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Precise estimation of Sea Surface Salinity (SSS) is of prime importance to know marine physical and biochemical processes. In this manuscript we have utilized the Random Forest (RF) and Support Vector Regression (SVR) to estimate the SSS concentration. Particularly, we have utilized the global SSS values provided by the Copernicus Marine Services. We have also extracted the Aqua MODIS and Sentinel-3 data from the Google Earth Engine platform. The complete range of SSS varies approximately from 6 psu to 38 psu which enables the global application of the dataset. We have also studied the importance of different wavelength bands and their significance to SSS using the RF model. It is found that wavelengths around 400 nm have their highest significance due to the sensitiveness to Chromophoric Dissolved Organic Matter (CDOM) and other related constituents. For the regression analysis we have also utilized the Laplacian kernel in the SVR. It is found that the SVR with Laplace kernel outperforms the RF and SVR with Radial Basis Function (RBF) kernel. The trained algorithms are used to estimate the SSS content over the Bay of Bengal and the Arabian Sea regions which show interesting variations in SSS content due to diverse climatological conditions. [ABSTRACT FROM AUTHOR]

The rapid expansion of exotic saltcedar along riparian corridors has dramatically altered the landscape structure and ecological function of riparian habitats in the western United States. The development of accurate and reproducible mapping methods with remote sensing plays an indispensable role in the timely monitoring of saltcedar, re-evaluating its ecological functions, and establishing effective control measures. The utmost challenge for achieving this goal is manifested as the lack of time series of remote sensing images to capture the saltcedar phenology adequately. To this end, the newly available Landsat 9 images, combined with its counterpart of Landsat 8, offer a precious opportunity to compensate for the temporal image shortage. To understand Landsat 9 in the saltcedar classification and to discover helpful information for its application, this study presents the first attempt to classify saltcedar using intra-annual Landsat 8 and Landsat 9 images. We adopted two machine learning algorithms, support vector machine (SVM) and random forest (RF), to compare the performance of Landsat 9 and Landsat 8 for intra-annual saltcedar classification. In addition, we investigated the respective contribution of each spectral band to the overall performance and identified the optimal time window for saltcedar classification. The results indicated that the difference in classification performance between Landsat 9 and Landsat 8 was insignificant. The shortwave infrared bands associated with both Landsat 8 & 9 have contributed most to the process of saltcedar identification. Image acquired in July, November, and December yielded better results than other months for saltcedar classification. It is concluded that Landsat 8 & 9 constellation has the potential to refine saltcedar classification accuracy on larger spatial and temporal scales. [ABSTRACT FROM AUTHOR]

Land-use and land-cover (LULC) mapping in the complex area is a challenging task due to the mixed vegetation patterns, and rough mountains with fast-flowing rivers. In Vietnam, LULC update is not frequently. In this study, we applied a supervised machine learning (Random forest—RF) approach to mapping LULC in Thanh Hoa province, Vietnam from 2011 to 2015 utilizing multitemporal Normalized Difference Vegetation Index (NDVI) data from MODIS, combined with topographic features. Random forest classification (RFC) reached a total prediction accuracy of 91% and Kappa coefficient (K) of 0.89 across eight LULCs. Besides, the results showed that the features extracted from time-series NDVI comprising the mean of yearly NDVI, the sum of NDVI, and the topography were the important variables controlling the LULC classification. For similar studies on the distribution of LULC, the method proposed in this study could be helpful. [ABSTRACT FROM AUTHOR]

This study aims to predict channel unit types (CUTs) by combining remotely sensed data with morphological variables using machine learning algorithms (random forest, support vector machines, multiple adaptive regression splines, extreme gradient boosting and adaptive boosting) within the Upper Ogun River Basin, Southwestern Nigeria. In achieving the aim of this study, we identified the most important variable(s) in CUT discrimination using the random forest – recursive feature elimination (RF-RFE). A total of 249 cross-sections across 83 reaches were sampled during the fieldwork. Landsat 8 and Sentinel-1 bands were retrieved for days the fieldwork was carried and mosaiced using the Google Earth Engine platform. The RF-RFE identified five top variables (accuracy: 0.79 ± 0.14; kappa: 0.39) discriminating the CUT as dimensionless stream power, slope, width, wetted perimeter and Band 4. In essence, there is much hope in the use of remote sensing in CUT mapping at the reach scale. [ABSTRACT FROM AUTHOR]

One of the main applications of machine learning (ML) in remote sensing (RS) is the pixel-level classification of satellite images into land cover types. Although classes with different spectral signatures can be easily separated, e.g. aquatic and terrestrial land cover types, others have similar spectral signatures and are hard to separate using only the information within a single pixel. This work focused on the separation of two cover types with similar spectral signatures, cocoa agroforest and forest, over an area in Pará, Brazil. For this, we study the training and application of several ML algorithms on datasets obtained from a single composite image, a time-series (TS) composite obtained from the same location and by preprocessing the TS composite using simple TS preprocessing techniques. As expected, when ML algorithms are applied to a dataset obtained from a composite image, the median producer's accuracy (PA) and user's accuracy (UA) in those two classes are significantly lower than the median overall accuracy (OA) for all classes. The second dataset allows the ML models to learn the evolution of the spectral signatures over 5 months. Compared to the first dataset, the results indicate that ML models generalize better using TS data, even if the series are short and without any preprocessing. This generalization is further improved in the last dataset. The ML models are subsequently applied to an area with different geographical bounds. These last results indicate that, out of seven classifiers, the popular random forest (RF) algorithm ranked fourth, while XGBoost (×GB) obtained the best results. The best OA, as well as the best PA/UA balance, were obtained by performing feature construction using the M3GP algorithm and then applying XGB to the new extended dataset. [ABSTRACT FROM AUTHOR]

