scholarly journals Mapping Forest Canopy Height over Continental China Using Multi-Source Remote Sensing Data

2015 ◽  
Vol 7 (7) ◽  
pp. 8436-8452 ◽  
Author(s):  
Xiliang Ni ◽  
Yuke Zhou ◽  
Chunxiang Cao ◽  
Xuejun Wang ◽  
Yuli Shi ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Forest Canopy ◽  
Remote Sensing Data ◽  
Canopy Height ◽  
Sensing Data

scholarly journals Mapping Forest Canopy Height in Mountainous Areas Using ZiYuan-3 Stereo Images and Landsat Data

Forests ◽  
2019 ◽  
Vol 10 (2) ◽  
pp. 105 ◽  
Author(s):  
Mingbo Liu ◽  
Chunxiang Cao ◽  
Yongfeng Dang ◽  
Xiliang Ni
Keyword(s):  
Remote Sensing ◽  
Forest Canopy ◽  
Remote Sensing Data ◽  
Canopy Height ◽  
Landsat 8 ◽  
Surface Reflectance ◽  
Landsat Data ◽  
Sensing Data ◽  
Forest Height ◽  
Reflectance Data

Forest canopy height is an important parameter for studying biodiversity and the carbon cycle. A variety of techniques for mapping forest height using remote sensing data have been successfully developed in recent years. However, the demands for forest height mapping in practical applications are often not met, due to the lack of corresponding remote sensing data. In such cases, it would be useful to exploit the latest, cheaper datasets and combine them with free datasets for the mapping of forest canopy height. In this study, we proposed a method that combined ZiYuan-3 (ZY-3) stereo images, Shuttle Radar Topography Mission global 1 arc second data (SRTMGL1), and Landsat 8 Operational Land Imager (OLI) surface reflectance data. The method consisted of three procedures: First, we extracted a digital surface model (DSM) from the ZY-3, using photogrammetry methods and subtracted the SRTMGL1 to obtain a crude canopy height model (CHM). Second, we refined the crude CHM and correlated it with the topographically corrected Landsat 8 surface reflectance data, the vegetation indices, and the forest types through a Random Forest model. Third, we extrapolated the model to the entire study area covered by the Landsat data, and obtained a wall-to-wall forest canopy height product with 30 m × 30 m spatial resolution. The performance of the model was evaluated by the Random Forest’s out-of-bag estimation, which yielded a coefficient of determination (R2) of 0.53 and a root mean square error (RMSE) of 3.28 m. We validated the predicted forest canopy height using the mean forest height measured in the field survey plots. The validation result showed an R2 of 0.62 and a RMSE of 2.64 m.

scholarly journals Forest Canopy Height Mapping by Synergizing ICESat-2, Sentinel-1, Sentinel-2 and Topographic Information Based on Machine Learning Methods

2022 ◽  
Vol 14 (2) ◽  
pp. 364
Author(s):  
Zhilong Xi ◽  
Huadong Xu ◽  
Yanqiu Xing ◽  
Weishu Gong ◽  
Guizhen Chen ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Forest Canopy ◽  
Remote Sensing Data ◽  
Canopy Height ◽  
Gradient Boosting ◽  
Forest Types ◽  
Height Estimation ◽  
Sensing Data ◽  
Continuous Forest ◽  
Estimation Models

Spaceborne LiDAR has been widely used to obtain forest canopy heights over large areas, but it is still a challenge to obtain spatio-continuous forest canopy heights with this technology. In order to make up for this deficiency and take advantage of the complementary for multi-source remote sensing data in forest canopy height mapping, a new method to estimate forest canopy height was proposed by synergizing the spaceborne LiDAR (ICESat-2) data, Synthetic Aperture Radar (SAR) data, multi-spectral images, and topographic data considering forest types. In this study, National Geographical Condition Monitoring (NGCM) data was used to extract the distributions of coniferous forest (CF), broadleaf forest (BF), and mixed forest (MF) in Hua’ nan forest area in Heilongjiang Province, China. Accordingly, the forest canopy height estimation models for whole forest (all forests together without distinguishing types, WF), CF, BF, and MF were established, respectively, by Radom Forest (RF) and Gradient Boosting Decision Tree (GBDT). The accuracy for established models and the forest canopy height obtained based on estimation models were validated consequently. The results showed that the forest canopy height estimation models considering forest types had better performance than the model grouping all types of forest together. Compared with GBDT, RF with optimal variables had better performance in forest canopy height estimation with Pearson’s correlation coefficient (R) and the root-mean-squared error (RMSE) values for CF, BF, and MF of 0.72, 0.59, 0.62, and 3.15, 3.37, 3.26 m, respectively. It has been validated that a synergy of ICESat-2 with other remote sensing data can make a crucial contribution to spatio-continuous forest canopy height mapping, especially for areas covered by different types of forest.

scholarly journals Pattern recognition of forest canopy using the airborne hyperspectral data and multi-bands high spatial resolution satellite sensor worldview-2 data. A results comparison and accuracy estimation

