International Journal of Geoinformatics

Investigating the Relationship between Precipitable Water Vapor and Rainfall Data during Flood Events: A GNSS-Based Study in Thailand

2024-11-21T00:33:11-08:00

This research investigates the correlation between Precipitable Water Vapor (PWV), derived from Global Navigation Satellite System (GNSS) measurements, and rainfall during flood events in Chanthaburi, Surat Thani, and Songkhla, Thailand. Utilizing GPS-PWV and meteorological data collected from 2007 to 2016, the study reveals a significant increase in average PWV during flood events at CHAN, SRTN, and SOKA stations, suggesting its potential as an anticipatory indicator for impending rainfall. The robust correlation between PWV and rainfall patterns underscores the pivotal role of meteorological parameters in shaping PWV distribution. Categorized by flood events, consistent correlations were observed, with Case 1 (2009) showing correlation coefficients of 0.78 for CHAN, Case 2 (2010) displaying correlation coefficients of 0.70 for SRTN and 0.27 for SOKA, and Case 3 (2011) exhibiting correlation coefficients of 0.64 for SRTN and 0.71 for SOKA. These findings contribute valuable insights for precipitation forecasting and flood management, emphasizing the utility of PWV as a reliable tool. Future studies incorporating an expanded network of GNSS CORS stations aim to refine PWV distribution understanding for enhanced monitoring and prediction of precipitation events in the region.

Structural Biases and Sensitivities of Vegetation Indices

2024-11-21T00:38:05-08:00

Since the epoch of climate change, observation of forest post-disturbance regeneration by satellite remote sensing has become a major research frontier. However, the monotonic saturation effects of specific reflectance bands may hinder the interpretation of post-disturbance vegetation indexing. We examine how spectral vegetation enhancement index limitations negate widespread implementation. The structural biases and sensitivities of four vegetation indices with potential usefulness for observing post-disturbance forest regeneration are assessed and clarified: the normalized difference vegetation index (NDVI), normalized burn ratio (NBR), near-infrared vegetation index (VI_NIR), and the infrared vegetation index (VI_IR). Index structures are partitioned in calculation space to model every possible output. Simulated burned, unburned, and global vegetation computational domains for each index are assessed using complex statistical visualizations. Cross-comparison among indices shows that NDVI and NBR exhibit saturation given the upper range of simulated near-infrared (NIR) reflectance inputs (> 0.30) while VI_NIR and VI_IR display increasing variability given lower inputs in the Green (> 0.07) and Shortwave-infrared (SWIR) (> 0.10), regions of the electromagnetic spectrum. NDVI and NBR display potential for vegetation class separability, while VI_NIR and VI_IR also display a linear association with forest post-disturbance regeneration stages. VI_NIR and VI_IR display significant potential for observing forest post-disturbance regeneration compared to traditional vegetation indices.

Design and Estimation of the Accuracy Parameters of a Digital Surface Model (DSM) Using a DJI Phantom 4 Pro Drone and Photogrammetry Technique

2024-11-21T00:41:15-08:00

A Digital Surface Model (DSM) represents terrain elevation, incorporating natural and artificial features within an environment. These models are valuable in fields like telecommunications for analyzing electromagnetic wave propagation. However, DSMs are approximations due to errors from acquisition tools, data collection conditions, and processing methods. This study presents a methodology for producing a DSM and calculating its accuracy using a DJI Phantom 4 Pro drone at altitudes of 30 m, 50 m, and 70 m. Images captured were processed with photogrammetry techniques using Pix4Dmapper software. For the respective altitudes, 374, 154, and 80 images were collected at a resolution of 5472×3648 pixels. The photogrammetry technique, employing the Triangulated Irregular Network (TIN) interpolation method, achieved optimal accuracies during the 30 m flight, with planimetric accuracies of 13 cm in the East-West direction and 17 cm in the North-South direction, and distance and height accuracies of 12 cm and 15 cm. This study demonstrates that high planimetric precision and accurate ground structure representation are achievable using drone-based DSM generation.

Development of Spatial Database System Based on Cloud Computing Remote Sensing for Monitoring of Oil Palm Plantation in Indonesia

2024-11-21T00:45:53-08:00

Oil palm plantations provide the highest amount of foreign exchange earnings for the Indonesian government. However, according to the Indonesian Ministry of Agriculture, the foreign exchange has dropped to 20%. Therefore, to increase oil palm productivity, the President Republic of Indonesia issued Instruction No. 6 of 2019 regarding National Action Plan for Sustainable Oil Palm Plantation 2019–2024, one of the actions to increase national oil palm productivity is strengthening data infrastructure. Considering that the oil palm plantations in Indonesia are highly extensive and spread across almost all parts of Indonesia, cloud computing remote sensing technology is a satisfactory solution for monitoring oil palm plantations. This study aims to develop a spatial database system for monitoring oil palm plantation in Indonesia based on cloud computing remote sensing data. The methodology includes user needs and spatial data identification as Indonesian regulations, combined with focus group discussions, normalized geographic data normalization, and spatial database development using conceptual, logical, and physical models and system design and data visualization. The resulting spatial database system for monitoring oil palm plantations in Indonesia based on cloud computing remote sensing has been constructed by leveraging data saved in the cloud on ArcGIS Living Atlas of The World. The data recorded in ArcGIS Online are then linked to a visualization system created using the ArcGIS operation dashboard. The dashboard has been developed based on user needs and displays information about oil palm plantation age, administration boundaries, rainfall, terrestrial ecosystems, hot spots, and weather conditions.

