Urban Flood Mapping Based on Unmanned Aerial Vehicle Remote Sensing and Random Forest Classifier—A Case of Yuyao, China

Water 2015, 7(4), 1437-1455; doi:10.3390/w7041437

Author

Quanlong Feng ¹

, Jiantao Liu ¹

 and Jianhua Gong ^1,2,* 

¹

State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth Chinese Academy of Sciences, No. 20 Datun Road, Chaoyang District, Beijing 100101, China

²

Zhejiang-Chinese Academy of Sciences (CAS) Application Center for Geoinformatics, No. 568 Jinyang East Road, Jiashan 314100, China

^*

Author to whom correspondence should be addressed.

Academic Editor: Yong Wang

Received: 30 November 2014 / Revised: 13 March 2015 / Accepted: 25 March 2015 / Published: 31 March 2015

(This article belongs to the Special Issue Advances in Remote Sensing of Flooding)

View Full-Text   |     Download PDF [11645 KB, uploaded 9 June 2015]   |    

 Browse Figures

 

Abstract 

Flooding is a severe natural hazard, which poses a great threat to human life and property, especially in densely-populated urban areas. As one of the fastest developing fields in remote sensing applications, an unmanned aerial vehicle (UAV) can provide high-resolution data with a great potential for fast and accurate detection of inundated areas under complex urban landscapes. In this research, optical imagery was acquired by a mini-UAV to monitor the serious urban waterlogging in Yuyao, China. Texture features derived from gray-level co-occurrence matrix were included to increase the separability of different ground objects. A Random Forest classifier, consisting of 200 decision trees, was used to extract flooded areas in the spectral-textural feature space. Confusion matrix was used to assess the accuracy of the proposed method. Results indicated the following: (1) Random Forest showed good performance in urban flood mapping with an overall accuracy of 87.3% and a Kappa coefficient of 0.746; (2) the inclusion of texture features improved classification accuracy significantly; (3) Random Forest outperformed maximum likelihood and artificial neural network, and showed a similar performance to support vector machine. The results demonstrate that UAV can provide an ideal platform for urban flood monitoring and the proposed method shows great capability for the accurate extraction of inundated areas. View Full-Text

Keywords: UAV;  flood mapping;  urban landscape;  random forest;  texture analysis

▼ Figures

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

For further details log on website :
http://www.mdpi.com/2073-4441/7/4/1437

Estimation of Airborne Lidar-Derived Tropical Forest Canopy Height Using Landsat Time Series in Cambodia

Remote Sens. 2014, 6(11), 10750-10772; doi:10.3390/rs61110750

Author

Tetsuji Ota ^1,†,* , Oumer S. Ahmed ^2,†

, Steven E. Franklin ³

, Michael A. Wulder ⁴

, Tsuyoshi Kajisa ⁵

, Nobuya Mizoue ¹

, Shigejiro Yoshida ¹

, Gen Takao ⁶

, Yasumasa Hirata ⁶

, Naoyuki Furuya ⁷

, Takio Sano ⁸

, Sokh Heng ⁹

 and Ma Vuthy ⁹

¹

Faculty of Agriculture, Kyushu University, 6-10-1 Hakozaki, Fukuoka 812-8581, Japan

²

Geomatics, Remote Sensing and Land Resources Laboratory, Department of Geography, Trent University, 1600 West Bank Drive Peterborough, Ontario K9J 7B8, Canada

³

Department of Environmental and Resource Studies/Science, Department of Geography, and Office of the President, Trent University, 1600 West Bank Drive Peterborough, Ontario K9J 7B8, Canada

⁴

Canadian Forest Service (Pacific Forestry Centre), Natural Resources Canada, 506 West Burnside Road, Victoria, British Columbia V8Z 1M5, Canada

⁵

Faculty of Agriculture, Kagoshima University, Korimoto 1-21-24, Kagoshima 890-8580, Japan

⁶

Department of Forest Management, Forestry and Forest Products Research Institute, Matsunosato 1, Tsukuba 305-8687, Japan

⁷

Hokkaido Research Center, Forestry and Forest Products Research Institute, Hitsujigaoka 7, Toyohiraku, Sapporo 062-8516, Japan

⁸

Asia Air Survey Co., LTD, Shinyuri 21 Building, 1-2-2 Manpukuji, Asao-ku, Kawasaki 215-0004, Japan

⁹

Forest-Wildlife Research and Development Institute, Forestry Administration, Khan Sen Sok, Phnom Penh 12157, Cambodia

^†

These authors contributed equally to this work.

^*

Author to whom correspondence should be addressed. 

