Vegetation Growth Status Research Articles

Using remote sensing data to evaluate the ecological restoration in Taiyuan from the SDGs perspective

ABSTRACTRemote sensing technology plays a critical role in evaluating large-scale ecological restoration efforts. However, previous studies tend to focus on specific aspects of ecological restoration, such as water conservation, vegetation restoration, or land rehabilitation. As a sustainable development demonstration city in China, Taiyuan has undertaken numerous ecological projects to mitigate environmental degradation caused by its long-standing energy-driven economic expansion. To comprehensively evaluate Taiyuan’s ecological restoration efforts, this study used remote sensing technology to assess its progress from the SDGs perspectives, such as water conservation (SDG 6.3.2), air quality improvement (SDG 11.6.2), and ecosystem preservation (SDG 6.6.1, 15.1.1, 15.3.1, 15.4.2). Firstly, ecological restoration indicators were selected from the aforementioned SDGs perspectives. Secondly, a new vegetation growth condition index (VGCI) was developed to accurately monitor changes in vegetation growth. Finally, Taiyuan’s ecological restoration efforts were evaluated based on the selected indicators. The results showed that Taiyuan has made significant progress in ecological restoration, including improved water and air quality, increased forest coverage, and restored mine’s vegetation, likely due to its ecological projects. This study provides a useful framework for evaluating ecological restoration efforts and advancing sustainability goals.

Influence of Climate, Topography, and Hydrology on Vegetation Distribution Patterns—Oasis in the Taklamakan Desert Hinterland

Vegetation in natural desert hinterland oases is an important component of terrestrial ecosystems. Determining how desert vegetation responds to natural variability is critical for a better understanding of desertification processes and their future development. The aim of this study is to characterize the spatial distribution of vegetation in the natural desert hinterland and to reveal how different environmental factors affect vegetation changes. Taking a Taklamakan Desert hinterland oasis as our research object, we analyzed the effects of different environmental factors on desert vegetation using a time-series normalized difference vegetation index (NDVI) combined with meteorological, topographic, and hydrological data, including surface water and groundwater data. Vegetation was distributed in areas with high surface water frequency, shallow groundwater levels, relatively flat terrain, and dune basins. NDVI datasets show greening trends in oasis areas over the past 20 years. The frequency of surface water distribution influences water accessibility and effectiveness and shapes topography, thus affecting the spatial distribution pattern of vegetation. In this study, areas of high surface water frequency corresponded with vegetation distribution. The spatial distribution of groundwater depth supports the growth and development of vegetation, impacting the pattern of vegetation growth conditions. Vegetation is most widely distributed in areas where the groundwater burial depth is 3.5–4.5 m. This study provides data for restoring riparian vegetation, ecological water transfer, and sustainable development.

Restoration Effects of Supplementary Planting Measures on the Abandoned Mining Areas in the Altay Mountain, Northwest China

Ecological restoration of abandoned mining areas in the Altay Mountain, northwest China, has always been considered a challenging issue due to the wide restoration area and serious devastation. To examine the restoration effects of the experimental measures carried out by our research team in an abandoned mining area in the Altay Mountain since 2010, short-term (2 years) and long-term (9 years) restoration efficiency of two types of supplementary planting measures, sowing commercial seeds (M1) and sowing soil seed banks (M2), were analyzed by calculating the vegetation growth indexes, soil–rock ratio, soil bulk density, and soil moisture content. Results show that both supplementary planting methods can significantly improve the growth status of surface vegetation, which is reflected by the variation of vegetation growth indices. The short-term restoration effect of M1 on vegetation is faster but, over time, the effect of M2 was more prominent. Restoration effects of the M2 on the soil condition were more significant at 30–40 cm of the soil layer, reflected in the higher soil–rock ratio, which is 7.2%. The water storage function of soil has significantly improved under both supplementary planting methods; the remediation effect of method M1 on the soil bulk density is mainly reflected in the surface layer, while the effect of method M2 on the soil bulk density is reflected in deeper (40–50 cm) layer. The results of this study would be helpful to explore the new practices for the restoration of mining areas.

