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.

scholarly journals Estimating the Growing Stem Volume of Coniferous Plantations Based on Random Forest Using an Optimized Variable Selection Method

Sensors ◽  
2020 ◽  
Vol 20 (24) ◽  
pp. 7248
Author(s):  
Fugen Jiang ◽  
Mykola Kutia ◽  
Arbi J. Sarkissian ◽  
Hui Lin ◽  
Jiangping Long ◽  
...  
Keyword(s):  
Random Forest ◽  
Variable Selection ◽  
Texture Features ◽  
Estimation Accuracy ◽  
Stem Volume ◽  
Estimation Model ◽  
Red Edge ◽  
Variable Combination ◽  
Coniferous Plantations ◽  
Sentinel 2

Forest growing stem volume (GSV) reflects the richness of forest resources as well as the quality of forest ecosystems. Remote sensing technology enables robust and efficient GSV estimation as it greatly reduces the survey time and cost while facilitating periodic monitoring. Given its red edge bands and a short revisit time period, Sentinel-2 images were selected for the GSV estimation in Wangyedian forest farm, Inner Mongolia, China. The variable combination was shown to significantly affect the accuracy of the estimation model. After extracting spectral variables, texture features, and topographic factors, a stepwise random forest (SRF) method was proposed to select variable combinations and establish random forest regressions (RFR) for GSV estimation. The linear stepwise regression (LSR), Boruta, Variable Selection Using Random Forests (VSURF), and random forest (RF) methods were then used as references for comparison with the proposed SRF for selection of predictors and GSV estimation. Combined with the observed GSV data and the Sentinel-2 images, the distributions of GSV were generated by the RFR models with the variable combinations determined by the LSR, RF, Boruta, VSURF, and SRF. The results show that the texture features of Sentinel-2’s red edge bands can significantly improve the accuracy of GSV estimation. The SRF method can effectively select the optimal variable combination, and the SRF-based model results in the highest estimation accuracy with the decreases of relative root mean square error by 16.4%, 14.4%, 16.3%, and 10.6% compared with those from the LSR-, RF-, Boruta-, and VSURF-based models, respectively. The GSV distribution generated by the SRF-based model matched that of the field observations well. The results of this study are expected to provide a reference for GSV estimation of coniferous plantations.

National-scale mangrove forest mapping by using Sentinel-1 SAR and Sentinel-2 MSI imagery on the Google Earth Engine Platform

2020 ◽  
Author(s):  
Luojia Hu ◽  
Wei Yao ◽  
Zhitong Yu ◽  
Lei Wang
Keyword(s):  
Spatial Resolution ◽  
Mangrove Forest ◽  
Large Scale ◽  
Landsat Imagery ◽  
Google Earth ◽  
Accuracy Evaluation ◽  
Mangrove Forests ◽  
Forest Mapping ◽  
Google Earth Engine ◽  
Sentinel 2

<p>Mangrove forest is considered as one of the pivotal ecosystems to near-shore environment health, adjacent terrestrial ecosystems and even global climate change migration. However, for past two decades, they are declining rapidly. In order to take effective steps to prevent the extinction of mangroves, high spatial resolution information of large-scale mangrove distribution is urgent. Recent study has indicated that a suitable pixel size for extracting mangroves should be at least equal to 10 m. Hence, Sentinel imagery (Sentinel-1 C-band synthetic aperture radar (SAR) and Sentinel-2 Multi-Spectral Instrument (MSI) imagery) whose spatial resolution is 10 m may hold great potentials to achieve this goal, but there are limited researches investigating it. Therefore, in this study, we will explore the potential of Sentinel imagery to extract mangrove forests in China on the Google Earth Engine platform. Specifically, our study was mainly conducted around 3 questions: (1) Which Sentinel imagery provides a higher accuracy for mangrove forest mapping, Sentinel-1 SAR data or Sentinel-2 multi-spectral data? (2) which combination of features from Sentinel imagery provides the most accurate mangrove forest map? (3) Compared to 30-m resolution mangrove products derived from Landsat imagery, how does 10-m resolution map improve our knowledge about the distribution of mangrove forest in China?</p><p> </p><p>Our results show that: (1) The highest producer’s accuracies (the reason why using producer’s accuracy as an accuracy evaluation indicator here is that the omission errors in mangrove forest extent map are much larger than commission errors) of mangrove forest maps derived from Sentinel-1 and Sentinel-2 imagery are 91.76% and 90.39%, respectively, which means that the contributions of Sentinel-1 SAR and Sentinel-2 MSI imagery to mangrove mapping are similar; (2) The highest producer’s accuracy of mangrove forest map at 10-m resolution is 95.4%. The mangrove forest map with the highest accuracy is obtained by combining quantiles of spectral and backscatter bands, spectral index, and texture index derived from time series of Sentinel-1 and Sentinel-2 imagery, indicating that the combination of Sentinel-1 SAR and Sentinel-2 MSI imagery is more useful in mangrove forest mapping than using them separately; (3) In China, the total area of mangrove forest extent at 10-m resolution is similar to that at 30-m resolution (20003 ha vs. 19220 ha). However, compared to 30-m resolution mangrove products, the 10-m resolution mangrove map identifies 1741 ha (occupying 8.7% of total mangrove forest area in China) mangrove forests in size smaller than 1 ha, which are especially important to low-lying coastal zone. This study demonstrates the feasibility of Sentinel imagery in large-scale mangrove forest mapping and gives guidance to map global mangrove forest at 10-m resolution in the future.  </p><p> </p>

