time of Landsat-8 and Sentinel-2. The study area was the Citarum River Basin area in Indonesia with monsoonal climate type and various landcover, e.g., Lake, agricultural area, rain forest, and settlement areas. Surface reflectance of visible, Near Infrared, Shortwave Infrared and spectral indices, i.e., Enhanced Vegetation Index (EVI), Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI) generated from a valid pair of pixels, which free from cloud, cloud-shadow, saturated, dark area and cirrus of Landsat-8 and Sentinel-2 images, were compared. Ordinary Least Square and t-test for mean difference analysis were accomplished to perform the relationship and the spectral changes of surface reflectance and spectral indices of Landsat-8 and Sentinel-2. The results showed a significant difference in mean values of surface reflectance and spectral indices of Landsat-8 and Sentinel-2 with a higher value of R² in the dry season (July) than in the rainy season (January). SWIR performed the highest relationships among the other spectra with R² of 0.68 in the wet season and R² of 0.8 in the dry season. However, spectral indices comparison is likely performed well over the dry and wet seasons. A single band of surface reflectance provided a lower relationship (R² < 0.8) than from spectral indices (R² > 0.8). NDWI provided best relationships on water pixels while EVI and NDVI well performed on soil and vegetation pixels, respectively. We found a lower relationships of surface reflectance between Landsat-8 and Sentinel-2 in tropical environments than were reported in subtropical regions. The highest relationship of this spectral comparison was found on water pixels, then decreased on soil pixels and lastly on vegetation pixels. Higher variability of tropical atmospheric conditions and mixed pixels issue may influence this lower relationship, particularly during the rainy season. An improvement of cloud masking product in tropical environment can be investigated further to provide a better relationship of this comparison for further research.

Land Surface Temperature (LST) is an important factor in geophysical parameters such as hydrological modeling, soil moisture, monitoring crop, etc. LST data with detailed resolution and the large-scale area is very helpful data in many research fields. Satellite imagery with thermal infrared sensors can be used to produce LST using a retrieval algorithm. Currently, Landsat 8 with TIRS sensor is freely available thermal infrared bands with the highest spatial resolution (resampled from 100m to 30m). Based on that situation, this study aims to build a model from optical bands of Landsat-8 as the input data and LST from Landsat-8 as the target data using Deep Neural Network regression (DNNr) architecture and then applied to Sentinel-2 to get LST at 10m resolution. The main difference of DNNr architecture with DNN for classification is we use linear activation function in the output layer. The study area is located in Yilan County, Taiwan. The input data from Landsat-8 and Sentinel-2 are optical bands (Blue, Green, Red, NIR), NDVI, and emissivity from NDVI. Both the input data have been standardized using the standardscaler function before feeding into the model. The input data were separated as 70% for training, 20% for validation, and the other 10% as testing data. We use air temperature data to calculate indirect validation with LST from Sentinel-2. The result shows, the mean absolute error and mean squared of testing data from DNNr are 0.581oC and 0.766oC. The correlation and maximum difference of air temperature with LST Sentinel-2 from DNNr are 0.92 and 2.94oC. Based on the experiments, our DNNr achieved a more good result than other regression architecture. Our DNNr architecture has been tested in other areas and also shows acceptable result. Based on that results, our LST product at 10m resolution can be used in others research fields.

Monitoring the growth of built-up land finds its challenge as a never-ending mapping process. Since the built-up maps are taken into account in development planning and measuring the achievement of SDGs objectives (Goal 11 - indicator 11.3.1), the best mapping method should be attempted. Therefore, all efforts were worked on to speed up the mapping process without compromising accuracy. Various methods have been proposed, but numerous difficulties remain in accurate and efficient built-up and settlement extraction. With the combination of passive and active sensors images, we try several machine learning methods using Google Earth Engine (GEE) platforms to map built-up land and settlements in Purwokerto, Banyumas, Central Java. Around 369 samples were occupied to distinguish four classes of land covers: settlements, built-up land, waters, and others. The decision tree-based algorithms give the best performances, scilicet Random Forest (RF) and Gradient Tree Boost (GTB). Random forest is a collection of many decision trees, while Gradient Boosting is a machine learning algorithm that uses an ensemble of decision trees to predict values. Thus, the algorithms can handle complex patterns and data when linear models cannot. On the whole, RF and GTB classifiers can distinguish between settlements and non-settlement with an overall accuracy of 80%. The Support Vector Machine (SVM) classifier produces 71.43% accuracy with Kappa = 0.61, and the Minimum Distance (Mahalanobis) classifier gain overall accuracy of 74.29% (Kappa = 0.64).

Paddy fields are complex land-use entities with various surface covers depending on the timing of the planting stages. Therefore, the best practice to map paddy fields using remote sensing has benefited from the availability of multi-temporal data which were used to characterize the phenology related to the paddy fields. However, this practice may require more RS data to be obtained and processed. Other mapping methods by capitalizing the spatial configuration, such as image segmentation in Object-Based Image Analysis (OBIA) and object recognition in Deep Learning using Convolutional- Neural Network (CNN) architecture has been used in the mapping application. This study aims to assess the accuracy from using mean-shift image segmentation and Random Forests and Extreme Gradient Boosting as the classifiers, with the accuracy from simple CNN architecture, by using Worldview-3 (WV3) full-spectrum image (16 bands). The image segmentation and deep learning analysis were conducted by using 16-bands from the WV3 image and classified by using RF and XGB, and CNN. The results showed that RF was able to identify the paddy fields with an accuracy of 88.09 % (User’s accuracy (UA)) and 81.61 % (Producer’s accuracy(PA)), while XGB produced an accuracy of 85.71 % (User’s accuracy (UA)) and 82.44 % (Producer’s accuracy (PA)), respectively. While CNN produced the accuracies of 49.5 % (PA), 96.3 % (UA) and 82.9 % (OA). The lower producer’s accuracy indicated the higher omission error where more paddy fields were classified as non-paddy fields. CNN produced promising accuracy results for identifying paddy field tiles with 82.9 % accuracy without using data augmentation, although it will be needed to increase the accuracy and more complex CNN architecture such as U-net is needed to determine the boundary of the mapped objects.

