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As per Ayurvedic system of medicine, Indian Ficus (Fam. – Moraceae) plants are used in the treatment of various diseases. The plants are characterized by a specific class of closed inflorescence, named syconia, and are distributed in different states of India. Total 97 species of Ficus genus are naturalized in Indian states. Indian Ficus species possess anti-inflammatory, antimicrobial, antioxidant, antidiabetic, antiarthritic, antistress, anticancer, hepatoprotective, neuroprotective and wound healing properties. The phytochemical analysis reveals the presence of alkaloids, triterpenoids, flavonoids, furanocoumarins, and polyphenolic compounds in different species. Recently, bioavailability of Indian Ficus has been increased due to the presence of antioxidative agents. However, large number of reports have been published on phytochemistry and biological activities of 31 Indian Ficus species but, no reports are available in literature on 66 species. This review summarizes and describes the current knowledge of ethnomedicinal uses, phytochemistry, pharmacological activities, bioavailability, and pharmacokinetic profiles of 31 Indian Ficus species. Moreover, it includes clinical and toxicological studies with an aim to explore their potential in the pharmaceutical industries.

Total 34 species of Tinospora genus are found in Africa, Australia, Asia, Madagascar and Pacific regions of the World. Nine species of Tinospora are naturalized in the different states of India. In traditional medicine, different parts of Tinospora are used in the treatment of syphilis, ulcers, bronchitis, jaundice, urinary disease, piles, skin and liver diseases. The information of traditional uses, phytochemical and pharmacological properties of Indian Tinospora species was collected from the published books, MSc/MTech, PhD dissertations, PubMed, Wiley, and Elsevier. Moreover, the reference books, the relevant reviews, and the digital records were critically examined to present a complete overview of Indian Tinospora species. Indian Tinospora species possess various pharmacological attributes such as antioxidant, hepatopotective, radioprotective, neuroprotective, antidiabetic, anthelmintic, antmicrobial, analgesic, anti-fertility, antiarthritic, anti-tumor, antistress, anti-inflammatory, immunomodulatory, wound healing, and antiulcer activities. These biological activities of Indian Tinospora species can be attributed to the presence of a wide range of phytoconstituents including alkaloids (tinoscorsides A-B, palmatine, tembetarine, jatrorrhizine, magnoflorine, berberine, isocolumbin), clerodane furano diterpene glucosides (amritosides A-D, tinoscorside C, borapetoside B and F, and cordifolide C), flavonoids (diosmetin, genkwanin, genkwanin 7-glucoside, and rutin), lignans (Secoisolariciresinol, syringaresinol, makisterone C), and sterols (campesterol, β-sitosterol, stigmasterol). This review describes the detailed botany, ethnomedicinal uses, phytochemistry, pharmacological attributes of 9 Indian Tinospora species. Moreover, we also included the clinical importance and toxicological effects of Indian Tinospora species with the aim to investigating its potential uses as ingredients for pharmaceutical industry.

Abstract Background Based on associations between sleep spindles, cognition, and sleep-dependent memory processing, here we evaluated potential relationships between levels of CSF Aβ42, P-tau, and T-tau with sleep spindle density and other biophysical properties of sleep spindles in a sample of cognitively normal elderly individuals. Methods One-night in-lab nocturnal polysomnography (NPSG) and morning to early afternoon CSF collection were performed to measure CSF Aβ42, P-tau and T-tau. Seven days of actigraphy were collected to assess habitual total sleep time. Results Spindle density during NREM stage 2 (N2) sleep was negatively correlated with CSF Aβ42, P-tau and T-tau. From the three, CSF T-tau was the most significantly associated with spindle density, after adjusting for age, sex and ApoE4. Spindle duration, count and fast spindle density were also negatively correlated with T-tau levels. Sleep duration and other measures of sleep quality were not correlated with spindle characteristics and did not modify the associations between sleep spindle characteristics and the CSF biomarkers of AD. Conclusions Reduced spindles during N2 sleep may represent an early dysfunction related to tau, possibly reflecting axonal damage or altered neuronal tau secretion, rendering it a potentially novel biomarker for early neuronal dysfunction. Given their putative role in memory consolidation and neuroplasticity, sleep spindles may represent a mechanism by which tau impairs memory consolidation, as well as a possible target for therapeutic interventions in cognitive decline.