SEASONAL CHEMICAL STRUCTURE VARIABILITY IN BETULA TORTUOSA LEDEB. TREE RINGS IN ALPINE FOREST-TUNDRA OF KUZNETSK ALA TAU MOUNTAINS

Woody vegetation growth conditions have marked effects on hemicellulose, cellulose and lignin formation. In this study, we analyzed the climatic responses of these major cell wall polymers in Betula tortuosa. We studied 35 annual rings (1980–2015) of Betula Tortuosa Ledeb. trees growing in the alpine forest-tundra of Kuznetsk Ala Tau using and pyrolysis gas chromatography mass spectrometry (Py-GC/MS). Our analysis of the correlation of the resulting spectra and Py-GC/MS values with mean air temperature and precipitation showed that the polymeric composition of Betula Tortuosa Ledeb. was mainly determined by June-August climate. The major factor limiting the development of the “unique” cell wall polymer composition of Betula Tortuosa Ledeb. found in alpine forest-tundra of Kuznetsk Ala Tau was a deficit of heat. At the end of the growing season, precipitation had largely a negative impact on polymer formation in Betula Tortuosa Ledeb. cell walls. We believe that Py-GC/MS is an effective approach to quantifying the consequences of current climate trends for Siberian forest ecosystems.

Research on Vegetation Ecological Security in Arid Region Mountain Front River Valleys Based on Ecological Water Consumption and Water Demand

The central region of the Eurasian continent is widely affected by arid conditions, but the valleys in front of the mountains nurture ecosystems consisting of forests, shrubs, and grasslands. Preserving the ecological balance in these arid valley areas is an essential aspect of water resource planning and management. This study utilizes calculations of vegetation’s ecological water consumption and water requirements to quantitatively simulate groundwater levels. These simulated levels are then compared with the threshold depth suitable for vegetation, ultimately leading to the development of an ecological security assessment method for valley areas. The results show the following: (1) During 30 years, the water demand of river valley vegetation increased slowly, and the overall stability is about 4.82 × 108 m3. Among them, the ecological water demand of grassland is the largest. The water demand from June to August is about 68% of the whole year. (2) The results indicate that over a period of 30 years, the groundwater levels in the valley area have shown a gradual decline. The rate of decline in groundwater levels is approximately twice as fast in areas farther away from the river compared to areas closer to the river. The decline in groundwater levels typically begins in May each year. During the period of valley flooding in June, there is a temporary rise in water levels, followed by a continued decline afterwards. (3) The study area has a significant proportion of groundwater suitable areas, accounting for approximately 65% on average annually. Over the course of 30 years, the area experiencing groundwater deficiency has increased from 31% to 37%. (4) Over the past 30 years, the ratio of annual vegetation water consumption to water demand in the river valley has been slowly decreasing, and the vegetation growth status has changed from good growth to normal growth. (5) In the past 30 years, the area of ecological quality areas has decreased significantly, and most of them have been transformed into general areas. The area of ecologically fragile areas is increasing, and the area of fencing protected areas is slowly declining.

Identification of Robust Hybrid Inversion Models on the Crop Fraction of Absorbed Photosynthetically Active Radiation Using PROSAIL Model Simulated and Field Multispectral Data

The fraction of absorbed photosynthetically active radiation (FPAR), which represents the capability of vegetation-absorbed solar radiation to accumulate organic matter, is a crucial indicator of photosynthesis and vegetation growth status. Although a simplified semi-empirical FPAR estimation model was easily obtained using vegetation indices (VIs), the sensitivity and robustness of VIs and the optimal inversion method need to be further evaluated and developed for canola FPAR retrieval. The objective of this study was to identify the robust hybrid inversion model for estimating the winter canola FPAR. A field experiment with different sow dates and densities was conducted over two growing seasons to obtain canola FPARs. Moreover, 29 VIs, two machine learning algorithms and the PROSAIL model were incorporated to establish the FPAR inversion model. The results indicate that the OSAVI, WDRVI and mSR had better capability for revealing the variations of the FPAR. Three parameters of leaf area index (LAI), solar zenith angle (SZA) and average leaf inclination angle (ALA) accounted for over 95% of the total variance in the FPARs and OSAVI exhibited a greater resistance to changes in the leaf and canopy parameters of interest. The hybrid inversion model with an artificial neural network (ANN-VIs) performed the best for both datasets. The optimal hybrid inversion model of ANN-OSAVI achieved the highest performance for canola FPAR retrieval, with R2 and RMSE values of 0.65 and 0.051, respectively. Finally, the work highlights the usefulness of the radiation transfer model (RTM) in quantifying the crop canopy FPAR and demonstrates the potential of hybrid model methods for retrieving the canola FPAR at each growth stage.

