Vegetation Primary Productivity Research Articles

Monitoring Net Primary Productivity of Vegetation and Analyzing Its Drivers in Support of SDG Indicator 15.3.1: A Case Study of Northeast China

Monitoring Net Primary Productivity of Vegetation and Analyzing Its Drivers in Support of SDG Indicator 15.3.1: A Case Study of Northeast China

Hurricane disturbance drives trophic changes in neotropical mountain stream food webs.

Food webs are complex ecological networks that reveal species interactions and energy flow in ecosystems. Prevailing ecological knowledge on forested streams suggests that their food webs are based on allochthonous carbon, driven by a constant supply of organic matter from adjacent vegetation and limited primary production due to low light conditions. Extreme climatic disturbances can disrupt these natural ecosystem dynamics by altering resource availability, which leads to changes in food web structure and functioning. Here, we quantify the response of stream food webs to two major hurricanes (Irma and María, Category 5 and 4, respectively) that struck Puerto Rico in September 2017. Within two tropical forested streams (first and second order), we collected ecosystem and food web data 6 months prior to the hurricanes and 2, 9, and 18 months afterward. We assessed the structural (e.g., canopy) and hydrological (e.g., discharge) characteristics of the ecosystem and monitored changes in basal resources (i.e., algae, biofilm, and leaf litter), consumers (e.g., aquatic invertebrates, riparian consumers), and applied Layman's community-wide metrics using the isotopic composition of 13 C and 15 N. Continuous stream discharge measurements indicated that the hurricanes did not cause an extreme hydrological event. However, the sixfold increase in canopy openness and associated changes in litter input appeared to trigger an increase in primary production. These food webs were primarily based on terrestrially derived carbon before the hurricanes, but most taxa (including Atya and Xiphocaris shrimp, the consumers with highest biomass) shifted their food source to autochthonous carbon within 2 months of the hurricanes. We also found evidence that the hurricanes dramatically altered the structure of the food web, resulting in shorter (i.e., smaller food-chain length), narrower (i.e., lower diversity of carbon sources) food webs, as well as increased trophic species packing. This study demonstrates how hurricane disturbance can alter stream food webs, changing the trophic base from allochthonous to autochthonous resources via changes in the physical environment (i.e., canopy defoliation). As hurricanes become more frequent and severe due to climate change, our findings greatly contribute to our understanding of the mechanisms that maintain forested stream trophic interactions amidst global change.

Impacts of Human Activities on the Net Primary Productivity of Vegetation in Chengde's Transitional Region from Plateau to Plain in the Context of Climate Change

Chengde's transitional region from plateau to plain is located in the transition zone of agriculture and livestock and is extremely sensitive to climate change and human activities. This study used the net primary productivity(NPP) of vegetation as an evaluation index to quantify the degree impacts of climate change and human activities on vegetation change in the region. The Thornthwaite Memorial model was used to calculate the potential NPP, and the actual NPP was obtained based on MODIS NPP remote sensing images, using the difference between the actual and potential NPPs to express the amount of change in NPP owing to human activities. We used the slope trend and coefficient of variation method to analyze the trend and stability distribution of the actual NPP, potential NPP, and NPP influenced by human activities, and the correlation between actual NPP and annual precipitation and annual average temperature was analyzed using the correlation coefficient method. Finally, we quantified the impact of climate change and human activities on vegetation change in the region. The results showed that 99.87% of the vegetation in the region was improved and changed steadily, and the proportions of the areas showing positive correlation between actual NPP and annual precipitation and annual average temperature were 99.87% and 91.66%, respectively. The potential NPP showed an increasing trend from northwest to southeast, whereas the trend and stability of the potential NPP both showed an increasing trend from west to east. The area where climate change and human activities played a role in vegetation improvement accounted for 99.71%, and that affected by climate change accounted for 0.14%, with the proportion of human activities leading to vegetation degradation being 0.15%.