Nowadays it is a main challenge to find theoretically a high heat transfer efficient fluid for heating and cooling process of industries because heat transfer is a common factor of all types of fluids. The article explores the effects of highly thermal active three nanoparticles i.e. gold (Au), graphene (Gr), and copper (Cu) in a carboxymethyl cellulose (CMC)-water base fluid and their behaviors of time-dependent flow between two horizontal plates. The considering ternary nanofluid (TNF) model contains variable physical (i.e. viscosity) properties, chemical processes, cross-diffusion, activation energy, and non-uniform heat sources with nonlinear stratifications. The partial differential equations (PDEs) are developed under the assumptions that governing the flow, energy, and concentration are transformed into nonlinear ordinary differential equations (ODEs) via similarity transformations. Compiling with bvp4c code based on Runge–Kutta 4th order method with a shooting mechanism is used to solve the proposed model. A thorough graphical analysis illustrates how significant parametric behavior affects the distribution of momentum, energy, and concentration in addition to the rate of drag force, rate of heat transfer, and Sherwood number. This work illustrates the characteristics of mixed nonlinear stratification and multiple slip boundaries of the fluid model and underlines the important uses of stratification in natural processes.

Nepal's burgeoning poultry industry which is a key sector in its economy leads to a considerable generation of slaughterhouse waste (SHW), necessitating effective and sustainable disposal methods. This study explores anaerobic digestion as an optimal solution for poultry SHW management, aiming to produce energy-rich biogas while efficiently mitigating pollution in these facilities. Particularly, the feasibility and effectiveness of co-digestion with chicken litter (CL) were investigated to enhance biogas production and waste utilization. Four distinct runs of anaerobic digestion were performed, each utilizing varying substrate compositions with SHW to CL ratios of 1:0, 4:1, 1:1, and 1:4. Throughout the process, essential parameters, including total solids (TS), volatile solids (VS), biological oxygen demand (BOD5), pH, temperature, biogas generation, were meticulously measured. The cumulative biogas production for each run was as follows: 14.87 liters and a biogas yield of 88.73 ml/gVS with a 17.39 % VS reduction for Run 1, 25.98 liters and a biogas yield of 147.21 ml/gVS with a 28.64 % VS reduction for Run 2, 78.32 liters and a biogas yield of 314.69 ml/gVS with a 54.32 % VS reduction for Run 3, and 89.195 liters and a biogas yield of 344.36 ml/gVS with a 63.05 % VS reduction for Run 4. Notably, as the proportion of CL increased in the mixture from 4:1 to 1:1, a considerable VS reduction was observed. Furthermore, when the ratio of SHW to CL reached 1:1 and 1:4, a significant BOD5 reduction of 50 % and 63.13 % was achieved, respectively, surpassing previous runs. The results reveal that the addition of CL in an appropriate ratio effectively manages poultry SHW, with the optimum SHW:CL ratio for significant biogas yield lying between 1:1 and 1:4.

1. Forest ecosystem is a basic component of terrestrial ecosystem that mitigates the impact of climate change through absorbing significant amount of carbon dioxide. Forest is crucial in maintaining ecological equilibrium, reducing greenhouse gas emission, and preserving biodiversity. Thus, standardised and precise methodologies for measuring forest carbon sequestration and assessing its potential are of paramount importance in evaluating the impacts of climate change.2. We presented the “forestat”, an approach for assessing forest carbon sequestration and its potential based on annual growth of biomass, considering site class and forest age conditions, wrapped in an R package, which is applicable to all forest types. By using a comprehensive system of forest stand models, “forestat” contributes to a refined comprehension of relationships between forest biomass and carbon sequestration and offers an evaluation of the carbon sequestration potential in forest ecosystem, unifying the methods of carbon measurement and its potential estimation in both natural and plantation forests.3. Based on the two applications, this study (a) introduces and demonstrates the methods in R package “forestat”; (b) uses the 6th, 7th, 8th, and 9th National Forest Inventory data to investigate carbon sequestration and potential productivity of degraded forests in China; and (c) conducts suitability analysis for tree species in the Yellow River Basin.4. “forestat” provides a unified, efficient, and standardized analytical tool for forest carbon sequestration and its potential. This study also highlights the primary forest functionalities.

