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.

An integrated agricultural drought monitoring model based on multi-source remote sensing data: model development and application

2019 ◽  
Vol 39 (20) ◽  
Author(s):  
温庆志 WEN Qingzhi ◽  
孙鹏 SUN Peng ◽  
张强 ZHANG Qing ◽  
刘嘉敏 LIU Jiamin ◽  
史培军 SHI Peijun
Keyword(s):  
Remote Sensing ◽  
Data Model ◽  
Model Development ◽  
Remote Sensing Data ◽  
Agricultural Drought ◽  
Drought Monitoring ◽  
Sensing Data ◽  
Model Based ◽  
Monitoring Model

scholarly journals Construction of Comprehensive Drought Monitoring Model in Jing-Jin-Ji Region Based on Multisource Remote Sensing Data

Water ◽  
2019 ◽  
Vol 11 (5) ◽  
pp. 1077 ◽  
Author(s):  
Haozhe Yu ◽  
Lijuan Li ◽  
Yang Liu ◽  
Jiuyi Li
Keyword(s):  
Remote Sensing ◽  
Correlation Coefficient ◽  
Remote Sensing Data ◽  
Condition Index ◽  
Agricultural Drought ◽  
Drought Monitoring ◽  
Sensing Data ◽  
Monitoring Model ◽  
Crop Area ◽  
Unit Yield

Drought is a complex hazard that has more adverse effects on agricultural production and economic development. Studying drought monitoring techniques and assessment methods can improve our ability to respond to natural disasters. Numerous drought indices deriving from meteorological or remote sensing data are focused mainly on monitoring single drought response factors such as soil or vegetation, and the ability to reflect comprehensive information on drought was poor. This study constructed a comprehensive drought-monitoring model considering the drought factors including precipitation, vegetation growth status, and soil moisture balance during the drought process for the Jing-Jin-Ji region, China. The comprehensive drought index of remote sensing (CDIR), a drought indicator deduced by the model, was composed of the vegetation condition index (VCI), the temperature condition index (TCI), and the precipitation condition index (PCI). The PCI was obtained from the Tropical Rainfall Measuring Mission (TRMM) satellite. The VCI and TCI were obtained from a moderate-resolution imaging spectroradiometer (MODIS). In this study, a heavy drought process was accurately explored using the CDIR in the Jing-Jin-Ji region in 2016. Finally, a three-month scales standardized precipitation index (SPI-3), drought affected crop area, and standardized unit yield of wheat were used as validation to evaluate the accuracy of this model. The results showed that the CDIR is closely related to the SPI-3, as well as variations in the drought-affected crop area and standardized unit yield of crop. The correlation coefficient of the CDIR with SPI-3 was between 0.45 and 0.85. The correlation coefficient between the CDIR and drought affected crop was between −0.81 and −0.86. Moreover, the CDIR was positively correlated with the standardized unit yield of crop. It showed that the CDIR index is a decent indicator that can be used for integrated drought monitoring and that it can synthetically reflect meteorological and agricultural drought information.

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.

Study on Agricultural Drought Monitoring Based on Multi-Sources Remote Sensing Data

2014 ◽  
Vol 12 (3) ◽  
pp. 659-666
Author(s):  
He Huang ◽  
Yida Fan ◽  
Siquan Yang ◽  
Wenbo Li ◽  
Haixia He ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Data ◽  
Agricultural Drought ◽  
Drought Monitoring ◽  
Sensing Data

scholarly journals Agricultural Drought Monitoring by MODIS Potential Evapotranspiration Remote Sensing Data Application

2020 ◽  
Vol 12 (20) ◽  
pp. 3411
Author(s):  
Kamil Szewczak ◽  
Helena Łoś ◽  
Rafał Pudełko ◽  
Andrzej Doroszewski ◽  
Łukasz Gluba ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Potential Evapotranspiration ◽  
Meteorological Data ◽  
Measurement Data ◽  
Remote Sensing Data ◽  
Absolute Error ◽  
Agricultural Drought ◽  
Drought Monitoring ◽  
Trend Test ◽  
Sensing Data

The current Polish Agricultural Drought Monitoring System (ADMS) adopted Climatic Water Balance (CWB) as the main indicator of crop losses caused by drought conditions. All meteorological data needed for CWB assessment are provided by the ground meteorological stations network. In 2018, the network consisted of 665 stations, among which in only 58 stations full weather parameters were registered. Therefore, only these stations offered a possibility to estimate the exact values of potential evapotranspiration, which is a component of the CWB algorithm. This limitation affects the quality of CWB raster maps, interpolated on the basis of the meteorological stations network for the entire country. However, the interpolation process itself may introduce errors; therefore, the adaptation of satellite data (that are spatially continuous) should be taken into account, even if the lack of data due to cloudiness remains a serious problem. In this paper, we involved the remote sensing data from MODIS instrument and considered the ability to integrate those data with values determined by using ground measurements. The paper presents results of comparisons for the CWB index assessed using ground station data and those obtained from potential evapotranspiration as the product from Moderate Resolution Imaging Spectroradiometer (MODIS) remote sensing instrument. The comparisons of results were performed for specific points (locations of ground stations) and were expressed by differences in means values. Analysis of Pearson’s correlation coefficient (r), Mann–Kendal trend test (Z-index), mean absolute error (MAE) and root mean square error (RMSE) for ten years’ series were evaluated and are presented. In addition, the basic spatial interpretation of results has been proposed. The correlation test revealed the r coefficient in the range from 0.06 to 0.68. The results show good trend agreement in time between two types of CWB with constantly higher values of this index, which is estimated using ground measurement data. In results for 34 (from 43 analyzed) stations the Mann–Kendal test provide the consistent trend, and only nine trends were inconsistent. Analyses revealed that the disagreement between the two considered indices (determined in different ways) increased significantly in the warmer period with a significant break point between R7 and R8 that falls at the end of May for each examined year. The value of MAE varied from 80 mm to 135 mm.

The Intelligent Classifier’s Building of Typical Vegetation Based on Hyper-Spectral Data

2014 ◽  
Vol 1073-1076 ◽  
pp. 1972-1976
Author(s):  
Jie Zhang ◽  
Hao Yan Zhao ◽  
Min Xia Zhang
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Data ◽  
Dynamic Monitoring ◽  
Large Area ◽  
Desert Vegetation ◽  
Sensing Data ◽  
Monitoring Model ◽  
Hyper Spectral ◽  
Bp Neural Network Model ◽  
Intelligent Process

With 3S comprehensive analysis on vegetation and the further development of hyper-spectral technology, the dynamic monitor of large area vegetation in long-term has become the trend. Intelligent process, combined the remote sensing data and field data, constructing dynamic monitoring model, plays an important guilding role in ecological security and balance. By using hyper-spectral remote sensing data of desert vegetation, three groups of spectral characteristic parameters were selected as input data of typical desert vegetation in the research, and vegetation types were selected as output data. Typical vegetation classifier was constructed based on the BP neural network model to study the vegetation classification.

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