Mapping Mangrove Species Using Combined UAV-LiDAR and Sentinel-2 Data: Feature Selection and Point Density Effects

2021 ◽  
Author(s):  
Dezhi Wang ◽  
Wan Bo ◽  
Penghua Qiu ◽  
Xiang Tan ◽  
Quanfa Zhang
Keyword(s):  
Feature Selection ◽  
Mangrove Species ◽  
Density Effects ◽  
Point Density ◽  
Sentinel 2

scholarly journals Feature Selection on Sentinel-2 Multispectral Imagery for Mapping a Landscape Infested by Parthenium Weed

2019 ◽  
Vol 11 (16) ◽  
pp. 1892 ◽  
Author(s):  
Zolo Kiala ◽  
Onisimo Mutanga ◽  
John Odindi ◽  
Kabir Peerbhay
Keyword(s):  
Feature Selection ◽  
Invasive Plant ◽  
Feature Selection Method ◽  
Sparse Learning ◽  
High Temporal Resolution ◽  
Multispectral Imagery ◽  
Selection Methods ◽  
Multi Temporal ◽  
Parthenium Weed ◽  
Sentinel 2

In the recent past, the volume of spatial datasets has significantly increased. This is attributed to, among other factors, higher sensor temporal resolutions of the recently launched satellites. The increased data, combined with the computation and possible derivation of a large number of indices, may lead to high multi-collinearity and redundant features that compromise the performance of classifiers. Using dimension reduction algorithms, a subset of these features can be selected, hence increasing their predictive potential. In this regard, an investigation into the application of feature selection techniques on multi-temporal multispectral datasets such as Sentinel-2 is valuable in vegetation mapping. In this study, ten feature selection methods belonging to five groups (Similarity-based, statistical-based, Sparse learning based, Information theoretical based, and wrappers methods) were compared based on f-score and data size for mapping a landscape infested by the Parthenium weed (Parthenium hysterophorus). Overall, results showed that ReliefF (a Similarity-based approach) was the best performing feature selection method as demonstrated by the high f-score values of Parthenium weed and a small size of optimal features selected. Although svm-b (a wrapper method) yielded the highest accuracies, the size of optimal subset of selected features was quite large. Results also showed that data size affects the performance of feature selection algorithms, except for statistically-based methods such as Gini-index and F-score and svm-b. Findings in this study provide a guidance on the application of feature selection methods for accurate mapping of invasive plant species in general and Parthenium weed, in particular, using new multispectral imagery with high temporal resolution.

scholarly journals Crop Mapping Using Random Forest and Particle Swarm Optimization based on Multi-Temporal Sentinel-2

2020 ◽  
Vol 12 (9) ◽  
pp. 1449
Author(s):  
Elahe Akbari ◽  
Ali Darvishi Boloorani ◽  
Najmeh Neysani Samany ◽  
Saeid Hamzeh ◽  
Saeid Soufizadeh ◽  
...  
Keyword(s):  
Feature Selection ◽  
Particle Swarm Optimization ◽  
Importance Measure ◽  
Swarm Optimization ◽  
Feature Sets ◽  
Classification Feature ◽  
Spectral Bands ◽  
Multi Temporal ◽  
Crop Mapping ◽  
Sentinel 2

Timely and accurate information on crop mapping and monitoring is necessary for agricultural resources management. Accordingly, the applicability of the proposed classification-feature selection ensemble procedure with different feature sets for crop mapping is investigated. Here, we produced various feature sets including spectral bands, spectral indices, variation of spectral index, texture, and combinations of features to map different types of crops. By using various feature sets and the random forest (RF) classifier, the crop maps were created. In aiming to determine the most relevant and distinctive features, the particle swarm optimization (PSO) and RF-variable importance measure feature selection methods were examined. The classification-feature selection ensemble procedure was adapted to combine the outputs of different feature sets from the better feature selection method using majority votes. Multi-temporal Sentinel-2 data has been used in Ghale-Nou county of Tehran, Iran. The performance of RF was efficient in crop mapping especially by spectral bands and texture in combination with other feature sets. Our results showed that the PSO-based feature selection leads to a more accurate classification than the RF-variable importance measure, in almost all feature sets for all crop types. The RF classifier-PSO ensemble procedure for crop mapping outperformed the RF classifier in each feature set with regard to the class-wise and overall accuracies (OA) (of about 2.7–7.4% increases in OA and 0.48–3.68% (silage maize), 0–1.61% (rice), 2.82–15.43% (alfalfa), and 10.96–41.13% (vegetables) improvement in F-scores for all feature sets). The proposed method could mainly be useful to differentiate between heterogeneous crop fields (e.g., vegetables in this study) due to their more obtained omission/commission errors reduction.

scholarly journals Probabilistic Mangrove Species Mapping with Multiple-Source Remote-Sensing Datasets Using Label Distribution Learning in Xuan Thuy National Park, Vietnam

2020 ◽  
Vol 12 (22) ◽  
pp. 3834 ◽  
Author(s):  
Junshi Xia ◽  
Naoto Yokoya ◽  
Tien Dat Pham
Keyword(s):  
Remote Sensing ◽  
Spatial Resolution ◽  
Tree Species ◽  
National Park ◽  
Mangrove Species ◽  
Mangrove Tree ◽  
Label Distribution Learning ◽  
Label Distribution ◽  
Mangrove Communities ◽  
Sentinel 2

