scholarly journals Allometric Models for Estimation of Forest Biomass in North East India

Forests ◽  
2019 ◽  
Vol 10 (2) ◽  
pp. 103 ◽  
Author(s):  
Arun Nath ◽  
Brajesh Tiwari ◽  
Gudeta Sileshi ◽  
Uttam Sahoo ◽  
Biplab Brahma ◽  
...  
Keyword(s):  
Forest Biomass ◽  
Forest Degradation ◽  
Accurate Estimation ◽  
Biomass Estimation ◽  
Forest Types ◽  
Tropical Regions ◽  
Carbon Cycles ◽  
North East India ◽  
North East ◽  
Harvest Data

In tropical and sub-tropical regions, biomass carbon (C) losses through forest degradation are recognized as central to global terrestrial carbon cycles. Accurate estimation of forest biomass C is needed to provide information on C fluxes and balances in such systems. The objective of this study was to develop generalized biomass models using harvest data covering tropical semi-evergreen, tropical wet evergreen, sub-tropical broad leaved, and sub-tropical pine forest in North East India (NEI). Among the four biomass estimation models (BEMs) tested AGBest = 0.32(D2Hδ)0.75 × 1.34 and AGBest = 0.18D2.16 × 1.32 were found to be the first and second best models for the different forest types in NEI. The study also revealed that four commonly used generic models developed by Chambers (2001), Brown (1989), Chave (2005) and Chave (2014) overestimated biomass stocks by 300–591 kg tree−1, while our highest rated model overestimated biomass by 197 kg tree−1. We believe the BEMs we developed will be useful for practitioners involved in remote sensing, biomass estimation and in projects on climate change mitigation, and payment for ecosystem services. We recommend future studies to address country scale estimation of forest biomass covering different forest types.

scholarly journals Linear vs non-linear learning methods A comparative study for forest above ground biomass, estimation from texture analysis of satellite images

2014 ◽  
Vol Volume 18, 2014 ◽  
Author(s):  
Hippolyte Tapamo ◽  
Adamou Mfopou ◽  
Blaise Ngonmang ◽  
Pierre Couteron ◽  
Olivier Monga
Keyword(s):  
Texture Analysis ◽  
Democratic Republic Of Congo ◽  
Forest Biomass ◽  
Forest Degradation ◽  
Support Vector ◽  
Biomass Estimation ◽  
K Nearest Neighbors ◽  
Tropical Regions ◽  
Very High Spatial Resolution ◽  
International Audience

International audience The aboveground biomass estimation is an important question in the scope of Reducing Emission from Deforestation and Forest Degradation (REDD framework of the UNCCC). It is particularly challenging for tropical countries because of the scarcity of accurate ground forest inventory data and of the complexity of the forests. Satellite-borne remote sensing can help solve this problem considering the increasing availability of optical very high spatial resolution images that provide information on the forest structure via texture analysis of the canopy grain. For example, the FOTO (FOurier Texture Ordination) proved relevant for forest biomass prediction in several tropical regions. It uses PCA and linear regression and, in this paper, we suggest applying classification methods such as k-NN (k-nearest neighbors), SVM (support vector machines) and Random Forests to texture descriptors extracted from images via Fourier spectra. Experiments have been carried out on simulated images produced by the software DART (Discrete Anisotropic Radiative Transfer) in reference to information (3D stand mockups) from forests of DRC (Democratic Republic of Congo), CAR (Central African Republic) and Congo. On this basis, we show that some classification techniques may yield a gain in prediction accuracy of 18 to 20%

scholarly journals The estimation model of mangrove forest biomass using a medium resolution satellite imagery in the concession area of forest consession company in West Kalimantan

2016 ◽  
Vol 6 (2) ◽  
pp. 69-81
Author(s):  
SENDI YUSANDI ◽  
I NENGAH SURATI JAYA
Keyword(s):  
Satellite Imagery ◽  
Mangrove Forest ◽  
Forest Biomass ◽  
Accurate Estimation ◽  
Biomass Estimation ◽  
Mean Deviation ◽  
Landsat 8 ◽  
Estimation Model ◽  
West Kalimantan ◽  
Medium Resolution