Investigation of possible variations of the combination of VV and VH polarization available for SENTINEL-1 Synthetic Aperture Radar (SAR) imagery has been conducted for plantation detection. The dual-polarization (VV and VH) Interferometric Wide Swath (IW) along descending orbit over Tesso Nilo Ecosystem collected between 1st to 25th March 2021 stored inside Google Earth Engine dataset catalog is used for this objective. Thus initial preprocessing is excluded. The Random Forest method is implemented for two purposes, (1) to identify variable importance of variable used in the classification process, (1) the supervised pixel-based classification. Training samples used in the classification process were collected from visual interpretation of SENTINEL-1 composite image whereas the validation sample is obtained from the Google earth high-resolution imagery. The result shows that variable (VV/VH), (VV-VH), and RVI has the highest degree of importance for oil palm, pulpwood, and forest detection respectively. There is a pattern where the derivative variables of the VV and VH polarization have a high degree of importance. The same pattern appears in the classification results, where scenario 13 ((VV-VH), (VV/VH), ((VV+VH)/2), RVI) has the highest overall accuracy value of 91.74%. Scenario 13 produces the user accuracy of 94.22%, 93.89%, 81.82%, and 95.45% for oil palm, pulpwood, forest, and other land use respectively. The scenario also produces a high producer accuracy of 93.86%, 93.89%, 81.82%, and 97.67%. The combination of available polarization derivative variables with SAR data capabilities can be utilized to build wide-scale plantation monitoring and management systems.

The 2019 Indian Ocean Dipole (IOD) event has impacted Indonesia in many ways. The extreme weather events due to the changes in rainfall patterns and increased average temperature were causing severe agricultural drought in some areas, including Bekasi Regency. Monitoring agricultural drought is challenging due to the nature and extent of the damage caused by the event. This study aims to identify some of the agricultural drought events in Bekasi based on soil moisture features (SMI). The approximate agricultural drought model was generated from Normalized Difference Drought Index (NDDI), while soil moisture information was derived from the Soil Moisture Index (SMI). Landsat 8 OLI-TIRS was utilized for generating both indexes. The analysis was carried out in the dry months of 2019, including July, August, September, and October, where the lowest rainfalls were found. The study founds that more than 50% of the area was damaged by severe drought every month. Most of the severe drought occurred in September, damaging 50,919.32 hectares (91%) of rice fields. Statistical-based Pearson’s correlation shows a significant relationship between NDDI and SMI, with R coefficient ranges from -0.37 to -0.74, especially from July to October. Conclusively, both indexes were successfully applied to picture agricultural drought phenomena in Bekasi Regency.

Drought explains a stage of water scarcity at a particular time in a certain location. In land, drought has impacts in many ways. Crop failure or “puso” will occur from a short-term drought, while economic disruption might happen in a long-term drought period. Several parts of Indonesia were periodically impacted by drought during the dry season, including Indramayu Regency. As one of the largest rice barns in West Java, 63% of Indramayu is covered by the rice field. Therefore, monitoring agricultural drought in such locations is challenging due to the extensive damage caused by the event. This study aims to analyze the spatial and temporal distribution of drought in Indramayu’s rice fields. This study utilizes the Normalized Difference Drought Index (NDDI) generated from Landsat 8 OLI-TIRS to identify drought locations. Observation is carried out during the dry months of July, August, September, and October. Most severe drought areas were found in October, especially in three sub-districts, including Gantar, Kroya, and Haurgeulis. Statistical-based correlation analysis shows a significant relationship between NDDI and distance from the irrigation network, although a relatively stronger correlation was found between NDDI and crop productivity. Conclusively, NDDI was successfully applied to identify drought areas in Indramayu Regency.

Updating information on rice fields is very important to pay attention to environmental quality and food security. This is related to Indonesia's commitment to achieving Sustainable Development Goal number two in terms of agricultural data collection and analysis. Remote sensing can be used as an alternative method for identifying and mapping land cover, for instance paddy fields. Land cover in paddy fields varies greatly according to paddy growth phase, wherein these growth phase can be shown by different spectral reflectance values in remote sensing imagery. Mapping of paddy fields based on their spectral reflectance began to be widely carried out in Indonesia. Therefore, the aims of this study were to determine the spectral reflectance pattern of temporal paddy growth phase then form a map of the paddy fields based on those spectral libraries. This study used Spectral Angle Mapper (SAM) method to identify paddy fields on Landsat-8 OLI determined from spectral reflectance pattern of paddy-growth phase in some areas of Subang and Indramayu Regency in one growing season. The results succeeded in classifying paddy fields and non-paddy fields area. Classified paddy fields consisted of several land covers comprising the bare-land, inundation-land, vegetative, generative, and ripening. The accuracy test showed an overall accuracy of 70.07%. Misclassification in this study occurred due to the existence of thin cloud cover, besides there was a misclassification between built-up area and the bare land.

Agricultural land conversion is considered the most important type of land use change, particularly in the countries where agriculture is the primary source of income, including Indonesia. The government of Republik Indonesia has issued a number of programs for preserving agricultural land and controlling the conversion rate. One of the programs is known as sustainable agricultural/cropland. To support the program, any information regarding agricultural land conversion is indispensable. The objective of the study is to provide spatial information called the sensitivity of agricultural land. The study was conducted in the urban area of Yogyakarta. As urban growth or expansion mainly changes land use, agricultural land in this area is highly vulnerable to conversion. The sensitivity of agricultural land was analyzed and mapped using a combination of spatial and statistical analysis. Multitemporal land use maps ,i.e., 2000 and 2020 previously derived from remotely sensed imagery, were used as the primary data for the analysis. Logistic regression was used to analyze the conversion probability and determine the sensitivity index. This study shows that about 650,1 hectares of agricultural land had been converted between 2000 and 2020. It left around 1364,55 hectares of agricultural land in 2020. The remaining agricultural lands were classified into three categories regarding their sensitivity, i.e., high, moderate and low. The proportion of area for each category is 7.5%, 42.7% and 49.8%, respectively. Regarding agricultural land preservation, authorized agencies could utilize this information to determine the preservation priority.

