scholarly journals Mapping Canopy Heights of Poplar Plantations in Plain Areas Using ZY3-02 Stereo and Multispectral Data

2019 ◽  
Vol 8 (3) ◽  
pp. 106 ◽  
Author(s):  
Mingbo Liu ◽  
Chunxiang Cao ◽  
Wei Chen ◽  
Xuejun Wang
Keyword(s):  
Forest Canopy ◽  
Remote Sensing Data ◽  
Canopy Height ◽  
Coefficient Of Determination ◽  
Surface Model ◽  
Green Band ◽  
Entire Study ◽  
Multispectral Data ◽  
Poplar Plantations ◽  
Entire Study Area

Forest canopy height plays an important role in forest management and ecosystem modeling. There are a variety of techniques employed to map forest height using remote sensing data but it is still necessary to explore the use of new data and methods. In this study, we demonstrate an approach for mapping canopy heights of poplar plantations in plain areas through a combination of stereo and multispectral data from China’s latest civilian stereo mapping satellite ZY3-02. First, a digital surface model (DSM) was extracted using photogrammetry methods. Then, canopy samples and ground samples were selected through manual interpretation. Canopy height samples were obtained by calculating the DSM elevation differences between the canopy samples and ground samples. A regression model was used to correlate the reflectance of a ZY3-02 multispectral image with the canopy height samples, in which the red band and green band reflectance were selected as predictors. Finally, the model was extrapolated to the entire study area and a wall-to-wall forest canopy height map was obtained. The validation of the predicted canopy height map reported a coefficient of determination (R2) of 0.72 and a root mean square error (RMSE) of 1.58 m. This study demonstrates the capacity of ZY3-02 data for mapping the canopy height of pure plantations in plain areas.

scholarly journals Estimates of Forest Canopy Height Using a Combination of ICESat-2/ATLAS Data and Stereo-Photogrammetry

2020 ◽  
Vol 12 (21) ◽  
pp. 3649
Author(s):  
Xiaojuan Lin ◽  
Min Xu ◽  
Chunxiang Cao ◽  
Yongfeng Dang ◽  
Barjeece Bashir ◽  
...  
Keyword(s):  
Forest Canopy ◽  
Multiple Scales ◽  
Airborne Lidar ◽  
Canopy Height ◽  
Coefficient Of Determination ◽  
Future Research ◽  
Surface Model ◽  
Landsat 8 ◽  
Stereo Images ◽  
Vegetation Height

Forest canopy height is an indispensable forest vertical structure parameter for understanding the carbon cycle and forest ecosystem services. A variety of studies based on spaceborne Lidar, such as ICESat, ICESat-2 and airborne Lidar, were conducted to estimate forest canopy height at multiple scales. However, while a few studies have been conducted based on ICESat-2 simulated data from airborne Lidar data, few studies have analyzed ATL08 and ATL03 products derived from the ATLAS sensor onboard ICESat-2 for regional vegetation canopy height mapping. It is necessary and promising to explore how data obtained by ICESat-2 can be applied to estimate forest canopy height. This study proposes a new means to estimate forest canopy height, defined as the mean height of trees within a given forest area, using a combination of ICESat-2 ATL08 and ATL03 data and ZY-3 satellite stereo images. Five procedures were used to estimate the forest canopy height of the city of Nanning in China: (1) Processing ground photons in a 30 m × 30 m grid; (2) Extracting a digital surface model (DSM) using ZY-3 stereo images; (3) Calculating a discontinuous canopy height model (CHM) dataset; (4) Validating the DSM and ground photon height using GEDI data; (5) Estimating the regional wall-to-wall forest canopy height product based on the backpropagation artificial neural network (BP-ANN) model and Landsat 8 vegetation indices and independent accuracy assessments with field measured plots. The validation shows a root mean square error (RMSE) of 3.34 m to 3.47 m and a coefficient of determination R2 = 0.51. The new method shows promise and can be used for large-scale forest canopy height mapping at various resolutions or in combination with other data, such as SAR images. Finally, this study analyzes resolutions and how to filter effective data when ATL08 data are directly used to generate regional or global vegetation height products, which will be the focus of future research.

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 Synthesis of Leaf-on and Leaf-off Unmanned Aerial Vehicle (UAV) Stereo Imagery for the Inventory of Aboveground Biomass of Deciduous Forests

2019 ◽  
Vol 11 (7) ◽  
pp. 889 ◽  
Author(s):  
Wenjian Ni ◽  
Jiachen Dong ◽  
Guoqing Sun ◽  
Zhiyu Zhang ◽  
Yong Pang ◽  
...  
Keyword(s):  
Aboveground Biomass ◽  
Point Cloud ◽  
Forest Canopy ◽  
Deciduous Forest ◽  
Ground Surface ◽  
Digital Terrain Model ◽  
Canopy Height ◽  
Surface Model ◽  
Lidar Data ◽  
Stereo Imagery