Yusandi S, Jaya INS. 2016. The estimation model of mangrove forest biomass using a medium resolution satellite imagery in the concession area of forest consession company in West Kalimantan. Bonorowo Wetlands 6: 69-81. Mangrove forest is one of forest ecosystem types having the highest carbon stock in the tropics. Mangrove forests have a good assimilation capability with their environmental elements as well as have a high capability on carbon sequestration. Up to now, however, the availability of data and information on carbon storage, especially on tree biomass content of mangrove is still limited. Conventionally, an accurate estimation of biomass could be obtained from terrestrial measurements, but those methods costly and time-consuming. This study offered an alternative solution to overcome these limitations by using remote sensing technology, i.e., by using the moderate resolution imageries Landsat 8. The objective of this study is to formulate the biomass estimation model using medium resolution satellite imagery, as well as to develop a biomass distribution map based on the selected model. The study found that the NDVI has a considerably high correlation coefficient of larger than > 0.7071 with the stand biomass. On the basis of the values of aggregation deviation, mean deviation, bias, RMSE, χ², R², and s, the best model for estimating the mangrove stand biomass is B=0.00023404 with the R² value of 77.1%. In general, the concession area of BSN Group (PT Kandelia Alam Semesta and PT Bina Ovivipari) have the potential of biomass ranging from 45 to 100 ton per ha.

scholarly journals Stratification-Based Forest Aboveground Biomass Estimation in a Subtropical Region Using Airborne Lidar Data

2020 ◽  
Vol 12 (7) ◽  
pp. 1101 ◽  
Author(s):  
Xiandie Jiang ◽  
Guiying Li ◽  
Dengsheng Lu ◽  
Erxue Chen ◽  
Xinliang Wei
Keyword(s):  
Aboveground Biomass ◽  
Forest Type ◽  
Forest Stand ◽  
Airborne Lidar ◽  
Accurate Estimation ◽  
Biomass Estimation ◽  
Lidar Data ◽  
Forest Types ◽  
Estimation Model ◽  
Forest Aboveground Biomass

Species-rich subtropical forests have high carbon sequestration capacity and play important roles in regional and global carbon regulation and climate changes. A timely investigation of the spatial distribution characteristics of subtropical forest aboveground biomass (AGB) is essential to assess forest carbon stocks. Lidar (light detection and ranging) is regarded as the most reliable data source for accurate estimation of forest AGB. However, previous studies that have used lidar data have often beenbased on a single model developed from the relationships between lidar-derived variables and AGB, ignoring the variability of this relationship in different forest types. Although stratification of forest types has been proven to be effective for improving AGB estimation, how to stratify forest types and how many strata to use are still unclear. This research aims to improve forest AGB estimation through exploring suitable stratification approaches based on lidar and field survey data. Different stratification schemes including non-stratification and stratifications based on forest types and forest stand structures were examined. The AGB estimation models were developed using linear regression (LR) and random forest (RF) approaches. The results indicate the following: (1) Proper stratifications improved AGB estimation and reduced the effect of under- and overestimation problems; (2) the finer forest type strata generated higher accuracy of AGB estimation but required many more sample plots, which were often unavailable; (3) AGB estimation based on stratification of forest stand structures was similar to that based on five forest types, implying that proper stratification reduces the number of sample plots needed; (4) the optimal AGB estimation model and stratification scheme varied, depending on forest types; and (5) the RF algorithm provided better AGB estimation for non-stratification than the LR algorithm, but the LR approach provided better estimation with stratification. Results from this research provide new insights on how to properly conduct forest stratification for AGB estimation modeling, which is especially valuable in tropical and subtropical regions with complex forest types.

Climate-based Allometric Biomass Equations for Five Major Canadian Timber Species

2021 ◽  
Author(s):  
Kun Xu ◽  
Jinghe Jiang ◽  
Fangliang He
Keyword(s):  
Aboveground Biomass ◽  
Forest Biomass ◽  
Allometric Equations ◽  
Accurate Estimation ◽  
Biomass Estimation ◽  
Change Scenario ◽  
Mean Annual Temperature ◽  
Timber Species ◽  
Trembling Aspen ◽  
Allometric Models

Accurate estimation of forest biomass is essential to quantify the role forests play at balancing terrestrial carbon. Allometric equations based on tree size have been used for this purpose worldwide. There is little quantitative understanding on how environmental variation may affect tree allometries. Even less known is how to incorporate environmental factors into such equations to improve estimation. Here we tested the effects of climate on tree allometric equations and proposed to model forest biomass by explicitly incorporating climatic factors. Among the five major Canadian timber species tested, the incorporation of climate was not found to improve the allometric models. For trembling aspen and tamarack, the residuals of their conventional allometric models were found strongly related to frost-free period and mean annual temperature, respectively. The predictions of the two best climate-based models were significantly improved, which indicate that trembling aspen and tamarack store more aboveground biomass when growing in warmer than in colder regions. We showed that, under the RCP4.5 modest climate change scenario, there would be a 10% underestimation of aboveground biomass for these two species if the conventional non-climate models would still be in use in 2030. This study suggests the necessity to proactively develop climate-based allometric equations for more accurate and reliable forest biomass estimation.