Airborne laser scanning technique (ALS) is the most appealing remote sensing technique for the precise estimation of forest above-ground biomass (AGB). Significantly strong correlations (collinearity) among the independent variables derived from ALS data decrease the accuracy of developed AGB models. To address this issue, we propose a novel variable selection algorithm-an improved sure independence screening (SPV), which integrates the Pearson correlation coefficient, threshold μ, and variance inflation factor. We further compared the performance of SPV-based, stepwise feature selection (SFS)-based, and least absolute shrinkage and selection operator (LASSO)-based AGB models developed with different regression approaches that were sensitive to strong collinearity. Field-measured data and corresponding ALS data, acquired from 1002 sample plots distributed across four distinct forest types within the Guangxi Zhuang Autonomous Region in Southern China, were used to evaluate variable selection techniques and develop AGB models. Results indicated that ALS variables selected by SPV exhibited weaker collinearity compared to those selected by SFS and LASSO. SPV-based AGB models outperformed SFS-based AGB models with higher leave-one-out cross-validation adjusted R2 (LOOCV Radj2) by 0.1% − 27.8%. SPV-based AGB models outperformed LASSO-based AGB models with higher LOOCV Radj2 by 0.4% − 16.3%. Hence, for variable selection in constructing AGB models (linear regression model, log–log regression model, and generalized additive model (GAM)) based on strongly collinear ALS variables, SPV is most preferred, followed by SFS and LASSO. The smooth curves from our GAMs developed using SPV-selected variables revealed that five canopy height variables (whp40, hmean, hp25, hp70, hskew), one canopy density variable (whp85), three density-related variables (nVegPts, bcc, and mcc), and one vertical structural variable (LADmean) were positively correlated with AGB. The canopy height variables (whp40, hmean, hp25, and hp70) were identified as the most important variables in estimating AGB for four forest types. The canopy density variable wdp85 showed a strong effect on AGB of the coniferous forests, whereas it had almost no effect on the AGB of broadleaved forests. Overall, this manuscript proposes a novel variable selection algorithm named SPV, aimed at addressing collinearity of variables derived from ALS data, which has significant implications for the application of ALS in forest inventory and forest modeling.

Abstract Background Murraya koenigii (L.) Spreng. (Rutaceae) is an important medicinal plant in natural products research for its diverse pharmacological activities. Carbazole alkaloids were the major classes of phytoconstituents obtained from different parts of this plant, such as leaves, stems, and roots. Mahanimbine and koenimbine are two important carbazole alkaloids obtained from the M. koenigii plant and are known for their anti-cancer, anti-oxidant, anti-diarrhoeal agents, etc. Standardization plays a vital role in the herbal drug industry for maintaining the quality, purity, safety, and efficacy of herbal formulations, and hyphenated analytical techniques like HPTLC, HPLC, GC–MS, and LC–MS were utilized for this purpose. In the present study, a specific, simple, and rapid semi-automated TLC method was developed to quantify mahanimbine and koenimbine in some marketed herbal formulations, and the same was validated based on (ICH)-Q2-(R1) guidelines. Results This study revealed that the powder formulation (F3) contains the highest amount of mahanimbine (62.32 µg), but the tablet formulation (F1) contains both mahanimbine (41.19 µg) and koenimbine (143.6 µg). Conclusion A simple, specific, and reproducible semi-automated TLC method was developed and validated successfully as per (ICH)-Q2-(R1) guidelines and can be utilized for analysing marketed herbal formulations containing M. koenigii powder/extracts.