A spatio-temporal fusion method based on Landsat and MODIS normalized vegetation index data

Abstract. Normalized difference vegetation index (NDVI) can simply and effectively reflect the growth status of plants, and has a linear relationship with vegetation coverage, thus it is an important indicator to identify vegetation growth status and coverage. However, the commonly used MODIS NDVI or Landsat NDVI cannot simultaneously achieve the high temporal and spatial resolution. To this end, we proposed a generic and automatic method to fuse MODIS and Landsat satellite vegetation index (NDVI) into daily high-resolution products which takes time series NDVI data from MODIS/Landsat as input, filters noise pixels, and generates high-resolution and high-frequency products through a fusion process of temporal-linearly interpolation to increase the temporal resolution and spatial filtering to alleviate the blocky artifacts from MODIS pixel border. The fused NDVI maps are visually compared to evaluate the performance of this method, and more quantitative assessment will be continued in the next step. This study can provide important support for large-scale long-term surface vegetation monitoring.

Analysis of Spatial-Temporal Changes and Driving Forces of Desertification in the Mu Us Sandy Land from 1991 to 2021

Desertification is one of the most critical environmental and socioeconomic issues in the world today. Located in the transitional region between the desert and the Loess Plateau, the Mu Us Sandy Land is one of the nine most environmentally sensitive areas in the world. Remote sensing provides an effective technical method for desertification monitoring. In order to analyze the spatiotemporal distribution of desertification in the Mu Us Sandy Land from 1991 to 2021, the “MSAVI-Albedo” model was employed to extract desertification data in 1991, 2002, 2009 and 2021. The clustering characteristics of desertification were analyzed based on Moran’s I statistic. Subsequently, the driving forces in desertification changes were investigated using a geographical detector to analyze the influence of soil, meteorology, and topography on desertification. Additionally, the impact of meteorological and human factors on desertification change in the Mu Us Sandy Land was assessed. From 1991 to 2021, the degree of desertification of the Mu Us Sandy Land showed an overall decreasing trend, and the percentage of land classified as undergoing extremely severe, severe, moderate and mild desertification was improved by 86.11%, 81.82%, 52.5% and 37.42%, respectively. The proportion of land classified as undergoing extremely severe desertification decreased from 29.22% to 5.62%, and the proportion of land undergoing no desertification increased from 4.16% to 18.33%. At the same time, the desertification center shifted westward, and the desertification distribution showed a clustering trend. It is known that different factors affect the formation and distribution of desertification in the Mu Us Sandy Land in the following order: soil, meteorology, and topography. Over the past 30 years, the mean annual temperature and annual precipitation increased at rates of 0.01871 °C/a and 1.0374 mm/a, respectively, while the mean annual wind speed decreased at a rate of 0.00945 m/s·a. These changes provided more favorable natural conditions for vegetation growth and sand fixation. Human factors, such as economic development, agriculture and animal husbandry practices, and the policy of returning farmland to forest (grassland) also had a significant impact on the desertification process, leading to a year-by-year improvement in the ecological environment of the Mu Us Sandy Land.

