Vegetation Index Variables Research Articles

Enhancing Aboveground Biomass Estimation for Three Pinus Forests in Yunnan, SW China, Using Landsat 8

The estimation of forest aboveground biomass (AGB) using Landsat 8 operational land imagery (OLI) images has been extensively studied, but forest aboveground biomass (AGB) is often difficult to estimate accurately, in part due to the multi-level structure of forests, the heterogeneity of stands, and the diversity of tree species. In this study, a habitat dataset describing the distribution environment of forests, Landsat 8 OLI image data of spectral reflectance information, as well as a combination of the two datasets were employed to estimate the AGB of the three common pine forests (Pinus yunnanensis forests, Pinus densata forests, and Pinus kesiya forests) in Yunnan Province using a parametric model, stepwise linear regression model (SLR), and a non-parametric model, such as random forest (RF) and support vector machine (SVM). Based on the results, the following conclusions can be drawn. (1) As compared with the parametric model (SLR), the non-parametric models (RF and SVM) have a better fitting performance for estimating the AGB of the three pine forests, especially in the AGB segment of 40 to 200 Mg/ha. The non-parametric model is more sensitive to the number of data samples. In the case of the Pinus densata forest with a sample size greater than 100, RF fitting provides better fitting performance than SVM fitting, and the SVM fitting model is better suited to the AGB estimation of the Pinus yunnanensis forest with a sample size of less than 100. (2) Landsat 8 OLI images exhibit superior accuracy in estimating the AGB of the three pine forests using a single dataset. Variables, such as texture and vegetation index variables, which can reflect the comprehensive reflection information of ground objects, play a significant role in estimating AGBs, especially the texture variables. (3) By incorporating the combined dataset with characteristics of tree species distribution and ground object reflectance spectrum, the accuracy and stability of AGB estimation of the three pine forests can be improved. Moreover, the employment of a combined dataset is also effective in reducing the number of estimation errors in cases with AGB less than 100 Mg/ha or exceeding 150 Mg/ha.

Utilization of Spectral Indices for High-Throughput Phenotyping.

The conventional plant breeding evaluation of large sets of plant phenotypes with precision and speed is very challenging. Thus, consistent, automated, multifaceted, and high-throughput phenotyping (HTP) technologies are becoming increasingly significant as tools to aid conventional breeding programs to develop genetically improved crops. With rapid technological advancement, various vegetation indices (VIs) have been developed. These VI-based imaging approaches, linked with artificial intelligence and a variety of remote sensing applications, provide high-throughput evaluations, particularly in the field of precision agriculture. VIs can be used to analyze and predict different quantitative and qualitative aspects of vegetation. Here, we provide an overview of the various VIs used in agricultural research, focusing on those that are often employed for crop or vegetation evaluation, because that has a linear relationship to crop output, which is frequently utilized in crop chlorophyll, health, moisture, and production predictions. In addition, the following aspects are here described: the importance of VIs in crop research and precision agriculture, their utilization in HTP, recent photogrammetry technology, mapping, and geographic information system software integrated with unmanned aerial vehicles and its key features. Finally, we discuss the challenges and future perspectives of HTP technologies and propose approaches for the development of new tools to assess plants’ agronomic traits and data-driven HTP resolutions for precision breeding.

Floristic Diversity and Species Composition in Urban Area at sSub-Himalayan Region of West Bengal, India

Urban green landscapes are crucial in the period of drastic population increase, climate change concerns and pollution. Urban forestry conserves biodiversity and provides ecosystem services, thereby increasing the aesthetic value of cities. However, studies on the urban vegetation cover and their quantification are rare in India. Hence, the present analysis focused on the vegetation composition, quantitative characters and diversity indices in urban areas. The study was carried out through stratified random quadrat sampling and line transect method in urban green areas (three parks and one institutional area) of Cooch Behar ity, West Bengal, India. Around 76 plant c species, which belong to 67 genera and 39 families, were documented in the studied areas. The highest species richness was reported in Nripendra Narayan Park, followed by Royal Eco Heritage Park. Royal Eco Heritage Park is highly diverse according to the species diversity index. The species and are reported from all the urban forestry Codiaeum variegatum, Polyalthia longifolia Neolamarckia cadamba landscapes. The study serves as a managerial planning tool for the proper maintenance and management of urban green spaces.