Long term experiments with nine organic and inorganic fertilizer treatments. Control, 20 kg N+13 kg P, 30 kg N+20 kg P, 40 kg N+26 kg P, 60 kg N+35 kg P, FYM 6 t/ha+20 kg N+13 kg P, soybean residue 5 t/ha+20 kg N+13 kg P, FYM 6 t/ha and soybean residue 5 t/ha were conducted in semi-arid vertisols at Indore, Madhya Pradesh, India, during 10 kharif seasons from 1992 to 2001 with the objective of identifying the most sustainable nutrient management practice for soybean (cv. JS-335) in the region. For this purpose, procedures of regression and sustainability index were used for evaluating the impact of integrated fertility management practices on soil health and crop productivity. There was a significant land degradation effect on the yield over the years as indicated by the negative slope of years in the regression model. The predictability of yield through land degradation variable ranged from 0.176 for control to 0.381 for 60 kg N+35 kg P per hectare treatment. Predictability of the yield through land degradation and rainfall was found to range between 0.277 for FYM 6 t/ha+20kg N+13 kg P to 0.507 for FYM 6 t/ha treatment. Using the treatment means, estimates of error and yield potential of the crop, i.e. the maximum yield of 3051 kg/ha obtained in kharif 1993. The study indicated that application of FYM 6 t /ha+20 kg N+13 kg P per hectare is highly sustainable followed by 60 kg N+35 kg P per hectare for attaining maximum yield of soybean and significant build up of organic carbon and available N and P status in the soil. The paper also presents the experimental evidence of the impact of joint use of in-situ and ex-situ residue application, vegetative barriers coupled with cost effective land treatments on checking the erosion of carbon and available nutrients from agricultural lands.

Abstract Considering the major source of plant-derived low/non-calorie steviol glycosides (SGs), comprehensive physiological, biochemical, and deep transcriptional investigations were conducted to explicit deeper insight into multiple abiotic stress responses in Stevia rebaudiana. The physiological indicators including photosynthesis, chlorophyll, relative water content, shoot growth, electrolyte leakage, and SG biosynthesis were negatively impacted under drought (DS), followed by salinity (SS) and waterlogging (WS). Global transcriptional analysis revealed significant upregulated expression of the genes encoding for ROS detoxification (GST, SOD, APX, glutathione peroxidase), osmotic adjustment (alpha-trehalose-phosphate and S-adenosylmethionine decarboxylase), ion transporters (CAX, NHX, CNGS, VPPase, VATPase), water channel (PIP1, TIP) and abiotic stress-responsive candidate genes (LEA, HSPs, and Dehydrins) regulating abiotic stress response in S. rebaudiana. These inferences were complemented with predicted interactome network that revealed regulation of energy metabolism by key stress-responsive genes (GST, HKT1, MAPKs, P5CSs, PIP), transcription factors (HSFA2, DREB1A, DREB2A), and abiotic stress responsive pathways (ABA, ethylene, ion stress). This is the first detailed study to comprehend the molecular regulation of stress response and their interplay under DS, SS, and WS. The key genes and regulators can be functionally validated, and will facilitate targeted gene editing for genetic improvement of crop sustainability under changing environmental conditions in S. rebaudiana.

Mango is a commercial fruit crop of India that suffers huge postharvest losses every year. The application of biocontrol agents (BCAs) bears a vast potential for managing the same, which is yet to be exploited to its fullest extent. Hence, studies were conducted for BCAs application of Debaryomyces hansenii, Bacillus subtilis and Pseudomonas fluorescens strains on mango fruit under in-vitro, in-vivo conditions to know the efficacy of these BCAs on the postharvest pathogen, shelf life and quality retention of mango fruit. The ‘poisoned food technique’ was attempted for in-vitro studies. For the in-vivo studies, fruit of the commercial cultivar ‘Amrapali’ were un-inoculated and pre-inoculated with major postharvest pathogens (anthracnose: Colletotrichum gloeosporioides and stem-end rot: Botryodiplodia theobromae) were treated with BCA, followed by ambient storage at (24 ± 4 °C, 75 ± 5 % RH). From the results, it has been observed that under in vitro studies, BCA Debaryomyces hansenii (Strain: KP006) and Bacillus subtilis (Strain: BJ0011) at the treatment level 108 CFU mL−1 while, the Pseudomonas fluorescens at 109 CFU mL−1 (Strain: BE0001) were significantly effective for pathogen inhibition. However, under the in vivo studies, the BCA Debaryomyces hansenii (Strain: KP006) at 108 CFU mL−1 treatment level was found to significantly reduce the pathogen's decay incidence while positively influencing the shelf life and biochemical (quality) attributes. This treatment increased the storage life of mango fruit by more than three days over control fruit. Therefore, BCA Debaryomyces hansenii (Strain: KP006) at 108 CFU mL−1 can be used to control the postharvest pathological loss of mango fruit without affecting its internal quality.

