Random Forest Ensemble for River Turbidity Measurement From Space Remote Sensing Data

2020 ◽  
Vol 69 (11) ◽  
pp. 9028-9036
Author(s):  
Ke Gu ◽  
Yonghui Zhang ◽  
Junfei Qiao
Keyword(s):  
Remote Sensing ◽  
Random Forest ◽  
Remote Sensing Data ◽  
Sensing Data ◽  
Turbidity Measurement

scholarly journals STUDY ON REMOTE SENSING MONITORING MODEL OF AGRICULTURAL DROUGHT BASED ON RANDOM FOREST DEVIATION CORRECTION

2021 ◽  
pp. 413-422
Author(s):  
Shao Li ◽  
Xia Xu
Keyword(s):  
Remote Sensing ◽  
Random Forest ◽  
Remote Sensing Data ◽  
Agricultural Drought ◽  
Data Sources ◽  
Drought Monitoring ◽  
Monitoring Methods ◽  
Large Area ◽  
Sensing Data ◽  
Monitoring Model

Using remote sensing data to monitor large area drought is one of the important methods of drought monitoring at present. However, the traditional remote sensing drought monitoring methods mainly focus on monitoring single drought response factors such as soil moisture or vegetation status, and the research on comprehensive multi-factor drought monitoring is limited. In order to improve the ability to resist drought events, this paper takes Henan Province of China as an example, takes multi-source remote sensing data as data sources, considers various disaster-causing factors, adopts random forest method to model, and explores the method of regional remote sensing comprehensive drought monitoring using various remote sensing data sources. Compared with neural network, classification regression tree and linear regression, the performance of random forest is more stable and tolerant to noise and outliers. In order to provide a new method for comprehensive assessment of regional drought, a comprehensive drought monitoring model was established based on multi-source remote sensing data, which comprehensively considered the drought factors such as soil water stress, vegetation growth status and meteorological precipitation profit and loss in the process of drought occurrence and development.

scholarly journals Comparison of Machine Learning Algorithms for Wildland-Urban Interface Fuelbreak Planning Integrating ALS and UAV-Borne LiDAR Data and Multispectral Images

Drones ◽  
2020 ◽  
Vol 4 (2) ◽  
pp. 21 ◽  
Author(s):  
Francisco Rodríguez-Puerta ◽  
Rafael Alonso Ponce ◽  
Fernando Pérez-Rodríguez ◽  
Beatriz Águeda ◽  
Saray Martín-García ◽  
...  
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Random Forest ◽  
Learning Algorithms ◽  
Remote Sensing Data ◽  
Machine Learning Algorithms ◽  
Data Sources ◽  
Lidar Data ◽  
Sensing Data ◽  
Sentinel 2

Controlling vegetation fuels around human settlements is a crucial strategy for reducing fire severity in forests, buildings and infrastructure, as well as protecting human lives. Each country has its own regulations in this respect, but they all have in common that by reducing fuel load, we in turn reduce the intensity and severity of the fire. The use of Unmanned Aerial Vehicles (UAV)-acquired data combined with other passive and active remote sensing data has the greatest performance to planning Wildland-Urban Interface (WUI) fuelbreak through machine learning algorithms. Nine remote sensing data sources (active and passive) and four supervised classification algorithms (Random Forest, Linear and Radial Support Vector Machine and Artificial Neural Networks) were tested to classify five fuel-area types. We used very high-density Light Detection and Ranging (LiDAR) data acquired by UAV (154 returns·m−2 and ortho-mosaic of 5-cm pixel), multispectral data from the satellites Pleiades-1B and Sentinel-2, and low-density LiDAR data acquired by Airborne Laser Scanning (ALS) (0.5 returns·m−2, ortho-mosaic of 25 cm pixels). Through the Variable Selection Using Random Forest (VSURF) procedure, a pre-selection of final variables was carried out to train the model. The four algorithms were compared, and it was concluded that the differences among them in overall accuracy (OA) on training datasets were negligible. Although the highest accuracy in the training step was obtained in SVML (OA=94.46%) and in testing in ANN (OA=91.91%), Random Forest was considered to be the most reliable algorithm, since it produced more consistent predictions due to the smaller differences between training and testing performance. Using a combination of Sentinel-2 and the two LiDAR data (UAV and ALS), Random Forest obtained an OA of 90.66% in training and of 91.80% in testing datasets. The differences in accuracy between the data sources used are much greater than between algorithms. LiDAR growth metrics calculated using point clouds in different dates and multispectral information from different seasons of the year are the most important variables in the classification. Our results support the essential role of UAVs in fuelbreak planning and management and thus, in the prevention of forest fires.

scholarly journals Tree height inversion combining light detection and ranging and optical remote sensing data

2020 ◽  
Vol 165 ◽  
pp. 03020
Author(s):  
Kunlin Wang ◽  
Yi Ma ◽  
Fangrong Zhou
Keyword(s):  
Remote Sensing ◽  
Random Forest ◽  
Vegetation Index ◽  
Remote Sensing Data ◽  
Tree Height ◽  
Edge Length ◽  
Optical Remote Sensing ◽  
Sensing Data ◽  
Inversion Model ◽  
Feature Parameters

