Improvement of Digital Elevation Model (DEM) using data fusion technique for oil palm replanting phase

Abstract

Digital elevation models (DEMs) play an important role in producing terrain-related applications such as curvature and contour maps for planning and management of oil palm plantation. Data fusion of DEMs derived from terrestrial laser scanning (TLS) and interferometric aperture radar (IfSAR) was developed with the intention to increase the accuracy of IfSAR-derived DEM at a lower cost thus, provide a high-quality data for plantation management. In this research, fusion by weights was carried out after applying regression analysis to integrate both TLS and IfSAR data. The results showed a significant reduction in root mean square error (RMSEs) after fusion. RMSEs of both stations reduced from 1.83 m to 0.35 m and from 3.13 m to 0.41 m for Station 1 and Station 2, respectively. In addition, data fusion technique for an area with no TLS data were tested around the stations at 200 m distance. The RMSEs decreased from 2.52 m to 2.33 m for Station 1 but the value increased from 2.09 m to 2.13 m for Station 2. It was concluded that the proposed fusion technique in the extension area could be done in a relatively flat area but not be used in a steep-slope area.

Satellite DEM Improvement Using Multispectral Imagery and an Artificial Neural Network

“…Using more data from other remote sensing data sources (data fusion technique) should be considered to further improve the DEM scheme, as one sensor's limitations could be compensated by another sensor's strengths [26]. Additionally, different types of machine learning models should be investigated to assess the model performance for different land cover areas.…”

“…There are many studies on improving or correcting satellite DEMs using other remote sensing data and various methods to overcome the aforementioned limitation [22][23][24][25]. Muhadi et al (2019) derived a DEM using the data fusion technique. It exploited laser scanning and aperture radar to create a new dataset for oil farm plantation.…”

“…It exploited laser scanning and aperture radar to create a new dataset for oil farm plantation. The performance of using two datasets showed less error than using one dataset [26]. Meadows et al (2021) applied a fully convolutional neural network to improve SRTM using multispectral imagery, nighttime lights, and other freely available global datasets.…”

Simple-Yet-Effective SRTM DEM Improvement Scheme for Dense Urban Cities Using ANN and Remote Sensing Data: Application to Flood Modeling

“…The data fusion technique can be applied to this DEM improvement scheme using more data from other satellites (e.g., TanDEM-X, ASTER DEM, AW3D DEM, Landsat 8, Sentinel 1 and ASTER imagery). This technique would increase the performance of the output as the limitation of one sensor could be compensated by the other sensors [16]. Also, the methodology developed is quite flexible in data selection and can be applied to the other space-borne DEM data sources mentioned above for their improvement.…”

“…Data fusion is one of the techniques used for eliminating errors from space-borne DEMs [11,[13][14][15]. Muhadi et al (2019) used a data fusion technique for deriving DEM that exploits two or more data to create a new data set for the planning and management of an oil farm plantation [16]. The idea is the limitation of one sensor could be compensated by the other sensors, so the combination of different data sets overcome the limitations.…”