Abstract Microclimate ecology is attracting renewed attention because of its fundamental importance in understanding how organisms respond to climate change. Many hot issues can be investigated in desert ecosystems, including the relationship between species distribution and environmental gradients (e.g., elevation, slope, topographic convergence index, and solar insolation). Species Distribution Models (SDMs) can be used to understand these relationships. We used data acquired from the important desert plant Nitraria tangutorum Bobr. communities and desert topographic factors extracted from LiDAR (Light Detection and Ranging) data of one square kilometer in the inner Mongolia region of China to develop SDMs. We evaluated the performance of SDMs developed with a variety of both the parametric and nonparametric algorithms (Bioclimatic Modelling (BIOCLIM), Domain, Mahalanobi, Generalized Linear Model, Generalized Additive Model, Random Forest (RF), and Support Vector Machine). The area under the receiver operating characteristic curve was used to evaluate these algorithms. The SDMs developed with RF showed the best performance based on the area under curve (0.7733). We also produced the Nitraria tangutorum Bobr. distribution maps with the best SDM and suitable habitat area of the Domain model. Based on the suitability map, we conclude that Nitraria tangutorum Bobr. is more suited to southern part with 0–20 degree slopes at an elevation of approximately 1010 m. This is the first attempt of modelling the effects of topographic heterogeneity on the desert species distribution on a small scale. The presented SDMs can have important applications for predicting species distribution and will be useful for preparing conservation and management strategies for desert ecosystems on a small scale.

This paper presents the Dominant Species-Physiognomy-Ecological (DSPE) classification system developed for large-scale differentiation of plant ecological communities from high-spatial resolution remote sensing images. In this system, the plant ecological communities are defined with the inference of dominant species, physiognomy, and shared ecological settings by incorporating multiple strata. The DSPE system was implemented in a cool-temperate climate zone at a regional scale. The deep recurrent neural networks with bootstrap resampling method were employed for evaluating performance of the DSPE classification using Sentinel-2 images at 10 m spatial resolution. The performance of differentiating DSPE communities was compared with the differentiation of higher, Dominant Genus-Physiognomy-Ecological (DGPE) communities. Overall, there was a small difference in the classification between 58 DSPE communities (F1-score = 85.5%, Kappa coefficient = 84.7%) and 45 DGPE communities (F1-score = 86.5%, Kappa coefficient = 85.7%). However, the class wise accuracy analysis showed that all 58 DSPE communities were differentiated with more than 60% accuracy, whereas more than 70% accuracy was obtained for the classification of all 45 DGPE communities. Since all 58 DSPE communities were classified with more than 60% accuracy, the DSPE classification system was still effective for the differentiation of plant ecological communities from satellite images at a regional scale, indicating its applications in other regions in the world.