Lectotypification of Ischaemum molle (Andropogoneae, Poaceae)

Phytotaxa ◽  
2016 ◽  
Vol 272 (3) ◽  
pp. 228 ◽  
Author(s):  
ARJUN PRASAD TIWARI ◽  
SANDIP GAVADE ◽  
MANOJ LEKHAK
Keyword(s):  
Peninsular India ◽  
Tropical Regions ◽  
North East India ◽  
North East ◽  
The World ◽  
Infraspecific Taxa ◽  
C 71

The genus Ischaemum Linnaeus (1753: 1049) was established with two species, viz. I. muticum Linnaeus (1753: 1049) and I. aristatum Linnaeus (1753: 1049). Presently, it comprises c. 71 species distributed in warm and tropical regions of the world (Mabberley 2008). In India, the genus is represented by about 56 species and six infraspecific taxa, of which 43 are endemic and confined to Peninsular India, including Maharashtra and North-East India (Shrivastava & Nair 2010).

Optimizing spaceborne LiDAR and very high resolution optical sensor parameters for biomass estimation at ICESat/GLAS footprint level using regression algorithms

2017 ◽  
Vol 41 (3) ◽  
pp. 247-267 ◽  
Author(s):  
P Dhanda ◽  
S Nandy ◽  
SPS Kushwaha ◽  
S Ghosh ◽  
YVN Krishna Murthy ◽  
...  
Keyword(s):  
High Resolution ◽  
Forest Biomass ◽  
Optical Data ◽  
Accurate Estimation ◽  
Support Vector ◽  
Biomass Estimation ◽  
Multiple Sensors ◽  
Forest Height ◽  
Regression Algorithms ◽  
Single Sensor

Forests sequester large quantity of carbon in their woody biomass and hence accurate estimation of forest biomass is extremely crucial. The present study aims at combining information from spaceborne LiDAR (ICESat/GLAS) and high resolution optical data to estimate forest biomass. Estimation of aboveground biomass (AGB) at ICESat/GLAS footprint level was done by integrating data from multiple sensors using two regression algorithms, viz. random forest (RF) and support vector machine (SVM). The study used forest height and canopy return ratio (rCanopy) for determination of effective size of ICESat/GLAS footprints for field data collection. The forest height was predicted with root mean square error (RMSE) of 1.35 m. The study showed that six most important parameters derived from LiDAR, and passive optical data were able to explain 78.7% (adjusted) variation in the observed AGB with an RMSE of 13.9 Mg ha–1. It was also observed that 15 most important parameters were able to explain 83% (adjusted) variation in the observed AGB. It was found that SVM regression algorithm explained 88.7% of variation in AGB with an RMSE of 13.6 Mg ha–1 on the combined datasets while RF regression algorithm explained 83.5% of variation in AGB with an RMSE of 20.57 Mg ha–1. The study demonstrated that RF regression algorithm performs equally well on datasets irrespective of the correlation of underlying variables with the predicted variable whereas SVM regression was found to perform well on those datasets which had a subset of underlying variables that are correlated with the predicted variable. The study highlighted that sensor integration approach is more accurate than single sensor approach in predicting the AGB.

Forest biomass estimation over three distinct forest types using TanDEM-X InSAR data and simulated GEDI lidar data

2019 ◽  
Vol 232 ◽  
pp. 111283 ◽  
Author(s):  
Wenlu Qi ◽  
Svetlana Saarela ◽  
John Armston ◽  
Göran Ståhl ◽  
Ralph Dubayah
Keyword(s):  
Forest Biomass ◽  
Biomass Estimation ◽  
Lidar Data ◽  
Forest Types

Population dynamics of earthworms and their activity in forest ecosystems of North-east India

1991 ◽  
Vol 7 (3) ◽  
pp. 305-318 ◽  
Author(s):  
Tuneera Bhadauria ◽  
P. S. Ramakrishnan
Keyword(s):  
Monsoon Season ◽  
Pine Forest ◽  
Sacred Grove ◽  
Forest Types ◽  
Forest Stands ◽  
North East India ◽  
North East ◽  
Wide Range ◽  
Earthworm Populations ◽  
Earthworm Activity

ABSTRACTA comparative analysis of earthworm populations in seral Khasi pine forest represented byPinus kesiya5- and 35-year old stands, and a climax broad-leaved mixed forest represented by a sacred grove was done at altitudes of 1500 m in Meghalaya in north-east India.Tonoscolax horaiioccurred under all forest types whereasAmynthas diffringensandEulyphoeus feslivuswere confined to pine forest stands only.Perionyxsp. andDrawida assamensiswere restricted to the sacred grove.T. horaiihad larger numbers in all three different forest types. This species offers possibilities of vermicullurc for biologically improving soil fertility in manmade ecosystems because of its wide range of tolerance.Generally earthworm populations were more active during the monsoon season;A. diffringenswas however, more active during the winter, thereby conferring an advantage on this species as it was enabled to avoid competition during the monsoon season when other species dominate. Earthworm activity was generally higher in the sacred grove than in the pine forest stands. Population size was significantly correlated with soil moisture, temperature and pH. Wormcasts had a higher pH and nutrient status than the soil.In the highly leached soils of the humid tropics where there is a large concentration of fine root biomass in the surface soil layers, earthworm activity is beneficial because it helps incorporate detritus into the mineral soil rapidly and locally concentrates nutrients in the surface layers.