Exploring the Effects of Land use Land Cover (LULC) Change on Menhir in 2053: Utilizing the Cellular Automata-Artificial Neural Network (CA-ANN) Algorithm: A Case Study of Menhir Tourist Site in Nagari Maek, Indonesia

2024-11-21T00:50:17-08:00

The construction of tourism facilities related to physical aspects triggers changes in land use. This research aims to model Land Use Land Cover (LULC) by 2053 and determine the influence of LULC on the existence of the menhir tourist attraction as the main object in historical tourism in Nagari Maek. The method used to produce LULC predictions is the CA-ANN model via the MOLUSCE plugin in QGIS Desktop 2.18.11. The research results show that the 2023 prediction map has an accuracy value of 68.43 with a correctness percentage of 83.11458%. This means the resulting model is highly suitable, and the prediction results can be trusted. The LULC prediction for 2023 to 2053 results in significant land changes occurring in the dry land forests, which experienced a decrease of 6.75%, and plantations of 13.67%, while the increase occurred in the mixed plantations by 17.46%, rice fields 1.57% and built-up area 1.39%. If the LULC conversion is linked to the existence of menhirs (the main object in historical tourism), then by 2053, there will be a reduction from 30 sites to 24 menhir sites. Each site has a different number of menhirs, and there will be at least 20. The loss of 6 sites or 120 menhirs caused the inherent characteristic of Nagari Maek, as it was named as the Nagari of 1000 menhirs, to disappear.

Morphometric Inferences of the Euphrates River Islands and the Possibility of their Development through the Analysis of Remote Sensing Data and Geographic Information Systems

2024-11-21T00:54:26-08:00

River islands are considered one of the most important components of the development of the river environment, due to their function as a ground appearance that hinders the speed of the flow of the river stream and the distribution of that flow over the seasons of the year, and these islands are important in creating environmental balance, as their components of sand have a role in reducing pollutants due to the mechanism of denitrogenization as a weakening element for aquatic organisms, and their cadastral development is of great importance in the possibilities of creating development opportunities for the surrounding areas, which are still the mechanism of investment of these islands is still limited to a very high degree, and therefore the aim of the study is to identify and compare the engineering characteristics of one of the most important islands of the Euphrates River by measuring its areas and height from the river and comparing its cadastral development in all its engineering dimensions using remote sensing data and processing it in the GIS environment, and the results showed that the area of the island has developed between 1973, which amounted to 289.07 m² to 2024 4,681.18 m² to reach the final length of the island 3,587 meters The results showed that the island takes a shape close to a rectangle with a strip extension, where the values according to the results of the aspect ratio reached (30.94), Its heights range between 32 - 58 meters above sea level, with a height difference of up to 2 meters from the riverbed. Its climatic effects are evident through the dense vegetation cover, which ranged according to NDVI between 1 - 0.92 in 1973 to 0.74-1 in 2024, and it has also become one of the most important lands that humans have begun to exploit for many purposes, including grazing. There is a need for further study on the development of river islands over time, and the importance of the impact of this study appears through alerting the community and farmers living in the region to the possibility of investing in these islands for many purposes, while being careful of the possibility of their exposure to sudden river floods.

Potential Surface Analysis in Urban Sustainability Planning: A Case Study in Mae Ka, Phayao, Thailand

2024-11-21T00:58:50-08:00

This study aimed to achieve three objectives: firstly, to analyze the urban growth around the Mae Ka community by examining the land use changes that occurred in 2001, 2011, and 2021; secondly, to identify the factors that affect the potential areas for development; and thirdly, to propose sustainable development guidelines for both the Mae Ka community and the surrounding areas. To accomplish this, the study employed Potential Surface Analysis (PSA) as a method for evaluating suitability and development potential. The study utilized Geographic Information System (GIS) to process three major factors: physical, infrastructural, and risk area factors. The overlay mapping technique was crucial in analyzing and classifying areas based on their potential for supporting the development plan for the Mae Ka community. The areas were divided into three categories: high potential areas, moderate potential areas, and low potential areas. The results of the analysis land use changes revealed that in 2001, 2011 and 2021, forest areas decreased by 11.74%, with a conversion to agricultural areas, build-up areas, and miscellaneous areas, resulting in a decrease from 149.92 square kilometers to 132.32 square kilometers. Furthermore, the results of the potential surface analysis using physical, infrastructural, and risk area factors demonstrated that the high potential areas constituted 23.74 square kilometers or 8.58%, the moderate potential areas comprised 64.39 square kilometers or 23.28%, the low potential areas covered 55.75 square kilometers or 20.16%, and the excluded areas, such as forest areas, reserved or preserved areas, and natural resource areas, accounted for 132.70 square kilometers or 47.98% of the total area. Based on the level of potential areas, recommendations for stable and sustainable land use plans, transportation plans, and infrastructure plans can be developed for Mae Ka in the near future.

