Balancing water supply demand is vital for sustaining livelihoods. Spatial mapping and calculating water yield dynamics due to land use changes over decades are needed to manage land resources and formulate ecological protection policies. This study mapped the supply, demand, and matching status of water product service using the Integrated Valuation of Ecosystem Service and Tradeoff (InVEST) biophysical models in the Citarum Watershed (CW) in 2000, 2010, and 2020. Moreover, this study used Exploratory Spatial Data Analysis (ESDA) and Geographic Information System (GIS) techniques to study the agglomeration characteristics and evolutionary trajectories of supply–demand over two decades. The results showed that between 2000–2010 and 2010–2020, the water supply decreased by 19.01 × 10⁸ m³ (18.28%) and 12.97 × 10⁸ m³ (15.27%), respectively. However, the water demand in the same period increased by 6.17 × 10⁸ m³ (23%) and 15.74 × 10⁸ m³ (47%), respectively. Over the decades, the contribution of land use land cover (LULC) changes to variations in water supply has yielded values ranging from 2.87% to 6.37%. The analysis of the water supply–demand imbalance indicated that the entire CW experienced water shortage, and the type of spatial matching for supply and demand is dominated by a high supply and high demand class (16.09% of the total area). Based on the level of water deficit calculation, the upstream and downstream areas were identified as zones that require ecological conservation, while the middle CW area requires ecological restoration or ecological improvement.

The application of remote sensing data has been significant in modeling soil erosion. However, previous studies have fallen short in elucidating and lacked an understanding of the multifactor influencing erosion. This study addresses these limitations by employing the InVEST and the Geodetector models. Specifically, it aims (1) to delineate both spatial and temporal variations in soil erosion within the Citarum watershed from 2010 to 2020, (2) to identify the key drivers of soil erosion and unravel the underlying mechanisms, and (3) to identify the high-risk zones for soil erosion. Both models consider a range of natural predictors, including topography (slope factor), climate (precipitation factor), and vegetation cover (vegetation factor). In addition, they incorporate social parameters such as income per capita and population density, which interact with the watersheds position in the downstream, middle, and upper streams. The results reveal that, over a decade, the average soil erosion increased by 15.50×10⁶ tons, marking a 16.65 surge. The impact of factors varies significantly across different subwatershed areas. For example, fraction vegetation cover interactions influence upper- and middle-stream regions, while the downstream area is notably affected by precipitation interactions. The high-risk erosion areas in the watershed are primarily influenced by slope, precipitation, and fractional vegetation cover. In these areas, factors causing high erosion risks include slope, precipitation, and other environmental variables categorized into strata. The study highlights the varying influential factors in different watershed areas.

This research aims to protect Digital Elevation Model (DEM) data from piracy or counterfeiting. An invisible watermark inserted into the data, which will not considerably change the data value, is necessary. The proposed method involves the use of the two-dimensional discrete cosine transform (2D DCT), a combination of 2D DCT and discrete wavelet transform (DWT), and two-dimensional discrete Fourier transform (2D DFT) in the frequency domain. The data used include a National DEM file downloaded from the geoportal of the Geospatial Information Agency (Badan Informasi Geospasial—BIG). Three files represent mountainous, lowland/urban, and coastal areas. An “attack” is also conducted on the watermarked DEM by cropping. The results indicate that the watermarked DEM is well recognized. The watermark can be read 100% for 2D DCT, while that for 2D DFT can be read 90.50%. The distortion value of the elevation data under the DCT technique demonstrates the smallest maximum value of 0.1 m compared with 4.5 and 1.1 m for 2D DFT and 2D DCT–DWT. Meanwhile, the height difference (Max Delta), the peak signal-to-noise ratio, and the root mean squared error (RMSE) are highest in mountainous, lowland, and coastal areas, respectively. Overall, the 2D DCT is also superior to the 2D DFT and the2D DCT–DWT. Although only one can recognize the nine watermarks inserted on each sheet, DEMs attacked by the cropping process can still be identified. However, this finding can sufficiently confirm that DEMs belong to BIG.

Weld defect identification requires radiographic operator experience, so the interpretation of weld defect type could potentially bring subjectivity and human error factor. This paper proposes Statistical Texture and Support Vector Machine method for weld defect type classification in radiographic film. Digital image processing technique applied in this paper implements noise reduction using median filter, contrast stretching, and image sharpening using Laplacian filter. Statistical method feature extraction based on image histogram was proposed for describing weld defects texture characteristic of a radiographic film digital image. Multiclass Support Vector Machine (SVM) algorithm was used to perform classification of weld defects type. The result of classification testing shows that the proposed method can classify 83.3% correctly from 60 testing data of weld defects radiographic films.

Exploring the drivers of changes in ecosystem services is crucial to maintain ecosystem functionality, especially in the diverse Central Citarum watershed. This study utilises the integrated valuation of ecosystem service and trade-offs (InVEST) model and multiscale geographically weighted regression (MGWR) model to examine ecosystem services patterns from 2006 to 2018. The InVEST is a hydrological model to calculate water availability and evaluate benefits provided by nature through simulating alterations in the amount of water yields driven by land use/cover changes. Economic, topographic, climate, and vegetation factors are considered, with an emphasis on their essential components. The presence of a geographical link between dependent and explanatory variables was investigated using a multiscale geographic weighted regression model. The MGWR model is employed to analyse spatial impacts. The integration of both models simplified the process and enhanced its understanding. The findings reveal the following patterns: 1) decreasing land cover and increasing ecosystem services demand in the watershed, along with a decline in water yield, e.g. certain sub-districts encounter water scarcity, while others have abundant water resources; 2) the impact of natural factors on water yield shifts along vegetation > climate > topography (2006) changes to climate > vegetation > topography (2018).

This study examines the potential outcomes of uniting the resources of Muslim countries. Despite the challenges in obtaining accurate data, various representative approaches are proposed. Among the institutions that collect such data is the Statistical, Economic and Social Research and Training Center for Islamic Countries (SESRIC), which is under the Organization of Islamic Cooperation. The analysis focuses on the sufficiency of energy, food, and industrial raw materials in Muslim nations. However, it emphasizes the need for adjustments to improve economic interdependence among these countries, reducing their reliance on developed nations that once colonized them. Furthermore, the study acknowledges the ongoing work required to tap into intangible resources effectively. The findings underscore the anticipated benefits and ease that can be achieved through collaborative efforts among Muslim countries, paving the way for improved resource utilization and collective advancement.