Weather radar waves experience attenuation due to meteorological objects in their path or referred to as wave attenuation. Attenuation can cause a large enough error to reflect the weather radar as input data for the calculation of QPE through the Z-R relation which eventually also causes an error in the QPE value. Attenuation can occur along the wave propagation path that contains meteorological objects, so that the farther from the weather radar location, the higher the potential for QPE error caused as the consequence of unapplied attenuation correction. Weather radar applies a local Z-R relation model where the backscatter of meteorological objects received by the radar is processed into a reflectivity value (Z) and then processed through an empirical equation to produce an estimated rainfall (R). There has been no research on attenuation correction using the Z-R relation model which was developed in Indonesia to determine the effect of attenuation correction from QPE in Sidoarjo weather radar. This research is intended to apply the attenuation correction preprocessing based on Kraemer Verworn preprocessing algorithm and Z-R relation model processing algorithm, Arida et al., developed in Indonesia for the QPE calculation considering the radius from the weather radar location. QPE that is incorrected for attenuation will be used as a comparison in this study to determine the differences that occur before and after the attenuation correction is applied. Furthermore, validation is carried out with rainfall observations from rain gauges through correlation analysis and RMSE. The results show that rainfall without attenuation correction tends to be of lower value, especially at greater distances from the weather radar. A higher correlation with a lower RMSE is generated by QPE with attenuation-corrected raw data input and at the relatively close distance to the weather radar.

Whirlwinds are a meteorological disaster that often strikes Indonesia's territory, including the Special Region of Yogyakarta on Java Island. The formation of this small-diameter vortex of rapidly spiraling air depends on the presence of convective clouds, particularly Cumulonimbus (Cb). This research was intended to analyze the spatiotemporal distribution of convective clouds in the region according to whirlwinds recorded in 2019. The research used historical data consisting of the dates and locations of whirlwinds throughout 2019 to determine their spatial distribution. For this purpose, Himawari-8 images with bands 13 (IR1) and 15 (IR2) captured on said dates were processed using the split windows technique in SATAID software. Other data like daily synoptic weather, landscape, topography, and land use were also analyzed to provide atmospheric and land conditions surrounding the weather events. The analysis results showed convective clouds, especially Cumulonimbus (Cb), were present on every date of the event with various distribution and massiveness and always appeared preceding the whirlwinds. On March 17, 2019, the most massive convective cloud coincided with three whirlwinds striking Sleman, Bantul, and Kulon Progo. Tropical cyclone Savannah that hit the southern part of Java Island, including the study area, created atmospheric anomalies responsible for this cloud formation. Low air pressure, high humidity, rainfall, level topography, and land used for settlements and rice fields are concluded as contributing to the convective cloud and whirlwind formation in the region.

Extreme weather occurs when meteorological conditions in a specific location are unusually lower or higher than the average pattern, for example, tropical cyclones and extreme rain events. The southern margin of Yogyakarta, a province in Indonesia, abuts the Indian Ocean as one of the active tropical cyclone basins on Earth. Therefore, tropical cyclones potentially affect the rainfall characteristics in the region. This study aimed to ascertain the threshold values and frequencies of extreme rainfall and the impact of tropical cyclones on daily rainfall in Yogyakarta. It used 30-year rainfall data (1991‒2020) from the Climate Hazards Group InfraRed Precipitation with Station (CHIRPS) and three methods for determining extremes: the percentile method and two fixed thresholds set by Indonesia’s Meteorological, Climatological, and Geophysical Agency (BMKG) and World Meteorological Organization (WMO). Details on tropical cyclone occurrences were obtained from Australia’s Bureau of Meteorology. The results showed that the 99th percentile made the nearest threshold values to the fixed ones set for extreme rainfall by WMO and heavy rain by BMKG (R50mm). Extreme rainfall occurred in the west more often than in other parts of the province. In 1991‒2020, four tropical cyclones entered the Yogyakarta area, including Tropical Cyclone Cempaka on November 28, 2017, which induced inordinately high rainfall that exceeded the percentile threshold and the WMO’s fixed threshold of 50 mm (R50mm) for extremes and fell in the category Heavy Rainfall by the BMKG standard.

Coral reefs is are an important community in coastal and marine ecosystems. Currently, they are under high environmental pressures and suffer damages from human activities and increased sea surface temperature, narrowing the live coral cover. This study aimed to assess the mapping accuracy of the live and dead coral covers using PlanetScope satellite images around Mandangin Island, Madura, Indonesia. Minimum Noise Fraction (MNF) was applied to the bands corrected for the effect of energy attenuation by the water column using the Depth Invariant Bottom Index method, and Random Forest (RF) algorithm was used for mapping. The classification results showed five classes of benthic habitat 2021, namely live coral, dead coral, rubble, seagrass, and sand. Using the confusion matrix, it was found that the live and dead coral cover models had 72.5% accuracy. The mean live coral and dead coral covers were 18.87% and 36.40%, respectively.

As a marine protected area, the water quality of Wangi-wangi Island, located in South East Sulawesi, should be monitored regularly to detect potential eutrophication. However, in situ water quality monitoring is expensive and unpractical due to its remote area. The Landsat 8 satellite is able to retrieve water quality information, such as chlorophyll-a and total suspended matter (TSM) over period of times using several neuronal nets of Coast 2 Regional CoastColor / C2RCC algorithm. The C2RCC is a neural network algorithm, trained on simulated top of atmosphere reflectance. There are 2 sets of neuronal nets on C2RCC algorithm which is C2RCC-Nets and C2X-Nets. The aim of this study is to obtain the best neuronal nets of C2RCC algorithm to retrieve chlorophyll-a and TSM from Landsat 8 image over Wangi-wangi island. The in situ measurement of chlorophyll-a and TSM were measured during satellite pass of Landsat 8. Four scenes of Landsat 8 data overpass of Wangi-wangi Island in 2016 were selected in this study. The result showed that the C2RCCnets is more accurate to retrieve chlorophyll-a information with R²=0.0747, RMSE = 0.1046 mg/m³ and MAE = 0.0834 mg/m³. The C2RCC-nets showed good performance to retrieve TSM information with R²=0.1586, RMSE = 0.4327 g/m³ and MAE = 0.4258 g/m³. During our study timeframe, the trophic status of Wangi-wangi Island was oligotrophic with no signs of eutrophication.