Sustainable community forest management in West Kalimantan: A case study of the Dayak Katab Kebahan community

West Kalimantan has great potential of forests and indigenous people. Therefore, it is crucial to study on forest management by indigenous people. The study is aimed at analyzing the forest management carried out by the Dayak Katab Kebahan community; and the condition of the forest ecosystem. The research area is in Melawi Regency, West Kalimantan. Observations and in-depth interviews were conducted to explore the customs in managing forests. A vegetation survey using purposive sampling was performed. The fauna study was conducted by the exploration method. Vegetation data analysis was carried out by calculating the importance value and diversity indices. One-way ANOVA analysis was done to determine the presence of forest ecosystem differences between two forests. The findings showed forest managed by the Dayak Katab Kebahan community has a core zone which is a zone that should not be disturbed, and a cultivation zone which can be utilized and can be intercropped with plantation crops; and the forest ecosystem managed by the Dayak Katab Kebahan community has a higher fauna richness, vegetation diversity index and the importance value index of each species. It is suggested that the government be involved in supporting the customary law of local communities in forest conservation.

Macrozoobenthic community assemblage as key indicator for mangrove restoration success in North Sumatra and Aceh, Indonesia

The recognition of the high value of mangrove forests and the wide array of ecosystem services they provide has motivated investment in worldwide restoration efforts. However, current metrics of functional restoration (other than seedling survival rates and plant community composition) are often not readily available for local community managers, highlighting an urgency to identify easy‐to‐measure indicators to assess the functionality of restored mangroves. The macrozoobenthic community could be such practical indicator, as macrozoobenthic communities are sensitive to changes in their environment, and can be easily surveyed within local managing programs. Focusing on three main mangrove management conditions (natural, planted, and naturally regenerated) in North Sumatra and the province of Aceh, Indonesia, we compared vegetation and macrozoobenthic community diversity indices and identified environmental variables that best describe the forest management conditions and their associated macrozoobenthic community assemblage. Results showed that community assemblage, rather than macrozoobenthic diversity index, was associated with management conditions. The highest dissimilarity in macrozoobenthic community assemblages occurred between planted versus natural mangroves, with nonsignificant dissimilarity between natural and naturally regenerated mangroves. The Lined Nerite gastropod (Nerita balteata) was identified as an indicator of natural mangroves, and the invasive Giant African snail (Achatina fulica) was abundant in mangrove plantations, but also in natural mangroves bordering harbors, oil palm plantations, and aquaculture ponds, suggesting associated anthropogenic pressures. This study showed that the macrozoobenthic community can be used as restoration indicator and could serve as a baseline to empower monitoring activities and community‐based adaptive management practices to improve the outcomes of restoration efforts.

Analysis of the effect of CO and PM 2.5 parameters on vegetation conditions in red brick industrial area, Srimulyo Village, Sragen Regency

Red brick industrial activity which is developing in Srimulyo Village generates pros and cons related to CO gas emissions and the resulting particulates, which affect to the flora diversity. This study aims to identify flora biodiversity in the red brick industrial area of Srimulyo Village, then analyze the effect of the emission value of CO and Particulate Matter (PM) 2.5 on vegetation in the area. This study conducted at 4-point locations for sampling, using Shannon-Wiener formula to determine the biodiversity index. Result obtain low vegetation diversity index and low stability with the average index value was 1.91. The low vegetation diversity index has correlation with the measured value of CO and PM 2.5 gases which above the standard thresholds. The average range of CO gas levels is 353.3-844.7 ppm, and PM 2.5 levels is 1928.7-9517.7 µg/m3. In conclusion, this study shows that the CO gases and PM 2.5 resulted from Red Brick Industrial Area affect to the flora diversity index into a low cluster in the observed location.

