Vegetation Spectra Research Articles

Quantitative evaluation of biological spectrum and phenological pattern of vegetation of a sacred grove of West Midnapore District, Eastern India

Abstract. Sen UK, Bhakat RK. 2021. Quantitative evaluation of biological spectrum and phenological pattern of vegetation of a sacred grove of West Midnapore District, Eastern India. Asian J For 5: 83-100. Sacred groves, small forests patches devoted to deities and ancestral spirits, are classic examples of community-based, culturally aware, natural resource management. To indigenous groups that care for them, display rich biodiversity, and provide ecological services to local groups that have sustained the environment over the years, sacred groves have cultural and spiritual significance. This studied sacred forest hosts 312 plant species belonging to 257 genera under 78 families of 34 orders according to APG IV. Poales, 73, 23.40% and Poaceae, 48, 15.38% were the dominant order and family. Therophytes, cryptophytes and chamaephytes constitute a higher percentage 16.81%, 3.62% and 3.18% respectively than the normal spectrum exhibiting “thero-crypto-chamaephytic” phytoclimate. Leaf size spectra showed that the plant with leptophyll, 83, 26.60% and ovate, 59, 18.91% type's leaf lamina were dominant. The findings may have a heuristic value in developing future monitoring schemes and assessing the effects of global change in this varied but poorly studied area.

The leaf economic and plant size spectra of European forest understory vegetation

Forest understories play a vital role in ecosystem functioning and the provision of ecosystem services. However, the extent to which environmental conditions drive dominant ecological strategies in forest understories at the continental scale remains understudied. Here, we used ~29 500 forest vegetation plots sampled across Europe and classified into 25 forest types to explore the relative role of macroclimate, soil pH and tree canopy cover in driving abundance‐weighted patterns in the leaf economic spectrum (LES) and plant size spectrum (PSS) of forest understories (shrub and herb layers). We calculated LES using specific leaf area (SLA) and leaf dry matter content (LDMC) and PSS using plant height and seed mass of vascular plant species found in the understories. We found that forest understories had more conservative leaf economics in areas with more extreme mean annual temperatures (mainly Fennoscandia and the Mediterranean Basin), more extreme soil pH and under more open canopies. Warm and summer‐dry regions around the Mediterranean Basin and areas of Atlantic Europe also had taller understories with heavier seeds than continental temperate or boreal areas. Understories of broadleaved deciduous forests, such asFagusforests on non‐acid soils, or ravine forests, more commonly hosted species with acquisitive leaf economics. In contrast, some coniferous forests, such asPinus,LarixandPiceamire forests, orPinus sylvestrislight taiga and sclerophyllous forests, more commonly hosted species with conservative leaf economics. Our findings highlight the importance of macroclimate and soil factors in driving trait variation of understory communities at the continental scale and the mediator effect of canopy cover on these relationships. We also provide the first maps and analyses of LES and PSS of forest understories across Europe and give evidence that the understories of European forest types are differently positioned along major axes of trait variation.

A novel spectral index for estimating fractional cover of non-photosynthetic vegetation using near-infrared bands of Sentinel satellite