Sardinella lemuru is important species and only exists in Bali Strait. In recent years (1980-2019), the fishing condition in Bali Strait is highly volatile. This condition makes lemuru catching locations are challenging to predict. According to catch data, Sardinella lemuru has collapsed and dramatically vanished in 1999, 2011, and 2017. Here, we examined the collapse was due to the impact of IOD or ENSO. According to wavelet coherence analysis, there was a strong association between IOD or ENSO phenomenon with decrease or increase in lemuru catches occurred in 2007-2012 with power 0.6 for IOD and 0.5 for ENSO. After those years, the coherence shows a weak association. The result of wavelet coherency in this study found that the incidence of lemuru collapse in the years 2011 and 2017 was caused by different factors. In 2011 decrease of lemuru was strongly coherent with the occurrence of positive IOD and La Nina events, but in 2017 lemuru decreased was not coherent with IOD (positive/negative) or ENSO (El Nino/La Nina) event.

Ecological sensitivity can explain spatial ecological conditions that have the potential to face disturbances and stresses. Spatial ecological sensivity assessment helps to understand the relationship between ecological component in specific spatial scale. The level of ecological sensitivity depends on spatial regional characteristics. Banyumas regency are the area with Slamet mountainous-type National Parks, which is a priority area for conservation and relatively sensitive to various disturbances. Eruption hazard zone, elevation, slope, distance from main river, vegetation cover, land surface temperature, and built-up densitiy are components that considered in ecological sensitivity assessments. This research aims to assess the level of spatial ecological sensitivity based on ecological sensitivity index (ESI) for areas with vulnerability to volcanic eruptions. The mathematical measurement model of the ESI system is combined with geographic information system (GIS) technology to generate location attributes. Spatial data is obtained through remote sensing digital image processing that highlight synoptic aspects in understanding region heterogeneity. Each parameters have different influence on ecological sensitivity levels, that are represented by the priority weight determined by analytical hierarchy process (AHP) techniques. There are four classes of ecological sensitivity in Banyumas: very low (51.3 km2 ), low (900.6 km2 ), moderate (325.6 km2 ), and high sensitivity (108.97 km2 ). The distribution pattern of ESI values shows that poor sensitivity conditions are surrounded by areas with improved sensitivity due to the spatial distribution of slope and elevation conditions. The ESI spatial pattern can be used as a reference for policy making to maintain regional functions.

Jakarta is the government and economic capital of Indonesia with a consistently increasing population, resulting in high demand for infrastructure development, which has several socio-economic benefits. However, the environmental issues related to an increasing population and built-up area have come to the public’s attention in recent years. This research analyses the effects of land use and land cover changes (LULCC) on land surface temperature (LST), the development of urban heat island (UHI), and weather conditions from 2000-2019. Focusing the analysis on remote sensing data, satellite images at two time points (2000 and 2019) were used to investigate the LULCC and its impact on UHI development that were associated with meteorological data from ground-based stations. The normalized difference vegetation index (NDVI) and normalized difference built-up index (NDBI) were used to describe the spatial distribution evolution of LULCC along with the urban thermal field variance index (UTFVI) that illustrates the potential impacts of UHI on the quality of life in the urban area. Supervised classification was employed to describe LULCC together with the accuracy of the classification result. UHI areas were extended to the southern and eastern part of Jakarta during the time with an increment of 13%, followed by increasing urban heat island index over a selected urban and rural area. Meanwhile, the decreased number of waters, vegetation, and agriculture area was observed during 2000-2019, followed by the increased number of residence and industry areas. The overall results indicate LULCC plays a critical role in defining the change of LST and meteorological conditions.

Batam, one of the largest cities in Kepulauan Riau Province, Indonesia, has been established as a free-trade zone (FTZ). It is undergoing rapid development and is thus characterized by built-up areas expanding every year. This condition is due to the increasing demand for space and leads to conflicts of needs for land use and incompatibility with the allocation plans. The land cover maps of Batam City in 2000 and 2015 were obtained from the Landsat 7 ETM+ and Landsat 8 OLI image interpretation and classification, which used maximum likelihood. These maps were also checked with high-resolution imagery (Google Earth image) to produce a confusion matrix to validate or test their accuracy. Land Change Modeler (LCM) was an instrument used to determine changes in land cover from 2000 to 2015. Based on land cover change from 2000 to 2015, the results showed a total increase of 2,401.65 hectares or 43.48% in the built-up area. From 2000 to 2015, it was persistently expanding toward the city’s outskirts. The confusion matrixes revealed that the land cover maps in 2000 and 2015 had an overall accuracy of 95.5% and 96.3%, respectively.

Visible Infrared Imaging Radiometer Suite (VIIRS) instruments produce nighttime light (NTL) images showing artificial light emissions, which are closely related to human existence as an indicator of built-up areas, especially settlements. This study was designed to determine the capability of NTL data to estimate population based on its correlation with the intensity of artificial light emission and lit area by conducting multivariate linear regression analysis using Python in Google Colaboratory. The research area consisted of regencies/cities on Java Island, home to the largest population in Indonesia, that had different rates of development. The samples were city/regency population data divided randomly with a 7:3 ratio into training and testing samples. The model was created using a training samples with correlation coefficients of 0.857 for 2015, 0.855 for 2017, and 0.852 for 2019 and then validated by calculating the percent error (% error) between the estimated and actual populations using the testing samples. The results showed an average of 1.44% error, and from this high accuracy indicator, the study concludes that NTL can be used to estimate the population. However, this estimate only serves as an overview because the model was developed based on small-scale cases, resulting in less detailed outcomes.

Expansion of built-up land can lead to changes in land use and land cover (LULC), especially in urban areas. Expansion of built up land can occur due to an increase in population and the level of urbanization. Urbanization and changes in LULC have led to a substantial increase in the urban population due to migration, which has brought many acute problems in the urban environmental system. This study aims to map LULC conditions in 2008, 2013, and 2018 and to analyze the spatial pattern of urban growth from 2008 to 2018. The maximum likelihood method was used to classify LULC based on Landsat imagery and to analyze urban growth spatial patterns. Spatial analysis was carried out by dividing the four locations into 4 (four) quadrants adjusted to the cardinal directions. From the analysis conducted, the accuracy of the research in 2008 was 88%, in 2013 was 86%, and in 2018 was 88%. From this study, the urban growth of the city in Purwokerto spreads in all directions, but urban growth is more dominated in quadrant I or the northeast direction. Quadrant 1 has a strong pull from Purbalingga Regency and is traversed by the main connecting road between districts and provinces to the north and east so that it can cause expansion of built-up land in quadrant 1. The results of this study can be used as recommendations in making urban spatial plans in Purwokerto City.