Increased sensitivity of greening to afforestation in China over the recent 20 years

China has experienced extensive afforestation over the recent 20 years. The observed terrestrial vegetation greening in China, characterized by increasing leaf area index (LAI), is mainly associated with afforestation illustrated by increasing tree cover fraction. The increasing tree cover fraction not only contributed directly to greening but also impacted the vegetation growth conditions. Quantifying the combined direct and indirect effect of tree cover fraction change on vegetation greening is challenging and not yet conducted. Here we propose an index, namely the sensitivity of greening to afforestation (SGA=∂LAI∂TCF) to evaluate this effect. Comparing satellite-based land cover observations of 2003–2012 with 2010–2019, SGA in China increased by 1.03, 0.49, 3.3, and 2.46 (10−2 m2 m−2 %−1) for evergreen needleleaf, deciduous broadleaf, evergreen broadleaf, and mixed forests, respectively. This indicates greening (increasing LAI) becomes more sensitive to afforestation (increasing tree cover fraction), particularly in evergreen broadleaf forests. To explore possible eco-hydrothermal factors contributing to the observed SGA increase, a diagnostic framework synergizing atmospheric supply (precipitation and potential evaporation) and biological use (vegetation transpiration) of water and energy was constructed. The results suggest that an eased water-energy constraint mostly facilitates the observed SGA increase.

Exploring the performance of spectral and textural information for leaf area index estimation with homogeneous and heterogeneous surfaces

AbstractLeaf area index (LAI) is one of the key parameters of vegetation structure, which can be applied in monitoring vegetation growth status. Currently, abundant spatial information (e.g., textural information), provided by the developing remote sensing satellite techniques, could boost the accuracy of LAI estimation. Thus, the performance of spectral and textural information must be evaluated for different vegetation types of LAI estimation in different surface types. In this study, different spectral vegetation indices (SVIs) and grey‐level co‐occurrence matrix‐based textural variables under different moving window sizes were extracted from Landsat TM satellite data. First, the ability of different types of SVIs for LAI estimation in different surface types was analysed. Subsequently, the effect of different texture variables with different moving window sizes towards LAI estimation accuracy in different vegetation types was explored. Lastly, the performance of SVIs combined with textural information for the LAI estimation in different vegetation types was evaluated. Results indicated that SVIs performed better for LAI estimation in the homogeneous region than that in the heterogeneous region, and difference vegetation index was more remarkable for LAI estimation in different vegetation types than other SVIs. In addition, variations in texture variables and moving window sizes had a large influence on LAI estimation of natural vegetation with high canopy heterogeneity. SVI combined with textural information can efficiently improve the accuracy of LAI estimation in different vegetation types (R2 = 0.672, 0.455 and 0.523 for meadow, shrub and cantaloupe, respectively.) compared with SVI alone (R2 = 0.189, 0.064 and 0.431 for meadow, shrub and cantaloupe, respectively.). Especially for natural vegetation (meadow, shrub), the addition of textural information can greatly improve the accuracy of LAI estimation.

Monitoring the Impact of Heat Damage on Summer Maize on the Huanghuaihai Plain, China

As an important food crop, summer maize is widely planted all over the world. Monitoring its growth and output is of great significance for world food security. With the trend of global warming and deterioration, the frequency of high temperature and heat damage affecting summer corn has been increasing in the past ten years. Therefore, there is an increasing demand for monitoring the high temperature and heat damage of summer maize. At present, there are nearly a hundred indices or methods for research on high temperature and heat damage. However, research based on the vegetation index cannot fully describe the damage caused by high-temperature thermal damage, and there is an obvious asynchrony effect. Research based on hyperspectral remote sensing has many inconveniences in data acquisition and complex physical model construction. Therefore, this study uses remote sensing data, including MODIS surface reflection data, MODIS land surface temperature products, as well as ground observation data and statistical data, combined with multiple remote sensing indices and land surface temperature, to construct a remote sensing index, LSHDI (land surface heat damage index). The LSHDI first searches for a location with the worst vegetation growth conditions in the three-dimensional feature space based on the LST (land surface temperature), the normalized difference vegetation index (NDVI), and the land surface water index (LSWI). Then, it calculates the distance between each point and this location to measure the degree of vegetation affected by high temperature and heat damage. Finally, because there is no reliable disaster verification dataset that has been published at present, this study uses soil moisture as a reference to explain the performance and stability of the LSHDI. The results showed that their coefficient of determination was above 0.5 and reached a significance level of 0.01. The LSHDI can well-reflect the high temperature and heat damage of land surface vegetation and can provide important data support and references for agricultural management departments.

