Accuracy comparison of various remote sensing data sources in the retrieval of forest stand attributes

Currently, the worsening impacts of urbanizations have been impelled to the importance of monitoring and management of existing urban trees, securing sustainable use of the available green spaces. Urban tree species identification and evaluation of their roles in atmospheric Carbon Stock (CS) are still among the prime concerns for city planners regarding initiating a convenient and easily adaptive urban green planning and management system. A detailed methodology on the urban tree carbon stock calibration and mapping was conducted in the urban area of Brussels, Belgium. A comparative analysis of the mapping outcomes was assessed to define the convenience and efficiency of two different remote sensing data sources, Light Detection and Ranging (LiDAR) and WorldView-3 (WV-3), in a unique urban area. The mapping results were validated against field estimated carbon stocks. At the initial stage, dominant tree species were identified and classified using the high-resolution WorldView3 image, leading to the final carbon stock mapping based on the dominant species. An object-based image analysis approach was employed to attain an overall accuracy (OA) of 71% during the classification of the dominant species. The field estimations of carbon stock for each plot were done utilizing an allometric model based on the field tree dendrometric data. Later based on the correlation among the field data and the variables (i.e., Normalized Difference Vegetation Index, NDVI and Crown Height Model, CHM) extracted from the available remote sensing data, the carbon stock mapping and validation had been done in a GIS environment. The calibrated NDVI and CHM had been used to compute possible carbon stock in either case of the WV-3 image and LiDAR data, respectively. A comparative discussion has been introduced to bring out the issues, especially for the developing countries, where WV-3 data could be a better solution over the hardly available LiDAR data. This study could assist city planners in understanding and deciding the applicability of remote sensing data sources based on their availability and the level of expediency, ensuring a sustainable urban green management system.

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Assessment of Forest-Stand Destruction by Fires Based on Remote-Sensing Data on the Seasonal Distribution of Burned Areas

Contemporary Problems of Ecology ◽

10.1134/s1995425521070027 ◽

2021 ◽

Vol 14 (7) ◽

pp. 711-716

Author(s):

S. A. Bartalev ◽

F. V. Stytsenko

Keyword(s):

Remote Sensing ◽

Remote Sensing Data ◽

Forest Stand ◽

Seasonal Distribution ◽

Sensing Data ◽

Burned Areas

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Remote Sensing Data Sources

Swiss National Forest Inventory – Methods and Models of the Fourth Assessment - Managing Forest Ecosystems ◽

10.1007/978-3-030-19293-8_3 ◽

2019 ◽

pp. 95-100

Author(s):

Christian Ginzler

Keyword(s):

Remote Sensing ◽

Remote Sensing Data ◽

Data Sources ◽

Sensing Data

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Forest Stand Extraction: Which Optimal Remote Sensing Data Source(S)?

IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium ◽

10.1109/igarss.2018.8518803 ◽

2018 ◽

Author(s):

Clement Dechesne ◽

Clement Mallet ◽

Arnaud Le Bris ◽

Valerie Gouet-Brunet

Keyword(s):

Remote Sensing ◽

Remote Sensing Data ◽

Forest Stand ◽

Sensing Data ◽

Data Source

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

INMATEH Agricultural Engineering ◽

10.35633/inmateh-64-41 ◽

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.

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

Drones ◽

10.3390/drones4020021 ◽

2020 ◽

Vol 4 (2) ◽

pp. 21 ◽

Cited By ~ 1

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.

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