Snow and cloud discrimination is a main factor contributing to errors in satellite-based snow cover. To address the error, satellite-based snow cover performs snow reclassification tests on the cloud pixels of the cloud mask, but the error still remains. Machine Learning (ML) has recently been applied to remote sensing to calculate satellite-based meteorological data, and its utility has been demonstrated. In this study, snow and cloud discrimination errors were analyzed for GK-2A/AMI snow cover, and ML models (Random Forest and Deep Neural Network) were applied to accurately distinguish snow and clouds. The ML-based snow reclassified was integrated with the GK-2A/AMI snow cover through post-processing. We used the S-NPP/VIIRS snow cover and ASOS in situ snow observation data, which are satellite-based snow cover and ground truth data, as validation data to evaluate whether the snow/cloud discrimination is improved. The ML-based integrated snow cover detected 33–53% more snow compared to the GK-2A/AMI snow cover. In terms of performance, the F1-score and overall accuracy of the GK-2A/AMI snow cover was 73.06% and 89.99%, respectively, and those of the integrated snow cover were 76.78–78.28% and 90.93–91.26%, respectively. [ABSTRACT FROM AUTHOR]

The occurrence of drought is related to complicated interactions between many factors, such as precipitation, temperature, evapotranspiration and vegetation. In this study, the relationships between drought and precipitation, temperature, vegetation and evapotranspiration were investigated with a random forest (RF), and a new combined drought monitoring index (CDMI) was constructed. The effectiveness of the CDMI in monitoring drought in Shaanxi Province was verified by the in situ 1 ∼ 12-month standardized precipitation index (SPI); relative soil moisture (RSM) and four other commonly used remote sensing drought monitoring indices. The results show that CDMI is more correlated with the SPI and RSM than the four indices. Moreover, the spatial distributions of drought for the CDMI and RSM are similar. Therefore, the CDMI can be used to monitor droughts in Shaanxi Province, and machine learning can explore the relationships between various factors and establish a drought index without knowledge of the causal mechanisms of these factors. [ABSTRACT FROM AUTHOR]

Machine learning algorithms such as Random Forest (RF) are being increasingly applied on traditionally geographical topics such as population estimation. Even though RF is a well performing and generalizable algorithm, the vast majority of its implementations is still 'aspatial' and may not address spatial heterogenous processes. At the same time, remote sensing (RS) data which are commonly used to model population can be highly spatially heterogeneous. From this scope, we present a novel geographical implementation of RF, named Geographical Random Forest (GRF) as both a predictive and exploratory tool to model population as a function of RS covariates. GRF is a disaggregation of RF into geographical space in the form of local sub-models. From the first empirical results, we conclude that GRF can be more predictive when an appropriate spatial scale is selected to model the data, with reduced residual autocorrelation and lower Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE) values. Finally, and of equal importance, GRF can be used as an effective exploratory tool to visualize the relationship between dependent and independent variables, highlighting interesting local variations and allowing for a better understanding of the processes that may be causing the observed spatial heterogeneity. [ABSTRACT FROM AUTHOR]

Classification of 3D point cloud in urban scenes has been widely applied in the fields of automatic driving, map updating, and change detection. To develop the accurate and effective method for the classification remains a challenge. We propose a three-stage framework to classify 3D point clouds acquired in the urban road environment. First, an efficient segmentation approach is applied to generate segments as the entity to be classified. This is achieved by using the pairwise linkage (P-Linkage) algorithm for the initial point cloud segmentation, followed by two-step post-processing approach to improve the segmentation results. Second, a set of novel features is extracted from each segment and its performance is evaluated using four classifiers: Support Vector Machine, Random Forests, Naïve Bayesian, and Extreme Learning Machine. Third, the contextual constraints among objects are used to refine the classification via graph cuts. The results showed that the initial classification can obtain a high average precision of 80.8−92.9% and a good average recall rate of 77.5−79.1%. After refinement via graph cuts, the precision and recall rate increase by approximately 0.3% and 3.1%, respectively. Our framework is effective for classifying 3D urban point clouds acquired by a mobile laser scanning (MLS) system in the road environment. [ABSTRACT FROM AUTHOR]

Newfoundland and Labrador (NL) is the only province in Atlantic Canada that does not have a wetland inventory system. As a consequence, both classifying and monitoring wetland areas are necessary for wetland conservation and human services in the province. In this study, wetlands in 5 pilot sites, distributed across NL, were classified using multi-source and multi-temporal optical remote sensing images. The procedures involved the application of an object-based method to segment and classify the images. To classify the areas, 5 different machine learning algorithms were examined. The results showed that the Random Forest (RF) algorithm in combination with an object-based approach was the most accurate method to classify wetlands. The average producer and user accuracies of wetland classes considering all pilot sites were 68% and 73%, respectively. The overall classification accuracies, which considered the accuracy of all wetland and non-wetland classes varied from 86% to 96% across all pilot sites confirming the robustness of the methodology despite the biological, ecological, and geographical differences among the study areas. Additionally, we assessed the effects of the tuning parameters on the accuracy of results, as well as the difference between pixel-based and object-based methods for wetland classification in this study. [ABSTRACT FROM PUBLISHER]