scholarly journals Improved Multi-Sensor Satellite-Based Aboveground Biomass Estimation by Selecting Temporally Stable Forest Inventory Plots Using NDVI Time Series

Forests ◽  
2016 ◽  
Vol 7 (12) ◽  
pp. 169 ◽  
Author(s):  
Mikhail Urbazaev ◽  
Christian Thiel ◽  
Mirco Migliavacca ◽  
Markus Reichstein ◽  
Pedro Rodriguez-Veiga ◽  
...  
Keyword(s):  
Time Series ◽  
Aboveground Biomass ◽  
Forest Inventory ◽  
Biomass Estimation ◽  
Ndvi Time Series

scholarly journals Application of Synthetic NDVI Time Series Blended from Landsat and MODIS Data for Grassland Biomass Estimation

2015 ◽  
Vol 8 (1) ◽  
pp. 10 ◽  
Author(s):  
Binghua Zhang ◽  
Li Zhang ◽  
Dong Xie ◽  
Xiaoli Yin ◽  
Chunjing Liu ◽  
...  
Keyword(s):  
Time Series ◽  
Biomass Estimation ◽  
Modis Data ◽  
Ndvi Time Series

scholarly journals Forest Inventory and Aboveground Biomass Estimation with Terrestrial LiDAR in the Tropical Forest of Malaysia

2020 ◽  
Vol 46 (2) ◽  
pp. 130-145 ◽  
Author(s):  
Solomon M. Beyene ◽  
Yousif A. Hussin ◽  
Henk E. Kloosterman ◽  
Mohd Hasmadi Ismail
Keyword(s):  
Tropical Forest ◽  
Aboveground Biomass ◽  
Forest Inventory ◽  
Biomass Estimation ◽  
Terrestrial Lidar

Analysis on Three NDVI Time-series Reconstruction Methods and Their Applications in North Tibet

2011 ◽  
Vol 13 (1) ◽  
pp. 133-143 ◽  
Author(s):  
Chunqiao SONG ◽  
Songcai YOU ◽  
Linghong KE ◽  
Gaohuan LIU
Keyword(s):  
Time Series ◽  
Reconstruction Methods ◽  
Ndvi Time Series

Biomass of understory species of Pinus halepensis Mill. forests in the Kassandra Peninsula - Chalkidiki

1998 ◽  
Vol 63 ◽  
Author(s):  
P. Smiris ◽  
F. Maris ◽  
K. Vitoris ◽  
N. Stamou ◽  
P. Ganatsas
Keyword(s):  
Moisture Content ◽  
Aboveground Biomass ◽  
Quercus Ilex ◽  
Pinus Halepensis ◽  
Biomass Estimation ◽  
Regression Equations ◽  
Pistacia Lentiscus ◽  
Quercus Coccifera ◽  
Understory Species ◽  
Cistus Incanus

This  study deals with the biomass estimation of the understory species of Pinus halepensis    forests in the Kassandra peninsula, Chalkidiki (North Greece). These  species are: Quercus    coccifera, Quercus ilex, Phillyrea media, Pistacia lentiscus, Arbutus  unedo, Erica arborea, Erica    manipuliflora, Smilax aspera, Cistus incanus, Cistus monspeliensis,  Fraxinus ornus. A sample of    30 shrubs per species was taken and the dry and fresh weights and the  moisture content of    every component of each species were measured, all of which were processed  for aboveground    biomass data. Then several regression equations were examined to determine  the key words.

A clustering method for inter-annual NDVI time series

2021 ◽  
Vol 12 (8) ◽  
pp. 819-826
Author(s):  
Zhen Yang ◽  
Yingying Shen ◽  
Jing Li ◽  
Huawei Jiang ◽  
like Zhao
Keyword(s):  
Time Series ◽  
Clustering Method ◽  
Ndvi Time Series

scholarly journals Estimating Aboveground Biomass Using Sentinel-2 MSI Data and Ensemble Algorithms for Grassland in the Shengjin Lake Wetland, China

2021 ◽  
Vol 13 (8) ◽  
pp. 1595
Author(s):  
Chunhua Li ◽  
Lizhi Zhou ◽  
Wenbin Xu
Keyword(s):  
Aboveground Biomass ◽  
Carbon Accounting ◽  
Dynamic Monitoring ◽  
Gradient Boosting ◽  
Biomass Estimation ◽  
Floodplain Wetlands ◽  
Red Edge ◽  
Extreme Gradient Boosting ◽  
Ensemble Algorithms ◽  
Sentinel 2

Wetland vegetation aboveground biomass (AGB) directly indicates wetland ecosystem health and is critical for water purification, carbon cycle, and biodiversity conservation. Accurate AGB estimation is essential for the monitoring and supervision of ecosystems, especially in seasonal floodplain wetlands. This paper explored the capability of spectral and texture features from the Sentinel-2 Multispectral Instrument (MSI) for modeling grassland AGB using random forest (RF) and extreme gradient boosting (XGBoost) algorithms in Shengjin Lake wetland (a Ramsar site). We use five-fold cross-validation to verify the model effectiveness. The results indicated that the RF and XGBoost models had a robust and efficient performance (with root mean square error (RMSE) of 126.571 g·m−2 and R2 of 0.844 for RF, RMSE of 112.425 g·m−2 and R2 of 0.869 for XGBoost), and the XGBoost models, by contrast, performed better. Both traditional and red-edge vegetation indices (VIs) obtained satisfactory results of AGB estimation (RMSE = 127.936 g·m−2, RMSE = 125.879 g·m−2 in XGBoost models, respectively), with the red-edge VIs contributed more to the AGB models. Moreover, we selected eight gray-level co-occurrence matrix (GLCM) textures calculated by four processing window sizes using the mean value of four offsets, and further analyzed the results of three analysis sets. Textures derived from traditional and red-edge bands using a 7 × 7 window size performed better in biomass estimation. This finding suggested that textures derived from the traditional bands were as important as the red-edge bands. The introduction of textures moderately improved the accuracy of modeling AGB, whereas the use of textures alo ne was not satisfactory. This research demonstrated that using the Sentinel-2 MSI and the two ensemble algorithms is an effective method for long-term dynamic monitoring and assessment of grass AGB in seasonal floodplain wetlands, which can support sustainable management and carbon accounting of wetland ecosystems.

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