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 Using Remote Sensing to Monitor the Spring Phenology of Acadia National Park across Elevational Gradients

2019 ◽  
Author(s):  
Yan Liu ◽  
Caitlin McDonough MacKenzie ◽  
Richard B. Primack ◽  
Michael J. Hill ◽  
Xiaoyang Zhang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Spatial Resolution ◽  
National Park ◽  
Vegetation Index ◽  
Landsat 8 ◽  
Mountainous Area ◽  
Acadia National Park ◽  
Fusion Model ◽  
Elevational Gradients ◽  
Spring Phenology

Abstract. Greenup dates of the mountainous Acadia National Park, were monitored using remote sensing data (including Landsat 8 surface reflectances (at a 30 m spatial resolution) and VIIRS reflectances adjusted to a nadir view (gridded at a 500 m spatial resolution)) during the 2013–2016 growing seasons. Ground-level leaf-out monitoring in the areas alongside the north-south-oriented hiking trails on three of the park's tallest mountains (466 m, 418 m, and 380 m) was used to evaluate satellite derived greenup dates in this study. While the 30 m resolution would be expected to provide a better scale for phenology detection in this mountainous region than the 500 m resolution, the daily temporal resolution of the 500 m data would be expected to offer vastly superior monitoring of the rapid variations experienced during vegetation greenup along elevational gradients. Therefore, the greenup dates derived from the Landsat 8 Enhanced Vegetation Index (EVI) data, augmented with Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) simulated EVI values, does provide more spatial details than VIIRS data alone and agree well with field monitored leaf out dates. Satellite derived greenup dates from the 30 m of Acadia National Park vary among different elevational zones, although the date of greenup is not always the most advanced at the lowest elevation. This indicates that the spring phenology is not only determined by microclimates associated with different elevations in this mountainous area, but is also possibly affected by the species mixture, localized temperatures, and other factors in Acadia.

scholarly journals Deep Learning Approaches for the Mapping of Tree Species Diversity in a Tropical Wetland Using Airborne LiDAR and High-Spatial-Resolution Remote Sensing Images

Forests ◽  
2019 ◽  
Vol 10 (11) ◽  
pp. 1047 ◽  
Author(s):  
Ying Sun ◽  
Jianfeng Huang ◽  
Zurui Ao ◽  
Dazhao Lao ◽  
Qinchuan Xin
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
Species Diversity ◽  
Spatial Resolution ◽  
Tree Species ◽  
High Spatial Resolution ◽  
Diversity Index ◽  
Tree Species Diversity ◽  
Individual Tree ◽  
Rgb Images

The monitoring of tree species diversity is important for forest or wetland ecosystem service maintenance or resource management. Remote sensing is an efficient alternative to traditional field work to map tree species diversity over large areas. Previous studies have used light detection and ranging (LiDAR) and imaging spectroscopy (hyperspectral or multispectral remote sensing) for species richness prediction. The recent development of very high spatial resolution (VHR) RGB images has enabled detailed characterization of canopies and forest structures. In this study, we developed a three-step workflow for mapping tree species diversity, the aim of which was to increase knowledge of tree species diversity assessment using deep learning in a tropical wetland (Haizhu Wetland) in South China based on VHR-RGB images and LiDAR points. Firstly, individual trees were detected based on a canopy height model (CHM, derived from LiDAR points) by the local-maxima-based method in the FUSION software (Version 3.70, Seattle, USA). Then, tree species at the individual tree level were identified via a patch-based image input method, which cropped the RGB images into small patches (the individually detected trees) based on the tree apexes detected. Three different deep learning methods (i.e., AlexNet, VGG16, and ResNet50) were modified to classify the tree species, as they can make good use of the spatial context information. Finally, four diversity indices, namely, the Margalef richness index, the Shannon–Wiener diversity index, the Simpson diversity index, and the Pielou evenness index, were calculated from the fixed subset with a size of 30 × 30 m for assessment. In the classification phase, VGG16 had the best performance, with an overall accuracy of 73.25% for 18 tree species. Based on the classification results, mapping of tree species diversity showed reasonable agreement with field survey data (R2Margalef = 0.4562, root-mean-square error RMSEMargalef = 0.5629; R2Shannon–Wiener = 0.7948, RMSEShannon–Wiener = 0.7202; R2Simpson = 0.7907, RMSESimpson = 0.1038; and R2Pielou = 0.5875, RMSEPielou = 0.3053). While challenges remain for individual tree detection and species classification, the deep-learning-based solution shows potential for mapping tree species diversity.