Non-photosynthetic vegetation (NPV) is an essential component in various vegetation-soil ecosystems. Both phenology and disturbance lead to a transition from photosynthetic vegetation to NPV and vice versa. Due to the similar spectral reflectance of NPV and bare soil (BS) in the visible-near infrared region (400–1000 nm), NPV and BS separation is relying on the shortwave infrared (SWIR) bands in most cases. The lignin and cellulose absorption feature is around 2100 nm, which is the most distinctive feature of NPV. However, the water absorption feature is much stronger in the SWIR, increasing the difficulty for NPV-BS separation when wet. Recently, Sentinel-2/3 satellites add more bands in the near infrared (NIR), which provide an extra opportunity for index building and application. Based on the difference captured by derivative spectra, a spectral index, NPV-Soil Separation Index (NSSI), is proposed to realize the separation using two NIR bands within 750–900 nm range in this study. Using spectra of photosynthetic vegetation (PV), NPV, and BS acquired from world-recognized spectral libraries, NSSI is built and validated as effective for lab-collected data. With the triangle method, one of the linear spectral unmixing methods, the fractional cover of PV, NPV, and BS can be estimated. Over a woodland study area, the fractional cover retrieved by cellulose absorption index (CAI) and NDVI combination of ZY1-02D AHSI hyperspectral image is 26.41%, 37.56%, 36.03% for PV, NPV, and BS in order. With the proposed NSSI-NDVI combination, the corresponding estimated fractional cover is 23.31%, 38.44%, 38.25% using Sentinel-2 MSI and 24.58%, 36.74%, and 38.68% using Sentinel-3 OLCI image. The comparable validation result confirms that the proposed NSSI is effective for NPV-BS separation. Moreover, the triangle method of NSSI-NDVI combination is applied on both grassland and cropland images to examine its feasibility on varied types of typical vegetation-soil ecosystems, and the well-built triangular space supports its feasibility. Relying on NIR bands, NSSI can avoid strong water absorption in the SWIR. Also, the feasibility of NSSI being used on multiple multispectral satellite sensors, especially the Sentinel series, makes continuous mapping for NPV over a large spatial scale possible.

Algorithm for preliminary processing of charge coupled devices array data based on the adaptive Wiener filter

The researcher should choose the modes of recording spectra which allow to achieve the highest accuracy of spectral measurements in remote sensing systems. When registering a signal from aircraft which provide maximum coverage of the studied area, it is important to obtain a signal with the maximum signal-to- noise ratio in a minimum time, since the accumulation of spectra samples for averaging is impossible. The paper presents the experimental results of determining the noise components (readout noise, photon, electronic shot, pattern noise) for a monochrome uncooled CCD-line detector Toshiba TCD1304DG (CCD – charge-coupled devices) with various conditions of spectrum registration: detector temperature, exposition. Obtained dependences of the noise components make it possible to estimate the noise level for well-known conditions of spectra registration. The algorithm for processing CCD data based on an adaptive Wiener filter is proposed to increase the signal-to-noise ratio by using a priori information about the statistical parameters of the noise components. Such approach has allowed to increase the signal-to-noise ratio of sky spectral brightness by 4–9 dB for exposure times. The practical application of the algorithm has reduced the uncertainty in the vegetation index NDVI by 1.5 times when recording the reflection spectra of vegetation from the aircraft in the nadir measurement geometry.

New effective spectral matching measures for hyperspectral data analysis

ABSTRACT The successful implementation of Spectral Matching Measures (SMMs) often plays a crucial role in material discrimination and classification using hyperspectral dataset. The commonly exploited SMMs, such as Spectral Angle Mapper (SAM), Spectral Information Divergence (SID), and their hybrid, i.e., SIDSAMtan, show limited discrimination ability while discriminating spectrally similar materials. This study presents three new effective SMMs named Dice Spectral Similarity Coefficient (DSSC), Kumar–Johnson Spectral Similarity Coefficient (KJSSC), and a hybrid of DSSC and KJSSC, i.e., KJDSSCtan, for accurate discrimination of spectrally similar materials. A wide range of hyperspectral datasets of minerals and vegetation acquired under laboratory and real atmospheric conditions were used to compare and evaluate the performance of newly proposed and existing SMMs using Relative Spectral Discrimination Power (RSDPW) statistics. We also assessed the discrimination ability of the proposed and existing SMMs using spectra of selected minerals and vegetation species with an added component of random noise and linearly synthesized mixed spectra. An in-depth comparison and evaluation of different SMMs demonstrated that the discrimination power of the proposed SMMs is significantly higher than existing SMMs. The proposed SMMs also outperform existing SMMs when discriminating noisy and linearly synthesized mixed counterparts. The KJSSC and DSSC show similar efficacy in discriminating spectra of minerals and vegetation, whereas their hybrid measure, i.e., KJDSSCtan shows significantly higher spectral discrimination ability. Therefore, the newly proposed hybrid measure, i.e., KJDSSCtan is recommended over existing SMMs for successful material discrimination and classification using hyperspectral data.