scholarly journals Large-Scale High-Resolution Coastal Mangrove Forests Mapping Across West Africa With Machine Learning Ensemble and Satellite Big Data

2021 ◽  
Vol 8 ◽  
Author(s):  
Xue Liu ◽  
Temilola E. Fatoyinbo ◽  
Nathan M. Thomas ◽  
Weihe Wendy Guan ◽  
Yanni Zhan ◽  
...  
Keyword(s):  
Machine Learning ◽  
High Resolution ◽  
West Africa ◽  
Spatial Resolution ◽  
Mangrove Forest ◽  
Large Scale ◽  
Google Earth ◽  
Mangrove Forests ◽  
Data Products ◽  
Sentinel 2

Coastal mangrove forests provide important ecosystem goods and services, including carbon sequestration, biodiversity conservation, and hazard mitigation. However, they are being destroyed at an alarming rate by human activities. To characterize mangrove forest changes, evaluate their impacts, and support relevant protection and restoration decision making, accurate and up-to-date mangrove extent mapping at large spatial scales is essential. Available large-scale mangrove extent data products use a single machine learning method commonly with 30 m Landsat imagery, and significant inconsistencies remain among these data products. With huge amounts of satellite data involved and the heterogeneity of land surface characteristics across large geographic areas, finding the most suitable method for large-scale high-resolution mangrove mapping is a challenge. The objective of this study is to evaluate the performance of a machine learning ensemble for mangrove forest mapping at 20 m spatial resolution across West Africa using Sentinel-2 (optical) and Sentinel-1 (radar) imagery. The machine learning ensemble integrates three commonly used machine learning methods in land cover and land use mapping, including Random Forest (RF), Gradient Boosting Machine (GBM), and Neural Network (NN). The cloud-based big geospatial data processing platform Google Earth Engine (GEE) was used for pre-processing Sentinel-2 and Sentinel-1 data. Extensive validation has demonstrated that the machine learning ensemble can generate mangrove extent maps at high accuracies for all study regions in West Africa (92%–99% Producer’s Accuracy, 98%–100% User’s Accuracy, 95%–99% Overall Accuracy). This is the first-time that mangrove extent has been mapped at a 20 m spatial resolution across West Africa. The machine learning ensemble has the potential to be applied to other regions of the world and is therefore capable of producing high-resolution mangrove extent maps at global scales periodically.

scholarly journals Influencia del ángulo de observación en la estimación del índice de área foliar (LAI) mediante imágenes PROBA/CHRIS

2016 ◽  
pp. 45 ◽  
Author(s):  
J. Delegido ◽  
C. M. Meza ◽  
N. Pasqualotto ◽  
J. Moreno
Keyword(s):  
Remote Sensing ◽  
Viewing Angle ◽  
Area Index ◽  
Red Edge ◽  
The Subject ◽  
Remote Sensing Techniques ◽  
Normalized Difference Index ◽  
The Relationship ◽  
Chlorophyll Absorption ◽  
Sentinel 2

<p>The estimation of biophysical variables, such as the Leaf Area Index (LAI), using remote sensing techniques, is still the subject of numerous studies, since these variables allow obtaining valuable information on the vegetation status. In this work, we estimate LAI from multiangular PROBA/CHRIS images, by analyzing the reflectance measured in its 5 observation angles, for the bands centered in 665 and 705 nm. These wavelengths correspond to the chlorophyll absorption band and the Red-Edge region, respectively. The Normalized Difference Index (NDI) calculated from this wavelengths, shows good correlation with LAI and allows its remote sensing estimation and its applicability to the recently launched ESA Sentinel 2, thanks to its new bands in the Red-Edge. This research analyzed the influence on the acquisition geometry in the NDI, calibrating the relationship between this index and the LAI for each of the five observation angles in the PROBA / CHRIS images. As a result, we have obtained a relationship capable of providing LAI from the viewing angle and the NDI index.</p>

scholarly journals Dampak alih fungsi hutan mangrove terhadap ekonomi masyarakat di Telaga Wasti Sowi IV Manokwari Papua Barat