Mangrove forests play an important role in maintaining water quality, mitigating climate change impacts, and providing a wide range of ecosystem services. Effective identification of mangrove species using remote-sensing images remains a challenge. The combinations of multi-source remote-sensing datasets (with different spectral/spatial resolution) are beneficial to the improvement of mangrove tree species discrimination. In this paper, various combinations of remote-sensing datasets including Sentinel-1 dual-polarimetric synthetic aperture radar (SAR), Sentinel-2 multispectral, and Gaofen-3 full-polarimetric SAR data were used to classify the mangrove communities in Xuan Thuy National Park, Vietnam. The mixture of mangrove communities consisting of small and shrub mangrove patches is generally difficult to separate using low/medium spatial resolution. To alleviate this problem, we propose to use label distribution learning (LDL) to provide the probabilistic mapping of tree species, including Sonneratia caseolaris (SC), Kandelia obovata (KO), Aegiceras corniculatum (AC), Rhizophora stylosa (RS), and Avicennia marina (AM). The experimental results show that the best classification performance was achieved by an integration of Sentinel-2 and Gaofen-3 datasets, demonstrating that full-polarimetric Gaofen-3 data is superior to the dual-polarimetric Sentinel-1 data for mapping mangrove tree species in the tropics.

scholarly journals Incorporating the Plant Phenological Trajectory into Mangrove Species Mapping with Dense Time Series Sentinel-2 Imagery and the Google Earth Engine Platform

2019 ◽  
Vol 11 (21) ◽  
pp. 2479 ◽  
Author(s):  
Huiying Li ◽  
Mingming Jia ◽  
Rong Zhang ◽  
Yongxing Ren ◽  
Xin Wen
Keyword(s):  
Time Series ◽  
Vegetation Index ◽  
Google Earth ◽  
Late Winter ◽  
Mangrove Forests ◽  
Mangrove Species ◽  
Ndvi Time Series ◽  
Google Earth Engine ◽  
Species Mapping ◽  
Sentinel 2

Information on mangrove species composition and distribution is key to studying functions of mangrove ecosystems and securing sustainable mangrove conservation. Even though remote sensing technology is developing rapidly currently, mapping mangrove forests at the species level based on freely accessible images is still a great challenge. This study built a Sentinel-2 normalized difference vegetation index (NDVI) time series (from 2017-01-01 to 2018-12-31) to represent phenological trajectories of mangrove species and then demonstrated the feasibility of phenology-based mangrove species classification using the random forest algorithm in the Google Earth Engine platform. It was found that (i) in Zhangjiang estuary, the phenological trajectories (NDVI time series) of different mangrove species have great differences; (ii) the overall accuracy and Kappa confidence of the classification map is 84% and 0.84, respectively; and (iii) Months in late winter and early spring play critical roles in mangrove species mapping. This is the first study to use phonological signatures in discriminating mangrove species. The methodology presented can be used as a practical guideline for the mapping of mangrove or other vegetation species in other regions. However, future work should pay attention to various phenological trajectories of mangrove species in different locations.

scholarly journals Species-Level Classification and Mapping of a Mangrove Forest Using Random Forest—Utilisation of AVIRIS-NG and Sentinel Data

2021 ◽  
Vol 13 (11) ◽  
pp. 2027
Author(s):  
Mukunda Dev Behera ◽  
Surbhi Barnwal ◽  
Somnath Paramanik ◽  
Pulakesh Das ◽  
Bimal Kumar Bhattyacharya ◽  
...  
Keyword(s):  
Random Forest ◽  
Spatial Resolution ◽  
Mangrove Forest ◽  
Dominant Species ◽  
Species Level ◽  
Mangrove Species ◽  
Central Wavelength ◽  
Red Edge ◽  
Chlorophyll Absorption ◽  
Sentinel 2

Although studies on species-level classification and mapping using multisource data and machine learning approaches are plenty, the use of data with ideal placement of central wavelength and bandwidth at appropriate spatial resolution, for the classification of mangrove species is underreported. The species composition of a mangrove forest has been estimated utilising the red-edge spectral bands and chlorophyll absorption information from AVIRIS-NG and Sentinel-2 data. In this study, three dominant species, Heritiera fomes, Excoecaria agallocha and Avicennia officinalis, have been classified using the random forest (RF) model for a mangrove forest in Bhitarkanika Wildlife Sanctuary, India. Various combinations of reflectance/backscatter bands and vegetation indices derived from Sentinel-2, AVIRIS-NG, and Sentinel-1 were used for species-level discrimination and mapping. The RF model showed maximum accuracy using Sentinel-2, followed by the AVIRIS-NG, in discriminating three dominant species and two mixed compositions. This study indicates the potential of Sentinel-2 data for discriminating various mangrove species owing to the appropriate placement of central wavelength and bandwidth in Sentinel-2 at ≥10 m spatial resolution. The variable importance plots proved that species-level classification could be attempted using red edge and chlorophyll absorption information. This study has wider applicability in other mangrove forests around the world.

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