Gerbangkertosusila (Gresik-Bangkalan-Mojokerto-Surabaya-Sidoarjo-Lamongan) is one of the biggest metropolitan areas in Indonesia impacted hardest by COVID-19 after social restriction. High temperature conditions are an issue in the Gerbangkertosusila area. Reduced mobility and industrial activity lead to decrease in surface temperature. The research was carried out using the Statistical Mono Windows (SMW) algorithm in separate periods of time (July 2019, July 2020, October 2020, May 2021) to represent the changes between social restriction policy and the weather. This research goal is to examine the relationship between land surface temperature with changes of spectral indices, such as NDVI (Normalized Difference Vegetation Index) and NDBI (Normalized Difference Built-up Index) data. These three parameters are correlated with a simple linear regression equation to calculate how much influence occurs in each different period, then the qualitative analysis is carried out to explain the variations between the distribution of hotspot and annual temperature chart to the real conditions. The result shows strong positive correlation coefficient between changes of NDBI pixel and the LST in each period of time such as 0.62; 0.80; 0.70; and 0.80. Meanwhile the NDVI-LST correlation coefficient shows negative results such as -0.57; -0.43; -0.38; -0.41. This research also concludes that in the social restriction period, the Land Surface Temperature doesn't affect the variability of NDVI

Mangroves store significant carbon content that, when managed properly, will contribute to combating the climate crisis. Despite having the largest mangrove forest, Indonesia’s mangrove annual damage rate turns out to be the highest globally, one of the most significant factors is extensive plastic waste exposure, exacerbated by mangroves’ deforestation for conversion into agricultural land. Many efforts initiated by the government and other stakeholders have been targeting mangrove rehabilitation and plastic waste abatement. Labor-intensive and time-consuming ground checking have been the main source of information to determine priority areas for mangrove rehabilitation so far. This study aims to introduce a more effective and efficient identification of priority areas for rehabilitation. The study utilizes vulnerability index by optimizing remote sensing satellite data modeling. The study covers all mangroves in Indonesia, and for the purpose of this study, four mangrove vulnerability classes are formed to help categorize the severity of the damage. The classes are formed through integration, scoring, and classifying plant health, water turbidity, land temperature, plant carbon sequestration capability, and plastic waste distribution in Indonesian coastal area data. The modeling demonstrates its ability to distinguish the classes through machine learning. This study identifies that 65.74% of Indonesia’s coastal mangroves are highly exposed to plastic waste. Bali and Surabaya are two of the most severely damaged areas. This study, along with further analysis of socio-cultural, economic, and development priorities, will enable decision-makers to prioritize and mobilize necessary resources to rehabilitate the mangroves guided by a suitable mangrove management regime for each class.

Samarinda is one of the big cities on Kalimantan Island, which is also the capital of East Kalimantan, Indonesia. The proximity with the prospective new Indonesia State Capital, Penajam Paser Utara, and increasingly massive urban activities can potentially increase considerable changes in green open space in the future. The availability of trees in green open spaces has an important role in mitigating climate change because of its ability to store carbon stock. Therefore, it is necessary to carry out an inventory, monitoring, and evaluate the above ground carbon stock value in Samarinda. This study aims to find a suitable vegetation index for carbon stock estimation, as well as determine the total and spatial distribution of carbon stocks using the best vegetation index in green open space vegetation in Samarinda City using Sentinel-2 imagery. Sentinel-2 L2 MSI imagery was utilized to build a carbon stock model based on field sample calculation. Empirical modeling with the allometric equation was carried out, wherein carbon stocks at points of samples correlate with the index value of each transformation selected by the ability to assess vegetation (DVI, EVI2, GNDVI, NDVI, OSAVI, SARVI, and SARVI). Statistical analysis performed is normality test, correlation analysis with Pearson Product Moment method, and regression using simple linear analysis. The significance test was carried out using the ANOVA Test and Partial T-Test, while the accuracy-test used the Standard error of estimate (SEE) method on independent validation samples. The results showed that the best vegetation indices were GNDVI with the highest coefficient of determination of 0.552. Moreover, the significance test shows that all indices significantly affect the estimation of carbon stocks in Samarinda. The accuracy test shows that GNDVI has a maximum accuracy of 55.816% by an estimated error of 1.775 tons/pixel.

Mangrove ecosystems have a significant role in absorbing carbon emissions in the atmosphere used as photosynthetic materials. Absorbed carbon emission is stored in the form of biomass in vegetation, of which 47% of the biomass is aboveground carbon stock (AGC). Monitoring the AGC needs to be carried out efficiently, consistently, and sustainably to increase efforts to prevent global warming, and remote sensing imagery has the potentials to address this issue. This study aims to (1) explore the relationship between the selected vegetation indices derived from the WorldView-2 image and the corresponding AGC measurement in the field and (2) estimate and map the spatial distribution of AGC of mangroves. This research was conducted in a mangrove forest in Clungup Mangrove Conservation area, East Java Province, Indonesia. We applied allometric equations to determine the value of vegetation biomass in the study area based on the tree diameter at breast height (DBH), which will then be converted into carbon stock value. For AGC estimation and mapping purposes, we used Normalized Difference Vegetation Index (NDVI), Simple Ratio (SR), Rededge Simple Ratio (SRre), and Combined Mangrove Recognition Index (CMRI). We used correlation and regression analysis to evaluate the statistical relationship between these vegetation indices and field AGC data. Our findings suggested that the SR has the highest accuracy in modeling AGC with an R² value of 0.124. Thus, it results in a range of AGC from 0.127 tons/pixel to 0.414 tons/pixel in the study site.

