Optimal method of linear regression in laser remote sensing

Machine learning has been successfully used for object recognition within images. Due to the complexity of the spectrum and texture of construction and demolition waste (C&DW), it is difficult to construct an automatic identification method for C&DW based on machine learning and remote sensing data sources. Machine learning includes many types of algorithms; however, different algorithms and parameters have different identification effects on C&DW. Exploring the optimal method for automatic remote sensing identification of C&DW is an important approach for the intelligent supervision of C&DW. This study investigates the megacity of Beijing, which is facing high risk of C&DW pollution. To improve the classification accuracy of C&DW, buildings, vegetation, water, and crops were selected as comparative training samples based on the Google Earth Engine (GEE), and Sentinel-2 was used as the data source. Three classification methods of typical machine learning algorithms (classification and regression trees (CART), random forest (RF), and support vector machine (SVM)) were selected to classify the C&DW from remote sensing images. Using empirical methods, the experimental trial method, and the grid search method, the optimal parameterization scheme of the three classification methods was studied to determine the optimal method of remote sensing identification of C&DW based on machine learning. Through accuracy evaluation and ground verification, the overall recognition accuracies of CART, RF, and SVM for C&DW were 73.12%, 98.05%, and 85.62%, respectively, under the optimal parameterization scheme determined in this study. Among these algorithms, RF was a better C&DW identification method than were CART and SVM when the number of decision trees was 50. This study explores the robust machine learning method for automatic remote sensing identification of C&DW and provides a scientific basis for intelligent supervision and resource utilization of C&DW.

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Laser remote sensing of sulfur dioxide and ozone with the mobile differential absorption lidar ARGOS

Optical Engineering ◽

10.1117/12.213584 ◽

1995 ◽

Vol 34 (11) ◽

pp. 3097 ◽

Cited By ~ 6

Author(s):

Uta-Barbara Goers

Keyword(s):

Remote Sensing ◽

Sulfur Dioxide ◽

Differential Absorption ◽

Differential Absorption Lidar ◽

Laser Remote Sensing

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LASER REMOTE SENSING FOR ENVIRONMENTAL APPLICATIONS

Photonics for Safety and Security ◽

10.1142/9789814412971_0008 ◽

2013 ◽

pp. 175-205

Author(s):

Antonella Boselli ◽

Gianluca Pisani ◽

N. Spinelli ◽

Xuan Wang

Keyword(s):

Remote Sensing ◽

Environmental Applications ◽

Laser Remote Sensing

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Laser Remote Sensing Techniques

Laser Spectroscopy and Its Applications ◽

10.1201/9780203749104-8 ◽

2017 ◽

pp. 565-621 ◽

Cited By ~ 4

Author(s):

William B. Grant

Keyword(s):

Remote Sensing ◽

Remote Sensing Techniques ◽

Laser Remote Sensing

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Remote Sensing-Based Estimation of Advanced Perennial Grass Biomass Yields for Bioenergy

Land ◽

10.3390/land10111221 ◽

2021 ◽

Vol 10 (11) ◽

pp. 1221

Author(s):

Yuki Hamada ◽

Colleen R. Zumpf ◽

Jules F. Cacho ◽

DoKyoung Lee ◽

Cheng-Hsien Lin ◽

...

Keyword(s):

Remote Sensing ◽

Linear Regression ◽

Regression Model ◽

Linear Regression Model ◽

Environmental Sustainability ◽

Vegetation Index ◽

Mean Squared Error ◽

Spectral Band ◽

Great Promise ◽

Optical Remote Sensing

A sustainable bioeconomy would require growing high-yielding bioenergy crops on marginal agricultural areas with minimal inputs. To determine the cost competitiveness and environmental sustainability of such production systems, reliably estimating biomass yield is critical. However, because marginal areas are often small and spread across the landscape, yield estimation using traditional approaches is costly and time-consuming. This paper demonstrates the (1) initial investigation of optical remote sensing for predicting perennial bioenergy grass yields at harvest using a linear regression model with the green normalized difference vegetation index (GNDVI) derived from Sentinel-2 imagery and (2) evaluation of the model’s performance using data from five U.S. Midwest field sites. The linear regression model using midsummer GNDVI predicted yields at harvest with R2 as high as 0.879 and a mean absolute error and root mean squared error as low as 0.539 Mg/ha and 0.616 Mg/ha, respectively, except for the establishment year. Perennial bioenergy grass yields may be predicted 152 days before the harvest date on average, except for the establishment year. The green spectral band showed a greater contribution for predicting yields than the red band, which is indicative of increased chlorophyll content during the early growing season. Although additional testing is warranted, this study showed a great promise for a remote sensing approach for forecasting perennial bioenergy grass yields to support critical economic and logistical decisions of bioeconomy stakeholders.