Quantifying the Impact of Hydrological Connectivity on Salt Marsh Vegetation in the Liao River Delta Wetland.

The online version contains supplementary material available at 10.1007/s13157-023-01693-4.

Temporal and spatial variations and influencing factors of gross primary productivity in Changbai Mountain Nature Reserve, China.

Changbai Mountain Nature Reserve (CNR) is a typical temperate forest ecosystem, and gross primary production (GPP) of which is closely related to topography and climate change. Research on the spatio-temporal variations and influencing factors of GPP in the CNR is of great significance for assessing growth status of vegetation and the quality of ecological environment. We calculated GPP in CNR using the vegetation photosynthesis model (VPM), and analyzed the influences of slope, altitude, temperature, precipitation, and total radiation. The results showed that the range of annual average GPP in CNR was 63-1706 g C·m-2·a-1 from 2000 to 2020 and that GPP decreased with the increases of altitude. Temperature played the most important role in driving the spatial varia-tion of GPP, with a significant positive correlation with GPP. During the study period, the overall annual GPP showed a significant increase trend in CNR, with an average annual increase of 13 g C·m-2·a-1. The areas with increase of annual GPP accounted for 79.9% of the total area, and the area proportion of annual GPP increase differed in each plant functional type. Annual precipitation was significantly negatively correlated with GPP in 43.2% of CNR, while annual mean temperature and annual total radiation were significantly positively correlated with GPP in 47.2% and 82.4% of CNR. GPP would increase continuously in CNR under the scenario of future global warming.

Spatial and temporal trends and drivers of fractional vegetation cover in Heilongjiang Province, China during 1990-2020.

Fractional vegetation cover (FVC) is a quantitative indicator for vegetation growth conditions and ecosystem change. Clarifying the spatial and temporal trends and driving factors of FVC is an important research content of global and regional ecological environment. Based on Google Earth Engine (GEE) cloud computing platform, we estimated FVC in Heilongjiang Province from 1990 to 2020 using the pixel dichotomous model. We analyzed the temporal and spatial trends and drivers of FVC using Mann-Kendall mutation test, Sen's slope analysis with Mann-Kendall significance test, correlation analysis, and structural equation model. The results showed that the estimated FVC based on the pixel dichotomous model had high accuracy (R2>0.7, root mean square error <0.1, relative root mean square error <14%). From 1990 to 2020, the annual average FVC in Heilongjiang was 0.79, with a fluctuating upward trend (0.72-0.85) and an average annual growth rate of 0.4%. The annual average FVC at the municipal administrative districts level also showed different levels of increase of FVC. The area with extremely high FVC dominated the Heilongjiang Province with a gradual increase proportion. The area with increasing trend of FVC accounted for 67.4% of the total area, whereas the area with decreasing trend only accounted for 26.2%, and the rest remained unchanged. The correlation of human activity factor on annual average FVC was higher than that of growing season monthly average meteorological factor. The human activity factor was the main driver for FVC change in Heilongjiang Province, followed by land use type. The total effect of monthly average meteorological factor during the growing season on FVC change was negative. The results would serve as technical support for long-term FVC monitoring and driving force analysis in Heilongjiang Province, and provide a reference for ecological environment restoration and protection, as well as the formulation of related land use policy.

Impact assessment of urbanization on vegetation net primary productivity: A case study of the core development area in central plains urban agglomeration, China