Using Digital Photography to Track Understory Phenology in Mediterranean Cork Oak Woodlands

Monitoring vegetation is extremely relevant in the context of climate change, and digital repeat photography is a method that has gained momentum due to a low cost–benefit ratio. This work aims to demonstrate the possibility of using digital cameras instead of field spectroradiometers (FS) to track understory vegetation phenology in Mediterranean cork oak woodlands. A commercial camera was used to take monthly photographs that were processed with the Phenopix package to extract green chromatic coordinates (GCC). GCC showed good agreement with the normalized difference vegetation index (NDVI) and normalized difference water index (NDWI) obtained with FS data. The herbaceous layer displayed a very good fit between GCC and NDVI (coefficient of determination, represented by r2 = 0.89). On the contrary, the GCC of shrubs (Cistus salviifolius and Ulex airensis) showed a better fit with NDWI (r2 = 0.78 and 0.55, respectively) than with NDVI (r2 = 0.60 and 0.30). Models show that grouping shrub species together improves the predictive results obtained with ulex but not with cistus. Concerning the relationship with climatic factors, all vegetation types showed a response to rainfall and temperature. Grasses and cistus showed similar responses to meteorological drivers, particularly mean maximum temperature (r = −0.66 and −0.63, respectively). The use of digital repeat photography to track vegetation phenology was found to be very suitable for understory vegetation with the exception of one shrub species. Thus, this method proves to have the potential to monitor a wide spectrum of understory vegetation at a much lower cost than FS.

Exploring the potential of canopy reflectance spectra for estimating organic carbon content of aboveground vegetation in coastal wetlands

ABSTRACT Accurate estimation of organic carbon content or carbon storage of coastal wetland vegetation is essential for understanding the carbon cycle of coastal wetland ecosystems. This study aimed to explore the potential of canopy spectra of coastal wetland vegetation in estimating the organic carbon content of aboveground vegetation (OCCAV). A total of 54 representative vegetation quadrats were selected from coastal wetlands in Jiangsu Province, China, and their canopy reflectance spectra were measured using an in situ field spectrometer. In particular, two advanced spectral algorithms, fractional-order derivative (FOD) and optimal band combination algorithm, were used for spectral pre-treatments. Partial least squares regression (PLSR) and support vector machine (SVM) were employed to establish the OCCAV estimation models. Accuracies of the models constructed by the processed spectral parameters were compared with that of the models corresponding to five traditional pre-treatment methods and 15 common vegetation indices. Results showed that the FOD method captured subtler spectral characteristics than the first and second derivatives. The optimal estimation accuracies of PLSR and SVM models were obtained based on 0.75 and 1.50 order derivative spectra, respectively, and the ratios of performance to interquartile range (RPIQ) of the models were 2.57 and 2.97, respectively. Moreover, the optimal band combination algorithm effectively extracted the sensitive spectral parameters related to OCCAV, and the accuracy of the model established based on the spectral parameters extracted by this algorithm was generally better than that of the model obtained by the common vegetation indices. The optimal estimation result was achieved by the SVM model based on the optimized ratio vegetation index, with an RPIQ of 3.10. In summary, this research provides a theoretical basis for future studies on estimating the organic carbon and carbon storage of wetland vegetation based on large-scale canopy hyperspectral images and also helps to improve the knowledge of the carbon cycle of wetland ecosystems.