Identification of Agricultural Drought through Vegetation Health Analysis at Erap Station under the Markham Valley of Papua New Guinea

2024-11-21T01:02:47-08:00

Drought is a phenomenon caused by insufficient soil moisture resulting from prolonged periods of low rainfall and the absence of proper irrigation systems. Its impact on crops, agricultural productivity, and the overall environment is significant. The use of multispectral satellite remote sensing data greatly facilitates the identification of agricultural drought. This study outlines a practical approach for assessing drought using multispectral satellite data. The main objective of this research is to estimate drought indicators and their relationship with ground conditions by analyzing remote sensing datasets through vegetation health index (VHI) analysis. The study is conducted at Erap, which is situated on the northern bank of the Markam River. Spectral bands, like near-infrared, red, and thermal bands of the Landsat 8 satellite are used to produce the VHI database. Multispectral data from the Landsat 8 satellite is the key dataset used to calculate the normalized differential vegetation index (NDVI) and land surface temperature (LST). The vegetation condition index (VCI) and temperature condition index (TCI) are derived from NDVI and LST, respectively. Ultimately, the VHI is calculated by combining VCI and TCI. The resulting VHI varies from 8 to 87, where the lower VHI values refer to extreme drought conditions and higher values denote normal conditions. Mild to moderate drought conditions were observed in the central and some areas in the western part of the research area. The R²between NDVI and VHI is calculated higher (0.72) compared to LST and VHI (0.40). The research indicates that multispectral satellite data is highly beneficial in predicting agricultural drought in any region based on temperature and vegetation conditions. VHI has the potential to be utilized in scientific contexts across different fields, particularly in agriculture and land use planning.

Modeling Rice Growth and Yield using Integrated Remote Sensing Data on Google Earth Engine

2024-11-21T01:04:53-08:00

Assessing the growth stages, health, and yield of rice is crucial for agriculture, economy, sustainability, and food security. Such assessments provide valuable insights for farmers to optimize agricultural practices, effectively manage pests and diseases, and enhance crop management, leading to improved yields and efficient resource management. This study focuses on paddy fields in Buak Khang sub-district, San Kamphaeng district, Chiang Mai, Thailand, with the objective of developing models to assess rice growth stages, evaluate rice health using the NDVI, and model rice yield using Sentinel-2 MSI and Sentinel-1 SAR (VV and VH polarizations) satellite imagery. The study was conducted during the rainy season of 2023 (June-November). Various parameters were identified to establish correlations and develop models, with field data collected to validate the models. The study yielded three models for rice growth stage assessment, with the best model achieving an R² of 0.67. Monitoring rice health revealed that on September 21, the NDVI values ranged between 0.51 and 0.91, indicating optimal growth conditions and healthy crops. Similarly, three models were developed for yield estimation, with the best model achieving an R² of 0.51. Validation showed that the growth stage model had a Kappa coefficient of 0.80, while the yield estimation model had a RMSE of 0.887 kg/m². These models, demonstrating high accuracy, provide a robust framework for agricultural agencies to develop effective agricultural policies.

Impact of Varying Ground Control Points Configurations on the Accuracy of Unmanned Aerial Vehicle-Based Digital Elevation Models

2024-11-23T18:43:55-08:00

Ensuring the vertical and horizontal accuracy of UAV-DEM is crucial for precision of results in any geohazard modeling like flood modeling. Various factors affecting UAV-DEMs accuracy, particularly the number and spatial distribution of Ground Control Points (GCPs) used in the surveyed area. This study aimed to identify the most effective GCP configuration for the UAV-DEM by assessing its impact on the vertical and horizontal accuracy of UAV-DEMs, considering GCP configurations involving different numbers (4, 8, 12, 16, and 20) and distributions (Outer, Center, and Stratified). The investigation employed a Factorial Experimental Design with three replications. The results revealed that UAV-DEMs with 8, 12, 16, and 20 GCPs exhibited higher vertical accuracy compared to those with 4 GCPs. While 4, 8, 12 and 16 number of GCPs will have the same effect on the horizontal accuracy. Additionally, both outer and stratified distributions surpassed center distribution for both vertical and horizontal accuracy. Furthermore, the study produced 20-cm Vertical Accuracy Class with 10-cm Horizontal Accuracy class based on ASPRS Positional Accuracy Standards for Digital Geospatial Data of 2023. Consequently, the study recommends an optimal GCP configuration involving 8 GCPs with either Outer or Stratified Distribution for every 100-hectare (1 km²) study area.