Amidst the compounded challenges posed by global climate change and urbanization on forest ecosystems, the integration of urbanization control measures within a climate-focused framework may offer an avenue for breakthroughs. This study delves into the impact of climate, specifically hydrothermal conditions, on the complex interplay between urbanization (Urb) and forest ecological function (Eco) in the Yellow River Basin (YRB) in China. Our findings reveal: (1) The application of a coupled coordination model reveals a stronger alignment between urbanization and forest ecological function in the warm and humid regions in the YRB. (2) Through the cross-sectional threshold regression model, we elucidate the diverse responses of Urb to Eco across varying climate gradients. Among them, annual precipitation shows a double-threshold effect at 532.34 mm and 694.18 mm. As precipitation increases, the impact of Urb shifts from negative to positive on Eco. Moreover, in regions with precipitation below 532.34 mm and above 694.18 mm, the absolute value of response coefficients of Eco to Urb is amplified. Annual average temperature displays a single-threshold effect at 10.11 °C, leading to a transition from negative to positive impact as temperature rises. This study establishes the climate-based threshold system that governs the urbanization–forest ecological function relationship.

Research on the inversion of forest aboveground biomass based on airborne light detection and ranging (LiDAR) data focuses on finding the relationship between the two, such as established linear or nonlinear models. However, these models may have poorer estimation accuracy for tree-components biomass and cannot guarantee the additivity of each component. Therefore, we aimed to develop an error-in-variable biomass model system that ensures both the compatibility of the individual tree component biomass with the diameter at breast height and the additivity of component biomass. The system we developed used the airborne LiDAR data and field-measured data of principis-rupprechtii (Larix gmelinii var.) trees, collected from north China. Our model system not only ensured the additivity of nonlinear biomass models, it also accounted for the impact of measurement errors. We first selected the airborne LiDAR-derived variable with the highest contribution to the biomass of each component and then developed an inversion model system with that variable as an independent variable and with the biomass of each component as the dependent variable using allometric functions. Moreover, two model estimation methods, two-stage error (TSEM) and nonlinear seemingly unrelated regression (NSUR) with one-step, two-step, and summation methods, were also applied, and their performances were compared. The results showed that both NSUR-one-step and TSEM-one-step led to similar parameter estimates and performance for a system, and the fitting accuracy of a model system was not very attractive. The variance function included in a model system reduced the heteroscedasticity effectively and improved the model accuracy. Overall, this study successfully combined the error-in-variable modeling with the airborne LiDAR data, proposed methods that can be used for the extension of component biomass from an individual tree to a stand and that might improve the estimation accuracy of carbon storage. A compatible model system can be further improved if various sources of error in the variables are identified, and their impacts on the system are effectively accounted for.

Vegetation mapping and monitoring is important as the composition and distribution of vegetation has been greatly influenced by land use change and the interaction of land use change and climate change. The purpose of vegetation mapping is to discover the extent and distribution of plant communities within a geographical area of interest. The paper introduces the Genus-Physiognomy-Ecosystem (GPE) system for the organization of plant communities from the perspective of satellite remote sensing. It was conceived for broadscale operational vegetation mapping by organizing plant communities according to shared genus and physiognomy/ecosystem inferences, and it offers an intermediate level between the physiognomy/ecosystem and dominant species for the organization of plant communities. A machine learning and cross-validation approach was employed by utilizing multi-temporal Landsat 8 satellite images on a regional scale for the classification of plant communities at three hierarchical levels: (i) physiognomy, (ii) GPE, and (iii) dominant species. The classification at the dominant species level showed many misclassifications and undermined its application for broadscale operational mapping, whereas the GPE system was able to lessen the complexities associated with the dominant species level classification while still being capable of distinguishing a wider variety of plant communities. The GPE system therefore provides an easy-to-understand approach for the operational mapping of plant communities, particularly on a broad scale.