Hyperspectral remote sensing monitoring of cyanobacteria blooms in a large South American reservoir: high- and medium-spatial resolution satellite algorithm simulation

2020 ◽  
Vol 71 (5) ◽  
pp. 593 ◽  
Author(s):  
A. Drozd ◽  
P. de Tezanos Pinto ◽  
V. Fernández ◽  
M. Bazzalo ◽  
F. Bordet ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Spatial Resolution ◽  
Near Infrared ◽  
Monitoring Program ◽  
Hyperspectral Remote Sensing ◽  
Coefficient Of Determination ◽  
South American ◽  
Cell Abundance ◽  
Hyperspectral Signatures ◽  
Sentinel 2

We used hyperspectral remote sensing with the aim of establishing a monitoring program for cyanobacteria in a South American reservoir. We sampled at a wide temporal (2012–16; 10 seasons) and spatial (30km) gradient, and retrieved 111 field hyperspectral signatures, chlorophyll-a, cyanobacteria densities and total suspended solids. The hyperspectral signatures for cyanobacteria-dominated situations (n=75) were used to select the most suitable spectral bands in seven high- and medium-spatial resolution satellites (Sentinel 2, Landsat 5, 7 and 8, SPOT-4/5 and -6/7, WorldView 2), and for the development of chlorophyll and cyanobacteria cell abundance algorithms (λ550 – λ650+λ800) ÷ (λ550+λ650+λ800). The best-performing chlorophyll algorithm was Sentinel 2 ((λ560 – λ660+λ703) ÷ (λ560+λ660+λ703); R2=0.80), followed by WorldView 2 ((λ550 – λ660+λ720) ÷ (λ550+λ660+λ720); R2=0.78), Landsat and the SPOT series ((λ550 – λ650+λ800) ÷ (λ550+λ650+λ800); R2=0.67–0.74). When these models were run for cyanobacteria abundance, the coefficient of determination remained similar, but the root mean square error increased. This could affect the estimate of cyanobacteria cell abundance by ~20%, yet it still enable assessment of the alert level categories for risk assessment. The results of this study highlight the importance of the red and near-infrared region for identifying cyanobacteria in hypereutrophic waters, demonstrating coherence with field cyanobacteria abundance and enabling assessment of bloom distribution in this ecosystem.

scholarly journals Remote Sensing Phenology of Antarctic Green and Red Snow Algae Using WorldView Satellites

2021 ◽  
Vol 12 ◽  
Author(s):  
Andrew Gray ◽  
Monika Krolikowski ◽  
Peter Fretwell ◽  
Peter Convey ◽  
Lloyd S. Peck ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Cell Density ◽  
Spatial Resolution ◽  
Satellite Imagery ◽  
Algal Blooms ◽  
Vegetation Index ◽  
Net Primary Productivity ◽  
Snow Algae ◽  
Antarctic Snow ◽  
Sentinel 2

Snow algae are an important group of terrestrial photosynthetic organisms in Antarctica, where they mostly grow in low lying coastal snow fields. Reliable observations of Antarctic snow algae are difficult owing to the transient nature of their blooms and the logistics involved to travel and work there. Previous studies have used Sentinel 2 satellite imagery to detect and monitor snow algal blooms remotely, but were limited by the coarse spatial resolution and difficulties detecting red blooms. Here, for the first time, we use high-resolution WorldView multispectral satellite imagery to study Antarctic snow algal blooms in detail, tracking the growth of red and green blooms throughout the summer. Our remote sensing approach was developed alongside two Antarctic field seasons, where field spectroscopy was used to build a detection model capable of estimating cell density. Global Positioning System (GPS) tagging of blooms and in situ life cycle analysis was used to validate and verify our model output. WorldView imagery was then used successfully to identify red and green snow algae on Anchorage Island (Ryder Bay, 67°S), estimating peak coverage to be 9.48 × 104 and 6.26 × 104 m2, respectively. Combined, this was greater than terrestrial vegetation area coverage for the island, measured using a normalized difference vegetation index. Green snow algae had greater cell density and average layer thickness than red blooms (6.0 × 104 vs. 4.3 × 104 cells ml−1) and so for Anchorage Island we estimated that green algae dry biomass was over three times that of red algae (567 vs. 180 kg, respectively). Because the high spatial resolution of the WorldView imagery and its ability to detect red blooms, calculated snow algal area was 17.5 times greater than estimated with Sentinel 2 imagery. This highlights a scaling problem of using coarse resolution imagery and suggests snow algal contribution to net primary productivity on Antarctica may be far greater than previously recognized.