Efficient RTM-based training of machine learning regression algorithms to quantify biophysical & biochemical traits of agricultural crops

With an upcoming unprecedented stream of imaging spectroscopy data, there is a rising need for tools and software applications exploiting the spectral possibilities to extract relevant information on an operational basis. In this study, we investigate the potential of a scientific processor designed to quantify biophysical and biochemical crop traits from spectroscopic imagery of the upcoming Environmental Mapping and Analysis Program (EnMAP) satellite. Said processor relies on a hybrid retrieval workflow executing pre-trained machine learning regression models fast and efficiently based on training data from a lookup table of synthetic vegetation spectra and their associated parameterization of the well-known radiative transfer model (RTM) PROSAIL. The established models provide spatial information about leaf area index (LAI), average leaf inclination angle (ALIA), leaf chlorophyll content (Cab) and leaf mass per area (Cm). In contrast to using site-specific training data, the approach facilitates a universal application without the need to integrate a priori information into the processor. Four machine learning algorithms, namely artificial neural networks (ANN), random forest regression (RFR), support vector machine regression (SVR), and Gaussian process regression (GPR), were found to estimate biophysical and biochemical variables of unseen targets with high performance (relative error scores < 10%). ANNs excelled in terms of accuracy, model size and execution time when the 242 spectral bands were transformed into 15 principal components, the signals of which were scaled by a z-transformation. Validation using in situ data from the SPARC03 Barrax campaign dataset revealed an overall good estimation of measured functional traits, for instance for LAI with root mean squared error (RMSE) of 0.81 m2 m−2, and for Cab RMSE of 6.2 µg cm−2 with the ANN model. Moreover, both crop traits could be successfully mapped using a pseudo-EnMAP scene revealing plausible within-field patterns. Conformity with LAI output of the SNAP biophysical processor was found especially for grassland and maize in the vegetative stages. Based on these findings, ANN models are considered the best choice for implementation of a hybrid retrieval workflow within the context of operational agricultural crop traits monitoring from future satellite imaging spectroscopy.

Investigating different versions of PROSPECT and PROSAIL for estimating spectral and biophysical properties of photosynthetic and non-photosynthetic vegetation in mixed grasslands

ABSTRACT The PROSPECT and PROSAIL family of radiative transfer models (RTMs) are among the most popular for simulating vegetation spectra and estimating vegetation properties at the leaf and canopy levels. However, the main limitation of the radiative transfer model approach is that model performance depends on the exhaustiveness of the calibration database(s). The PROSPECT model was calibrated mainly with photosynthetic leaves, and thus does not contain specific absorption coefficients of decay pigments responsible for the spectral behavior of non-photosynthetic vegetation. Hence, PROSPECT and PROSAIL may be ill-suited to mixed ecosystems (e.g., grasslands and wetlands), especially in the late growing season when the non-photosynthetic vegetation is likely to obfuscate the quantification of green vegetation. This study investigates the performance of different versions of PROSPECT/PROSAIL models for simulating spectra and estimating biophysical properties of photosynthetic and non-photosynthetic vegetation, and aims to better understand the limitations of these RTMs and identify possible ways to improve their performance. Results show that the PROSPECT-5 and PROSPECT-D had challenges in simulating spectra of non-photosynthetic leaves in a mixed grassland area, while a modified version (PROSPECT-5M) that considered the absorption effects of decay pigments achieved higher accuracy (e.g., mean Root Mean Square Error (RMSE) of 0.014 compared to 0.026 for the PROSPECT-5). In comparison, there is minimal difference in RMSE between models for simulating green photosynthetic leaves. At the canopy level, the original PROSAIL model simulated the spectra well for homogeneous green canopies (with mean RMSE around 0.012), while a modified PROSAIL model simulated the spectra more accurately for mixed canopies that have photosynthetic and non-photosynthetic leaves (e.g., with a mean RMSE of 0.010 compared to 0.020 of original PROSAIL). The original and modified PROSAIL were then inverted using helicopter-based high-spatial resolution hyperspectral imagery to estimate vegetation properties, and achieved higher accuracies for green and mixed canopies, respectively (e.g., estimating canopy chlorophyll with R2 values over 0.75). Overall, different versions of PROSPECT/PROSAIL models have a varied performance for photosynthetic and non-photosynthetic vegetation. Understanding the limitations of the models and adopting corresponding measures to improve their performance is critical for successful applications of RTMs in the estimation of vegetation spectral and biophysical properties.

