Using machine learning models, remote sensing, and GIS to investigate the effects of changing climates and land uses on flood probability

For the issue of collapse susceptibility prediction (CSP), minimal attention has been paid to explore the uncertainty characteristics of different machine learning models predicting collapse susceptibility. In this study, six kinds of typical machine learning methods, namely, logistic regression (LR), radial basis function neural network (RBF), multilayer perceptron (MLP), support vector machine (SVM), chi-square automatic interactive detection decision tree (CHAID), and random forest (RF) models, are constructed to do CSP. In this regard, An’yuan County in China, with a total of 108 collapses and 11 related environmental factors acquired through remote sensing and GIS technologies, is selected as a case study. The spatial dataset is first constructed, and then these machine learning models are used to implement CSP. Finally, the uncertainty characteristics of the CSP results are explored according to the accuracies, mean values, and standard deviations of the collapse susceptibility indexes (CSIs) and the Kendall synergy coefficient test. In addition, Huichang County, China, is used as another study case to avoid the uncertainty of different study areas. Results show that 1) overall, all six kinds of machine learning models reasonably and accurately predict the collapse susceptibility in An’yuan County; 2) the RF model has the highest prediction accuracy, followed by the CHAID, SVM, MLP, RBF, and LR models; and 3) the CSP results of these models are significantly different, with the mean value (0.2718) and average rank (2.72) of RF being smaller than those of the other five models, followed by the CHAID (0.3210 and 3.29), SVM (0.3268 and 3.48), MLP (0.3354 and 3.64), RBF (0.3449 and 3.81), and LR (0.3496 and 4.06), and with a Kendall synergy coefficient value of 0.062. Conclusively, it is necessary to adopt a series of different machine learning models to predict collapse susceptibility for cross-validation and comparison. Furthermore, the RF model has the highest prediction accuracy and the lowest uncertainty of the CSP results of the machine learning models.

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Monitoring the Foliar Nutrients Status of Mango Using Spectroscopy-Based Spectral Indices and PLSR-Combined Machine Learning Models

Remote Sensing ◽

10.3390/rs13040641 ◽

2021 ◽

Vol 13 (4) ◽

pp. 641

Author(s):

Gopal Ramdas Mahajan ◽

Bappa Das ◽

Dayesh Murgaokar ◽

Ittai Herrmann ◽

Katja Berger ◽

...

Keyword(s):

Machine Learning ◽

Remote Sensing ◽

Partial Least Square ◽

Least Square ◽

Partial Least Square Regression ◽

Support Vector ◽

Spectral Indices ◽

Learning Models ◽

Leaf Nutrients ◽

Machine Learning Models

Conventional methods of plant nutrient estimation for nutrient management need a huge number of leaf or tissue samples and extensive chemical analysis, which is time-consuming and expensive. Remote sensing is a viable tool to estimate the plant’s nutritional status to determine the appropriate amounts of fertilizer inputs. The aim of the study was to use remote sensing to characterize the foliar nutrient status of mango through the development of spectral indices, multivariate analysis, chemometrics, and machine learning modeling of the spectral data. A spectral database within the 350–1050 nm wavelength range of the leaf samples and leaf nutrients were analyzed for the development of spectral indices and multivariate model development. The normalized difference and ratio spectral indices and multivariate models–partial least square regression (PLSR), principal component regression, and support vector regression (SVR) were ineffective in predicting any of the leaf nutrients. An approach of using PLSR-combined machine learning models was found to be the best to predict most of the nutrients. Based on the independent validation performance and summed ranks, the best performing models were cubist (R2 ≥ 0.91, the ratio of performance to deviation (RPD) ≥ 3.3, and the ratio of performance to interquartile distance (RPIQ) ≥ 3.71) for nitrogen, phosphorus, potassium, and zinc, SVR (R2 ≥ 0.88, RPD ≥ 2.73, RPIQ ≥ 3.31) for calcium, iron, copper, boron, and elastic net (R2 ≥ 0.95, RPD ≥ 4.47, RPIQ ≥ 6.11) for magnesium and sulfur. The results of the study revealed the potential of using hyperspectral remote sensing data for non-destructive estimation of mango leaf macro- and micro-nutrients. The developed approach is suggested to be employed within operational retrieval workflows for precision management of mango orchard nutrients.