Cassowary ◽  
2019 ◽  
Vol 2 (1) ◽  
pp. 30-48
Author(s):  
Yohan F. Rumwaropen ◽  
Bambang Nugroho ◽  
Anton Sineri
Keyword(s):  
Mangrove Forest ◽  
Dominant Species ◽  
Forest Vegetation ◽  
Rhizophora Mucronata ◽  
Social Culture ◽  
Mangrove Species ◽  
Rhizophora Apiculata ◽  
Southern District ◽  
Papua Barat ◽  
Important Value Index

Mangrove forest is a vegetation that grows in the estuary beaches and who has the function of ecological, biological, economic and social culture, but now its existence has been degraded by the use of a less appropriate or changing function. Research on the structure of mangrove forest vegetation in the Wasti Bay Sowi IV Manokwari District of Southern District Manokwari conducted in April 2018 with the aim to determine the structure of mangrove forest vegetation and utilization. The method used in this research is the approach of ecological (biological) and anthropological approach. From the analysis of vegetation, found as many as 8 species of mangrove plants. Rhizophora apiculata is the dominant species on the level of a tree with a Density Value of 784.66 Individuals/Ha with an Important Value Index (IVI) 50.06 followed Rhizophora mucronata with a Density Value of 770.34 Individuals/Ha with the Important Value Index (IVI) 41.01. At the level of belta Rhizophora mucronata is the dominant species with a Density Value of 385.66 Individuals/Ha with the Important Value Index (IVI) 45.13 then Rhizophora apiculata with a Density Value of 263.33 Individuals/Ha with the Important Value Index (IVI) 40.22. At the seedling stage Rhizophora mucronata a dominant species with a Density Value of 760.00 Individuals/Ha with the Important Value Index (IVI) 45.42 and Rhizophora apiculata had Density Value of 681.66 Individuals/Ha with the Important Value Index (IVI) 41.04. Based on interviews of 8 mangrove species found in the observation plot, 3 species used as building material, 6 species as a source of firewood, 3 species as drugs and 5 species for other purposes.

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 Landsat-8 vs. Sentinel-2: Landuse Landcover Change Analysis and Differences in Gudur Municipality

2021 ◽  
Vol 9 (VI) ◽  
pp. 160-166
Author(s):  
R. Sanjeeva Reddy
Keyword(s):  
Land Use ◽  
Spatial Resolution ◽  
Landsat Imagery ◽  
European Space Agency ◽  
Landsat 8 ◽  
Landsat Data ◽  
Red Edge ◽  
Edge Band ◽  
Sentinel 2

With the recent free availability of moderate to high spatial resolution data (10m-30m), land use analysis became more robust. The launch of Sentinel-2a by the European Space Agency, coupled with the availability of free Landsat data, availed more analysis capabilities for the science community with a wide variety of temporal, spatial, and spectral capabilities. This study compares the synergetic use of Landsat and Sentinel-2 in mapping Land Use Land cover themes in Gudur, explicitly utilizing the red edge band of Sentinel-2. A combination of both sentinel and Landsat data results in higher spatial resolution. Classification of the red edge band produces better resolution than the classification of Landsat Imagery.

scholarly journals Evaluating the Effectiveness of Using Vegetation Indices Based on Red-Edge Reflectance from Sentinel-2 to Estimate Gross Primary Productivity

2019 ◽  
Vol 11 (11) ◽  
pp. 1303 ◽  
Author(s):  
Shangrong Lin ◽  
Jing Li ◽  
Qinhuo Liu ◽  
Longhui Li ◽  
Jing Zhao ◽  
...  
Keyword(s):  
Spatial Resolution ◽  
Primary Productivity ◽  
Carbon Flux ◽  
High Spatial Resolution ◽  
Vegetation Indices ◽  
Gross Primary Productivity ◽  
Flux Tower ◽  
Red Edge ◽  
The Relationship ◽  
Sentinel 2

Gross primary productivity (GPP) is the most important component of terrestrial carbon flux. Red-edge (680–780 nm) reflectance is sensitive to leaf chlorophyll content, which is directly correlated with photosynthesis as the pigment pool, and it has the potential to improve GPP estimation. The European Space Agency (ESA) Sentinel-2A and B satellites provide red-edge bands at 20-m spatial resolution on a five-day revisit period, which can be used for global estimation of GPP. Previous studies focused mostly on improving cropland GPP estimation using red-edge bands. In this study, we firstly evaluated the relationship between eight vegetation indices (VIs) retrieved from Sentinel-2 imagery in association with incident photosynthetic active radiation (PARin) and carbon flux tower GPP (GPPEC) across three forest and two grassland sites in Australia. We derived a time series of five red-edge VIs and three non-red-edge VIs over the CO2 flux tower footprints at 16-day time intervals and compared both temporal and spatial variations. The results showed that the relationship between the red-edge index (CIr, ρ 783 ρ 705 − 1 ) multiplied by PARin and GPPEC had the highest correlation (R2 = 0.77, root-mean-square error (RMSE) = 0.81 gC∙m−2∙day−1) at the two grassland sites. The CIr also showed consistency (rRMSE defined as RMSE/mean GPP, lower than 0.25) across forest and grassland sites. The high spatial resolution of the Sentinel-2 data provided more detailed information to adequately characterize the GPP variance at spatially heterogeneous areas. The high revisit period of Sentinel-2 exhibited temporal variance in GPP at the grassland sites; however, at forest sites, the flux-tower-based GPP variance could not be fully tracked by the limited satellite images. These results suggest that the high-spatial-resolution red-edge index from Sentinel-2 can improve large-scale spatio-temporal GPP assessments.

