Journal ArticleDOI
A survey of remote sensing-based aboveground biomass estimation methods in forest ecosystems
TLDR
A survey of current biomass estimation methods using remote sensing data and discusses four critical issues – collection of field-based biomass reference data, extraction and selection of suitable variables fromRemote sensing data, identification of proper algorithms to develop biomass estimation models, and uncertainty analysis to refine the estimation procedure.Abstract:
Remote sensing-based methods of aboveground biomass (AGB) estimation in forest ecosystems have gained increased attention, and substantial research has been conducted in the past three decades. This paper provides a survey of current biomass estimation methods using remote sensing data and discusses four critical issues – collection of field-based biomass reference data, extraction and selection of suitable variables from remote sensing data, identification of proper algorithms to develop biomass estimation models, and uncertainty analysis to refine the estimation procedure. Additionally, we discuss the impacts of scales on biomass estimation performance and describe a general biomass estimation procedure. Although optical sensor and radar data have been primary sources for AGB estimation, data saturation is an important factor resulting in estimation uncertainty. LIght Detection and Ranging (lidar) can remove data saturation, but limited availability of lidar data prevents its extensive application. This...read more
Citations
More filters
Journal ArticleDOI
How can UAV bridge the gap between ground and satellite observations for quantifying the biomass of desert shrub community?
TL;DR: In this paper , the performance of generalized additive models between the upscaled UAV-based AGB and vegetation indices (VIs) generated from PlanetScope (resolution: 3 m), Sentinel-2A MSI and Landsat 8 OLI was evaluated in typical desert shrub communities in Inner Mongolia, China.
Journal ArticleDOI
Remote State Estimation With Asynchronous Mission-Critical IoT Sensors
TL;DR: This paper proposes a low complexity 2-D message passing state estimation algorithm, where the cyclic loops in the2-D factor graphs are removed based on the Gaussian-elimination-based quasi-diagonalization of the oversampled aggregated channel matrix of the IoT sensors.
Journal ArticleDOI
Estimation of Change in Forest Aboveground Carbon in Bhimbandh Wildlife Sanctuary, Bihar, India Between 2007 and 2016
Suman Sinha,Abhisek Santra +1 more
TL;DR: An integrated geospatial approach incorporating satellite synthetic aperture radar data with a continuous forest inventory over a tenyear period utilizing statistical up-scaling procedure over a tropical deciduous forest of India as a case study shows a significant decrease in carbon stock and the release of 918.5 Gg of carbon in the atmosphere from deforestation and forest degradation in the study area within the ten-year period.
Journal ArticleDOI
Estimation of above-ground biomass of reed (Phragmites communis) based on in situ hyperspectral data in Beijing Hanshiqiao Wetland, China
Wei Li,Dou Zhiguo,Wang Yan,Gaojie Wu,Manyin Zhang,Yinru Lei,Yunmei Ping,Jiachen Wang,Lijuan Cui,Wu Ma +9 more
TL;DR: In this paper, the authors compared the accuracy of commonly used empirical models in estimating above-ground biomass in dense swamp reeds in the Beijing Hanshiqiao Wetland Nature Reserve, northern China.
Journal ArticleDOI
Aboveground biomass estimation of an agro-pastoral ecology in semi-arid Bundelkhand region of India from Landsat data: a comparison of support vector machine and traditional regression models
Dibyendu Deb,Shovik Deb,Debashis Chakraborty,J. P. Singh,Amit Singh,Puspendu Dutta,Ashok Choudhury +6 more
TL;DR: In this paper, the authors compared the traditional regression models and support vector machine (SVM) for estimation of aboveground biomass (ABG) of an agro-pastoral ecology using vegetation indices derived from different vegetation types.
References
More filters
Journal ArticleDOI
Random Forests
TL;DR: Internal estimates monitor error, strength, and correlation and these are used to show the response to increasing the number of features used in the forest, and are also applicable to regression.
Journal ArticleDOI
Maximum entropy modeling of species geographic distributions
TL;DR: In this paper, the use of the maximum entropy method (Maxent) for modeling species geographic distributions with presence-only data was introduced, which is a general-purpose machine learning method with a simple and precise mathematical formulation.
Journal ArticleDOI
High-Resolution Global Maps of 21st-Century Forest Cover Change
Matthew C. Hansen,Peter Potapov,Rebecca Moore,M. Hancher,Svetlana Turubanova,Alexandra Tyukavina,David Thau,Stephen V. Stehman,Scott J. Goetz,Thomas R. Loveland,Anil Kommareddy,A. Egorov,Louise Chini,Christopher O. Justice,John R. Townshend +14 more
TL;DR: Intensive forestry practiced within subtropical forests resulted in the highest rates of forest change globally, and boreal forest loss due largely to fire and forestry was second to that in the tropics in absolute and proportional terms.
Journal ArticleDOI
Modeling of species distributions with Maxent: new extensions and a comprehensive evaluation
TL;DR: This paper presents a tuning method that uses presence-only data for parameter tuning, and introduces several concepts that improve the predictive accuracy and running time of Maxent and describes a new logistic output format that gives an estimate of probability of presence.
BookDOI
Assessing the accuracy of remotely sensed data : principles and practices
Russell G. Congalton,Kass Green +1 more
TL;DR: This chapter discusses Accuracy Assessment, which examines the impact of sample design on cost, statistical Validity, and measuring Variability in the context of data collection and analysis.