Area-Wide Prediction of Vertebrate and Invertebrate Hole Density and Depth across a Climate Gradient in Chile Based on UAV and Machine Learning

Burrowing animals are important ecosystem engineers affecting soil properties, as their burrowing activity leads to the redistribution of nutrients and soil carbon sequestration. The magnitude of these effects depends on the spatial density and depth of such burrows, but a method to derive this type of spatially explicit data is still lacking. In this study, we test the potential of using consumer-oriented UAV RGB imagery to determine the density and depth of holes created by burrowing animals at four study sites along a climate gradient in Chile, by combining UAV data with empirical field plot observations and machine learning techniques. To enhance the limited spectral information in RGB imagery, we derived spatial layers representing vegetation type and height and used landscape textures and diversity to predict hole parameters. Across-site models for hole density generally performed better than those for depth, where the best-performing model was for the invertebrate hole density (R2 = 0.62). The best models at individual study sites were obtained for hole density in the arid climate zone (R2 = 0.75 and 0.68 for invertebrates and vertebrates, respectively). Hole depth models only showed good to fair performance. Regarding predictor importance, the models heavily relied on vegetation height, texture metrics, and diversity indices.

Drought reduces the effectiveness of ecological projects: Perspectives from the inter-annual variability of vegetation index

To alleviate grassland degradation and improve ecosystem functions, the Chinese government has implemented some ecological projects since 2004 across the Tibetan Plateau, such as ‘Return Grazing Land to Grassland Project’ and the ‘Compensation and Rewards to Herders for Natural Grassland Conservation’. Meanwhile, the frequency and intensity of drought events have strengthened in this plateau. However, how grasslands respond to these projects and drought remains controversial. Here, taking the northern Tibetan Plateau (NTP) as the study area, we first quantified the spatiotemporal changes in the annual maximum grassland NDVI (NDVImax) after the implementation of the ecological projects (2005–2015). Then, we assessed the effects of drought (Standardized Precipitation-Evapotranspiration Index, SPEI) and ecological projects on the dynamics of NDVImax. The results showed that (1) the grazing intensity decreased significantly across the NTP after the start of ecological projects, but grassland NDVImax did not increase as expected. Statistically, 53.7% of grasslands showed a decreasing NDVImax, of which 5.7% had a significant decrease (P < 0.05), mainly in the central and eastern NTP. (2) NDVImax positively correlated with SPEI at the pixel, county, and regional scales, suggesting that drought was the driving force for the decreasing NDVImax. The severe and extreme drought events in 2015 contributed to 9.4% and 17.7% reduction of grassland NDVImax, respectively. (3) The residual analysis demonstrated that there were 4.4% of the grasslands experienced human-induced degradation. However, on the whole, the effects of ecological projects on vegetation changes of the NTP were positive. Our findings suggested that the positive effects of the ecological projects on vegetation growth were relatively mitigated or offset by drought. Thus, we question the validity of evaluating the effectiveness of ecological projects only according to the trend of vegetation productivity derived from remote-sensing based vegetation index.