Correlation between Landscape Pattern and Net Primary Productivity of Vegetation: A Case Study in the Arid and Semi-Arid Regions of Northwest China

Human activities and environmental changes have influenced the changes in landscape patterns, which in turn profoundly impact the variation in net primary productivity (NPP) of vegetation. Understanding the relationship between landscape patterns and NPP is of significant importance for maintaining ecosystem stability and improving the ecological environment. In this study, six land use types in the arid and semi-arid regions of Northwest China were selected, and five landscape pattern indices at the landscape level and four landscape pattern indices at the class level were used. Pearson correlation and multiple linear regression models were employed to analyze the relationship between landscape indices and NPP at a 100 km × 100 km grid scale. The results indicate that there are varying degrees of correlation between landscape pattern indices and NPP from 2001 to 2020, with different levels of variation over the 20-year period. The correlation between indices and NPP is higher at the class level than at the landscape level, and the increase in landscape abundance and fragmentation promotes an increase in NPP. At the landscape level, three landscape indices, namely NP (Number of Patches), PR (Patch Richness), and SHDI (Shannon’s Diversity Index), explain 45.4% of the variation in NPP. At the class level, NP, TE (Total Edge Length), and IJI (Dispersion and Juxtaposition Index) are the main influencing factors for NPP in cropland, forestland, and grassland. Therefore, in ecological governance, it is necessary to consider landscape pattern changes appropriately to maintain ecosystem stability.

Temporal and Spatial Evolution, Prediction, and Driving-Factor Analysis of Net Primary Productivity of Vegetation at City Scale: A Case Study from Yangzhou City, China

Net primary productivity (NPP) is an important index with which to evaluate the safety and quality of regional carbon sinks. Based on the improved CASA model, climate data, social data, remote-sensing ecological data, and other multi-source data types, this article took a Chinese city, Yangzhou, as the research object, used Theil–Sen medium-trend analysis and the Hurst index to analyze its spatial–temporal-evolution characteristics and future change trends, and used geographical detectors to analyze the impact of climate, social, ecological, and other factors on the change in NPP in the study area, with the intention of providing a theoretical exploration and practical basis for achieving the “dual carbon” goals in the region. The results showed that the annual average NPP levels of the vegetation in Yangzhou in the five sampling years were 445.343 gc/m2·a, 447.788 gc/m2·a, 427.763 gc/m2·a, 398.687 gc/m2·a, and 420.168 gc/m2·a, respectively, exhibiting a trend that first decreases and then increases, with a slight overall decrease from 2000 to 2020. The area in which the vegetation in Yangzhou had the higher grades of NPP increased by 203,874 km², and an increase of 321,769 km² in the lower levels was observed. The NPP level of vegetation showed polarization, with relatively high levels in the surrounding farmland and mountain–forest areas and relatively low levels in densely populated urban areas. The ranking was highest in Baoying and lowest in Gaoyou. From the average NPP of all the land types in the study area, the following trend was exhibited: forest land > farmland > bare soil > impermeable surface > water. The future change in vegetation NPP in Yangzhou City will mainly follow the trend of the past 20 years, with a slow decrease. The NDVI (q = 0.728) and LUCC (q = 0.5601) were the leading driving factors of vegetation NPP change in Yangzhou City, and the interaction effect of double driving factors was greater than that of single driving factors.

Spatiotemporal Analysis of Ecosystem Status in China’s National Key Ecological Function Zones

