1,720,974 research outputs found
Canopy Height Estimation Using Sentinel Series Images through Machine Learning Models in a Mangrove Forest
No full textCanopy height serves as a good indicator of forest carbon content. Remote sensing-based direct estimations of canopy height are usually based on Light Detection and Ranging (LiDAR) or Synthetic Aperture Radar (SAR) interferometric data. LiDAR data is scarcely available for the Indian tropics, while Interferometric SAR data from commercial satellites are costly. High temporal decorrelation makes freely available Sentinel-1 interferometric data mostly unsuitable for tropical forests. Alternatively, other remote sensing and biophysical parameters have shown good correlation with forest canopy height. The study objective was to establish and validate a methodology by which forest canopy height can be estimated from SAR and optical remote sensing data using machine learning models i.e., Random Forest (RF) and Symbolic Regression (SR). Here, we analysed the potential of Sentinel-1 interferometric coherence and Sentinel-2 biophysical parameters to propose a new method for estimating canopy height in the study site of the Bhitarkanika wildlife sanctuary, which has mangrove forests. The results showed that interferometric coherence, and biophysical variables (Leaf Area Index (LAI) and Fraction of Vegetation Cover (FVC)) have reasonable correlation with canopy height. The RF model showed a Root Mean Squared Error (RMSE) of 1.57 m and R2 value of 0.60 between observed and predicted canopy heights; whereas, the SR model through genetic programming demonstrated better RMSE and R2 values of 1.48 and 0.62 m, respectively. The SR also established an interpretable model, which is not possible via any other machine learning algorithms. The FVC was found to be an essential variable for predicting forest canopy height. The canopy height maps correlated with ICESat-2 estimated canopy height, albeit modestly. The study demonstrated the effectiveness of Sentinel series data and the machine learning models in predicting canopy height. Therefore, in the absence of commercial and rare data sources, the methodology demonstrated here offers a plausible alternative for forest canopy height estimation
Species-level classification of mangrove forest using AVIRIS-NG hyperspectral imagery
No full textSpecies-level classification of mangroves provides important inputs for conservation, rehabilitation and understanding of ecosystem functions. The hyperspectral sensor, Airborne Visible InfraRed Imaging Spectrometer-New Generation (AVIRIS-NG), holds promises for species-level discrimination by virtue of its coverage across a wider spectrum at very high spatial resolution. Using the continuum removal (CR) technique and absorption band depth (ABD), this study applied Random Forest (RF) model to classify the distribution of three species (Heritiera fomes, Excoecaria agallocha and Avicennia officinalis) and two of their combinations (Heritiera fomes-Excoecaria agallocha and Avicennia officinalis-Excoecaria agallocha). The classified map demonstrated good accuracy (overall accuracy = 88%; kappa coefficient = 0.84) using ABD as an independent variable. The important wavelengths (972, 1172, 1177 nm) identified for mangrove species discrimination correspond to water absorption bands. This characteristic may be replicated for species-level classification of other mangrove forests with similar species
Indicating saturation limits of multi-sensor satellite data in estimating aboveground biomass of a mangrove forest
No full textCarbon sequestration in aboveground biomass remains understudied because of the difficulties in conducting field observations and saturation of remote sensing datasets. This study aimed to address the challenges associated with estimating forest AGB using remote sensing data from a dense mangrove ecosystem that has reached saturation limits. A mangrove ecosystem can reach saturation limits when the vegetation density and AGB are so high that remote sensing instruments, such as radar and lidar, cannot accurately measure further increases in biomass because of the sensors' limited penetration and resolution capabilities. This study evaluated the potential and limitations of using dual-polarised microwave data from Sentinel-1A and PALSAR-2, as well as spectral reflectance data from Sentinel-2, to estimate the AGB of the Bhitarkanika Wildlife Sanctuary (BWS), which is the second largest mangrove site in India. Using stratified random sampling, 314 elementary sampling units of 20 m × 20 m (0.04 ha) were used to record the diameter at breast height, tree height, and stand density for estimating AGB. Different band combinations of multisensor datasets were utilised to identify the best predictor variables for estimating the AGB and their corresponding saturation limits. Sentinel-1A demonstrated saturation at 123 Mg/ha (R
2 = 0.17) for AGB using VV polarisation, followed by 93 Mg/ha (R
2 = 0.55), 91 Mg/ha (R
2 = 0.26), and 96 Mg/ha (R
2 = 0.17) for the three red-edge bands at wavelengths of 705, 749, and 783 nm, respectively, of Sentinel-2 data. Red-edge bands are sensitive to chlorophyll and vegetation structure, and therefore, S2REP and REIP attained the maximum saturation limit at an AGB of 80 Mg/ha (R
2 = 0.26, 0.3, respectively). The highest correlation (R
2 = 0.9) and the maximum AGB of 326.06 Mg/ha was captured by the variable HH × HV of the L-band of the PALSAR-2 due to its penetration and interaction capacity with biomass components and less susceptible to interference from surface conditions and atmospheric effects. This study confirmed the advantages of longer-wavelength L-band data over C-band and multispectral optical bands for AGB estimation and identified the best predictor variables. This approach highlights the efficacy of different predictor variables for AGB estimation and the complementary strengths of multisensor datasets for navigating saturation limits. This framework could offer a practical guide for variable selection based on forest density and canopy structure from upcoming SAR missions, including NISAR.</p
Assessment of tropical cyclone amphan affected inundation areas using sentinel-1 satellite data