Measuring land surface temperature, near-infrared and short-wave infrared reflectance for estimation of water availability in vegetation

The vegetation water status is a crucial variable for modelling of drought impact, vegetation productivity and water fluxes. Methods for spatial estimation of this variable still need to be improved. The integration of remotely sensed data of land surface temperature (LST) and water vegetation indices based on near-infrared (NIR) and short-wave infrared (SWIR) reflectance for estimation of vegetation water content and water available for evapotranspiration require more analysis. This study contains a detailed method and measurements of LST, NIR and SWIR reflectance of soybean, corn and barley taken in field campaigns in central Argentine Pampas and laboratory with a ST PRO Raytek (8–14 µm) and a spectrometer SVC HR-1024i (0.35 and 2.5 µm). Also, relative water content of leaves was measured in laboratory during the dehydration process. This method and dataset could be also used for researching other wavelengths between 0.35 and 2.5 µm as indicator of water vegetation status (e.g. solar-induced chlorophyll fluorescence, photosynthesis).•Procedures useful to measure field spectra of vegetation are presented.•Not only the traditional method to measure leaves spectra in laboratory, but also in field were applied.•The method allows the integration of spectra and thermal data as a proxy of vegetation water status.

Ecological succession in areas degraded by bauxite mining indicates successful use of topsoil

Brazilian ironstone outcrops (cangas) are nutrient‐poor stressful habitat dominated by slow‐growing woody species with high biodiversity and unique evolutionary history. Mining has produced great impacts on this ecosystem. Spontaneous regeneration of abandoned canga mined areas has not been observed. One of the active methods most widely used for ecological restoration in environments where soil has been lost or severely degraded is topsoil transposition due to the physical, chemical, and microbiological improvement of the substrate, in addition to the seed bank. Thus, plant succession was monitored for 40 months after topsoil transposition in a canga area degraded by aluminum mining, without any other type of management. A completely randomized design with 70 permanent plots (1 × 1 m) was used. Annual phytosociological surveys were carried out and floristic and vegetational spectra were constructed with the life‐forms proposed by Raunkiaer. Floristic composition was compared with a reference site. Overall, 105 species were identified. Both flora and vegetation changed over time, increasing resemblance to the reference areas. The floristic and vegetational spectra after 4 years of topsoil deposition are similar to pristine ones. The vegetation spectrum showed an increase in the dominance of phanerophytes and hemicryptophytes, while therophytes reduced their proportion. The early successional stage is dominated by weeds, like in other canga restoration studies, but did not impede the native species regeneration. Cangas's species recruited well from transposed topsoil. Unlike other studies with fertilized topsoil, our findings show the efficiency of topsoil transposition to provide initial conditions for the ecological restoration of this ecosystem.

Dust Dispersion and Its Effect on Vegetation Spectra at Canopy and Pixel Scales in an Open-Pit Mining Area

Dust pollution is severe in some mining areas in China due to rapid industrial development. Dust deposited on the vegetation canopy may change its spectra. However, a relationship between canopy spectra and dust amount has not been quantitatively studied, and a pixel-scale condition for remote sensing application has not been considered yet. In this study, the dust dispersion characteristics in an iron mining area were investigated using the American Meteorological Society (AMS) and the U.S. Environmental Protection Agency (EPA) regulatory model (AERMOD). Further, based on the three-dimensional discrete anisotropic radiative transfer (DART) model, the spectral characteristics of vegetation canopy under the dusty condition were simulated, and the influence of dustfall on vegetation canopy spectra was studied. Finally, the dust effect on vegetation spectra at the canopy scale was extended to a pixel scale, and the response of dust effect on vegetation spectra at the pixel scale was determined under different fractional vegetation covers (FVCs). The experimental results show that the dust pollution along a haul road was more severe and extensive than that in a stope. Taking dust dispersion along the road as an example, the variation of vegetation canopy spectra increased with the height of dust deposited on the vegetation canopy. At the pixel scale, a lower vegetation FVC would weaken the influence of dust on the spectra. The results derived from simulation spectral data were tested using satellite remote sensing images. The tested result indicates that the influence of dust retention on the pixel spectra with different FVCs was consistent with that created with the simulated data. The finding could be beneficial for those making decisions on monitoring vegetation under dusty conditions and reducing dust pollution in mining areas using remote sensing technology.