scholarly journals Diversity and characteristics of mangrove vegetation in Pulau Rimau Protection Forest, Banyuasin District, South Sumatra, Indonesia

2019 ◽  
Vol 20 (4) ◽  
pp. 1215-1221
Author(s):  
ERNIK YULIANA ◽  
YUNI TRI HEWINDATI ◽  
ADI WINATA ◽  
WIBOWO A DJATMIKO ◽  
ATI RAHADIATI
Keyword(s):  
Tree Species ◽  
Diversity Index ◽  
Forest Edge ◽  
Importance Value Index ◽  
Importance Value ◽  
Mangrove Species ◽  
Protection Forest ◽  
Mangrove Tree ◽  
Mangrove Vegetation ◽  
Study Sites

Abstract. Yuliana E, Hewindati YT, Winata A, Djatmiko WA, Rahadiati A. 2019. Diversity and characteristics of mangrove vegetation in Pulau Rimau Protection Forest, Banyuasin District, South Sumatra, Indonesia. Biodiversitas 20: 1215-1221. The purpose of the study was to analyze the flora diversity and characteristics of mangrove vegetation in Pulau Rimau Protection Forest, Banyuasin District, South Sumatra. Data collected were the number and girth diameter of mangrove tree species, and aquatic ecology parameters using transect method. The sample plots size were 2m×2 m; 5m×5 m; 10m×10 m; for seedling, sapling, and tree, respectively. The observation plots were arranged in a row of 120 m length on two sides of the forest edge, namely Calik Riverbank and Banyuasin Riverbank. Data were analyzed using importance value index (IVI), Simpson’s diversity index and Sørensen’s community similarity. The study revealed that there were differences in mangrove characteristics in two study sites. There were 57 plant species identified inside and outside sample plots, but only 15 species (26.32%) among them were categorized as true mangrove species. Inside the sample plots, there were 11 and 10 mangrove tree species recorded on the Calik Riverbank and Banyuasin Riverbank, respectively, but only 7 species among them were found in both sites. The mangroves on Calik Riverbank were dominated by Nypa (IVI 53.59%) and Bruguiera (51.12%), while those on Banyuasin Riverbank were dominated by Sonneratia (66.91%) and Avicennia (51.73%). The Simpson’s diversity index for Calik Riverbank and Banyuasin Riverbank was 0.82 and 0.78, respectively, whereas the Sørensen’s coefficient of community between the two sites was 0.67.

DIAGNÓSTICO DO USO E COBERTURA DA TERRA NOS RESERVATÓRIOS PÚBLICOS DO MUNICÍPIO DE PAU DOS FERROS, RN, BRASIL

2020 ◽  
Vol 11 (38) ◽  
pp. 146-161
Author(s):  
Aluizio Bezerra Júnior ◽  
◽  
Agassiel Medeiros Alves ◽  
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Natural Resources ◽  
Spatial Resolution ◽  
Sustainable Use ◽  
Rio Grande ◽  
Spatial Dimensions ◽  
Methodological Procedures ◽  
Remote Sensing Techniques ◽  
Sentinel 2

Research objective is to classify, measure and map the spatial dimensions of land use and land cover classes in public reservoirs 25 de Março and Dr. Pedro Diógenes Fernandes, both belonging to the municipality of Pau dos Ferros, state of Rio Grande do Norte. For the methodological procedures, remote sensing techniques (SIG Qgis version Lyon 2.12.3) were used, of the medium spatial resolution images of the SENTINEL 2 satellite, MSI sensor (Multispectral Instrument), accompanied by the interpretation key. The results showed that there is a possibility of sustainable use, since the exploration and conservation remains in balance, therefore, this research can subsidize the conservation of the use of natural resources around the reservoirs.

