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

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

Significant Inverse Influence of Tropical Indian Ocean SST on SIF of Indian Vegetation during the Summer Monsoon Onset Phase

Sea surface temperature (SST) substantially influences the land climate conditions through the co-variability of multiple climate variables, which in turn affect the structural and functional characteristics of terrestrial vegetation. Our study explored the varying responses of vegetation photosynthesis in India to the SST variations in the tropical Indian Ocean during the summer monsoon. To characterise the terrestrial photosynthetic activity, we used solar-induced chlorophyll fluorescence (SIF). Our results demonstrated a significant negative SST-SIF relationship during the onset phase of the summer monsoon: the SIF anomalies in the northern and central Indian regions decrease when strong warm SST anomalies persist in the tropical Indian Ocean. Further, SIF anomalies increase with cold anomalies of SST. However, the negative SST anomalies in the tropical Indian Ocean are less impactful on SIF anomalies relative to the positive SST anomalies. The observed statistically significant SST–SIF link is feasible through atmospheric teleconnections. During monsoon onset, positive SST anomalies in the tropical Indian Ocean favour weakened monsoon flow, decreasing moisture transport from the ocean to the Indian mainland. The resultant water deficiency, along with the high air temperature, created a stress condition and reduced the photosynthetic rate, thus demonstrating negative SIF anomalies across India. Conversely, negative SST anomalies strengthened monsoon winds in the onset period and increased moisture availability across India. Negative air temperature anomalies also dampen water stress conditions and increased photosynthetic activity, resulting in positive SIF anomalies. The identified SST-SIF relationship would be beneficial to generate a simple framework that aids in the detection of the probable impact on vegetation growth across India associated with the rapidly varying climate conditions in the Indian Ocean

Spatial heterogeneity of climate explains plant richness distribution at the regional scale in India.

IntroductionKnowledge of species richness patterns and their relation with climate is required to develop various forest management actions including habitat management, biodiversity and risk assessment, restoration and ecosystem modelling. In practice, the pattern of the data might not be spatially constant and cannot be well addressed by ordinary least square (OLS) regression. This study uses GWR to deal with spatial non-stationarity and to identify the spatial correlation between the plant richness distribution and the climate variables (i.e., the temperature and precipitation) in a 1° grid in different biogeographic zones of India.MethodologyWe utilized the species richness data collected using 0.04 ha nested quadrats in an Indian study. The data from this national study, titled 'Biodiversity Characterization at Landscape Level', were aggregated at the 1° grid level and adjudged for sampling sufficiency. The performances of OLS and GWR models were compared in terms of the coefficient of determination (R2) and the corrected Akaike Information Criterion (AICc).Results and discussionA comparative study of the R2 and AICc values of the models showed that all the GWR models performed better compared with the analogous OLS models. The climate variables were found to significantly influence the distribution of plant richness in India. The minimum precipitation (Pmin) consistently dominated individually (R2 = 0.69; AICc = 2608) and in combinations. Among the shared models, the one with a combination of Pmin and Tmin had the best model fits (R2 = 0.72 and AICc = 2619), and variation partitioning revealed that the influence of these parameters on the species richness distribution was dominant in the arid and the semi-arid zones and in the Deccan peninsula zone.ConclusionThe shift in climate variables and their power to explain the species richness of biogeographic zones suggests that the climate-diversity relationships of plants species vary spatially. In particular, the dominant influence of Tmin and Pmin could be closely linked to the climate tolerance hypothesis (CTH). We found that the climate variables had a significant influence in defining species richness patterns in India; however, various other environmental and non-environmental (edaphic, topographic and anthropogenic) variables need to be integrated in the models to understand climate-species richness relationships better at a finer scale

Species-level classification of mangrove forest using AVIRIS-NG hyperspectral imagery

Species-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

Agroforestry Suitability for Planning Site-Specific Interventions Using Machine Learning Approaches

Agroforestry in the form of intercropping, boundary plantation, and home garden are parts of traditional land management systems in India. Systematic implementation of agroforestry may help achieve various ecosystem benefits, such as reducing soil erosion, maintaining biodiversity and microclimates, mitigating climate change, and providing food fodder and livelihood. The current study collected ground data for agroforestry patches in the Belpada block, Bolangir district, Odisha state, India. The agroforestry site-suitability analysis employed 15 variables on climate, soil, topography, and proximity, wherein the land use land cover (LULC) map was referred to prescribe the appropriate interventions. The random forest (RF) machine learning model was applied to estimate the relative weight of the determinant variables. The results indicated high accuracy (average suitability >0.87 as indicated by the validation data) and highlighted the dominant influence of the socioeconomic variables compared to soil and climate variables. The results show that >90% of the agricultural land in the study area is suitable for various agroforestry interventions, such as bund plantation and intercropping, based on the cropping intensity. The settlement and wastelands were found to be ideal for home gardens and bamboo block plantations, respectively. The spatially explicit data on agroforestry suitability may provide a baseline map and help the managers and planners. Moreover, the adopted approach can be hosted in cloud-based platforms and applied in the different agro-ecological zones of India, employing the local ground data on various agroforestry interventions. The regional and national scale agroforestry suitability and appropriate interventions map would help the agriculture managers to implement and develop policies

