The advancement of remote sensing image acquisition through Unmanned Aerial Vehicles (UAVs) has seen rapid growth in the last five years, particularly in the field of agricultural mapping. The inclusion of multispectral sensors on UAVs holds potential and capabilities for distinguishing different growth stages of rice crops. However, with respect to this objective, there has been limited research investigating pixel-based and object-based classification approaches using multispectral UAV data. This study aims to assess the capabilities of multispectral aerial photos in identifying rice crop growth stages through both pixel-based and object-based classification methods within a portion of the Banyubiru Subdistrict, Semarang Regency. The Support Vector Machine (SVM) method is employed for pixel-based classification, while the object-based classification (OBIA) process employs the Segment Mean Shift algorithm for segmentation. Training samples and data accuracy are obtained through visual interpretation based on the developed orthomosaic data. Four rice crop growth stages are mapped, namely vegetative, reproductive, ripening, and bare-land phases. The two approaches yield differing accuracy performance. The pixel based approach using support vector machine (SVM) achieves an accuracy of 45% with a kappa coefficient of 0.28, whereas the Object Based Image Analysis (OBIA) approach attains an accuracy of 37% with a kappa coefficient of 0.24. The results indicate that, in this case, the pixel-based approach (SVM) demonstrates higher accuracy compared to the Object Based Image Analysis (OBIA) approach. However, the low accuracy indicates the limitations of pixel based image analysis using spectrometer inputs for mapping using UAV datasets.
Oil spills frequently occur on the sea surface due to heightened vessel activities. Oil spills can be detected by applying supervised and unsupervised classification methods to satellite images using radar sensors. Supervised classification methods such as visual interpretation are widely used, but the results are very subjective. Conversely, unsupervised methods, while less subjective, necessitate parameter tuning for accurate results. This study's primary goal is to assess the impact of parameter tuning on unsupervised K-Means and Clustering Large Applications (CLARA) algorithms for detecting sea surface oil spills. It can be concluded that the area of identified oil spills is closely related to the iteration parameters and the number of cluster centers. The results of identification using the unsupervised method with these two algorithms will be compared with reference data from Indonesia National Institute of Aeronautics and Space (LAPAN) as the official institution that provides information regarding oil spills pollution on the sea surface in Indonesia. The main conclusion from this study, parameter tuning is highly required before carrying out the process of identifying oil spills on sea level using the unsupervised method especially related to the number of iterations executed, the desired number of cluster centers, and the clustering type of the algorithm used. Using the tuned parameters, the K-Means algorithm is able to identify oil spill areas that are quantitatively close to the reference data area, but the CLARA algorithm is able to provide identification results that have fewer errors in terms of oil spills look-alikes.
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.
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.
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.
Remote sensing has been widely used in the estimation of forest aboveground biomass (AGB) which is essential for climate change mitigation,by using either optical or radar data and its combination. This estimation of AGB from remote sensing data is now supported by the availability of the freely available dual-polarization Sentinel 1 SAR data. However, the assessment of the accuracy for measuring AGB from the VV and VH polarization in Sentinel-1 data in Indonesia is still limited. This study aims to assess the performance of VV and VH polarization and the combination with texture data from Sentinel-1 for estimating AGB in tropical forest of Barru Regency, South Sulawesi. The AGB was calculated by using backscatter value from C-band SAR dual-polarization and Grey Level of Measure (GLCM) texture data from Sentinel-1 as the independent variables, and ground inventory plots as the dependent variable. Twenty-three plots of field inventory data were collected whereas 16 plots were used in the regression models and the remaining seven plots were used to validate the result. The allometric equation was used to calculate the biomass value of the field survey data then multilinear regression models were generated by using biomass value, backscatter data from VV and VH polarization, and texture data. The performance of the resulted multilinear regression models was compared by looking at the coefficient of determination (R2) and RMSE value using cross-validation. The results demonstrated that combination of VH and GLCM texture suggest as the best to estimate the AGB based on higher value of R2 = 0.44 and SE 83.7 kg/tree. In conclusion, VH polarization usage in vegetation AGB modelling has been able to predict 3 % higher than by using VV polarisation. The inclusion of texture also had been able to increase the model performance by 5 to 7 % which demonstrated the importance of having texture variables in the analysis of AGB.
Noise in SAR imagery was produced due to different backscatter response from the objects in the earth surface. This resulted in a grainy image, usually known as “salt and pepper” noise, which reducing the capability to identify object from radar imagery. Therefore, speckle filtering was conducted to decrease this noise from SAR imagery. This study aims to assess the performance of different types of speckle filters especially when used to construct forest aboveground biomass (AGB) model from Sentinel-1 data in Barru Regency, South Sulawesi. There were 4 filters used in this study i.e. Frost, Gamma-MAP, Median, and Refined Lee. AGB model was constructed by using dual polarization C-band SAR of Sentinel1 data and ground inventory plots. 23 plots were collected in the field and the allometric equation was used to calculate the biomass value of the field survey data then cross validation models were generated by using biomass value and backscatter data from VV and VH polarization. Quality control was performed by comparing the coefficient of determination (R2 ) of those filters. The result shows that Frost filter especially on VH polarization was chosen as the bestfit model to estimate the AGB based on higher value of R2 (0.3464158) and RMSE (33.5231). The result demonstrated Frost filter as the best filter for retaining and/or enhancing the backscatter signal in Sentinel-1 data to be used in vegetation bio-physical modelling.
Water is the resource and determinant factor that determines the performance of the agricultural sector, although the role is very strategic, the water management is still far from expected so that the water that should be a farmer's turn turned into a cause of disaster for farmers. Small farm reservoir is built to accommodate excess rainwater during the rainy season. The water collected is then used as a source of supplementary irrigation for the cultivation of high-value economic commodities in the dry season. This research aims to survey, inventory, and study the potential of small farm reservoir development, and to plan potential site locations to be developed into small farm reservoir based on the results of analysis of physical and socio-economic data and drought potential using advanced remote sensing technology in Jombang Regency. Research planning of small farm reservoir location in Jombang Regency is designed in four stages of activity that is data gathering, mapping, and compilation of database, analysis, planning of location, and recommendation of the small farm reservoir location. The results of this research state that the appropriate area built small farm reservoir based on the parameters used are Ngoro, Mojoagung, Kesamben, and Kabuh village.
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