Rapid urbanization process has a negative or positive impact on vegetation growth. Net primary productivity (NPP) is an effective indicator to characterize vegetation growth status. Taking the core development area of the Central Plains urban agglomeration as the study area, we estimated the NPP and its change trend in the past four decades using the Carnegie-Ames-Stanford Approach (CASA) model and statistical analysis based on meteorological and multi-source remote sensing data. Meanwhile, combined with the urbanization impact framework, we further analyzed urbanization's direct and indirect impact on NPP. The results showed that the urban area increased by 2688 km2 during a high-speed urbanization process from 1983 to 2019. As a result of the intense urbanization process, a continuous NPP decrease (direct impact) can be seen, which aggravated along with the acceleration of the urban expansion, and the mean value of direct impact was 130.84 g C·m−2·a−1. Meanwhile, urbanization also had a positive impact on NPP (indirect impact). The indirect impact showed an increasing trend during urbanization with a mean value of 10.91 g C·m−2·a−1. The indirect impact was mainly related to temperature in climatic factors. The indirect impact has a seasonal heterogeneity, and high-temperature environments of urban areas are more effective in promoting vegetation growth in autumn and winter than in summer. Among different cities, high-speed development cities have higher indirect impact values than medium's and low's because of better ecological construction. This study is of great significance for understanding the impact of urbanization on vegetation growth in the Central Plains urban agglomeration area, supporting urban greening plans, and building sustainable and resilient urban agglomerations.

Performance test and effect evaluation of guar gum-stabilized loess as a sustainable slope protection material

During the rainy season, many slopes are eroded by precipitation on the China Loess Plateau (CLP). Hence, Guar gum (GG) is used to stabilize the topsoil of loess slopes to control rainwater erosion. To test the engineering performance of GG-stabilized loess and its feasibility in slope protection practice, the mechanical and hydraulic properties of specimens with different GG contents were measured in the laboratory. The results show that GG can effectively improve loess mechanical behavior during shear and disintegration and endow loess with good impermeability and water retention ability. With increasing GG content, the cohesion initially increased and subsequently decreased, the disintegration rate and permeability coefficient decreased, and the height of soil–water characteristic curve increased. When the GG content was 1.0%, the comprehensive engineering performance of the treated specimens was optimized. The cohesion, residual water content, and Van Genuchten (VG) model empirical fitting parameter increased by 125.7%, 148.9%, and 34.5%, while the disintegration rate, permeability coefficient, and VG model scale parameter decreased by 84.3%, 84.1%, and 80%, respectively. According to the scanning electron microscopy images, the stabilization mechanism of GG on loess relied on the hydrogel adhesion of soil particles and pore filling. Finally, field model test was used to verify that the GG-stabilized loess had excellent vegetation growth status and erosion control effect. After 120d of natural rainfall, the vegetation coverage increased by 67.6%, and the cumulative erosion decreased by 47.5%. Thus, utilizing GG-stabilized loess as a slope protection material shows great potential and is expected to promote the green construction of engineering and the sustainable development of soil and water conservation in the CLP.

Estimating suitable hydrothermal conditions for vegetation growth for land use cover across China based on maximum-probability-density monthly NDVI

To provide a theoretical basis and key parameters for ecological process modeling, the maximum-probability-density (MPD) atmospheric temperature and precipitation on vegetation growth for various land cover types throughout China were estimated using a non-parametric statistical technique, based on MPD monthly Normalized Difference Vegetation Index (NDVI) during 2000–2021. The results revealed that more than half of all MPD monthly temperatures deviated from the average monthly air temperature. The spatial distribution of MPD monthly temperature closely resembled that of traditional temperature zones. The deviation from the average temperature was caused by the physiological rhythm of vegetation growth. Less than half of all MPD precipitations deviated from the average monthly precipitation. The deviation was caused by the ability of vegetation to absorb water. The suitable temperatures and precipitations on vegetation growth for vegetation cover types, temperature zones, and dry-wet zones were unique and varied widely. The same vegetation in different temperature zones and dry-wet zones had different MPD temperatures and precipitation. In the scenario of Representative Concentration Pathway (RCP) 2.6, global warming in the future would have negative impacts on vegetation growth in arid and semi-arid regions but have positive effects on vegetation growth in the Qinghai-Tibet Plateau. The rise in precipitation could partially offset the negative effects of rising temperatures. The parameters in the Gaussian kernel density estimation method and the traits of the original dataset (spatial resolution, variance, and length) were the primary influencing factors on the uncertainty of spatial distributions. Our results highlight the potential of MPD temperatures and precipitations in ecological process modeling, particularly in the simulation of NDVI under the influence of air temperature and precipitation changes.