Rice Yield Estimation Based on Vegetation Index and Florescence Spectral Information from UAV Hyperspectral Remote Sensing

Rice floret number per unit area as one of the key yield structure parameters is directly related to the final yield of rice. Previous studies paid little attention to the effect of the variations in vegetation indices (VIs) caused by rice flowering on rice yield estimation. Unmanned aerial vehicles (UAV) equipped with hyperspectral cameras can provide high spatial and temporal resolution remote sensing data about the rice canopy, providing possibilities for flowering monitoring. In this study, two consecutive years of rice field experiments were conducted to explore the performance of florescence spectral information in improving the accuracy of VIs-based models for yield estimates. First, the florescence ratio reflectance and florescence difference reflectance, as well as their first derivative reflectance, were defined and then their correlations with rice yield were evaluated. It was found that the florescence spectral information at the seventh day of rice flowering showed the highest correlation with the yield. The sensitive bands to yield were centered at 590 nm, 690 nm and 736 nm–748 nm, 760 nm–768 nm for the first derivative florescence difference reflectance, and 704 nm–760 nm for the first derivative florescence ratio reflectance. The florescence ratio index (FRI) and florescence difference index (FDI) were developed and their abilities to improve the estimation accuracy of models basing on vegetation indices at single-, two- and three-growth stages were tested. With the introduction of florescence spectral information, the single-growth VI-based model produced the most obvious improvement in estimation accuracy, with the coefficient of determination (R2) increasing from 0.748 to 0.799, and the mean absolute percentage error (MAPE) and the root mean squared error (RMSE) decreasing by 11.8% and 10.7%, respectively. Optimized by flowering information, the two-growth stage VIs-based model gave the best performance (R2 = 0.869, MAPE = 3.98%, RMSE = 396.02 kg/ha). These results showed that introducing florescence spectral information at the flowering stage into conventional VIs-based yield estimation models is helpful in improving rice yield estimation accuracy. The usefulness of florescence spectral information for yield estimation provides a new idea for the further development and improvement of the crop yield estimation method.

Estimating average tree height in Xixiaoshan Forest Farm, Northeast China based on Sentinel-1 with Sentinel-2A data

Forest resource survey is important for the sustainable development of forest ecosystem in China. The average tree height is a main structural parameter of forest resource survey, and also one of the key parameters with greatest difficulty to obtain. The purpose of this study was to explore the potential of joint active and passive remote sensing technology in estimating forest average height. Taking Xixiaoshan Forest Farm in Linjiang City of Jilin Province as the research area, we used Sentinel-1 SAR and Sentinel-2A data, extracted two backscatter coefficients and eight texture information of Sentinel-1, ten spectral bands and texture information of Sentinel-2A and eleven vegetation index variables, constructed five groups of average tree height estimation models based on above variables and fusion of four variables by multiple linear regression method. We further evaluated the influence of each variable on the inversion accuracy. The results showed that the texture information extracted from the Sentinel-2A spectral band of a single data source variable had a better modeling effect and could be used as effective data to estimate the average tree height. The height estimation model of the integrated four variables was optimal, with a R2 vaule of 0.56, a root mean square error of leave-one-out cross-validation of 2.92 m, and a relative root mean square error of leave-one-out cross-validation of 21.5%. The forest average height model based on Sentinel-1 and Sentinel-2a characteristic variables could improve the estimation accuracy of forest height, which could be used for regional forest average height estimation and mapping.

Species diversity and estimation of carbon stock in abandoned shrimp pond of mangrove ecosystem in East Kalimantan

The mangrove forest ecosystem plays an absorbent and carbon sink to reduce CO2 levels in the atmosphere, including in degraded mangrove areas. This research was conducted precisely on abandoned shrimp ponds in mangrove areas in the Mahakam Delta. Four different locations are locating on Sepatin ponds, ponds, ponds, and ponds Bayur headland. The purpose of this study was to estimate the above-ground carbon stock in abandoned shrimp ponds. Carbon stock measurement methods create a 125 m transect and quadrant divided into six plots at each location. These measurements were conducted on living vegetation and dead wood from felling vegetation. Measurement of above-ground biomass was done by measuring the tree diameter at breast height (1.3 m) on each tree diameter> 5 cm in radius quadrant plot 7 m. Vegetation has a diameter <5 cm categorized in seedling criteria and measured 30 cm from the ground within the quadrant plot radius of 2 m. The vegetation diversity experienced a succession calculated using the diversity index by calculating the dominance index, diversity index, and similarity index of vegetation. While the calculation of above-ground biomass accumulation using allometric equations according to the species. The results showed that vegetation analysis in the fourth study sites obtained a high dominance index value at each location. The study is inversely proportional to the value of diversity and similarity index. The highest total carbon stock at Tanjung Nipah location was 11.599 Mg C. Ha -1, followed by Sepatin 6.248 Mg C. Ha -1, Benati Dalam 3.579 Mg C. Ha -1, and lowest carbon stock at Bayur location 1.460 Mg C. Ha -1.