The influence of mineral content on spectral features of vine leaves

ABSTRACT The reflectance spectra of vegetation carry potentially useful information that can be used to determine chemical composition and discriminate between vegetation classes. If compared with analytical methods such as conventional chemical analysis, reflectance measurement provides non-destructive, economic, near real-time data. This paper reports results from reflectance measurements performed by spectroradiometry on leaves and branches of Vitis vinifera L. cv. Merlot and Cabernet Sauvignon from two vineyards in south Brazil. The vineyards had different geological origins but were subjected to the same management. The objectives were to detect spectral differences between the vineyards, and to correlate these differences to variations in foliar traits like the chemical composition of vine leaves. To that end, seven vine parcels were selected for reflectance measurements and chemical analyses (of eleven elements) of vine leaves, and correlations between reflectance and chemical composition were looked for. An initial investigation by discriminant analysis applied to reflectance data of leaves and branches and to grape varieties as well allowed for good separation between vineyards and varieties (> 90% accuracy). By further investigating the correlations between leaf chemical composition and reflectance along the wavelength domain covered by the measurements, we found several well-determined wavelengths with Pearson correlation coefficients r > 0.7. Concentrations of elements could be modelled up to 94% accuracy. These preliminary results, which have to be validated, suggest that variations in soil properties induce chemical differences in vine leaves that can be detected by reflectance measurements. Applications of this observation include the assessment of the chemical content of vine leaves by spectroradiometry as a fast, low-cost alternative to chemical analytical methods.

DEEP PHENOTYPING CONSIDERING TILE DRAINAGE FROM UAS-BASED MULTISPECTRAL IMAGERY BY CONVOLUTIONAL NEURAL NETWORKS

Abstract. Subsurface agriculture tile lines can greatly impact plant phenotypic characteristics through spatial variation of soil moisture, plant nutrient, and plant rooting depth. Therefore, location of subsurface tile lines plays a critical role in supporting the above ground plant phentoyping and needs to be considered in plant phenotyping analysis. Unnamed Aerial Systems (UAS) imagery together with deep learning methods can develop strong relations between the vegetation spectra and soil parameters.Here, we consider the capability of deep convolutional neural networks (CNN) to evaluate crop quality based on biomass production derived from soil moisture differences by using UAS-based multispectral imagery over soybean breeding fields. Results are still being evaluated, with particular attention to the temporal and spatial resolution of the data required to apply our approach.

Floristic and Phytoclimatic Study of an Indigenous Small Scale Natural Landscape Vegetation of Jhargram District, West Bengal, India

Sacred groves are distinctive examples of biotic components as genetic resources being preserved in situ and serve as secure heavens for many endangered and endemic taxa. From this point of view, the biological spectrum, leaf spectrum and conservation status of the current sacred grove vegetation, SBT (Swarga Bauri Than) in Jhargram district of West Bengal, India, have been studied. The area's floristic study revealed that SBT’s angiosperms were varied and consisted of 307 species belonging to 249 genera, distributed under 79 families of 36 orders as per APG IV. Fabales (12.05%) and Fabaceae (11.73%) are the dominant order and family in terms of species wealth. Biological spectrum indicates that the region enjoys “thero-chamae-cryptophytic” type of phytoclimate. With respect to the spectrum of the leaf size, mesophyll (14.05%) was found to be high followed by notophyll (7.84%), microphyll (7.19%), macrophyll (7.84%), nanophyll (6.86%), leptophyll (6.21%), and megaphyll (2.29%). The study area, being a sacred grove, it has a comparatively undisturbed status, and the protection of germplasm in the grove is based on traditional belief in the social system.

