Estimation of Coniferous Forest Above-Ground Biomass Using LiDAR and SPOT-5 Data

Author(s):  
Qisheng He
2016 ◽  
Vol 10 (4) ◽  
pp. 046003 ◽  
Author(s):  
Wang Li ◽  
Zheng Niu ◽  
Zengyuan Li ◽  
Cheng Wang ◽  
Mingquan Wu ◽  
...  

2013 ◽  
Vol 718-720 ◽  
pp. 360-365
Author(s):  
Jie Wu ◽  
Yu Huan Li ◽  
Zeng Bing Li ◽  
Jing Wang

The monitoring models to estimate the above-ground biomass weight and carbon and nitrogen (CN ) accumulation of corn at maturation stage were established based on SPOT-5 images to provide reference for prediction of land production, effective nutrient balance of cultivated soil and carbon and nitrogen cycles. The appropriate spectral parameters were filtered out and the optimal monitoring equations were established between spectral parameter and above-ground biomass weight and CN accumulation by using Pearson correlation analysis and linear and nonlinear simulations. The adjust brightness vegetation index (SAVI) showed a significant positive correlation with the above-ground biomass weight and carbon accumulation and the correlation coefficients were 0.831 and 0.846.The strongest correlation with nitrogen accumulation was ratio index (R3/R1) and the correlation coefficient was 0.844.The power function model based on SAVI inversed above-ground biomass weight and carbon accumulation was the best, R2 were 0.6982 and 0.7216.The linear function model based on R3 / R1 inversed nitrogen accumulation was the best and R2 was 0.7129.Based on the optimal monitoring models, the thematic maps were produced to monitor above-ground biomass and CN accumulation in corn at maturation stage. The estimating model based on the SPOT-5 images can achieve high precision in estimating the above-ground biomass weight and CN accumulation in corn and it has a wide application prospect.


Forests ◽  
2013 ◽  
Vol 4 (4) ◽  
pp. 984-1002 ◽  
Author(s):  
Qisheng He ◽  
Erxue Chen ◽  
Ru An ◽  
Yong Li

2017 ◽  
Vol 23 (2) ◽  
Author(s):  
AFSHAN ANJUM BABA ◽  
SYED NASEEM UL-ZAFAR GEELANI ◽  
ISHRAT SALEEM ◽  
MOHIT HUSAIN ◽  
PERVEZ AHMAD KHAN ◽  
...  

The plant biomass for protected areas was maximum in summer (1221.56 g/m2) and minimum in winter (290.62 g/m2) as against grazed areas having maximum value 590.81 g/m2 in autumn and minimum 183.75 g/m2 in winter. Study revealed that at Protected site (Kanidajan) the above ground biomass ranged was from a minimum (1.11 t ha-1) in the spring season to a maximum (4.58 t ha-1) in the summer season while at Grazed site (Yousmarag), the aboveground biomass varied from a minimum (0.54 t ha-1) in the spring season to a maximum of 1.48 t ha-1 in summer seasonandat Seed sown site (Badipora), the lowest value of aboveground biomass obtained was 4.46 t ha-1 in spring while as the highest (7.98 t ha-1) was obtained in summer.


2016 ◽  
Vol 13 (11) ◽  
pp. 3343-3357 ◽  
Author(s):  
Zun Yin ◽  
Stefan C. Dekker ◽  
Bart J. J. M. van den Hurk ◽  
Henk A. Dijkstra

Abstract. Observed bimodal distributions of woody cover in western Africa provide evidence that alternative ecosystem states may exist under the same precipitation regimes. In this study, we show that bimodality can also be observed in mean annual shortwave radiation and above-ground biomass, which might closely relate to woody cover due to vegetation–climate interactions. Thus we expect that use of radiation and above-ground biomass enables us to distinguish the two modes of woody cover. However, through conditional histogram analysis, we find that the bimodality of woody cover still can exist under conditions of low mean annual shortwave radiation and low above-ground biomass. It suggests that this specific condition might play a key role in critical transitions between the two modes, while under other conditions no bimodality was found. Based on a land cover map in which anthropogenic land use was removed, six climatic indicators that represent water, energy, climate seasonality and water–radiation coupling are analysed to investigate the coexistence of these indicators with specific land cover types. From this analysis we find that the mean annual precipitation is not sufficient to predict potential land cover change. Indicators of climate seasonality are strongly related to the observed land cover type. However, these indicators cannot predict a stable forest state under the observed climatic conditions, in contrast to observed forest states. A new indicator (the normalized difference of precipitation) successfully expresses the stability of the precipitation regime and can improve the prediction accuracy of forest states. Next we evaluate land cover predictions based on different combinations of climatic indicators. Regions with high potential of land cover transitions are revealed. The results suggest that the tropical forest in the Congo basin may be unstable and shows the possibility of decreasing significantly. An increase in the area covered by savanna and grass is possible, which coincides with the observed regreening of the Sahara.


2021 ◽  
Vol 21 ◽  
pp. 100462
Author(s):  
Sadhana Yadav ◽  
Hitendra Padalia ◽  
Sanjiv K. Sinha ◽  
Ritika Srinet ◽  
Prakash Chauhan

2020 ◽  
Vol 5 (1) ◽  
pp. 13
Author(s):  
Negar Tavasoli ◽  
Hossein Arefi

Assessment of forest above ground biomass (AGB) is critical for managing forest and understanding the role of forest as source of carbon fluxes. Recently, satellite remote sensing products offer the chance to map forest biomass and carbon stock. The present study focuses on comparing the potential use of combination of ALOSPALSAR and Sentinel-1 SAR data, with Sentinel-2 optical data to estimate above ground biomass and carbon stock using Genetic-Random forest machine learning (GA-RF) algorithm. Polarimetric decompositions, texture characteristics and backscatter coefficients of ALOSPALSAR and Sentinel-1, and vegetation indices, tasseled cap, texture parameters and principal component analysis (PCA) of Sentinel-2 based on measured AGB samples were used to estimate biomass. The overall coefficient (R2) of AGB modelling using combination of ALOSPALSAR and Sentinel-1 data, and Sentinel-2 data were respectively 0.70 and 0.62. The result showed that Combining ALOSPALSAR and Sentinel-1 data to predict AGB by using GA-RF model performed better than Sentinel-2 data.


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