The mitigation of global warming and conservation of biodiversity are two significant environmental challenges today. Estimating and comparing forest carbon stock and plant diversity under different management approaches provide insight into the choice of management approaches for carbon and plant diversity management. We investigated the variation in carbon stock and diversity of plant species in two forest managements under different approaches: the Kakrebihar protection forest (PF) and Sano Surkhet community forest (CF) in Karnali Province, Surkhet, Nepal. In total, 63 sample plots (30 plots in PF and 33 plots in CF) were laid out systematically across the forests. Dendrometric measurements were carried out for trees, poles, and saplings, and representative leaf litter and herb samples were collected. Soil samples were taken at 10 cm, 20 cm, and 30 cm depths using a soil auger. The existing tree volume equations of tree species of interest were used to estimate tree volume, and species-specific wood density and conversion factors were used to obtain total biomass and carbon content. Soil samples were analyzed using the Walkley-Black method to determine soil organic carbon. PF had higher carbon stock, plant species richness, and abundance at the landscape level than CF; however, the scenario differed at the plot level. At the plot level, PF had significantly higher total carbon stock and biomass carbon stock than CF. However, PF and CF were statistically indistinguishable in term of soil carbon stock. At the plot level, PF and CF were statistically indistinguishable regarding richness, Simpson diversity, and Shannon diversity, but PF had significantly higher plant abundance than CF. In conclusion, the value of PF for carbon stock and plant diversity surpassed those of CF. This study suggests that PF might be a better strategy to enhance carbon stock in forests and maintain habitat for various plant species.

Aboveground biomass (AGB) of shrub communities in the desert is a basic quantitative characteristic of the desert ecosystem and an important index to measure ecosystem productivity and monitor desertification. An accurate and efficient method of predicting the AGB of a shrub community is essential for studying the spatial patterns and ecological functions of the desert region. Even though there are several entries in the literature on the AGB prediction of desert shrub communities using remote sensing data, the applicability and accuracy of airborne LiDAR data and prediction methods have not been well studied. We first extracted the elevation, density and intensity variables based on the airborne LiDAR, and then sample plot-level AGB prediction models were constructed using the parametric regression (nonlinear regression) and nonparametric methods (Random Forest, Support Vector Machine, K-Nearest Neighbor, Gradient Boosting Machine, and Multivariate adaptive regression splines). We evaluated accuracies of all the AGB prediction models we developed based on the fit statistics. Results showed that: (1) the elevation, density and intensity variables obtained from LiDAR point cloud data effectively predicted the AGB of the desert shrub community at a sample plot level, (2) the kappa coefficient of nonlinear mixed-effects (NLME) model obtained was 0.6977 with an improvement by 13% due to the random effects included into the model, and (3) the nonparametric model, such as Support Vector Machine showed the best fit statistics (R2 = 0.8992), which is 28% higher than the NLME-model, and effectively reduced the heteroscedasticity. The AGB prediction model presented in this paper, which is based on the airborne LiDAR data and machine learning algorithm, will provide a valuable tool to the managers and researchers for evaluating desert ecosystem productivity and monitoring desertification.

This study was conducted in Tanahun district of Gandaki Province, Nepal, to analyze the land cover change over two decades, the migration effect in land cover, and the impact caused in crop production by Rhesus macaque. Landsat TM/ETM+ for land use of 2000 and 2010 extracted by ICIMOD and Landsat 8 OLI/TIRS satellite images for land cover 2019 were downloaded from the USGS website. A purposive sample for household survey was carried out based on crops damaged by the monkey. Two hundred and fifty households were taken as samples. The Landsat images were analyzed by ArcGIS, and the social data were analyzed using SPSS and MS Excel. Land cover change data revealed increment of forest cover from 36.57% to 40.91% and drastic decrease in agriculture crops from 57.52% to 43.78% in the period of 20 years. The accuracy of the data showed overall classification accuracy of 86.11%, 81.08%, and 75% with overall kappa statistics 0.83, 0.77, and 0.74, respectively. The migration effect in the land cover was related to remittance and migrated members and found a significant positive relationship. Analyzing the trend of production with an increase in the forest cover, 21% decrease in paddy, 5% decrease in maize, and 26% decrease in millet were found as compared to the production in 2000. The econometric model concluded that the quantity of crop damage was negatively significant in relation to distance from forest and distance from water body while positively significant to distance from settlements and distance from owner’s home. The quantity of crop damage was estimated 113.89 kg per household, and the cost was 78.82 USD. This study recommends active forest management; regular thinning, and weeding. Remittance generated should be invested in the agriculture field by the households. Damage relief should be made available for the damage cost by Rhesus macaque.