Factors explaining the composition and diversity of vascular plant understories along a transcontinental gradient in the Canadian boreal forest

AbstractAimImpacts of longitudinal gradients on species distributions have received less attention than latitudinal gradients. In Canada, precipitation varies longitudinally, with direct effects on plant growth and fire regimes. Despite the geographical extent of Canadian boreal forests, vascular plant diversity is relatively limited, with just under 300 species. Understorey communities comprise most of this diversity and play key roles in forests, affecting succession, nutrient cycling and wildlife habitat. Our objective was to evaluate the relative impacts of local and regional environmental conditions on vascular plant community composition and diversity in the boreal forest.LocationSampling took place in 33 trembling aspen (Populus tremuloides) stands along a transcontinental gradient in precipitation and fire regimes in boreal Canada.MethodsWe measured community composition and vegetation diversity indices of the vascular understorey in 400‐m2 plots and measured environmental variables at the local (drainage, nutrient and light availability, site heterogeneity) and regional (annual temperature and precipitation, fire weather index, landscape heterogeneity) scales. Multivariate analyses and hierarchical model selection were used to analyze patterns.ResultsSpecies composition varied longitudinally, with western and eastern species pools. Western indicator species tended to be fire‐adapted pioneer species, while indicator species at eastern sites were fire avoiders and late‐successional species. Species richness and intra‐site beta diversity seemed primarily driven by local variables and did not vary across the country. Species evenness, however, was slightly higher in the western region and decreased under higher precipitation, colder temperatures, and higher landscape‐level heterogeneity.ConclusionOur results suggested that even for similar canopy composition environmental variables play a key role in the establishment of plant communities and structure local plant assemblages by selecting or eliminating species from the regional pool, which was also controlled by fire regimes.

Revisiting the use of red and near-infrared reflectances in vegetation studies and numerical climate models

Surface reflectance data acquired in red and near-infrared spectra by remote sensing sensors are traditionally applied to construct various vegetation indices (VIs), which are related to vegetation biophysical parameters. Most VIs use pre-defined weights (usually equal to 1) for the red and NIR reflectance values, therefore constraining particular weights for red and NIR during the VI design phase, and potentially limiting capabilities of the VI to explain an independent variable. In this paper, we propose an approach to estimate biophysical variables, such as Leaf Area Index (LAI), Canopy Chlorophyll Content (CCC) and Fraction of Photosynthetically Active Radiation (FPAR) absorbed by green vegetation, represented as linear combinations of the red and NIR reflectances with weights determined empirically from observations and radiative transfer model (PROSAIL) simulations. The proof of concept is first tested on available close-range observations over maize and soybean crops in Nebraska, USA. The empirical results compare well with those from PROSAIL model simulations. The proposed LAI model is then used with data from Landsat 8, Sentinel-2 and Planet/Dove, and the results are validated with in situ LAI measurements in Ukraine. We show that the weights on red and NIR reflectances are vegetation-specific and stable in time. The approach is further tested on crops and forests in the conterminous USA and on a global scale using MODIS LAI and FPAR products as proxies for “ground observations”. These LAI and FPAR, however, are not independently measured but derived from the corresponding remotely sensed reflectances, which precludes recommending a final set of the weights/coefficients for the users, and, thus, should be considered mostly for demonstrating the concept. The results for crop types, other than maize and soybean, and for all forests are conceptual and need to be tested with real ground data. It was, however, encouraging to see that the derived maps of coefficients/weights exhibit regular patterns over the globe compatible with those of vegetation classes and crop types. Tedious and thorough work on compiling available in situ measurements on various crops and forests needs to be accomplished prior to large-scale applications, and the method needs to be further tested and proven that it works at a large scale.The proposed parameterization may be attractive for global studies of various sub-classes of vegetation, once the parameter coefficients are established, validated, tabulated and their stability verified. Ultimately, this approach may provide quantification of vegetation traits for the past decades and be a useful asset for climate models that include satellite-derived land cover classifications and vegetation variables for simulating surface fluxes.This is a conceptual paper, with a proof-of-concept supported by observations over two crops, for which we had close-range observations. It is not a technical note, which would provide users with a recommended set of coefficients for global applications. Our intent was to develop a paradigm, which could ultimately be useful in global models.