Estimation of Byram’s Fire Intensity and Rate of Spread from Spaceborne Remote Sensing Data in a Savanna Landscape

Fire ◽  
2021 ◽  
Vol 4 (4) ◽  
pp. 65
Author(s):  
Gernot Ruecker ◽  
David Leimbach ◽  
Joachim Tiemann
Keyword(s):  
Remote Sensing ◽  
Fuel Consumption ◽  
Spatial Resolution ◽  
Fire Behavior ◽  
Remote Sensing Data ◽  
Fire Intensity ◽  
Rate Of Spread ◽  
Radiative Power ◽  
Sentinel 2 ◽  
Fire Radiative Power

Fire behavior is well described by a fire’s direction, rate of spread, and its energy release rate. Fire intensity as defined by Byram (1959) is the most commonly used term describing fire behavior in the wildfire community. It is, however, difficult to observe from space. Here, we assess fire spread and fire radiative power using infrared sensors with different spatial, spectral and temporal resolutions. The sensors used offer either high spatial resolution (Sentinel-2) for fire detection, but a low temporal resolution, moderate spatial resolution and daily observations (VIIRS), and high temporal resolution with low spatial resolution and fire radiative power retrievals (Meteosat SEVIRI). We extracted fire fronts from Sentinel-2 (using the shortwave infrared bands) and use the available fire products for S-NPP VIIRS and Meteosat SEVIRI. Rate of spread was analyzed by measuring the displacement of fire fronts between the mid-morning Sentinel-2 overpasses and the early afternoon VIIRS overpasses. We retrieved FRP from 15-min Meteosat SEVIRI observations and estimated total fire radiative energy release over the observed fire fronts. This was then converted to total fuel consumption, and, by making use of Sentinel-2-derived burned area, to fuel consumption per unit area. Using rate of spread and fuel consumption per unit area, Byram’s fire intensity could be derived. We tested this approach on a small number of fires in a frequently burning West African savanna landscape. Comparison to field experiments in the area showed similar numbers between field observations and remote-sensing-derived estimates. To the authors’ knowledge, this is the first direct estimate of Byram’s fire intensity from spaceborne remote sensing data. Shortcomings of the presented approach, foundations of an error budget, and potential further development, also considering upcoming sensor systems, are discussed.

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 Short Communication: Species diversity of mangrove in Kutai National Park, East Kalimantan, Indonesia

2019 ◽  
Vol 20 (12) ◽  
Author(s):  
Erny Poedjirahajoe ◽  
Iin Sumbada Sulistyorini ◽  
Liris Lis Komara
Keyword(s):  
Species Diversity ◽  
National Park ◽  
Short Communication ◽  
Diversity Index ◽  
Line Transect ◽  
Mangrove Forests ◽  
Mangrove Species ◽  
Mangrove Tree ◽  
East Kalimantan ◽  
Study Results

Abstract. Poedjirahajoe E, Sulityorini IS, Komara LL. 2019. Short Communication: Species diversity of mangrove in Kutai National Park, East Kalimantan, Indonesia. Biodiversitas 20: 3641-3646. Kutai National Park (KNP) in East Kalimantan, Indonesia is blessed with natural mangrove forests. However, there is only very little information relating to the species composition and mangrove diversity. Therefore, this study was conducted to assess the mangrove tree species diversity and richness in Kutai National Park, East Kalimantan, Indonesia. The study results showed that the mangrove trees density was 1,214 trees/ha in the line transect vegetation survey with 1.7 ha plot. The number of mangrove species in KNP was 17 from 6 families. The dominant species with large numbers of individuals were Rhizophora mucronata, Rhizophora apiculata, and Bruguiera sexangula. The diversity index was low in mangroves in Pandan Bay Estuary, and Kaba Bay compared to that in Lombok bay’s mangroves.

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