Wind erosion is a major natural disaster worldwide, and it is a key problem in western Rajasthan in India. The Analytical Hierarchy Process (AHP), the Geographic Information System (GIS), and remote sensing satellite images are effective tools for modeling and risk assessment of land degradation. The present study aimed to assess and model the land degradation vulnerable (LDV) zones based on the AHP and geospatial techniques in the Luni River basin in Rajasthan, India. This study was carried out by examining important thematic layers, such as vegetation parameters (normalized difference vegetation index and land use/land cover), a terrain parameter (slope), climatic parameters (mean annual rainfall and land surface temperature), and soil parameters (soil organic carbon, soil erosion, soil texture, and soil depth), using the Analytical Hierarchical Process (AHP) and geospatial techniques in the Luni River basin in Rajasthan, India. The weights derived for the thematic layers using AHP were as follows: NDVI (0.27) > MAR (0.22) > LST (0.15) > soil erosion (0.12) > slope (0.08) > LULC (0.06) > SOC (0.04) > soil texture (0.03) > soil depth (0.02). The result indicates that nearly 21.4 % of the total area is prone to very high degradation risks; 12.3% is prone to high risks; and 16%, 24.3%, and 26% are prone to moderate, low, and very low risks, respectively. The validation of LDV was carried out using high-resolution Google Earth images and field photographs. Additionally, the Receiver Operating Characteristic (ROC) curve found an area under the curve (AUC) value of 82%, approving the prediction accuracy of the AHP technique in the study area. This study contributes by providing a better understanding of land degradation neutrality and sustainable soil and water management practices in the river basin.

Fractional vegetation cover (FVC) is an important indicator of ecosystem changes. Both satellite remote sensing and ground measurements are common methods for estimating FVC. However, desert vegetation grows sparsely and scantly and spreads widely in desert regions, making it challenging to accurately estimate its vegetation cover using satellite data. In this study, we used RGB images from two periods: images from 2006 captured with a small, light manned aircraft with a resolution of 0.1 m and images from 2019 captured with an unmanned aerial vehicle (UAV) with a resolution of 0.02 m. Three pixel-based machine learning algorithms, namely gradient enhancement decision tree (GBDT), k-nearest neighbor (KNN) and random forest (RF), were used to classify the main vegetation (woody and grass species) and calculate the coverage. An independent data set was used to evaluate the accuracy of the algorithms. Overall accuracies of GBDT, KNN and RF for 2006 image classification were 0.9140, 0.9190 and 0.9478, respectively, with RF achieving the best classification results. Overall accuracies of GBDT, KNN and RF for 2019 images were 0.8466, 0.8627 and 0.8569, respectively, with the KNN algorithm achieving the best results for vegetation cover classification. The vegetation coverage in the study area changed significantly from 2006 to 2019, with an increase in grass coverage from 15.47 ± 1.49% to 27.90 ± 2.79%. The results show that RGB images are suitable for mapping FVC. Determining the best spatial resolution for different vegetation features may make estimation of desert vegetation coverage more accurate. Vegetation cover changes are also important in terms of understanding the evolution of desert ecosystems.

This paper presents next-generation mapping of plant ecological communities including land cover and agricultural types at 10 m spatial resolution countrywide. This research introduces modelling and mapping of land cover and ecological communities separately in small regions-of-interest (prefecture level), and later integrating the outputs into a large scale (country level) for dealing with regional distribution characteristics of plant ecological communities effectively. The Sentinel-2 satellite images were processed for cloud masking and half-monthly median composite images consisting of ten multi-spectral bands and seven spectral indexes were generated. The reliable ground truth data were prepared from extant multi-source survey databases through the procedure of stratified sampling, cross-checking, and noisy-labels pruning. Deep convolutional learning of the time-series of the satellite data was employed for prefecture-wise classification and mapping of 29–62 classes. The classification accuracy computed with the 10-fold cross-validation method varied from 71.1–87.5% in terms of F1-score and 70.9–87.4% in terms of Kappa coefficient across 48 prefectural regions. This research produced seamless maps of 101 ecological communities including land cover and agricultural types for the first time at a country scale with an average accuracy of 80.5% F1-score.