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Canopy Height Estimation Using Sentinel Series Images through Machine Learning Models in a Mangrove Forest

Remote Sensing ◽

10.3390/rs12091519 ◽

2020 ◽

Vol 12 (9) ◽

pp. 1519 ◽

Cited By ~ 3

Author(s):

Sujit Madhab Ghosh ◽

Mukunda Dev Behera ◽

Somnath Paramanik

Keyword(s):

Machine Learning ◽

Remote Sensing ◽

Forest Canopy ◽

Canopy Height ◽

Series Data ◽

Learning Models ◽

Biophysical Parameters ◽

Height Estimation ◽

Area Index ◽

Machine Learning Models

Canopy height serves as a good indicator of forest carbon content. Remote sensing-based direct estimations of canopy height are usually based on Light Detection and Ranging (LiDAR) or Synthetic Aperture Radar (SAR) interferometric data. LiDAR data is scarcely available for the Indian tropics, while Interferometric SAR data from commercial satellites are costly. High temporal decorrelation makes freely available Sentinel-1 interferometric data mostly unsuitable for tropical forests. Alternatively, other remote sensing and biophysical parameters have shown good correlation with forest canopy height. The study objective was to establish and validate a methodology by which forest canopy height can be estimated from SAR and optical remote sensing data using machine learning models i.e., Random Forest (RF) and Symbolic Regression (SR). Here, we analysed the potential of Sentinel-1 interferometric coherence and Sentinel-2 biophysical parameters to propose a new method for estimating canopy height in the study site of the Bhitarkanika wildlife sanctuary, which has mangrove forests. The results showed that interferometric coherence, and biophysical variables (Leaf Area Index (LAI) and Fraction of Vegetation Cover (FVC)) have reasonable correlation with canopy height. The RF model showed a Root Mean Squared Error (RMSE) of 1.57 m and R2 value of 0.60 between observed and predicted canopy heights; whereas, the SR model through genetic programming demonstrated better RMSE and R2 values of 1.48 and 0.62 m, respectively. The SR also established an interpretable model, which is not possible via any other machine learning algorithms. The FVC was found to be an essential variable for predicting forest canopy height. The canopy height maps correlated with ICESat-2 estimated canopy height, albeit modestly. The study demonstrated the effectiveness of Sentinel series data and the machine learning models in predicting canopy height. Therefore, in the absence of commercial and rare data sources, the methodology demonstrated here offers a plausible alternative for forest canopy height estimation.

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Mapping of soil organic carbon using machine learning models: Combination of optical and radar remote sensing data

Soil Science Society of America Journal ◽

10.1002/saj2.20371 ◽

2021 ◽

Author(s):

Yang Zhou ◽

Xiaomin Zhao ◽

Xi Guo ◽

Yi Li

Keyword(s):

Machine Learning ◽

Remote Sensing ◽

Organic Carbon ◽

Soil Organic Carbon ◽

Remote Sensing Data ◽

Learning Models ◽

Radar Remote Sensing ◽

Sensing Data ◽

Machine Learning Models

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Application of statistical and machine learning models for grassland yield estimation based on a hypertemporal satellite remote sensing time series

2014 IEEE Geoscience and Remote Sensing Symposium ◽

10.1109/igarss.2014.6947634 ◽

2014 ◽

Cited By ~ 8

Author(s):

Iftikhar Ali ◽

Fiona Cawkwell ◽

Stuart Green ◽

Ned Dwyer

Keyword(s):

Machine Learning ◽

Remote Sensing ◽

Time Series ◽

Satellite Remote Sensing ◽

Learning Models ◽

Yield Estimation ◽

Sensing Time ◽

Machine Learning Models

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Landslide Susceptibility Prediction Using Sparse Feature Extraction and Machine Learning Models Based on GIS and Remote Sensing

IEEE Geoscience and Remote Sensing Letters ◽

10.1109/lgrs.2021.3054029 ◽

2021 ◽

pp. 1-5

Author(s):

Li Zhu ◽

Gongjian Wang ◽

Faming Huang ◽

Yan Li ◽

Wei Chen ◽

...

Keyword(s):

Machine Learning ◽

Remote Sensing ◽

Feature Extraction ◽

Landslide Susceptibility ◽

Learning Models ◽

Gis And Remote Sensing ◽

Machine Learning Models

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Improving Potato Yield Prediction by Combining Cultivar Information and UAV Remote Sensing Data Using Machine Learning

Remote Sensing ◽

10.3390/rs13163322 ◽

2021 ◽

Vol 13 (16) ◽

pp. 3322

Author(s):

Dan Li ◽

Yuxin Miao ◽

Sanjay K. Gupta ◽

Carl J. Rosen ◽

Fei Yuan ◽

...