Can patterns of habitat use by western Nearctic–Neotropical migratory landbirds in winter inform conservation priorities?

I use point-count survey data collected from 171 locations across 11 vegetation conditions in western Mexico to illustrate common patterns of winter habitat use by 97 Nearctic–Neotropical migratory landbird species. A number of bird species are relatively restricted in their habitat use, with some (e.g., Northern Waterthrush [Parkesia noveboracensis], American Redstart [Setophaga ruticilla]) occurring only in relatively undisturbed habitats, and others (e.g., Say's Phoebe [Sayornis saya], Horned Lark [Eremophila alpestris]) occurring only in relatively disturbed lands associated with agriculture. A large number of bird species (e.g., Cassin's Vireo [Vireo cassinii], MacGillivray's Warbler [Geothlypis tolmiei]) use every one of the vegetation types considered, from low-elevation tropical deciduous forests to high-elevation conifer forests. Bird species showing patterns of restricted habitat use deserve conservation attention, but even the more broadly distributed species might become significantly less abundant in human-altered portions of habitats. Identifying the latter will require the inclusion of a wider spectrum of altered vegetation types/conditions than what I included here, or than what is typically considered in wildlife–habitat relationship programs.

Estimation of rice plant potassium accumulation based on non-negative matrix factorization using hyperspectral reflectance

Rapid and accurate estimation of plant potassium accumulation (PKA) using hyperspectral remote sensing is of significance for the precise management of crop K fertilizer. This study focused on the separation of non-negative matrix factorization (NMF) for hyperspectral reflectance from the ground and unmanned aerial vehicle (UAV) platforms and its mitigation effect on the water and soil background. Pure vegetation spectra were extracted from the canopy mixed spectra using NMF, and then a partial least-squares regression (PLSR) model was established based on the extracted vegetation spectra and rice PKA to construct an estimation model of rice PKA. The results showed that the green light and red edge bands contributed significantly to the rice PKA estimation. NMF could effectively extract pure vegetation and water and soil spectra from mixed spectra, and enhance the green peak, red valley, and red edge information of the extracted vegetation spectra. Compared with spectral indices, the PLSR performed best for ground and UAV data. Besides, the R2 of the PLSR model based on NMF-extracted vegetation spectra increased by 15.15% to 0.76%, and the verified RMSE and RE decreased by 16.93% and 16.77% to 3.19 g m−2 and 45.07%, respectively. Hyperspectral dataset testing from different years, growth stages and varieties, and UAV platforms showed that NMF could improve the estimation accuracy of rice PKA. This study showed that NMF could be applied to both ground and UAV hyperspectral platforms to improve the estimation accuracy of rice K nutrition.

Assessment of Biological Spectrum and Phenological Behaviours of Coastal Vegetation in East Midnapore District, Eastern India

The open coastlines of the world are dominated by sand dunes and salt marshes. They are the primary areas for human recreation and provide a wide range of frequently overlooked ecosystem services. Their intrinsic ecological significance lies in having unique biological assemblies and supporting a range of ecological processes, such as water filtration and nutrient cycling. In addition, in the coastal zone, these areas link marine and terrestrial systems. The objective of this paper is to check the psammophytic species diversity, conservation status and threat; and also prepare a biological spectrum m in the Costal East Midnapore (CEM) district's sand dune and salt marsh areas. A part of floristic inventories in the CEM, various field trips in different season were carried out, quadrate method is done to examine the ecological impact and richness. Specimens were collected, prepared voucher specimen and enlisted according APG IV classification system to prepare biological spectrum. CEM vegetation was diverse and composed of 162 species of 132 genera distributed among 52 families of 28 orders according to APG IV classification, of which 37 species were exotics. Fabales (19.14%) and Fabaceae (19.14%) were the dominant order and family. Biological spectrum shows the study area was classified as “thero-chamaephytic” type of phytoclimate. Mesophyll (24.07%) and lanceolate as well as oblong (19.14%) were found to be high in the leaf size spectrum and lamina. The area of study was highly ecotonal potential and fairly undisturbed. This study is useful in comparing and contrasting the adjacent natural vegetation along the gradients of the environment; and recommends effective long-term conservation and better management of the coastal area.