The National Key Ecological Function Zones (NKEFZ) serve as crucial ecological security barriers in China, playing a vital role in enhancing ecosystem services. This study employed the theoretical framework of ecological benefits assessment in major ecological engineering projects. The primary focus was on the ecosystem macrostructure, ecosystem quality, and key ecosystem services, enabling quantitative analysis of the spatiotemporal changes in the ecosystem status of the NKEFZ from 2000 to 2019. To achieve this, remote sensing data, meteorological data, and model simulations were employed to investigate five indicators, including land use types, vegetation coverage, net primary productivity of vegetation, soil conservation services, water conservation services, and windbreak and sand fixation services. The analysis incorporated the Theil–Sen Median method to construct an evaluation system for assessing the restoration status of ecosystems, effectively integrating ecosystem quality and ecosystem services indicators. The research findings indicated that land use changes in NKEFZ were primarily characterized by the expansion of unused land and the in of grassland. The overall ecosystem quality of these zones improved, showing a stable and increasing trend. However, there were disparities in the changes related to ecosystem services. Water conservation services exhibited a decreasing trend, while soil conservation and windbreak and sand fixation services showed a steady improvement. The ecosystem of the NKEFZ, in general, displayed a stable and recovering trend. However, significant spatial heterogeneity existed, particularly in the southern region of the Qinghai–Tibet Plateau and at the border areas between western Sichuan and northern Yunnan, where some areas still experienced deteriorating ecosystem conditions. Compared to other functional zones, the trend in the ecosystem of the NKEFZ might not have been the most favorable. Nonetheless, this could be attributed to the fact that most of these areas were situated in environmentally fragile regions, and conservation measures may not have been as effective as in other functional zones. These findings highlighted the considerable challenges ahead in the construction and preservation of the NKEFZ. In future development, the NKEFZ should leverage their unique natural resources to explore distinctive ecological advantages and promote the development of eco-friendly economic industries, such as ecological industry, ecological agriculture, and eco-tourism, transitioning from being reliant on external support to self-sustainability.

Influence of aerosol on photosynthetically active radiation under haze conditions

Photosynthetically active radiation (PAR, 400-700 nm), as a sub-band in solar shortwave radiation (280-2800 nm), has profoundly affected vegetation primary productivity and biogeochemical cycles of carbon. However, few previous studies focused on the quantitative effects of aerosols on PAR, a sub-band of the shortwave spectrum, during the winter haze period. Based on ground-based remote sensing observations, we have quantitatively investigated the role of aerosol in PAR variation under haze period through radiative transfer simulations at Wuhan, China. The results indicate that direct solar PAR irradiance is attenuated more dramatically and diffuse downward irradiance is enhanced weaklier as aerosol optical depth (AOD) increases in the haze period, which leads to a result of more significant surface aerosol radiative effect (ARE) in PAR spectrum (AREPAR). Simultaneously, the percentage of AREPAR in ARESW spectrum decreases by 3.33%, indicating that PAR is attenuated more than other sub-bands in SW as pollution enhances. Meanwhile, the ARE efficiency (REE) in PAR weakens from -122 W/m2 under the clear-air condition to -80 W/m2 during the severe-pollution period. These phenomena are attributed to stronger forward scattering in PAR due to the hygroscopic growth of fine-mode aerosol particles during haze. The results provides support for further studies on the effect of pollutants on PAR, and then vegetation productivity.

Spatio-temporal analysis of the impact of land urbanization on the gross primary productivity of vegetation in the Middle Reaches of the Yangtze River Urban Agglomeration: new evidence from the township scale

The urgent need to maintain ecosystem provisioning services and achieve urban sustainable development goals has led to a long-standing focus on the driving effects of land urbanization (LU) in the Middle Reaches of the Yangtze River Urban Agglomeration (MRYRU) on vegetation cover system productivity. Previous studies have lacked consideration for the long-term correlation between land urbanization (LU) and the Gross Primary Productivity (GPP) of vegetation, as well as the spatial non-stationarity of LU’s impact in the region. This study is based on land cover remote sensing data and GPP imagery data to monitor the long-term evolution trends of LU and GPP in MRYRU from the perspectives of townships and grids. It further investigates the spatial correlation and clustering characteristics between the two using bivariate spatial autocorrelation method. Additionally, a Geographically and Temporally Weighted Regression (GTWR) model was employed to analyze the spatial effects of LU on GPP. The results indicate that the GPP of MRYRU grows to 1572.88gCm-2a-1 with an average annual growth rate of 0.848% from 2000 to 2020. High-value areas are distributed in the central and eastern mountainous of the urban agglomeration, while low-value areas gradually formed an “n”-shaped clustered distribution pattern. The negative spatial autocorrelation between LU and GPP shows a general upward trend, and Low-High is the dominant agglomeration type, concentrated in Hengyang and some mountainous cities in Jiangxi Province. The overall negative impact of land urbanization on GPP in MRYRU is maintained in the spatially nonstationary pattern of driving coefficients, with negative impact areas mostly concentrated in the city’s main city zone and positive impact areas concentrated in the urban agglomeration’s mountainous townships. This study provides support for MRYRU to adopt more resilient land optimization management strategies and vegetation ecological restoration plans.