NDVI Forecasting Model Based on the Combination of Time Series Decomposition and CNN – LSTM

Normalized difference vegetation index (NDVI) is the most widely used factor in the growth status of vegetation, and improving the prediction of NDVI is crucial to the advancement of regional ecology. In this study, a novel NDVI forecasting model was developed by combining time series decomposition (TSD), convolutional neural networks (CNN) and long short-term memory (LSTM). Two forecasting models of climatic factors and four NDVI forecasting models were developed to validate the performance of the TSD-CNN-LSTM model and investigate the NDVI's response to climatic factors. Results indicate that the TSD-CNN-LSTM model has the best prediction performance across all series, with the RMSE, NSE and MAE of NDVI prediction being 0.0573, 0.9617 and 0.0447, respectively. Furthermore, the TP-N (Temperature & Precipitation-NDVI) model has a greater effect than the T-N (Temperature-NDVI) and P-N (Precipitation-NDVI) models, according to the climatic factors-based NDVI forecasting model. Based on the results of the correlation analysis, it can be concluded that changes in NDVI are driven by a combination of temperature and precipitation, with temperature playing the most significant role. The preceding findings serve as a helpful reference and guide for studying vegetation growth in response to climate changes.

Monitoring Spatiotemporal Vegetation Response to Drought Using Remote Sensing Data.

Environmental factors such as drought significantly influence vegetation growth, coverage, and ecosystem functions. Hence, monitoring the spatiotemporal vegetation responses to drought in a high temporal and adequate spatial resolution is essential, mainly at the local scale. This study was conducted to investigate the aspatial and spatial relationships between vegetation growth status and drought in the southeastern South Dakota, USA. For this purpose, Landsat 8 OLI images from the months of April through September for the years 2016-2021, with cloud cover of less than 10%, were acquired. After that, radiometric calibration and atmospheric correction were performed on all of the images. Some spectral indices were calculated using the Band Math toolbox in ENVI 5.3 (Environment for Visualizing Images v. 5.3). In the present study, the extracted spectral indices from Landsat 8 OLI images were the Normalized Difference Vegetation Index (NDVI) and the Normalized Multiband Drought Index (NMDI). The results showed that the NDVI values for the month of July in different years were at maximum value at mostly pixels. Based on the statistical criteria, the best regression models for explaining the relationship between NDVI and NMDISoil were polynomial order 2 for 2016 to 2019 and linear for 2021. The developed regression models accounted for 96.7, 95.7, 96.2, 88.4, and 32.2% of vegetation changes for 2016, 2017, 2018, 2019, and 2021, respectively. However, there was no defined trend between NDVI and NMDISoil observed in 2020. In addition, pixel-by-pixel analyses showed that drought significantly impacted vegetation coverage, and 69.6% of the pixels were negatively correlated with the NDVI. It was concluded that the Landsat satellite images have potential information for studying the relationships between vegetation growth status and drought, which is the primary step in site-specific management.

Global Leaf Area Index Research over the Past 75 Years: A Comprehensive Review and Bibliometric Analysis

The leaf area index (LAI) is widely used as an important indicator and ecological parameter of vegetation structure and growth status, but the LAI lacks bibliometric analysis. To further understand the LAI’s research status and frontier dynamics, we used 75 years of data (1947–2021) from the Web of Science for scientific bibliometric analysis. The results showed that 22,276 LAI re-search papers were published from 1947 to 2021. According to the characteristics of the literature growth, LAI research can be divided into five stages: incubation, cultivation, acceleration, evolution, and outbreak periods. The research power at the different stages had different characteristics. The overall research power of the United States is number one globally, followed by China, Canada, and France. The related disciplines were widely varied, involving agriculture (the most studied field of LAI research), environmental science and ecology, remote sensing, and other fields. The development of the Google Earth engine, cloud computing platforms, and unmanned aerial vehicle technology will provide more critical support for LAI research. The results of this paper quantitatively show the development history, research hotspots, and application of LAI research and provide a reference for understanding the current situation and development trends of global LAI research.