2019 ◽  
pp. 89-102
Author(s):  
V. V. Kozoderov ◽  
V. D. Egorov
Keyword(s):  
Remote Sensing ◽  
Pattern Recognition ◽  
Spatial Resolution ◽  
Forest Canopy ◽  
High Spatial Resolution ◽  
Remote Sensing Data ◽  
Hyperspectral Data ◽  
Automatic Data ◽  
Sensing Data ◽  
Satellite Sensor

Pattern recognition of forest surface from remote sensing data: using the airborne hyperspectral data and using multi-bands high spatial resolution satellite sensor WorldView‑2 data are investigated. The early proposed method and standard QDA method for calculations were used. A comparison of calculations results were conducted. A recognition calculation accuracy range for airborne and satellite remote sensing data for three forest surface fragments for different created data bases for recognition system has been assessed. Some opportunities of automatic data preparing of created system were displayed. Some special features of pattern recognition of forest surfaces from hyperspectral airborne data and from multi-bands high spatial resolution satellite data were discussed.

scholarly journals Application of Forest Canopy Density (FCD) Model for the Hotspot Monitoring of Crown Fire in Tebo, Jambi Province

2020 ◽  
Vol 4 (1) ◽  
Author(s):  
Muhammad Attorik Falensky ◽  
Anggieani Laras Sulti ◽  
Ranggas Dhuha Putra ◽  
Kuswantoro Marko
Keyword(s):  
Remote Sensing ◽  
Spatial Analysis ◽  
Forest Canopy ◽  
Remote Sensing Data ◽  
Forest Area ◽  
Landsat 8 ◽  
Crown Fire ◽  
Canopy Density ◽  
Sensing Data ◽  
Forest Canopy Density

<p><em>Indonesia is one of the owners of the 9th largest forest area in the world. Forest area in Indonesia reaches 884,950 km<sup>2</sup>. Tebo Regency is a regency in Jambi Province which has a wide forest area of 628,003 Ha. However, this forest area has been reduced due to the conversion of functions of Industrial Plantation Forests (HTI), oil palm plantations, and forest clearing activities for both settlements and plantations which led to the phenomenon of forest and land fires (karhutla). This study aims to get a better knowledge of crowns of fire potential locations in forest areas using remote sensing technology. Remote sensing data used in this study is from the satellite imagery </em><em>of </em><em>Landsat 8 OLI - TIRS in 2019. Remote sensing data is used to produce a Forest Canopy Density (FCD) model that can be overlap</em><em>ped with</em><em> a hotspot location, so the crown fire potential locations will be explored in the forest area of Tebo Regency, Jambi Province. Identification of hotspot patterns in Forest Areas was analyzed using spatial analysis. The results of this study are useful for the government as the information of the hotspot area as the cause of fires in the Forest Region of Tebo Regency Jambi Province.</em></p><strong><em>Keywords</em></strong><em>: Spatial Analysis, Forest Cover Density (FCD), Hotspots, Forest Areas, Remote Sensing</em>

TECHNOLOGY OF INTEGRATED ASSESSMENT OF WOOD RESOURCES BY REMOTE SENSING METHODS OF THE EARTH

2021 ◽  
pp. 129-138
Author(s):  
V. K. KHLYUSTOV ◽  
◽  
S. A. YURCHUK ◽  
D. V. KHLYUSTOV ◽  
A. M. GANIKHIN
Keyword(s):  
Remote Sensing ◽  
Spatial Resolution ◽  
Aerial Photography ◽  
Laser Scanning ◽  
Forest Canopy ◽  
Remote Sensing Data ◽  
Economic Sectors ◽  
Sensing Data ◽  
The Earth ◽  
Photogrammetric Method

The relevance and significance of the problem of automated forest inventory is dictated by regulatory documents defining the main directions and principles of digitalization of the country’s economic sectors, including the forest sector. The article is devoted to the problem of automated inventory of forests and digitalization of wood resources by technical means of ground-based taxation of stands, as well as remote aerial photography methods, analytical decoding of the forest canopy and determination of the complex of taxation indicators through the use of information and reference systems of multidimensional forest taxation standards. To construct an orthophotoplane and obtain a digital vegetation model, aerial photography works that meet the requirements of the photogrammetric method and the method of air-laser scanning (ALS) are described. The requirements for the parameters of aerial photography using the photogrammetric method, as well as for the parameters in the BOS, are set out. Variants of the technology of inventory of stands are proposed, indicating the appropriate tools for obtaining remote sensing data of the Earth. An assessment of the reliability of contour decoding of the species composition of stands with different spatial resolution of remote sensing data is given. The accuracy of digital vegetation models with different spatial resolution of data, the possibility of evaluating morphometric and volumetric indicators of tree crowns, as well as the resulting indicators of canopy closeness as a result of automation are indicated. An important element of the automated digitalization of wood resources is the allocation and taxation of cutting areas, the assessment of the commodity-monetary potential of stands allocated for logging.

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