Keyword(s):

Machine Learning ◽

Remote Sensing ◽

Vegetation Indices ◽

Remote Sensing Data ◽

Growing Season ◽

Potato Yield ◽

Yield Variability ◽

Yield Prediction ◽

Learning Models ◽

Machine Learning Models

Accurate high-resolution yield maps are essential for identifying spatial yield variability patterns, determining key factors influencing yield variability, and providing site-specific management insights in precision agriculture. Cultivar differences can significantly influence potato (Solanum tuberosum L.) tuber yield prediction using remote sensing technologies. The objective of this study was to improve potato yield prediction using unmanned aerial vehicle (UAV) remote sensing by incorporating cultivar information with machine learning methods. Small plot experiments involving different cultivars and nitrogen (N) rates were conducted in 2018 and 2019. UAV-based multi-spectral images were collected throughout the growing season. Machine learning models, i.e., random forest regression (RFR) and support vector regression (SVR), were used to combine different vegetation indices with cultivar information. It was found that UAV-based spectral data from the early growing season at the tuber initiation stage (late June) were more correlated with potato marketable yield than the spectral data from the later growing season at the tuber maturation stage. However, the best performing vegetation indices and the best timing for potato yield prediction varied with cultivars. The performance of the RFR and SVR models using only remote sensing data was unsatisfactory (R2 = 0.48–0.51 for validation) but was significantly improved when cultivar information was incorporated (R2 = 0.75–0.79 for validation). It is concluded that combining high spatial-resolution UAV images and cultivar information using machine learning algorithms can significantly improve potato yield prediction than methods without using cultivar information. More studies are needed to improve potato yield prediction using more detailed cultivar information, soil and landscape variables, and management information, as well as more advanced machine learning models.

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Retrieval of Non-Optically Active Parameters for Small Scale Urban Waterbodies by a Machine Learning-Based Strategy

10.20944/preprints202004.0111.v1 ◽

2020 ◽

Author(s):

Jinhui Jeanne Huang ◽

Hongwei Guo ◽

Bowen Chen ◽

Xiaolong Guo ◽

Vijay P. Singh

Keyword(s):

Water Management ◽

Machine Learning ◽

Remote Sensing ◽

Water Quality ◽

Optically Active ◽

Monitoring Strategy ◽

Learning Models ◽

Small Waterbodies ◽

Machine Learning Models ◽

Sentinel 2

Water quality retrieval for small urban waterbodies by remote sensing get used to be difficult due to coarse spatial resolution of the remote sensing imagery. The recently launched Sentinel-2 produces imagery with a spatial resolution of 10 m. It provides an opportunity to solve the problem of retrieving water quality for small waterbodies. Additionally, many water management issues also require fine resolution of imagery, e.g. illegal discharge to an urban waterbody. Since illegal discharges are an important issue for urban water management, chemical oxygen demand (COD), total phosphorous (TP), and total nitrogen (TN) were chosen as the target parameters for water quality retrieval in this study. COD, TP and TN, however, are non-optically active parameters. There were limited studies in the past to retrieve these parameters in comparison with optically active parameters, e.g. Chlorophyll-A etc. This study compared three machine learning models, namely Random Forest (RF), Support Vector Regression (SVR), and Neural Networks (NN), to investigate the opportunity to retrieve the above non-optically active parameters. Results showed that R2 of TP, TN, and COD by NN, RF and SVR were 0.94, 0.88, and 0.86, respectively. The performances of water quality retrieval for these non-optically active parameters were significantly improved by the optimized machine learning models. These models hence solved the problem to use remote sensing data to retrieve these non-optically active water quality parameters and provided a new monitoring strategy for small waterbodies. Water quality mapping obtained by Sentinel-2 imagery provided a full spatial coverage of the water quality characterization for the entire water surface. Compared with water samples collecting and testing, it greatly reduced labor cost, reagents cost, and waste treatment cost. It also may help identify illegal discharges to urban waterbodies. The method developed in this research provides a new practical and efficient water quality monitoring strategy in managing water with consideration of environmental sustainability.

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DROUGHT FORECASTING BASED ON MACHINE LEARNING OF REMOTE SENSING AND LONG-RANGE FORECAST DATA

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xli-b8-157-2016 ◽

2016 ◽

Vol XLI-B8 ◽

pp. 157-158

Author(s):

J. Rhee ◽

J. Im ◽

S. Park

Keyword(s):

Machine Learning ◽

Remote Sensing ◽

Long Range ◽

Initial Conditions ◽

Interpolation Method ◽

Spline Interpolation ◽

Standardized Precipitation Index ◽

Learning Models ◽

Drought Forecasting ◽

Machine Learning Models

The reduction of drought impacts may be achieved through sustainable drought management and proactive measures against drought disaster. Accurate and timely provision of drought information is essential. In this study, drought forecasting models to provide high-resolution drought information based on drought indicators for ungauged areas were developed. The developed models predict drought indices of the 6-month Standardized Precipitation Index (SPI6) and the 6-month Standardized Precipitation Evapotranspiration Index (SPEI6). An interpolation method based on multiquadric spline interpolation method as well as three machine learning models were tested. Three machine learning models of Decision Tree, Random Forest, and Extremely Randomized Trees were tested to enhance the provision of drought initial conditions based on remote sensing data, since initial conditions is one of the most important factors for drought forecasting. Machine learning-based methods performed better than interpolation methods for both classification and regression, and the methods using climatology data outperformed the methods using long-range forecast. The model based on climatological data and the machine learning method outperformed overall.

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