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Remote Sensing
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Remote Sensing of Vegetation Proportion, Attribute, Condition, and Change
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Remote Sensing of Vegetation Proportion, Attribute, Condition, and Change

A special issue of Remote Sensing (ISSN 2072-4292).

Deadline for manuscript submissions: closed (31 December 2021) | Viewed by 4998

Special Issue Editors

Dr. Sanath Kumar Sathyachandran

E-Mail Website
Guest Editor
ASRC Federal Data Solutions, contractor to USGS EROS, 47914 252nd Street, Sioux Falls, SD 57198, USA
Interests: quantitative remote sensing; large data analysis and ecological hypothesis testing
Dr. Francis K. Dwomoh

E-Mail Website
Guest Editor
ASRC Federal Data Solutions, contractor to USGS EROS, 47914 252nd Street, Sioux Falls, SD 57198, USA
Interests: remote sensing; land cover and land use change; landscape ecology; forest disturbance

Special Issue Information

Dear Colleagues,

Remote sensing is a powerful and dynamic synoptic tool for global monitoring. In the last few decades, various methodologies have been developed for remote monitoring of vegetation. In recent years, we have also seen a significant increase in the number of Earth observation satellites and unmanned aerial vehicles, which is expanding remote sensing observations in spectral, spatial, radiometric, and temporal domains. Coupled with this progress, recent rapid advances in artificial intelligence/machine learning techniques and decreasing costs of computing are driving current cutting-edge research toward analysis of newer and spatiotemporally denser data sets.

This Special Issue invites papers that use remotely sensed data with state-of-the-art algorithms to quantify vegetation proportion, attribute, condition, and change over land.

We invite original research articles, letters, and short communications, including but not limited to the following research topics:

  • Remote sensing methods for characterization of vegetation structure and function;
  • Remote sensing methods to detect and quantify gradual and abrupt vegetation change;
  • LiDAR, RADAR and structure from motion techniques for vegetation characterization;
  • Vegetation proportion, attribute, and condition forecast;
  • Cloud computing resources for remote sensing vegetation;
  • Artificial intelligence/machine learning for remote characterization of vegetation.

It is hoped that this Special Issue will also be of interest across other scientific fields, and we encourage interdisciplinary research.

Dr. Sanath Kumar Sathyachandran
Dr. Francis K. Dwomoh
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Land cover land use
  • Classification
  • Change detection
  • Machine learning
  • UAV/drones
  • Statistical methods
  • Ecological modeling

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Published Papers (1 paper)

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Research

23 pages, 21830 KiB  
Article
Leaf Area Index Estimation Algorithm for GF-5 Hyperspectral Data Based on Different Feature Selection and Machine Learning Methods
by Zhulin Chen, Kun Jia, Chenchao Xiao, Dandan Wei, Xiang Zhao, Jinhui Lan, Xiangqin Wei, Yunjun Yao, Bing Wang, Yuan Sun and Lei Wang
Remote Sens. 2020, 12(13), 2110; https://doi.org/10.3390/rs12132110 - 1 Jul 2020
Cited by 51 | Viewed by 4512
Abstract
Leaf area index (LAI) is an essential vegetation parameter that represents the light energy utilization and vegetation canopy structure. As the only in-operation hyperspectral satellite launched by China, GF-5 is potentially useful for accurate LAI estimation. However, there is no research focus on [...] Read more.
Leaf area index (LAI) is an essential vegetation parameter that represents the light energy utilization and vegetation canopy structure. As the only in-operation hyperspectral satellite launched by China, GF-5 is potentially useful for accurate LAI estimation. However, there is no research focus on evaluating GF-5 data for LAI estimation. Hyperspectral remote sensing data contains abundant information about the reflective characteristics of vegetation canopies, but these abound data also easily result in a dimensionality curse. Therefore, feature selection (FS) is necessary to reduce data redundancy to achieve more reliable estimations. Currently, machine learning (ML) algorithms have been widely used for FS. Moreover, the same ML algorithm is usually conducted for both FS and regression in LAI estimation. However, no evidence suggests that this is the optimal solution. Therefore, this study focuses on evaluating the capacity of GF-5 spectral reflectance for estimating LAI and the performances of different combination of FS and ML algorithms. Firstly, the PROSAIL model, which coupled leaf optical properties model PROSPECT and the scattering by arbitrarily inclined leaves (SAIL) model, was used to generate simulated GF-5 reflectance data under different vegetation and soil conditions, and then three FS methods, including random forest (RF), K-means clustering (K-means) and mean impact value (MIV), and three ML algorithms, including random forest regression (RFR), back propagation neural network (BPNN) and K-nearest neighbor (KNN) were used to develop nine LAI estimation models. The FS process was conducted twice using different strategies: Firstly, three FS methods were conducted to search the lowest dimension number, which maintained the estimation accuracy of all bands. Then, the sequential backward selection (SBS) method was used to eliminate the bands having minimal impact on LAI estimation accuracy. Finally, three best estimation models were selected and evaluated using reference LAI. The results showed that although the RF_RFR model (RF used for feature selection and RFR used for regression) achieved reliable LAI estimates (coefficient of determination (R2) = 0.828, root mean square error (RMSE) = 0.839), the poor performance (R2 = 0.763, RMSE = 0.987) of the MIV_BPNN model (MIV used for feature selection and BPNN used for regression) suggested using feature selection and regression conducted by the same ML algorithm could not always ensure an optimal estimation. Moreover, RF selection preserved the most informative bands for LAI estimation so that each ML regression method could achieve satisfactory estimation results. Finally, the results indicated that the RF_KNN model (RF used as feature selection and KNN used for regression) with seven GF-5 spectral band reflectance achieved the better estimation results than others when validated by simulated data (R2 = 0.834, RMSE = 0.824) and actual reference LAI (R2 = 0.659, RMSE = 0.697). Full article
(This article belongs to the Special Issue Remote Sensing of Vegetation Proportion, Attribute, Condition, and Change)
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