Mapping small mammal optimal habitats using satellite-derived proxy variables and species distribution models.

Small mammal species play an important role influencing vegetation primary productivity and plant species composition, seed dispersal, soil structure, and as predator and/or prey species. Species which experience population dynamics cycles can, at high population phases, heavily impact agricultural sectors and promote rodent-borne disease transmission. To better understand the drivers behind small mammal distributions and abundances, and how these differ for individual species, it is necessary to characterise landscape variables important for the life cycles of the species in question. In this study, a suite of Earth observation derived metrics quantifying landscape characteristics and dynamics, and in-situ small mammal trapline and transect survey data, are used to generate random forest species distribution models for nine small mammal species for study sites in Narati, China and Sary Mogul, Kyrgyzstan. These species distribution models identify the important landscape proxy variables driving species abundance and distributions, in turn identifying the optimal conditions for each species. The observed relationships differed between species, with the number of landscape proxy variables identified as important for each species ranging from 3 for Microtus gregalis at Sary Mogul, to 26 for Ellobius tancrei at Narati. Results indicate that grasslands were predicted to hold higher abundances of Microtus obscurus, E. tancrei and Marmota baibacina, forest areas hold higher abundances of Myodes centralis and Sorex asper, with mixed forest-grassland boundary areas and areas close to watercourses predicted to hold higher abundances of Apodemus uralensis and Sicista tianshanica. Localised variability in vegetation and wetness conditions, as well as presence of certain habitat types, are also shown to influence these small mammal species abundances. Predictive application of the Random Forest (RF) models identified spatial hot-spots of high abundance, with model validation producing R2 values between 0.670 for M. gregalis transect data at Sary Mogul to 0.939 for E. tancrei transect data at Narati. This enhances previous work whereby optimal habitat was defined simply as presence of a given land cover type, and instead defines optimal habitat via a combination of important landscape dynamic variables, moving from a human-defined to species-defined perspective of optimal habitat. The species distribution models demonstrate differing distributions and abundances of host species across the study areas, utilising the strengths of Earth observation data to improve our understanding of landscape and ecological linkages to small mammal distributions and abundances.

Distribution of emergent aquatic vegetation leaf inclination angle and its simulation

Leaf normal inclination angle distribution (LAD) determines the interception of radiation by leaves and the transmission of radiation by the vegetation canopy, which affects the distribution of incident photosynthetically active radiation on plant leaves and ultimately directly affects the net primary productivity of vegetation. However, the contact measurement method for leaf inclination angle is difficult to perform due to the complex growing environment of wetland aquatic vegetation. In this study, the method of measuring normal leaf inclination angles was improved for wetland aquatic vegetation based on horizontal digital photography combined with the application of a protractor. Phragmites australis and Typha orientalis, two typical emergent aquatic vegetation (EAV) species growing in wetlands, were used to evaluate the robustness of leaf inclination measurement methods. In addition, LAD values were measured and analyzed for their variations across seasonal and vertical gradients within the canopy. Finally, the applicability of the two simulation algorithms for leaf normal inclination was evaluated and compared. Analysis revealed that Phragmites australis LAD exhibited a variation with seasonal changes and vertical canopy height, which is beneficial for light transmission within the canopy. The LAD of Typha orientalis was always an erectophile pattern and changed only slightly with the seasons. The new findings provided additional insights and evidence on aquatic plant resistance and environmental adaptation. Comparing simulation models for EAV, the trigonometric algorithm outperforms other algorithms in accuracy and deviation. The results of this research provide a valuable reference for exploring radiation transfer within aquatic vegetation canopies and understanding vegetation growth.