Applications of stereo imagery acquired by cameras onboard unmanned aerial vehicles (UAVs) as practical forest inventory tools are hindered by the unavailability of ground surface elevation. It is still a challenging issue to remove the elevation of ground surface in leaf-on stereo imagery to extract forest canopy height without the help of lidar data. This study proposed a method for the extraction of forest canopy height through the synthesis of UAV stereo imagery of leaf-on and leaf-off, and further demonstrated that the extracted forest canopy height could be used for the inventory of deciduous forest aboveground biomass (AGB). The points cloud of the leaf-on and leaf-off stereo imagery was firstly extracted by an algorithm of structure from motion (SFM) using the same ground control points (GCP). The digital surface model (DSM) was produced by rasterizing the point cloud of UAV leaf-on. The point cloud of UAV leaf-off was processed by iterative median filtering to remove vegetation points, and the digital terrain model (DTM) was generated by the rasterization of the filtered point cloud. The mean canopy height model (MCHM) was derived from the DSM subtracted by the DTM (i.e., DSM-DTM). Forest AGB maps were generated using models developed based on the MCHM and sampling plots of forest AGB and were evaluated by those of lidar. Results showed that forest AGB maps from UAV stereo imagery were highly correlated with those from lidar data with R2 higher than 0.94 and RMSE lower than 10.0 Mg/ha (i.e., relative RMSE 18.8%). These results demonstrated that UAV stereo imagery could be used as a practical inventory tool for deciduous forest AGB.

scholarly journals Comparison of Different Analytical Strategies for Classifying Invasive Wetland Vegetation in Imagery from Unpiloted Aerial Systems (UAS)

2021 ◽  
Vol 13 (23) ◽  
pp. 4733
Author(s):  
Louis Will Jochems ◽  
Jodi Brandt ◽  
Andrew Monks ◽  
Megan Cattau ◽  
Nicholas Kolarik ◽  
...  
Keyword(s):  
Invasive Plants ◽  
Canopy Height ◽  
Surface Model ◽  
Wetland Ecosystems ◽  
Multispectral Data ◽  
Emergent Plants ◽  
Analysis Workflow ◽  
Analytical Strategies ◽  
And Control ◽  
Aerial Systems

Detecting newly established invasive plants is key to prevent further spread. Traditional field surveys are challenging and often insufficient to identify the presence and extent of invasions. This is particularly true for wetland ecosystems because of difficult access, and because floating and submergent plants may go undetected in the understory of emergent plants. Unpiloted aerial systems (UAS) have the potential to revolutionize how we monitor invasive vegetation in wetlands, but key components of the data collection and analysis workflow have not been defined. In this study, we conducted a rigorous comparison of different methodologies for mapping invasive Emergent (Typha × glauca (cattail)), Floating (Hydrocharis morsus-ranae (European frogbit)), and Submergent species (Chara spp. and Elodea canadensis) using the machine learning classifier, random forest, in a Great Lakes wetland. We compared accuracies using (a) different spatial resolutions (11 cm pixels vs. 3 cm pixels), (b) two classification approaches (pixel- vs. object-based), and (c) including structural measurements (e.g., surface/canopy height models and rugosity as textural metrics). Surprisingly, the coarser resolution (11 cm) data yielded the highest overall accuracy (OA) of 81.4%, 2.5% higher than the best performing model of the finer (3 cm) resolution data. Similarly, the Mean Area Under the Receiving Operations Characteristics Curve (AUROC) and F1 Score from the 11 cm data yielded 15.2%, and 6.5% higher scores, respectively, than those in the 3 cm data. At each spatial resolution, the top performing models were from pixel-based approaches and included surface model data over those with canopy height or multispectral data alone. Overall, high-resolution maps generated from UAS classifications will enable early detection and control of invasive plants. Our workflow is likely applicable to other wetland ecosystems threatened by invasive plants throughout the globe.

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 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.

Zwischenevaluation des Wald-Wild-Management-Instruments (WWMI) im Pilotprogramm effor2 «Wald und Wild» | Preliminary evaluation of the Game-Forest-Management-Tool (GFMT) in the effor2 pilot programme «Game and Forest»

2003 ◽  
Vol 154 (8) ◽  
pp. 305-313 ◽  
Author(s):  
Roman Eyholzer ◽  
Martin Baumann ◽  
Rolf Manser
Keyword(s):  
Forest Management ◽  
Management Practice ◽  
Management Tool ◽  
Preliminary Evaluation ◽  
Optimal Contract ◽  
Technical Tool ◽  
Entire Study ◽  
Pilot Programme ◽  
Complex Problems ◽  
Entire Study Area

Faced with the challenging task of balancing forest interests and wildlife, the Swiss Forest Agency initiated the pilot programme«Game and Forest», which is committed to a philosophy of goal-oriented management practice and a redesign of forestry subsidizing. Within this programme the diverse goals of forestry and hunting have been amalgamated to a superimposed goal and set out in a corresponding contract. The Game-Forest-Management-Tool (GFMT) has been divised to simulate the effect of various strategies to deal with the complex problems of forest-wildlife. Optimal contract-fulfilling procedures can be simulated on a PC using this technical tool. The efficiency of the measures suggested by simulations that were carried out are being tested in a study area within the pilot programme, «Game and Forest». Half way through this trial, after two years, we can say that there has been no significant increase of non-browsed areas. In 2004, after the collection of data for the entire study area, we will be able to tell whether applying this computer simulated strategy truly leads to an augmentation of non-browsed area and a decrease in bark-peeled forests in the pilot area.

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