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NASA laser remote sensing technology needs for earth science in the next decade and beyond

10.1117/12.740063 ◽

2007 ◽

Author(s):

David M. Tratt ◽

Jon M. Neff ◽

Azita Valinia

Keyword(s):

Remote Sensing ◽

Earth Science ◽

Remote Sensing Technology ◽

Sensing Technology ◽

Laser Remote Sensing

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Effects of Winter Snow Cover on Spring Soil Moisture Based on Remote Sensing Data Product over Farmland in Northeast China

Remote Sensing ◽

10.3390/rs12172716 ◽

2020 ◽

Vol 12 (17) ◽

pp. 2716

Author(s):

Shuang Liang ◽

Xiaofeng Li ◽

Xingming Zheng ◽

Tao Jiang ◽

Xiaojie Li ◽

...

Keyword(s):

Remote Sensing ◽

Soil Moisture ◽

Linear Regression ◽

Snow Cover ◽

Snow Depth ◽

Northeast China ◽

Regression Models ◽

Linear Regression Models ◽

Winter Snow ◽

The Relationship

Spring soil moisture (SM) is of great importance for monitoring agricultural drought and waterlogging in farmland areas. While winter snow cover has an important impact on spring SM, relatively little research has examined the correlation between winter snow cover and spring SM in great detail. To understand the effects of snow cover on SM over farmland, the relationship between winter snow cover parameters (maximum snow depth (MSD) and average snow depth (ASD)) and spring SM in Northeast China was examined based on 30 year passive microwave snow depth (SD) and SM remote-sensing products. Linear regression models based on winter snow cover were established to predict spring SM. Moreover, 4 year SD and SM data were applied to validate the performance of the linear regression models. Additionally, the effects of meteorological factors on spring SM also were analyzed using multiparameter linear regression models. Finally, as a specific application, the best-performing model was used to predict the probability of spring drought and waterlogging in farmland in Northeast China. Our results illustrated the positive effects of winter snow cover on spring SM. The average correlation coefficient (R) of winter snow cover and spring SM was above 0.5 (significant at a 95% confidence level) over farmland. The performance of the relationship between snow cover and SM in April was better than that in May. Compared to the multiparameter linear regression models in terms of fitting coefficient, MSD can be used as an important snow parameter to predict spring drought and waterlogging probability in April. Specifically, if the relative SM threshold is 50% when spring drought occurs in April, the prediction probability of the linear regression model concerning snow cover and spring SM can reach 74%. This study improved our understanding of the effects of winter snow cover on spring SM and will be beneficial for further studies on the prediction of spring drought.

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Development of a Multiple Linear Regression Model for Meteorological Drought Index Estimation Based on Landsat Satellite Imagery

Water ◽

10.3390/w12123393 ◽

2020 ◽

Vol 12 (12) ◽

pp. 3393

Author(s):

Seon Woo Kim ◽

Donghwi Jung ◽

Yun-Jae Choung

Keyword(s):

Remote Sensing ◽

Linear Regression ◽

Multiple Linear Regression ◽

Drought Index ◽

Linear Regression Analysis ◽

Multiple Linear Regression Model ◽

Meteorological Drought ◽

Drought Indices ◽

Index Estimation ◽

Cumulative Precipitation

Climate polarization due to global warming has increased the intensity of drought in some regions, and the need for drought estimation studies to help minimize damage is increasing. In this study, we constructed remote sensing and climate data for Boryeong, Chungcheongnam-do, Korea, and developed a model for drought index estimation by classifying data characteristics and applying multiple linear regression analysis. The drought indices estimated in this study include four types of standardized precipitation indices (SPI1, SPI3, SPI6, and SPI9) used as meteorological drought indices and calculated through cumulative precipitation. We then applied statistical analysis to the developed model and assessed its ability as a drought index estimation tool using remote sensing data. Our results showed that its adj.R2 value, achieved using cumulative precipitation for one month, was very low (approximately 0.003), while for the SPI3, SPI6, and SPI9 models, the adj.R2 values were significantly higher than the other models at 0.67, 0.64, and 0.56, respectively, when the same data were used.

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