Received: 9 July 2014 / Revised: 3 October 2014 / Accepted: 8 October 2014 / Published: 6 November 2014

View Full-Text   |     Download PDF [2051 KB, uploaded 6 November 2014]   |    

 Browse Figures

 

Abstract 

In this study, we test and demonstrate the utility of disturbance and recovery information derived from annual Landsat time series to predict current forest vertical structure (as compared to the more common approaches, that consider a sample of airborne Lidar and single-date Landsat derived variables). Mean Canopy Height (MCH) was estimated separately using single date, time series, and the combination of single date and time series variables in multiple regression and random forest (RF) models. The combination of single date and time series variables, which integrate disturbance history over the entire time series, overall provided better MCH prediction than using either of the two sets of variables separately. In general, the RF models resulted in improved performance in all estimates over those using multiple regression. The lowest validation error was obtained using Landsat time series variables in a RF model (R² = 0.75 and RMSE = 2.81 m). Combining single date and time series data was more effective when the RF model was used (opposed to multiple regression). The RMSE for RF mean canopy height prediction was reduced by 13.5% when combining the two sets of variables as compared to the 3.6% RMSE decline presented by multiple regression. This study demonstrates the value of airborne Lidar and long term Landsat observations to generate estimates of forest canopy height using the random forest algorithm. View Full-Text

Keywords: Landsat time series;  airborne Lidar;  canopy height;  random forest

▼ Figures

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

For further details log on website :
http://www.mdpi.com/2072-4292/6/11/10750

Canopy Gap Mapping from Airborne Laser Scanning: An Assessment of the Positional and Geometrical Accuracy

Remote Sens. 2015, 7(9), 11267-11294; doi:10.3390/rs70911267

Author

Stéphanie Bonnet ^1,* , Rachel Gaulton ²

, François Lehaire ¹

 and Philippe Lejeune ¹

¹

Unit of Forest Management, Department of Biosystem Engineering, University of Liège,Gembloux Agro-Bio Tech, 2 Passage des déportés, 5030 Gembloux, Belgium

²

School of Civil Engineering and Geosciences, Newcastle University, Newcastle upon Tyne NE1-7RU, UK

^*

Author to whom correspondence should be addressed. 

Academic Editors: Nicolas Baghdadi, Heiko Balzter and Prasad S. Thenkabail

Received: 21 May 2015 / Revised: 12 August 2015 / Accepted: 27 August 2015 / Published: 2 September 2015

View Full-Text   |     Download PDF [15574 KB, uploaded 2 September 2015]   |    

 Browse Figures

 

Abstract 

Canopy gaps are small-scale openings in forest canopies which offer suitable micro-climatic conditions for tree regeneration. Field mapping of gaps is complex and time-consuming. Several studies have used Canopy Height Models (CHM) derived from airborne laser scanning (ALS) to delineate gaps but limited accuracy assessment has been carried out, especially regarding the gap geometry. In this study, we investigate three mapping methods based on raster layers produced from ALS leaf-off and leaf-on datasets: thresholding, per-pixel and per-object supervised classifications with Random Forest. In addition to the CHM, other metrics related to the canopy porosity are tested. The gap detection is good, with a global accuracy up to 82% and consumer’s accuracy often exceeding 90%. The Geometric Accuracy (GAc) was analyzed with the gap area, main orientation, gap shape-complexity index and a quantitative assessment index of the matching with reference gaps polygons. The GAc assessment shows difficulties in identifying a method which properly delineates gaps. The performance of CHM-based thresholding was exceeded by that of other methods, especially thresholding of canopy porosity rasters and the per-pixel supervised classification. Beyond assessing the methods performance, we argue the critical need for future ALS-based gap studies to consider the geometric accuracy of results. View Full-Text

Keywords: forest canopy gaps;  ALS;  raster-based approach;  segmentation;  random forest;  geometric accuracy;  stand types

▼ Figures

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

For further details log on website :
http://www.mdpi.com/2072-4292/7/9/11267

RAQ–A Random Forest Approach for Predicting Air Quality in Urban Sensing Systems

Sensors 2016, 16(1), 86; doi:10.3390/s16010086

Author

Ruiyun Yu ^1,* , Yu Yang ²

, Leyou Yang ¹

, Guangjie Han ³

 and Oguti Ann Move ¹

¹

Software College, Northeastern University, Shenyang 110819, China

²

Department of Computer Science, Rutgers University, New Brunswick, NJ 08854, USA

³

Department of Internet of Things Engineering, Hohai University, Changzhou 213022, China

^*

Author to whom correspondence should be addressed. 

Academic Editor: Leonhard M. Reindl

Received: 30 September 2015 / Revised: 26 December 2015 / Accepted: 7 January 2016 / Published: 9 January 2016

(This article belongs to the Section Sensor Networks)

View Full-Text   |     Download PDF [5733 KB, uploaded 11 January 2016]   |    

 Browse Figures

 

Abstract 

Air quality information such as the concentration of PM_2.5 is of great significance for human health and city management. It affects the way of traveling, urban planning, government policies and so on. However, in major cities there is typically only a limited number of air quality monitoring stations. In the meantime, air quality varies in the urban areas and there can be large differences, even between closely neighboring regions. In this paper, a random forest approach for predicting air quality (RAQ) is proposed for urban sensing systems. The data generated by urban sensing includes meteorology data, road information, real-time traffic status and point of interest (POI) distribution. The random forest algorithm is exploited for data training and prediction. The performance of RAQ is evaluated with real city data. Compared with three other algorithms, this approach achieves better prediction precision. Exciting results are observed from the experiments that the air quality can be inferred with amazingly high accuracy from the data which are obtained from urban sensing. View Full-Text

Keywords: air quality prediction;  random forest;  point of interest;  traffic

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

For further details log on website :
http://www.mdpi.com/1424-8220/16/1/86