scholarly journals SUBTROPICAL FOREST BIOMASS ESTIMATION USING AIRBORNE LiDAR AND HYPERSPECTRAL DATA

2016 ◽  
Vol XLI-B8 ◽  
pp. 747-749
Author(s):  
Yong Pang ◽  
Zengyuan Li
Keyword(s):  
Forest Ecosystem ◽  
Forest Biomass ◽  
Subtropical Forest ◽  
Hyperspectral Data ◽  
Airborne Lidar ◽  
Observation System ◽  
Biomass Estimation ◽  
Lidar Data ◽  
Forest Types ◽  
Waveform Lidar

Forests have complex vertical structure and spatial mosaic pattern. Subtropical forest ecosystem consists of vast vegetation species and these species are always in a dynamic succession stages. It is very challenging to characterize the complexity of subtropical forest ecosystem. In this paper, CAF’s (The Chinese Academy of Forestry) LiCHy (LiDAR, CCD and Hyperspectral) Airborne Observation System was used to collect waveform Lidar and hyperspectral data in Puer forest region, Yunnan province in the Southwest of China. The study site contains typical subtropical species of coniferous forest, evergreen broadleaf forest, and some other mixed forests. The hypersectral images were orthorectified and corrected into surface reflectance with support of Lidar DTM product. The fusion of Lidar and hyperspectral can classify dominate forest types. The lidar metrics improved the classification accuracy. Then forest biomass estimation was carried out for each dominate forest types using waveform Lidar data, which get improved than single Lidar data source.

scholarly journals Estimation of Forest Biomass in Beijing (China) Using Multisource Remote Sensing and Forest Inventory Data

Forests ◽  
2020 ◽  
Vol 11 (2) ◽  
pp. 163 ◽  
Author(s):  
Yan Zhu ◽  
Zhongke Feng ◽  
Jing Lu ◽  
Jincheng Liu
Keyword(s):  
Remote Sensing ◽  
Forest Inventory ◽  
Forest Biomass ◽  
Mixed Forest ◽  
Accurate Estimation ◽  
Landsat 8 ◽  
Forest Types ◽  
Landsat 8 Oli ◽  
Inventory Data ◽  
Forest Inventory Data

Forest biomass reflects the material cycle of forest ecosystems and is an important index to measure changes in forest structure and function. The accurate estimation of forest biomass is the research basis for measuring carbon storage in forest systems, and it is important to better understand the carbon cycle and improve the efficiency of forest policy and management activities. In this study, to achieve an accurate estimation of meso-scale (regional) forest biomass, we used Ninth Beijing Forest Inventory data (FID), Landsat 8 OLI Image data and ALOS-2 PALSAR-2 data to establish different forest types (coniferous forest, mixed forest, and broadleaf forest) of biomass models in Beijing. We assessed the potential of forest inventory, optical (Landsat 8 OLI) and radar (ALOS-2 PALSAR-2) data in estimating and mapping forest biomass. From these data, a wide range of parameters related to forest structure were obtained. Random forest (RF) models were established using these parameters and compared with traditional multiple linear regression (MLR) models. Forest biomass in Beijing was then estimated. The results showed the following: (1) forest inventory data combined with multisource remote sensing data can better fit forest biomass than forest inventory data alone. Among the three forest types, mixed forest has the best fitting model. Forest inventory variables and multisource remote sensing variables can match each other in time and space, capturing almost all spatial variability. (2) The 2016 forest biomass density in Beijing was estimated to be 52.26 Mg ha−1 and ranged from 19.1381–195.66 Mg ha−1. The areas with high biomass were mainly distributed in the north and southwest of Beijing, while the areas with low biomass were mainly distributed in the southeast and central areas of Beijing. (3) The estimates from the RF model are better than those from the MLR model, showing a high R 2 and a low root mean square error (RMSE). The R 2 values of the MLR models of three forest types were greater than 0.5, and RMSEs were less than 15.5 Mg ha−1, The R 2 values of the RF models were higher than 0.6, and the RMSEs were lower than 13.5 Mg ha−1. We conclude that the methods in this paper can help improve the accurate estimation of regional biomass and provide a basis for the planning of relevant forestry decision-making departments.

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