Evaluating variable selection and machine learning algorithms for estimating forest heights by combining lidar and hyperspectral data

Machine learning has been employed for various mapping and modeling tasks using input variables from different sources of remote sensing data. For feature selection involving high- spatial and spectral dimensionality data, various methods have been developed and incorporated into the machine learning framework to ensure an efficient and optimal computational process. This research aims to assess the accuracy of various feature selection and machine learning methods for estimating forest height using AISA (airborne imaging spectrometer for applications) hyperspectral bands (479 bands) and airborne light detection and ranging (lidar) height metrics (36 metrics), alone and combined. Feature selection and dimensionality reduction using Boruta (BO), principal component analysis (PCA), simulated annealing (SA), and genetic algorithm (GA) in combination with machine learning algorithms such as multivariate adaptive regression spline (MARS), extra trees (ET), support vector regression (SVR) with radial basis function, and extreme gradient boosting (XGB) with trees (XGbtree and XGBdart) and linear (XGBlin) classifiers were evaluated. The results demonstrated that the combinations of BO-XGBdart and BO-SVR delivered the best model performance for estimating tropical forest height by combining lidar and hyperspectral data, with R2 = 0.53 and RMSE = 1.7 m (18.4% of nRMSE and 0.046 m of bias) for BO-XGBdart and R2 = 0.51 and RMSE = 1.8 m (15.8% of nRMSE and −0.244 m of bias) for BO-SVR. Our study also demonstrated the effectiveness of BO for variables selection; it could reduce 95% of the data to select the 29 most important variables from the initial 516 variables from lidar metrics and hyperspectral data

Change Detection Analysis using Bitemporal PRISMA Hyperspectral Data: Case Study of Magelang and Boyolali Districts, Central Java Province, Indonesia

Satellite missions which collect hyperspectral data provide detailed spectral information at a lower cost than airborne missions. The newly launched PRISMA hyperspectral mission provides greater swath coverage than the previous Hyperion hyperspectral mission. This study aims to assess the potential use of bitemporal PRISMA datasets for change detection (CD), by means of the clustering of Gaussian mixture models (GMM) with inputs to the magnitude component derived from change vector analysis (CVA), distance metrics and principal component analysis (PCA) from stacked data, and image-differenced layers. In addition, a change detection method using a combination of the modified z-score from imagedifferenced layers and a spectral angle mapper (SAM), SAMZID-TAN, was also assessed. Overall accuracies for CD in our results varied between 50.90 and 78.83%, with the producer’s and user’s accuracies for the change class ranging from 69.74 to 84.21% and 38.13–66.29%, respectively. SAMZID-TAN was the most accurate method for CD. Moderate CD accuracy was achieved using PRISMA due to the effects of misregistration and image striping, which contributed to misclassification. In future research, proper pre-processing should be performed in order to avoid the detection of false positives when using hyperspectral data

Monthly Burned-Area Mapping using Multi-Sensor Integration of Sentinel-1 and Sentinel-2 and machine learning: Case Study of 2019's fire events in South Sumatra Province, Indonesia

Indonesia has experienced massive historical land and forest fire events, creating transnational environmental and socioeconomic issues. The extent of burned areas (BAs) is one of many indicators that reflect the magnitudes and impacts from fire events, and such information is also used for planning the response and recovery steps after the fire events. This information is usually derived using remote sensing (RS) data. However, the assessment on the performance using available RS data, possible RS data combinations, and existing methods is needed to regularly monitor the BA extent. This study aims to assess the performance from Sentinel-1 synthetic aperture radar polarization (Pol.) and gray-level of co-occurrence matrix (GLCM) textural features and the integration with Sentinel-2 spectral data (Spec.) for the monthly mapping of BA extent using machine learning algorithms, such as random forests (RFs) and extreme gradient boosting (XGB). The study took place in the parts of Ogan Komering Ilir Regency and Banyuasin in South Sumatra Province, Indonesia. This area has complex land-use classes, such as natural vegetation and plantations (pulpwood and oil palm), which were affected by the 2019's fire events. Our study demonstrated that the combination between Pol. from Sentinel-1 and spectral data from Sentinel-2 (Diff.Pol + Spec.) yielded the best classification accuracy with the overall accuracy (OA) values ranging from 91.80 (XGB) to 95.80 (RF) with the producer's accuracy (PA) from 73.33 to 97.66 and user's accuracy (UA) from 76.69 to 89.80 for the BA class. The integration of spectral data using Sentinel-2 reduced the source of misclassification of BAs from false detection from the non-fire-related land-cover conversion, such as logging activities. © 2022 Elsevier B.V

Crop Intensity Mapping Using Dynamic Time Warping and Machine Learning from Multi-Temporal PlanetScope Data

Crop intensity information describes the productivity and the sustainability of agricultural land. This information can be used to determine which agricultural lands should be prioritized for intensification or protection. Time-series data from remote sensing can be used to derive the crop intensity information; however, this application is limited when using medium to coarse resolution data. This study aims to use 3.7 m-PlanetScope™ Dove constellation data, which provides daily observations, to map crop intensity information for agricultural land in Magelang District, Indonesia. Two-stage histogram matching, before and after the monthly median composites, is used to normalize the PlanetScope data and to generate monthly data to map crop intensity information. Several methods including Time-Weighted Dynamic Time Warping (TWDTW) and the machine-learning algorithms: Random Forest (RF), Extremely Randomized Trees (ET), and Extreme Gradient Boosting (XGB) are employed in this study, and the results are validated using field survey data. Our results show that XGB generated the highest overall accuracy (OA) (95 ± 4%), followed by RF (92 ± 5%), ET (87 ± 6%), and TWDTW (81 ± 8%), for mapping four-classes of cropping intensity, with the near-infrared (NIR) band being the most important variable for identifying cropping intensity. This study demonstrates the potential of PlanetScope data for the production of cropping intensity maps at detailed resolutions

Crop Intensity Mapping Using Dynamic Time Warping and Machine Learning from Multi-Temporal PlanetScope Data

Crop intensity information describes the productivity and the sustainability of agricultural land. This information can be used to determine which agricultural lands should be prioritized for intensification or protection. Time-series data from remote sensing can be used to derive the crop intensity information; however, this application is limited when using medium to coarse resolution data. This study aims to use 3.7 m-PlanetScope™ Dove constellation data, which provides daily observations, to map crop intensity information for agricultural land in Magelang District, Indonesia. Two-stage histogram matching, before and after the monthly median composites, is used to normalize the PlanetScope data and to generate monthly data to map crop intensity information. Several methods including Time-Weighted Dynamic Time Warping (TWDTW) and the machine-learning algorithms: Random Forest (RF), Extremely Randomized Trees (ET), and Extreme Gradient Boosting (XGB) are employed in this study, and the results are validated using field survey data. Our results show that XGB generated the highest overall accuracy (OA) (95 ± 4%), followed by RF (92 ± 5%), ET (87 ± 6%), and TWDTW (81 ± 8%), for mapping four-classes of cropping intensity, with the near-infrared (NIR) band being the most important variable for identifying cropping intensity. This study demonstrates the potential of PlanetScope data for the production of cropping intensity maps at detailed resolutions