This study aimed to evaluate the effects of atmospheric and topographic corrections on the vegetation density estimates based on vegetation index transformation. The research was conducted in Arjuno-Welirang volcanic complex, East Java, using Landsat 8 OLI imagery at 30 m spatial resolution. The image was corrected at two levels, i.e., atmospheric correction to at-surface reflectance using FLAASH method, and topographic correction using SCS-C method. The topographic correction referred to ALOS PALSAR DSM data, which was resampled at 30 m pixel size. The vegetation indices used includes NDVI, SAVI, ARVI, EVI and MSARVI. Fieldwork for measuring vegetation density was carried out by vertical bottom-up photography of the canopy on each sample, supported by observations of vegetation density using high-spatial resolution Google Earth imagery. The results showed that—in comparison with the atmospheric correction—the topographic correction was able to increase the correlation coefficients between the spectral information and the measured vegetation density in the field, especially for SAVI, EVI and MSARVI transformations. On the other hand, the NDVI and ARVI showed slight decreases. Based on the vegetation density maps generated using regression equations, the SAVI, EVI and MSARVI showed slight increases from atmospheric to topographic corrections, while the NDVI and ARVI showed declines. The rugged terrain condition affected the accuracies of the models due to the difficulty of vegetation density measurement in the field and even distribution of the samples.

Pixel-based classification is considered as a classic method of extracting land-cover related information from remotely sensed imagery, and has been used in various applications, including vegetation mapping. However, several recent studies also mentioned the weakness of the pixel-based approach, including the vegetation index transformation, in mapping the structural composition of vegetation. This study aimed to test several pixel-based classification algorithms for mapping the structural composition of vegetation using Sentinel-2A (10 meters) imagery in Salatiga and its surrounding, Central Java. In this study three classification algorithms, namely Maximum Likelihood, Minimum Distance to Mean, and Support Vector Machine were compared with respect to their accuracy results in mapping the vegetation structural composition. The authors evaluated the effects of additional data in the classification process by comparing two different datasets, i.e. (i) the one using original bands only, and (ii) the one containing original bands and additional data in the form of several vegetation indices and Leaf Area Index (LAI). We collected field samples using stratified random strategy, which were separated into two sub-datasets, as a basis for structural composition classification reference and accuracy assessment. In addition, comparison was also carried out using the original results and the one which was majority filtered. The results showed that the Maximum Likelihood algorithm performed the highest accuracies at a range of 74-86% using a combination of original bands and RVI (Ratio Vegetation Index). The result that was processed using a 5x5 majority filter showed the highest accuracy 86.29%. These results demonstrated that the pixel-based classification of Sentinel 2A imagery using the Maximum Likelihood algorithm could be used to map the structural composition of vegetation in the study area.

The emergence of remote sensing images with high spatial resolution has increased the advancement of image-based information extraction methods. One of the rapidly developing approaches for mapping and analyzing high spatial resolution images is the object-based approach, also known as geographic object-based image analysis (GEOBIA). This development makes it possible to quickly and accurately distinguish between vegetated and non-vegetated objects in vegetation study. This study aims to (1) create a ruleset to discriminate vegetated and non-vegetated objects from a high spatial resolution image, (2) apply the GEOBIA approach to map vegetated and non-vegetated objects, and (3) calculate the accuracy of the mapping results. The GEOBIA approach was applied to a WorldView-2 image (2 m pixel size and eight multispectral bands) of the Clungup Mangrove Conservation area, Malang, East Java, Indonesia. We assessed the ability of all of the WorldView-2 image bands for discriminating the targeted objects. The segmentation process in GEOBIA used a multi-resolution segmentation algorithm using the normalized difference vegetation index (NDVI), and the image classification used a rule-based classification technique. The green, red, and near-infrared bands can effectively distinguish the targeted objects based on the developed ruleset. The classification result shows that the vegetated and non-vegetated classes fall within their corresponding objects on the image. We implemented an area-based accuracy assessment that assesses both positional and thematic accuracy of the mapping result, based on the visual interpretation of the pansharpened WV-2 image (0.5 m pixel size) as a reference for the accuracy assessment. This process results in a 74,06% accuracy, meaning that the combination of GEOBIA and WorldView-2 image produces high accuracy of vegetated and non-vegetated objects map.

The SAR imagery such as Sentinel-1 in general has a major problem with the speckle effects. There are many speckle filtering methods have been developed to reduce the speckle effect. This research aims to test the ability of a number of speckle filtering methods to extract vegetation biophysical information from Sentinel-1. The ground truth of vegetation biophysical information in this research were simulated using Sentinel-2 MSI imagery. That is, Leaf Area Index (LAI), Canopy Water Content (CWC), Canopy Chlorophyll Content (CCC), Fraction of Vegetation Cover (FVC), and Fraction of Absorbed Photosynthetically Active Radiation (FAPAR). The Sentinel-1 imagery was speckle filtered using various methods, namely Lee, Lee Sigma, Refined Lee, IDAN, Boxcar, Frost, Gamma Map, and Median. Some speckle filtering parameters were modified, i.e., the processing windows. The Dual Polarization SAR Vegetation Index (DPSVI) were then extracted from the speckle-filtered Sentinel-1. DPSVI were then tested for correlation with vegetation biophysical information using the Pearson Correlation Coefficient (r). The test results show that Boxcar produces the highest r values for all types of vegetation biophysical information, with values ranging from 0.6s to 0.7s. Followed by Lee, Gamma Map, Median, and Frost. Each with a processing window size of 21x21. Since there are no r values was found which reached 0.8 for processing window sizes up to 21x21, the simulation was then run using the regression method. The simulation results show that to achieve r values of 0.8, it is predicted that window sizes range from 35x35 to 93x93.

Vegetation is the key to the ecological conditions of an area, especially the highlands, which function as protected areas. The mapping of vegetation cover percentage is essential considering that highlands in Indonesia have massive changes, particularly in areas affected by eruptions. This study aims to map the changes in vegetation cover percentage of the area around Mount Agung after the eruption in 2017. Pre-eruption and post-eruption multi-temporal remote sensing data were used to extract the percentage of vegetation cover using an empirical model built from regression of NDVI values and visual observation of vegetation cover percentage based on high-resolution imagery. The estimated error value is 9.67% of pre-eruption condition cover and 14.45% of post-eruption condition cover, used as a threshold value to determine the area and location of percentage changes of vegetation cover. The area of 1.93 km² decreases vegetation cover percentage due to the eruption on the southern and southwest slopes of Mount Agung. The area that has not changed because of the eruption dominates at 24.92 km². The area that experienced increasing vegetation cover percentages was minimal due to vegetation growth during the temporal difference of imagery (5 months).

