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
Multi-Resolution Mapping and Accuracy Assessment of Forest Carbon Density by Combining Image and Plot Data from a Nested and Clustering Sampling Design
TL;DR: This study implied that the combinations of Landsat 8 images and SGCSWA or SGBCS with the systematical, nested and clustering sampling design provided the potential to formulate a methodological framework to map forest carbon density and conduct accuracy assessment at multiple spatial resolutions.
Journal ArticleDOI
GA-SVR Algorithm for Improving Forest Above Ground Biomass Estimation Using SAR Data
TL;DR: In this article, 14 SAR polarimetric features were extracted from C-band and L-band full-polarization SAR images and worked as input SAR features, respectively. And the results showed that the proposed GA-SVR algorithm improved the forest AGB estimation accuracy with cross-validation coefficient of 80.21% for GF-3 and 71.41% for ALOS-2 PALSAR-2 data.
Journal ArticleDOI
Forest Height Mapping Using Feature Selection and Machine Learning by Integrating Multi-Source Satellite Data in Baoding City, North China
TL;DR: Overall, this study proposed a methodological framework for quantifying the importance of data source, feature selection method, and machine learning algorithm in forest height estimation, and it was proved to be effective in estimating forest height by using freely accessible multi-source data, advanced feature selection algorithm, andMachine learning algorithm.
Journal ArticleDOI
Remote sensing-based biomass estimation of dry deciduous tropical forest using machine learning and ensemble analysis.
TL;DR: In this article , the authors proposed a framework to monitor above-ground biomass (AGB) at finer scales using open-source satellite data, which integrated four machine learning (ML) techniques with field surveys and satellite data to provide continuous spatial estimates of AGB at finer resolution.
Journal ArticleDOI
Rape (Brassica napus L.) Growth Monitoring and Mapping Based on Radarsat-2 Time-Series Data
TL;DR: From the mapping results, it is concluded that a model built at the P3 stage can be used for rape biomass inversion, with 90% of estimation errors being less than 100 g m−2.
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.