scholarly journals Mapping Aboveground Biomass of Four Typical Vegetation Types in the Poyang Lake Wetlands Based on Random Forest Modelling and Landsat Images

2019 ◽  
Vol 10 ◽  
Author(s):  
Rongrong Wan ◽  
Peng Wang ◽  
Xiaolong Wang ◽  
Xin Yao ◽  
Xue Dai
Keyword(s):  
Random Forest ◽  
Aboveground Biomass ◽  
Poyang Lake ◽  
Vegetation Types ◽  
Landsat Images ◽  
Forest Modelling ◽  
Lake Wetlands

scholarly journals Canopy Height Estimation at Landsat Resolution Using Convolutional Neural Networks

2020 ◽  
Vol 2 (1) ◽  
pp. 23-36
Author(s):  
Syed Aamir Ali Shah ◽  
Muhammad Asif Manzoor ◽  
Abdul Bais
Keyword(s):  
Neural Networks ◽  
Random Forest ◽  
Convolutional Neural Networks ◽  
Forest Structure ◽  
State Of The Art ◽  
Spatial Association ◽  
Canopy Height ◽  
Landsat Images ◽  
Structure Estimation ◽  
Vegetation Height

Forest structure estimation is very important in geological, ecological and environmental studies. It provides the basis for the carbon stock estimation and effective means of sequestration of carbon sources and sinks. Multiple parameters are used to estimate the forest structure like above ground biomass, leaf area index and diameter at breast height. Among all these parameters, vegetation height has unique standing. In addition to forest structure estimation it provides the insight into long term historical changes and the estimates of stand age of the forests as well. There are multiple techniques available to estimate the canopy height. Light detection and ranging (LiDAR) based methods, being the accurate and useful ones, are very expensive to obtain and have no global coverage. There is a need to establish a mechanism to estimate the canopy height using freely available satellite imagery like Landsat images. Multiple studies are available which contribute in this area. The majority use Landsat images with random forest models. Although random forest based models are widely used in remote sensing applications, they lack the ability to utilize the spatial association of neighboring pixels in modeling process. In this research work, we define Convolutional Neural Network based model and analyze that model for three test configurations. We replicate the random forest based setup of Grant et al., which is a similar state-of-the-art study, and compare our results and show that the convolutional neural networks (CNN) based models not only capture the spatial association of neighboring pixels but also outperform the state-of-the-art.

scholarly journals Vegetation Types Mapping Using Multi-Temporal Landsat Images in the Google Earth Engine Platform

2021 ◽  
Vol 13 (22) ◽  
pp. 4683
Author(s):  
Masoumeh Aghababaei ◽  
Ataollah Ebrahimi ◽  
Ali Asghar Naghipour ◽  
Esmaeil Asadi ◽  
Jochem Verrelst
Keyword(s):  
Vegetation Index ◽  
Google Earth ◽  
Optimal Time ◽  
Quantitative Detection ◽  
Landsat 8 ◽  
Vegetation Types ◽  
Landsat Images ◽  
Multi Temporal ◽  
Google Earth Engine ◽  
Land Covers

Vegetation Types (VTs) are important managerial units, and their identification serves as essential tools for the conservation of land covers. Despite a long history of Earth observation applications to assess and monitor land covers, the quantitative detection of sparse VTs remains problematic, especially in arid and semiarid areas. This research aimed to identify appropriate multi-temporal datasets to improve the accuracy of VTs classification in a heterogeneous landscape in Central Zagros, Iran. To do so, first the Normalized Difference Vegetation Index (NDVI) temporal profile of each VT was identified in the study area for the period of 2018, 2019, and 2020. This data revealed strong seasonal phenological patterns and key periods of VTs separation. It led us to select the optimal time series images to be used in the VTs classification. We then compared single-date and multi-temporal datasets of Landsat 8 images within the Google Earth Engine (GEE) platform as the input to the Random Forest classifier for VTs detection. The single-date classification gave a median Overall Kappa (OK) and Overall Accuracy (OA) of 51% and 64%, respectively. Instead, using multi-temporal images led to an overall kappa accuracy of 74% and an overall accuracy of 81%. Thus, the exploitation of multi-temporal datasets favored accurate VTs classification. In addition, the presented results underline that available open access cloud-computing platforms such as the GEE facilitates identifying optimal periods and multitemporal imagery for VTs classification.

scholarly journals Estimating leaf area index and aboveground biomass of grazing pastures using Sentinel-1, Sentinel-2 and Landsat images

2019 ◽  
Vol 154 ◽  
pp. 189-201 ◽  
Author(s):  
Jie Wang ◽  
Xiangming Xiao ◽  
Rajen Bajgain ◽  
Patrick Starks ◽  
Jean Steiner ◽  
...  
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Aboveground Biomass ◽  
Landsat Images ◽  
Area Index ◽  
Sentinel 2

Object-based random forest modelling of aboveground forest biomass outperforms a pixel-based approach in a heterogeneous and mountain tropical environment

2019 ◽  
Vol 78 ◽  
pp. 175-188 ◽  
Author(s):  
Eduarda M.O. Silveira ◽  
Sérgio Henrique G. Silva ◽  
Fausto W. Acerbi-Junior ◽  
Mônica C. Carvalho ◽  
Luis Marcelo T. Carvalho ◽  
...  
Keyword(s):  
Random Forest ◽  
Forest Biomass ◽  
Tropical Environment ◽  
Forest Modelling ◽  
Object Based

A generalized space-time OBIA classification scheme to map sugarcane areas at regional scale, using Landsat images time-series and the random forest algorithm

2019 ◽  
Vol 80 ◽  
pp. 127-136 ◽  
Author(s):  
Ana Cláudia dos Santos Luciano ◽  
Michelle Cristina Araújo Picoli ◽  
Jansle Vieira Rocha ◽  
Daniel Garbellini Duft ◽  
Rubens Augusto Camargo Lamparelli ◽  
...  
Keyword(s):  
Time Series ◽  
Random Forest ◽  
Classification Scheme ◽  
Regional Scale ◽  
Space Time ◽  
Random Forest Algorithm ◽  
Landsat Images

scholarly journals Has the Three‐Gorges Dam made the Poyang Lake wetlands wetter and drier?

2012 ◽  
Vol 39 (20) ◽  
Author(s):  
Q. Zhang ◽  
L. Li ◽  
Y.‐G. Wang ◽  
A. D. Werner ◽  
P. Xin ◽  
...  
Keyword(s):  
Poyang Lake ◽  
Three Gorges Dam ◽  
Three Gorges ◽  
The Three Gorges ◽  
The Three Gorges Dam ◽  
Lake Wetlands
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