Floods that occurred in the Citarum watershed are caused by land degradation and environmental damage following the population and economic pressure on its surrounding. To overcome this problem, proper planning and uninterrupted observation on the physical characteristics of the watershed are needed. This study aims to: 1) identify the physical characteristics of the Citarum Hilir Sub-Watershed that affect the flow coefficient; 2) calculate the flow coefficient by considering the physical characteristics of the sub-watershed; 3) provide management recommendations of the Citarum Hilir Sub-Watershed for flood control. The method to identify the flood characteristics is by calculating the coefficient of river flow using Cook method which incorporates parameters of slope, soil infiltration, land use, and drainage density, deducted from DEM SRTM and Landsat-8. Using Multiparametric GIS Analysis, the result shows that the physical characteristic of land use contributed the most, which is 34.55% to the flow coefficient assessment. The slope parameter has an influence of 25.44%, soil infiltration is 11.42%, and drainage density is 28.60%. The total flow coefficient value is 52.32%, so it is classified as high criteria. Therefore, this area has high flood potential that are prioritized for handling flood control. This area covers a land unit with high to extreme flow coefficient values, which is 73.96% of the total area. Several efforts can be made to follow up the flood control on these priority areas, such as land use management, forest and land rehabilitation, soil and water conservation, and integrated watershed management. Land use planning must refer to this analysis to determine the suitability of land use for water and forest conservation to remediate those high to extreme flow coefficient values. The evaluation on Regional Planning Map of West Java Province in 2009-2029, is needed as a recommendation to control the flood and enhance the Citarum Hilir management.

Land subsidence due to groundwater over-exploitation has already become a problem in many countries and also some cities in Indonesia. Although there have been many studies that explain the occurrence of land subsidence in various places, there is still limited research that discusses prediction techniques or mapping the potential of land subsidence in an area. This research aims to determine the susceptibility zone of land subsidence due to groundwater over-exploitation in Yogyakarta-Sleman Groundwater Basin. In this study, the land subsidence susceptibility will be determined based on three parameters namely aquifer stratification, a combination of aquitard and aquiclude layers thickness, and compressibility potential of aquitard and aquiclude layers. The data used in this research is secondary data of 126 drill logs along with their descriptions which are then processed to generate litho-stratigraphy modeling and are also processed using Geographic Information System (GIS) software for the interpolation and extrapolation also to generate zoning maps of each parameter. After that, all three parameters were overlayed by applying Analytic Hierarchy Process (AHP) weighting method to produce a susceptibility map of land subsidence due to groundwater over-exploitation. A prediction of land subsidence hazard map is also created by overlaying the susceptibility with relatively groundwater pumping rate zones. The research reveals the central part of the basin belonged to a severely susceptible and hazardous zone. According to this research, it can be concluded that there is a potential for land subsidence due to groundwater over-exploitation in the Yogyakarta-Sleman groundwater basin area, so the management to control of the groundwater usage is needed to ensure sustainable use of groundwater and protection from land subsidence occurrence.

The Luk Ulo River watershed, the largest river in Kebumen Regency, owns some environmental issues, including its declining quality due to land degradation, land conversion, rapid urbanization, and mining of sand and rock for building material. Rapid population growth also triggers the change of the land functions from forest to built-up area. This massive land-use changing lead to the watershed decreasing potential to absorb a large amount of water optimally, since most of the rainwater will flow to the surface due to reduced forest as a barrier to the rate of surface water. Therefore, watershed potential and actual identification in term of water resources availability is urgently needed. The development of remote sensing technology and geographic information systems has a possibility to study the spatial pattern of water catchment areas in a wide range. This study aims to determine the potential and actual of water catchment areas in the Luk Ulo watershed. The potential of water catchment obtained from the result of overlay from rainfall, soil type (Secondary data) and slope data which derived from DEM SRTM 30 meters. The result of the potential water catchment area is overlaid with land use information extracted from Sentinel-2A imagery through visual interpretation and resulting the actual potential of water catchment. The results show that the condition of the potential natural water catchment area in the Luk Ulo watershed is dominated by moderate infiltration potential with an area of 42,462.87 Ha (65.170%). Furthermore, for actual potential of water catchment areas dominated by good and quite critical conditions with an area of 24,979.85 Ha (38.23%) and 22,896.96 Ha (35.80%) 24,758.53 Ha (38.14%). This research contributes to the potential assessment of watershed revitalization planning, especially to provide the estimation extent area followed by its spatial distribution. Data validation using field observation and secondary data will be ideal for future study to measure the model accuracy followed by giving us local knowledge of the study area.

Major problems related to mapping activities in developing countries is lack and out of date spatial data. Although, spatial data is paramount important as a basis for decision-making. The same case occurs at Merapi volcano which often requires urgent decisions by local authorities based on spatial data of geological disasters. This study aims to build spatiotemporal data of pyroclastic deposits, and to determine the direction and the extent of pyroclastic deposits as well. The methodologies were focused on image interpretation, virtual fieldwork, and accuracy assessment. The preprocessing of remote sensing images included a radiometric correction and image sharpening. Radiometric correction was implemented to convert the original image value to the Bottom of Atmosphere (BOA) reflectance value. Due to the COVID-19 pandemic, fieldwork could not be conducted, hence we replaced it using virtual fieldwork in order to classify objects on Landsat satellite imagery manually. After the virtual fieldwork, we analyzed the direction and the area of pyroclastic deposits in order to produce a map of pyroclastic deposits of Merapi Volcano. The result indicates that the pyroclastic deposits of the Merapi Volcano in 2011 were spread to the southeast, west and north. While in 2020, the deposits were directed to the west. The sediment area is 39.935.176 m² in 2011 and 7.772.015 m² in 2020. The result can be used to support decision-making by authorities and stakeholders in this volcanic area.

Spatial data is very important for geomorphological analysis and disaster studies related to volcanic activities in Indonesia. Merapi volcano is known worldwide for its frequent eruptions. This volcano provides a good cases for GIS research since it has unique morphology. Therefore, the aim of this study is focused on developing GIS approach for determining morphometry of Merapi volcano. The methods used in this study was focused on DEM generation from LiDAR, development of topographic cross sections, and morphometric analysis of volcanic facies. The results of this research are facies map of southern part of Merapi volcano and the morphometry map of the Gendol river. It can be seen from this result that the facies of Mount Merapi in the study area consist of central (>2000 m), proximal (1350-2000 m), medial (500-1350 m) and distal (<500 m) facies. Gendol river is divided into zone 1, zone 2, and zone 3 which are characterized by the river's upstream which generally have a v-shaped valley and narrow channel. This findings would be important for local authorities to support mitigation strategies related to Merapi eruption.

