scispace - formally typeset
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

Comparative Analysis of Remote Sensing and Geo-Statistical Techniques to Quantify Forest Biomass

TL;DR: In this article , the estimation of biomass using Sentinel-2 remote sensing data in moist temperate forests in the Galies region of Abbottabad Pakistan was studied, and the results illustrate that the atmospherically resistant vegetation index (ARVI) is the best index (R2 = 0.67) for estimating AGB.
Journal ArticleDOI

A combination of climate, tree diversity and local human disturbance determine the stability of dry Afromontane forests

TL;DR: In this article, the authors explored remote sensing derived indicators of forest stability, using MODIS satellite derived NDVI time series from 2001 to 2018, and measured resilience and resistance using the anomalies (remainders) after time series decomposition into seasonality, trend and remainder components.
Journal ArticleDOI

Vegetation mapping of No Name Key, Florida using lidar and multispectral remote sensing

TL;DR: In this article, LiDAR data have been widely used in the areas of ecological studies due to lidar's ability to provide information on the vertical structure of vegetation in wildlife habitats.
Journal ArticleDOI

Modeling Forest Aboveground Carbon Density in the Brazilian Amazon with Integration of MODIS and Airborne LiDAR Data

TL;DR: This research aimed to explore an approach for estimating aboveground carbon density (ACD) in the Brazilian Amazon through integration of MODIS and a limited number of light detection and ranging (Lidar) data samples using linear regression and random forest algorithms, respectively.
Journal ArticleDOI

A comparative analysis of modeling approaches and canopy height-based data sources for mapping forest growing stock volume in a northern subtropical ecosystem of China

TL;DR: In this paper , a hierarchical Bayesian approach (HBA) was used for forest growing stock volume (FGSV) estimation in a northern subtropical forest ecosystem, and the results showed that L-CHM provides better predictions than L-Lidar using the same modeling approach.
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

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

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.
Related Papers (5)