Satellite Based Fraction of Absorbed Photosynthetically Active Radiation Is Congruent with Plant Diversity in India

A dynamic habitat index (DHI) based on satellite derived biophysical proxy (fraction of absorbed photosynthetically active radiation, FAPAR) was used to evaluate the vegetation greenness pattern across deserts to alpine ecosystems in India that account to different biodiversity. The cumulative (DHI-cum), minimum (DHI-min), and seasonal (DHI-sea) DHI were generated using Moderate Resolution Imaging Spectroradiometer (MODIS)-based FAPAR. The higher DHI-cum and DHI-min represented the biodiversity hotspots of India, whereas the DHI-sea was higher in the semi-arid, the Gangetic plain, and the Deccan peninsula. The arid and the trans-Himalaya are dominated with grassland or barren land exhibit very high DHI-sea. The inter-year correlation demonstrated an increase in vegetation greenness in the semi-arid region, and continuous reduction in greenness in the Northeastern region. The DHI components validated using field-measured plant richness data from four biogeographic regions (semi-arid, eastern Ghats, the Western Ghats, and Northeast) demonstrated good congruence. DHI-cum that represents the annual greenness strongly correlated with the plant richness (R2 = 0.90, p-value < 0.001), thereby emerging as a suitable indicator for assessing plant richness in large-scale biogeographic studies. Overall, the FAPAR-based DHI components across Indian biogeographic regions provided understanding of natural variability of the greenness pattern and its congruence with plant diversity

Species-level classification and mapping of a mangrove forest using random forest—utilisation of AVIRIS-NG and sentinel data

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

Impact of Environmental Gradients on Phenometrics of Major Forest Types of Kumaon Region of the Western Himalaya

Understanding ecosystem functional behaviour and its response to climate change necessitates a detailed understanding of vegetation phenology. The present study investigates the effect of an elevational gradient, temperature, and precipitation on the start of the season (SOS) and end of the season (EOS), in major forest types of the Kumaon region of the western Himalaya. The analysis made use of the Normalised Difference Vegetation Index (NDVI) time series that was observed by the optical datasets between the years 2001 and 2019. The relationship between vegetation growth stages (phenophases) and climatic variables was investigated as an interannual variation, variation along the elevation, and variation with latitude. The SOS indicates a delayed trend along the elevational gradient (EG) till mid-latitude and shows an advancing pattern thereafter. The highest rate of change for the SOS and EOS is 3.3 and 2.9 days per year in grassland (GL). The lowest rate of temporal change for SOS is 0.9 days per year in mixed forests and for EOS it is 1.2 days per year in evergreen needle-leaf forests (ENF). Similarly, the highest rate of change in SOS along the elevation gradient is 2.4 days/100 m in evergreen broadleaf forest (EBF) and the lowest is −0.7 days/100 m in savanna, and for EOS, the highest rate of change is 2.2 days/100 m in EBF and lowest is −0.9 days/100 m in GL. Winter warming and low winter precipitation push EOS days further. In the present study area, due to winter warming and summer dryness, despite a warming trend in springseason or springtime, onset of the vegetation growth cycle shows a delayed trend across the vegetation types. As vegetation phenology responds differently over heterogeneous mountain landscapes to climate change, a detailed local-level observational insight could improve our understanding of climate change mitigation and adaptation policies

Impact of Environmental Gradients on Phenometrics of Major Forest Types of Kumaon Region of the Western Himalaya

Understanding ecosystem functional behaviour and its response to climate change necessitates a detailed understanding of vegetation phenology. The present study investigates the effect of an elevational gradient, temperature, and precipitation on the start of the season (SOS) and end of the season (EOS), in major forest types of the Kumaon region of the western Himalaya. The analysis made use of the Normalised Difference Vegetation Index (NDVI) time series that was observed by the optical datasets between the years 2001 and 2019. The relationship between vegetation growth stages (phenophases) and climatic variables was investigated as an interannual variation, variation along the elevation, and variation with latitude. The SOS indicates a delayed trend along the elevational gradient (EG) till mid-latitude and shows an advancing pattern thereafter. The highest rate of change for the SOS and EOS is 3.3 and 2.9 days per year in grassland (GL). The lowest rate of temporal change for SOS is 0.9 days per year in mixed forests and for EOS it is 1.2 days per year in evergreen needle-leaf forests (ENF). Similarly, the highest rate of change in SOS along the elevation gradient is 2.4 days/100 m in evergreen broadleaf forest (EBF) and the lowest is −0.7 days/100 m in savanna, and for EOS, the highest rate of change is 2.2 days/100 m in EBF and lowest is −0.9 days/100 m in GL. Winter warming and low winter precipitation push EOS days further. In the present study area, due to winter warming and summer dryness, despite a warming trend in springseason or springtime, onset of the vegetation growth cycle shows a delayed trend across the vegetation types. As vegetation phenology responds differently over heterogeneous mountain landscapes to climate change, a detailed local-level observational insight could improve our understanding of climate change mitigation and adaptation policies

Assessment of tropical cyclone amphan affected inundation areas using sentinel-1 satellite data

Tropical 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