No full textTropical cyclones as natural disturbances, influence ecosystem structure, function and dynamics at the global scale. This study assesses the inundation due to the super cyclone Amphan in coastal districts of eastern India by leveraging the computational power of Google Earth Engine (GEE) and the availability of high resolution Sentinel-1 Synthetic Aperture Radar (SAR) data. A cloud-based image processing framework was developed and implemented in GEE for classification using Random Forest algorithm. The inundation areas due to storm surge owing to cyclone Amphan, were mapped and further categorised to different land use and land cover classes based on an existing land cover map. Sentinel-1 images were useful in post-cyclone studies for the change detection analysis due to its higher temporal resolution and cloud penetration ability. The study found that the majority of agricultural and agricultural fallow lands were inundated in the coastal districts. The availability of open-source cloud-based data processing platforms provides cost effective way to rapidly gather accurate geospatial information. Such information could be useful for emergency response planning and post-event disaster management including relief, rescue and rehabilitation measures; and crop yield loss assessment. Cyclone and Land Use and Land Cover (LULC) change can have significant impacts on the human population and if both coexist, the consequences for people and the surrounding environment may be severe
Leaf chlorophyll concentration estimation using absorption spectroscopy of AVIRIS-NG for a mangrove forest in India
No full textChlorophyll concentration is one of the important biochemical properties of vegetation as it relates to photosynthetic activity and health. The amount of chlorophyll in a vegetation canopy indicates the physiological status or the health condition. Compared to other terrestrial ecosystems, mangroves are highly productive, so there is a need for a better understanding of the dynamics of carbon sequestration by monitoring their health and nutrition status for ecological conservation and restoration processes. In spite of many ecosystem services, limited research has been conducted concerning mangrove chlorophyll assessment due to the challenges of field sampling. The majority of the chlorophyll assessments in mangroves are being executed with the help of remote sensing data-derived vegetation indices (VIs). However, they are site or species-specific, which prohibits a universal adaptation. Our study quantifies leaf chlorophyll concentration (LCC) distribution using the Airborne Visible InfraRed Imaging Spectrometer—Next Generation (AVIRIS-NG) hyperspectral imagery and field observed dataset for the Bhitarkanika National Park (BNP), a mangrove ecosystem of India. This study aims to predict the LCC utilizing absorption features such as absorption band depth (ABD) as a predictor variable. This was calculated using continuum removal techniques and further predicted using machine learning (Random Forest, RF). This study identifies the red-edge region (676–722 nm) as the prominent part of the electromagnetic spectrum that is useful for predicting LCC. Our model achieved an acceptable accuracy (R2 = 0.82, RMSE = 0.34) and comparable validation statistics (R2 = 0.44, RMSE = 0.38), despite on-field logistic constraints in LCC measurements. This study demonstrated a protocol for a rapid estimate of biochemical variables using (AVIRIS-NG) hyperspectral imagery
Species-Level Classification and Mapping of a Mangrove Forest Using Random Forest—Utilisation of AVIRIS-NG and Sentinel Data
No full textAlthough 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
Disentangle the short-term forest degradation over most fire-affected parts of Western Himalaya, India
No full textThe tropical forest contributes around 5% to 15% of atmospheric carbon emissions, which are mostly anthropogenic. But there are large uncertainties in the quantification of these emissions from its sources. The remote-sensing data offers a practical opportunity to monitor and assess different forest disturbances. Western Himalayan forest is often affected by fire events, mostly during (pre-monsoon) dry and warm periods. In this study, we present a way to monitor the forest degradation condition using spectral mixture analysis (SMA) and surface reflectance of Landsat-8 data from 2014 to 2019. The Normalized Degradation Fraction Index (NDFI) has been performed by using spectral end member fractions of green vegetation (GV), non-photosynthetic vegetation (NPV), soil, and shade in the Google Earth Engine (GEE) cloud platform. The NDFI shows considerable spatial correspondences with clusters of fire spots during the pre-monsoon period. Around 3% to 9% of the forest burned area transformed to partially to highly degraded forest. The overall trend of degradation fraction (NDFI) over total forest cover shows a significant negative trend over a considerable area. Thus, Landsat-8-based SMA and NDFI demonstrate a potential way to identify forest degradation mediated by forest fires, although remote sensing-based approaches are limited in their capacity to accurately detect forest disturbances. Furthermore, field-based studies are needed to monitor the potentialities of the NDFI approach in forest degradation identification
Geomorphic evolution of Sutlej valley catchment in Western Himalayas: Imprint of surface processes in modulating fluvial erosion
No full textInternational audienceClimate change is increasingly predisposing polar regions to large landslides. Tsunamigenic landslides have occurred recently in Greenland ( Kalaallit Nunaat ), but none have been reported from the eastern fjords. In September 2023, we detected the start of a 9-day-long, global 10.88-millihertz (92-second) monochromatic very-long-period (VLP) seismic signal, originating from East Greenland. In this study, we demonstrate how this event started with a glacial thinning–induced rock-ice avalanche of 25 × 10 6 cubic meters plunging into Dickson Fjord, triggering a 200-meter-high tsunami. Simulations show that the tsunami stabilized into a 7-meter-high long-duration seiche with a frequency (11.45 millihertz) and slow amplitude decay that were nearly identical to the seismic signal. An oscillating, fjord-transverse single force with a maximum amplitude of 5 × 10 11 newtons reproduced the seismic amplitudes and their radiation pattern relative to the fjord, demonstrating how a seiche directly caused the 9-day-long seismic signal. Our findings highlight how climate change is causing cascading, hazardous feedbacks between the cryosphere, hydrosphere, and lithosphere
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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