Changes in the understory diversity of secondary Pinus tabulaeformis forests are the result of stand density and soil properties

Although the responses of forests to the regulation of stand density are better understood, a further elucidation is required as to how it impacts understory vegetation and soil properties. The relationships between understory soil properties and vegetation also warrants attention; thus, for this study we endeavored to develop new strategies to explore changes in understory vegetation. To investigate the effects of stand density on the diversity of understory vegetation and soil properties of a secondary Pinus tabulaeformis forest, fifteen sample plots with different stand densities were established. Furthermore, the soil organic carbon (SOC), total nitrogen (TN), total phosphorus (TP), nitrate nitrogen (NN), ammonium nitrogen (AN), microbial biomass carbon (MBC), and microbial biomass nitrogen (MBN) contents were determined. The Simpson, Shannon Weiner, and Pielou indices, C:N, C:P, and N:P ratios, microbial entropy carbon (qMBC), and microbial entropy nitrogen (qMBN) were calculated. It was observed that stand density substantially influenced soil attributes and the variety of understory vegetation, whereas the soil nutrient content and the diversity index of different stand density levels had significant differences. Moderate stand density promoted the retention of soil nutrients and the organic decomposition rate of the soil in a secondary Pinus tabulaeformis forest. Excessive forest stand density limited the availability of soil P; thus, microorganisms and plants transitioned to a P-restricted state, and the diversity index was reduced with higher stand densities. There was a strong correlation between soil properties and the diversity index of understory vegetation. Soil properties were one of the primary drivers toward the formation of understory vegetation diversity, whereas those of herbs were more affected by soil properties. Stand density influenced the diversity of understory vegetation by altering the nature of the soil, which was also a critical factor for how stand density influenced understory vegetation.

Assessment of the feasibility of hybrid renewable power for supply pumping system for irrigation