Plant diversity, structure and regeneration potential in tropical forests of Western Ghats, India

Diversity, stand structure and regeneration potential are the key elements of any forest ecosystem. For the present study, seven sites were selected with the aims of assessing plant diversity, structure and regeneration potential in tropical forests across Kanyakumari Wildlife Sanctuary (KWLS), Western Ghats, India. The sites were classified based on the similarity: tropical dry deciduous sites (TDDs I and II), tropical semi-evergreen sites (TSEs I and II) and tropical evergreen sites (TEFs I-III). The phytosociological survey was done by laying a total of 70 plots (10 plots in each study site). Standard methods were followed for the assessment of diversity, structure and regeneration patterns. A total of 267 species (205 genera, 70 families) were recorded. The tree species richness ranged 24 (TDD II) – 76 (TEF III). Of the vegetation spectrum, trees, vines and understorey accounted 56.5, 15.3 and 28.2% respectively to the total flora documented. A total of 66 species were endemic. The total tree density and tree basal area (seedlings, saplings, juveniles and adults) were 18,790 individuals (mean 2684) and 137.6 m2 (mean 19.7 m2) in 70 plots respectively. The mean tree adult density and basal area ranged 370 (TDD I) – 900 (TEF I) individuals/ha and 24.2 (TDD I) – 75.3 (TEF III) m2/ha respectively. The overall species richness was highest in TDD I, but TEF III had the highest tree species richness. The diameter class-wise distribution showed the characteristic ‘reverse J-shaped’ curve. Most tree species were ‘newly recruited’. The dominant species had ‘fair’ to ‘good’ regeneration potential. However, 12 tree species showed ‘no’ regeneration. The overall regeneration pattern of trees was ‘good’, but ‘no’ or ‘poor’ regeneration patterns in some tree species, especially endemics is a point of concern. Since a majority of tree species were ‘new recruits’, species composition may likely change in the future. The results obtained would help in understanding diversity patterns, structural attributes and regeneration potential in tropical forests of protected areas for better forest management and conservation.

Altitudinal variation of woody vegetation in Tirthan valley of great Himalayan national park at Kullu, Himachal Pradesh

The present investigation entitled “Altitudinal variation of woody vegetation in Tirthan valley of Great Himalayan National Park at Kullu, Himachal Pradesh” was carried out in three different elevation i.e. lower zone (1500-2000 m.), mid zone (2000-2500 m.) and upper zone (2500-3000 m.) of Tirthan valley at Great Himalayan National Park. In every elevational zone, 12 quadrats of 30 m X 30 m (900 sq m) size determined by species area curve method were randomly laid to study tree species. In each quadrat, a sub-quadrat of 5m X 5m (25 sq m) size for study of shrubs was laid. The generic spectrum of vegetation in Tirthan valley of Great Himalayan National Park comprised of 113 woody elements which included 58 tree species and 98 shrub species. In Tirthan valley, number of forest species decreased periodically from lower elevation to upper elevation and mean density (Individual/ha.) of tree vegetation increased along elevation. Tree density (Individual/ha.) in Tirthan valley was 683.33, 758.34 and 816.67 for lower zone, mid zone and upper zone respectively. Whereas, shrub density (Individual/ha.) in valley was 2866.67, 1900, 1233.33 for lower, mid and upper zone respectively. In valley, Pinus wallichiana at lower zone (IVI 58.7), Picea smithiana at mid zone (IVI 50.43), Quercus semecarpifolia at upper zone (IVI 70.79) were the dominant tree species. In shrub layer of Tirthan forest, dominant species at lower zone was Desmodium triflorum, at mid zone was Indigofera heterantha while at upper zone it was Rosa sericea.