Tree barriers in transmission line corridors are an important safety hazard.Scientific prediction of tree height and monitoring tree height changes are essential to solve this hidden danger. In this paper, the advantages of airborne lidar and optical remote sensing data are combined to research the method of tree height inversion. Based on glas data of lidar,waveform parameters such as waveform length, waveform leading edge length and waveform trailing edge length were extracted from waveform data by gaussian decomposition method.Terrain feature parameters were extracted from aster gdem data.The tree crown information was extracted from the optical remote sensing image by means of the mean shift algorithm. Then extract the vegetation index with high correlation with tree height.Finally, the extracted waveform feature parameters, topographic feature parameters, and crown index and vegetation index with high correlation are used as model input variables. The tree height inversion model was established using four regression methods, including multiple linear regression (mlr), support vector machine (svm), random forest (rf), and bp neural network (bpnn). The accuracy evaluation was conducted, and it was concluded that the tree height inversion model based on random forest obtained the best accuracy effect.

scholarly journals RANDOM-FOREST-ENSEMBLE-BASED CLASSIFICATION OF HIGH-RESOLUTION REMOTE SENSING IMAGES AND NDSM OVER URBAN AREAS

2017 ◽  
Vol XLII-2/W7 ◽  
pp. 887-892
Author(s):  
X. F. Sun ◽  
X. G. Lin
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Random Forest ◽  
Urban Areas ◽  
Vegetation Index ◽  
Conditional Random Field ◽  
Remote Sensing Data ◽  
Intermediate Step ◽  
Sensing Data ◽  
3D Geometry

As an intermediate step between raw remote sensing data and digital urban maps, remote sensing data classification has been a challenging and long-standing research problem in the community of remote sensing. In this work, an effective classification method is proposed for classifying high-resolution remote sensing data over urban areas. Starting from high resolution multi-spectral images and 3D geometry data, our method proceeds in three main stages: feature extraction, classification, and classified result refinement. First, we extract color, vegetation index and texture features from the multi-spectral image and compute the height, elevation texture and differential morphological profile (DMP) features from the 3D geometry data. Then in the classification stage, multiple random forest (RF) classifiers are trained separately, then combined to form a RF ensemble to estimate each sample’s category probabilities. Finally the probabilities along with the feature importance indicator outputted by RF ensemble are used to construct a fully connected conditional random field (FCCRF) graph model, by which the classification results are refined through mean-field based statistical inference. Experiments on the ISPRS Semantic Labeling Contest dataset show that our proposed 3-stage method achieves 86.9% overall accuracy on the test data.

Thermal remote sensing data enhancement over Alpine Vegetated Areas for evapotranspiration modelling

2020 ◽  
Author(s):  
Paulina Bartkowiak ◽  
Mariapina Castelli ◽  
Roberto Colombo ◽  
Claudia Notarnicola
Keyword(s):  
Remote Sensing ◽  
Random Forest ◽  
Spatial Resolution ◽  
Missing Values ◽  
Remote Sensing Data ◽  
European Alps ◽  
Thermal Remote Sensing ◽  
Sensing Data ◽  
Fine Resolution ◽  
Availability Assessment

<p>The main objective of this study is to exploit thermal remote sensing data for evapotranspiration (ET) modelling in the European Alps. This geographic region has been noted as a hot spot of climate change triggered by increasing number of drought events in recent years, with impacts on natural and agricultural vegetation. Evapotranspiration is considered as one of the major indicators for examining water anomalies in plants. The state-of-art ET models exploiting thermal remote sensing data have shown a large potential in water cycle monitoring. However, existing satellite-derived products do not provide adequate spatial resolution for mountain ecosystems affected by complex orography, common overcast and land-cover heterogeneity. Even though fine resolution imagery fills the gap regarding non-homogenous areas, its long revisit time and frequent cloud contamination hamper spatially continuous ET modelling. In this context, our aim is to overcome these limitations by downscaling and gap-filling 1-km MODIS LST (MOD11A1) to retrieve daily LST maps at 250 m spatial resolution, which can be considered a reasonable scale in the selected area. Firstly, we downscale MODIS LST images with the Random Forest (RF) algorithm by exploiting the relationship between coarse resolution MODIS LST and 250-m explanatory variables, including digital elevation model and normalized difference vegetation index. The 1-km MODIS LST and the downscaled product were compared with fine resolution Landsat LST images. The random forest results show an improvement of about 20% in the agreement between Landsat and 250-m MODIS LST compared to statistics obtained for MOD11A1. Secondly, we propose to recover missing values of LST pixels beneath the clouds. Considering local-scale climate variability of the study area, we present a novel approach based on investigating the relationships between LST and meteorological data under clear- and cloudy-sky conditions. The abovementioned improvements are planned to be used for energy balance modelling of ET with relevant implications on water availability assessment in the Alpine region.</p>

Sign in / Sign up

Export Citation Format

Share Document