Crop identification is vital to make an inventory of the crops grown in a given area and their cultivation period. The Remote sensing (RS) techniques can provide information on the distribution of cultivated land, crop types, and areas for the agricultural sector's effective management.&#x0D; In remote sensing, various vegetation indices (VI) can analyze and evaluate multiple phenomena and themes. The Normalized Difference Vegetation Index (NDVI) is an essential and highly significant remote measurement widely used in agriculture for phenological monitoring and crop health (Ray and Dadhwal, 2001).&#x0D; In this work, we present a methodology for the contribution of NDVI from Landsat 7 (TM) and (ETM+) images to crop mapping in the Gharb region using a classification based on the pixel approach and estimating rice crop coefficient from NDVI.&#x0D; The classification results concern six main types of crops planted in this region (beet, maize, sugar cane, market gardening, cereals, and rice). The classification map showed differences in agricultural practices adopted by farmers in crop spatial distribution. The classification results showed the ability of this methodology to discriminate between crops.&#x0D; Crop coefficients were deduced from the NDVI extracted from the images. Due to meteorological data collected from the meteorological station TCSC of SK Tlet, the estimation of the reference evapotranspiration was made and subsequently the potential evapotranspiration of each crop during the agricultural season 2019-2020.&#x0D; The highest values for ETC were obtained when the crop was in its full development when water was mainly lost through transpiration after a slight decrease in the ratio values observed during the phase of the vegetative cycle (maturity).&#x0D; The water requirements (daily, monthly and annual) for the crops were determined and their electrical energy consumption.&#x0D; Renewable energy can be an effective solution to meet the energy needs of plots , greenhouses and large farms.&#x0D; A technical-economic study of different combinations of autonomous hybrid renewable energy systems (HRES) in order to meet the power supply needs of the above mentioned crops in the Gharb region. The renewable energy sources considered are solar, wind and biomass. The results show that for an average energy requirement of 92 kWh/day and a peak load of 6.5 kW, the unit energy cost of the optimal configuration scenario A (PV-wind-biomass-battery) is 0.19 $/kWh. Therefore, the design, development and implementation of the proposed system is a promising solution for the security of energy supply. For a 100% integration of renewable energy, the HRES produces electricity according to the following distribution: 11% from wind, 41% from solar and 48% from biomass.

Stand Structure, Biomass and Carbon Storage in Gmelina arborea Plantation at Agricultural Landscape in Foothills of Eastern Himalayas

In the modern era, Gmelina arborea plantations are a hotspot of future research because of their high carbon sequestration potential. The present work was conducted during 2018 to 2020 on a young unmanaged Gmelina farm to understand the ecosystem’s carbon and its dynamics. The study area was categorized into three age classes: ≤5, 5–10, and 10–15 years. In a plantation, Gmelina trees (10%) were randomly selected while other trees (90%) were also taken into the consideration for ecosystem carbon. A stratified random nested quadrate sampling method was adopted for analyzing other vegetation forms under study. Overall, 51 individual species in the studied Gmelina farm were found which includes 23 tree species, 7 shrub species, 16 herbs, 2 climbers, and 3 species of ferns. The estimated quantitative vegetation parameters and diversity indices indicate that the plant assemblages were heterogeneous with native diverse species evenly distributed with fairly higher densities, frequencies, and abundance. Herbs were the most important species followed by shrubs and trees. Consequently, with the increasing age of plantation, the richness of plant species increased. Soil properties were significantly influenced by the age of the plantation but exhibited no discreet trend. Total biomass density and total carbon density increased with increasing plantation age while no drastic variation was found in available soil organic carbon (SOC) because of insignificant variability in litter production. Total carbon, available SOC (up to 60 cm depth) and ecosystem carbon in the three age class plantations fell in the ranges of 54.51–59.91, 48.18–55.73, and 104.81–110.77 Mg ha−1, respectively. The carbon sequestration potential of Gmelina arborea is higher compared to other reported species and highly supportive of converting unutilized agricultural landscapes to reduce the atmospheric carbon dioxide in future.

Keanekaragaman Jenis Vegetasi Strata Herba di Bukit Gatan Kabupaten Musi Rawas

This study aims to determine the Importance Value Index (INP), Vegetation Diversity Index, and abiotic factors in herbal strata in Bukit Gatan, Musi Rawas Regency. Determination of Herbs Strata Vegetation Diversity in Bukit Gatan Musi Rawas Regency using the point intercept method, which uses herbal touch rods that are below the touch point line. This research was made into 3 research study areas, namely study area A (1-10 stands), study area B (1-10 stands) and study area C (1-10 stands), then the data were analyzed descriptively quantitative. the highest importance index (INP) has been conducted in the study area A, the caladium (Caladium sp) of 50.33%, the study area B is the arrowroot (Maranta arundiciae) plant at 69%, and in the study area C is the bandotan plant (bandotan plant) Ageratum conyzoidez) by 39%. The average diversity of the Study Area A diversity index was 0.55, Study area B was 0.26 and Study area C was 0.33. Abiotic factors in the Bukit Gatan Region, Musi Rawas Regency, greatly affect the life of herbaceous strata in the Bukit Gatan Forest Area, Musi Rawas Regency. The air temperature in the gatan hill protection forest area ranges (28.30C) with soil moisture (15) while the humidity ranges (92.6%) while the soil temperature ranges (27.10C) and soil pH ranges (6.6).