scholarly journals Crop Classification Using Multi-Temporal Sentinel-2 Data in the Shiyang River Basin of China

2020 ◽  
Vol 12 (24) ◽  
pp. 4052
Author(s):  
Zhiwei Yi ◽  
Li Jia ◽  
Qiting Chen
Keyword(s):  
River Basin ◽  
Spatial Resolution ◽  
Temporal Information ◽  
Shiyang River Basin ◽  
Spectral Bands ◽  
Red Edge ◽  
Multi Temporal ◽  
Crop Classification ◽  
Shortwave Infrared ◽  
Sentinel 2

Timely and accurate crop classification is of enormous significance for agriculture management. The Shiyang River Basin, an inland river basin, is one of the most prominent water resource shortage regions with intensive agriculture activities in northwestern China. However, a free crop map with high spatial resolution is not available in the Shiyang River Basin. The European Space Agency (ESA) satellite Sentinel-2 has multi-spectral bands ranging in the visible-red edge-near infrared-shortwave infrared (VIS-RE-NIR-SWIR) spectrum. Understanding the impact of spectral-temporal information on crop classification is helpful for users to select optimized spectral bands combinations and temporal window in crop mapping when using Sentinel-2 data. In this study, multi-temporal Sentinel-2 data acquired in the growing season in 2019 were applied to the random forest algorithm to generate the crop classification map at 10 m spatial resolution for the Shiyang River Basin. Four experiments with different combinations of feature sets were carried out to explore which Sentinel-2 information was more effective for higher crop classification accuracy. The results showed that the augment of multi-spectral and multi-temporal information of Sentinel-2 improved the accuracy of crop classification remarkably, and the improvement was firmly related to strategies of feature selections. Compared with other bands, red-edge band 1 (RE-1) and shortwave-infrared band 1 (SWIR-1) of Sentinel-2 showed a higher competence in crop classification. The combined application of images in the early, middle and late crop growth stage is significant for achieving optimal performance. A relatively accurate classification (overall accuracy = 0.94) was obtained by utilizing the pivotal spectral bands and dates of image. In addition, a crop map with a satisfied accuracy (overall accuracy > 0.9) could be generated as early as late July. This study gave an inspiration in selecting targeted spectral bands and period of images for acquiring more accurate and timelier crop map. The proposed method could be transferred to other arid areas with similar agriculture structure and crop phenology.

scholarly journals EXPLORING THE CAPABILITIES OF COMBINING THE SENTINEL-2 MSI DATA AND HIGH RESOLUTION GOOGLE EARTH IMAGE FOR MAPPING MANGROVE SPECIES

2020 ◽  
Vol XLIII-B3-2020 ◽  
pp. 1001-1007
Author(s):  
H. Z. Li ◽  
Y. Han ◽  
J. S. Chen
Keyword(s):  
High Resolution ◽  
Spatial Resolution ◽  
Remote Sensing Data ◽  
Google Earth ◽  
Svm Classifier ◽  
Textural Features ◽  
Species Classification ◽  
Mangrove Species ◽  
Spectral Bands ◽  
Sentinel 2

Abstract. Knowledge gained about the mangrove species mapping is essential to understand mangrove species development and to better estimate their ecological service value. Spectral bands and spatial resolution of remote sensing data are two important factors for accurate discrimination of mangrove species. In this study, mangrove species classification in Shenzhen Bay, China was performed by using Sentinel 2 (S2) Multi Spectral Instrument (MSI) data and Google Earth (GE) high resolution imagery as data sources and their suitability in mapping mangrove forest at a species level was examined. In the classification feature groups, the spectral bands were from the S2 MSI data and the textural features were based on GE imagery. The SVM classifier was used in mangrove species classification processing with eight groups of features, which were based on different S2 spectral bands and different GE spatial resolution textural features. The highest overall accuracy of our mapping results was 78.57% and the Kappa coefficient was 0.74, which indicated great potential of using the combination of S2 MSI and GE imagery for distinguishing and mapping mangrove species.

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