The term of Mudik (in Bahasa) is often interpreted as the return of migrants from foreign areas to their hometowns, or largely known as massive mobility among regions that especially carried out annually during the Eid al-Fitr holiday in Indonesia. However, due to the COVID-19 pandemic occurred in 2020 and 2021, restrictions were placed on mudik during the Eid holiday. This study was conducted to see the extent of the effectiveness of the mudik restrictions carried out by the Indonesian government. This study was conducted by reviewing the levels of NO₂ and CO in the months before and during the Eid al-Fitr holiday through spatiotemporal processing of images retrieved from Google Earth Engine. The data used is Sentinel-5p images to map air pollution levels from NO₂ values and CO values in January-June 2019-2021. The study area includes two districts in DKI Jakarta Province and two districts in Central Java Province. The statistical tests are useful to see the trend of data that is obtained from the zonal analysis process. The statistical tests were carried out using the Mann-Kendall Test method to detect trends and the results were equipped with Sen's Slope analysis to measure the magnitude of the changes that occurred. According to the trend of NO₂ and CO values obtained, the values in 2019 are higher than in 2021, and the values in 2021 are higher than in 2020. Thus, the policy of mudik restrictions in 2020 is assumed more effectively than in 2021. The trend in the levels of NO₂ and CO in the air is more significant a month before the Eid al-Fitr holiday than in the Eid al-Fitr holiday. It can illustrate that the production of NO₂ and CO from motor vehicles continues to increase before there are restrictions.

Malaria is a mosquito-borne disease caused by the existence of the Plasmodium parasite in female Anopheles mosquitoes. Tropical countries, such as Indonesia, provide a suitable place for these mosquitos to live and breed quite easily. Despite having an overall low case incidence, some regions in Java still have it worse than others. Purworejo Regency holds the highest place in this regard, with an Annual Parasite Incidence (API) value of 1.96 for every 1,000 population. This research aims to utilize physical, climate, and socio-demographic variables as a parameter for malaria incidence in Purworejo Regency, as well as to map the vulnerability of the said area towards malaria. Physical variables included vegetation density, landuse and landcover, elevation, and soil texture, while climate variables included rainfall and humidity. Settlement density was used as a socio-demographic variable in the emergence of malaria. Each of these variables weights was then calculated with Analytical Hierarchy Process (AHP) based on their ranks, resulting in elevation being the most prominent variable. The overlay was used to combine all the variables as well as to calculate the total score for existing polygons to decide their classification. The generated map from this particular scenario showed that Menoreh Mountains in the northern region and settlements close to dense vegetation in the southern region of Purworejo Regency were vulnerable to malaria. Moderately vulnerable class dominated 55.23% of Purworejo Regency, followed by 35.41% of the vulnerable class, and lastly 9.36% of the non-vulnerable class. Malaria cases from Purworejo Public Health Center year 2007-2011 were used as a comparison. Not all incidents that happened on-field fit perfectly within the generated map. As such, further research involving other variables and the latest malaria case as a comparison are needed.

The interaction between humans and the environment is the object of research on spatial dynamics modeling. The relationship between population growth, land demand, and land availability was modeled to analyze the environment's carrying capacity. In spatial dynamics modeling studies, models were made to predict future land availability based on the system dynamics model of land demand and population growth. The model was then analyzed with regional socioeconomic and environmental factors. The novelty of spatial dynamic modeling studies comes from the uniqueness of regional factors. No significant novelty was found in the fundamental model assumption. Therefore, this research tries to find the opportunity for novelty in the spatial dynamic modeling study. Six research publications were reviewed using 6 variables on the comparative study method. The result of this study suggests that similar research on spatial dynamics modeling should use 60-70% of environmental carrying capacity for standard in the system dynamics model. This method was proven to delay the growth of the built-up area. Moreover, the model assumptions on spatial dynamics modeling research can be further developed in 3-dimensional spatial studies. The land availability dynamics model also can be further developed based on land suitability dynamics maps.

In the past few years, a large amount of geolocation data related to human movement has allowed scientists to generate representative models and prediction techniques for modeling, predicting, and mapping population mobility. Multisource heterogeneous data from the ground to space, such as GPS, census data, social media, mobile phone records, and remote sensing, provide a new driving force for exploring population mobility. The mapping of population mobility plays a vital role in applications such as estimating migratory flows, urban planning, epidemic control, location-based services, and transportation management. This research aims to review: 1) Study of population mobility based on multisource heterogeneous data, 2) The approaches developed to reproduce various population mobility patterns, and 3) The application of GIS and remote sensing for population mobility mapping. This research uses the literature review method. This review can be used both to introduce the fundamental modeling principles of population mobility and as a collection of GIS and remote sensing technical methods applicable to mapping population mobility.

Nowadays, Twitter data is significant to many studies since there is a shift in the data collection paradigm. As one of the contemporary social media with many active users, Twitter provides geotagging facilities to create a geotagged Tweet. Various spatial based studies use geotagged Tweet data. This paper aims to review the geo-temporal characteristics of geotagged Twitter data in nine major cities in Indonesia, namely five cities in the Greater Area of Jakarta, Surabaya, Bandung, Medan, and Makassar. Twitter data was collected by the streaming method for two years (January 2019- December 2020). The temporal analysis was carried out by graphing the number of Tweets with 30-minute intervals. Weekly Twitter activities were also visualized to get a specific understanding of when the optimum time to post a Tweet was. Density analysis was employed to Twitter data to find out the spatial patterns in the study area. Kernel Density Estimation (KDE) was used to determine the Tweets Density in the day and night. This study also used a simple framework of text analysis of topic modelling using Latent Semantic Indexing (LSI) to use the Twitter data better. Overall, Central Jakarta and South Jakarta have a significant number of Tweets compared to other cities. The study results show that, in general, big cities in Indonesia have almost the same temporal curve and the peak time for making geotagged tweets occurs from 4 pm to 8 pm. Our finding also points out that a high number of the population in a city does not always produce a high number of Tweets. The results of topic modelling in the Greater Area of Jakarta show that the themes of traffic jams/congestion, entertainment, and culinary tourism are widely mentioned by Twitter users, thus opening opportunities for research on these subjects.