Spatial Variations of Vegetation Index from Remote Sensing Linked to Soil Colloidal Status

Recent decades have seen a progressive degradation of soils owing to an intensification of farming practices (weeding and high trafficking), increasing use of pesticides and fertilizers, mainly nitrogen, resulting in a steady decline in soil organic matter, a key component to maintain soil fertility. The work has coupled the normalized difference vegetation index (NDVI) of wheat cultivation in Central Italy to soil properties where the wheat was grown to identify the properties linked to within-field variability in productivity. NDVI was assessed through Copernicus Sentinel-2 (S-2) data during the wheat anthesis phase. The main outcome showed a significant correlation of NDVI variability to soil colloidal status and to the relative quantity in the exchange complex of the Ca2+ ions. No relationship emerged between NDVI and soil macronutrients (nitrogen, phosphorus, and potassium) concentration. The work suggested that such elements (nitrogen, especially) should not be provided solely considering the vegetation index spatial variations. Rational and sustainable management of soil fertility requires the integration of the NDVI data with the whole complex of soil physical/chemical status. In this way, the identification of the real key factors of fertility will avoid the negative impact of overfertilization. As an example, a fertilization plan was simulated for the sunflower–wheat sequence. The results showed that in the study area additional supplies of N and K would be unnecessary.

Crop and Soil Temperature Difference an Additional Factor for Analysis of the Condition of Crops

The study aims to study changes in land surface temperature (LST) of soil and vegetation on agricultural land planted with barley based on unmanned LST data. Simultaneously with the LST data, the spectral characteristics (NDVI) of crops were measured using the DJI P4 Multispectral. The paper shows the variability of vegetation indices and radiation temperature during the growing season. A significant relationship was found between the dynamics of NDVI and the dynamics of radiation temperature. The features of the variability of the spatial distribution of temperatures depending on precipitation are shown. The paper gives an example of a temperature map of the studied areas in the middle of the growing season, which shows the features of the spatial distribution of temperatures.

Increased association between climate change and vegetation index variation promotes the coupling of dominant factors and vegetation growth

Vegetation productivity dynamics are closely related to climate change, and water availability determines vegetation growth in water-limited ecosystems. Nevertheless, how changes in the interactions between climatic factors and vegetation activity variation regulate the relationship between their trends remains unclear. The Normalized Difference Vegetation Index (NDVI) is an effective proxy of vegetation growth. First, we investigated the NDVI trends, and the results revealed a vegetation activity with weaker greening and greater spatial heterogeneity after an obvious land-cover breakpoint in 1999 compared with that before 1999 in northwest China. Notably, the Loess Plateau greatly led the greenness trends, but the Tibet Plateau showed mean browning after 1999, which implied that the coupling of climate change and vegetation trends varied with spatio-temporal changes. Subsequently, using the Geographical Detector Method (GDM), we quantified and compared the association between climate change and the interannual variability of NDVI in the two stages. Vegetation productivity variation is more closely related to changes in climatic factors after 1999 compared with that before 1999. Precipitation (PPT) and vapor pressure deficit (VPD) are the primary constraints to vegetation growth in both stages. Patterns in NDVI trend increases are consistent with those of increased PPT and decreased VPD and vice versa after 1999. However, the same patterns were not observed before 1999 because of the weak association between climate change and NDVI variation. This implicated a great significance of the association between climate change and changes in vegetation activity for the prediction of potential carbon sequestration due to the shift of dominant factors and their trends under future climate change.