Global enhanced vegetation photosynthesis in urban environment and its drivers revealed by satellite solar-induced chlorophyll fluorescence data

Investigation on the future impacts of climatic and environmental change on vegetation photosynthesis has been largely restricted to controlled field experiments, which can hardly be extended to global scale due to limited spatial, species and ecosystem coverages. However, in urban areas plants experience altered environments that mimic potential future conditions, with higher air temperature, atmospheric carbon dioxide (CO2) concentration and pollution levels. Cities can therefore be used as global, unplanned experiments for assessing the photosynthetic response to multiple climatic and environmental drivers. Following this logic, here we investigate the urbanization impact on vegetation primary productivity and its drivers at global 160 mega-cities, using high-spatial resolution satellite solar-induced chlorophyll fluorescence (SIF) data as the proxy of photosynthesis. SIF enhancements were observed across most of the urban-rural gradients, accounting for more than 85% of the investigated land pixels. More importantly, SIF enhancements due to indirect urbanization impact (i.e., the impacts of climatic and environmental factors on vegetation growth) offset approximately 47% of the SIF reductions due to land cover change, a value significantly higher than that observed for a greenness spectral index (Enhanced Vegetation Index, EVI) (30%). Atmospheric CO2, air temperature, radiation and atmospheric nitrogen dioxide (NO2) were found to be the main drivers accounting for the enhanced SIF in urban areas. These results prove a dominant and global enhancement of vegetation photosynthesis in urban conditions, and reveal the specific contribution of climatic and environmental factors. Our findings can help to forecast the impacts of future environmental conditions on vegetation photosynthesis, and enhance our knowledge on the overall response of terrestrial biomes to climate change.

Investigating the mechanism of urbanization on the net primary productivity of vegetation in the Yangtze River Economic Belt: a comprehensive analysis from global and local effects

The stressful effects of urbanization on vegetation net primary productivity (NPP) and vegetation–carbon cycle functions within the Yangtze River Economic Belt (YEB) under the sustainable development goals (SDG) and the concept of coordinated regional development, have garnered growing attention. Existing studies have been insufficient in comprehensively examining both the global effects and local variations resulting from urbanization in the region. Additionally, insufficient attention has been given to the heterogeneity of the ecological negative effects of urbanization in the three major urban agglomerations within the YEB. Based on multivariate remote sensing image and socioeconomic statistics data, this study integrates population, economic and land dimensions to construct comprehensive urbanization indexes and quantify the spatio-temporal evolution patterns of NPP and urbanization in the YEB. The focus is on detecting the global response of NPP to urbanization using the Spatial Durbin model and discussing the local heterogeneity of the effect in the Yangtze River Delta urban agglomeration (YRD), the middle reaches of Yangtze River urban agglomeration (MRYRU), and the Chengdu-Chongqing urban agglomeration (CCU) based on a geographically weighted regression model. The results show that the average NPP of the YEB increased from 592g*c/m2 to 670g*c/m2 at a rate of 0.621% from 2000 to 2020, with the most significant growth in the CCU, and the overall pattern of change is “increased in the north and decreased in the south”. The negative spatial autocorrelation between urbanization and NPP is becoming increasingly significant, and the negatively correlated clusters is the dominant type of local autocorrelation, among which the number of “High-Low” type cities is the largest and growing, mainly located in the northern regions of Anhui and Jiangsu. The significant negative effect of urbanization on the productive capacity of vegetation cover systems was verified, and a negative spillover effect that far exceeded the local negative effect was also confirmed. The effect of urbanization on NPP has significant local variability and gradually shifts to the effect pattern of positive effect in the east and negative effect in the west, while the pattern of urbanization-driven effects in the three urban agglomerations also continues to change. This study increases the concern about the negative ecological effects of urbanization, and more importantly, provides a basis for the joint action of ecological restoration and management in the Yangtze River Economic Belt and the implementation of differentiated governance policies around urban agglomerations.

Impacts of climate, phenology, elevation and their interactions on the net primary productivity of vegetation in Yunnan, China under global warming

Both net primary productivity (NPP) and vegetation phenology play essential roles in influencing the carbon sequestration of terrestrial ecosystems. However, the relationship between vegetation phenology and NPP remains unclear under the effects of global warming. This study used Geodetector to analyze the interaction mechanisms between climate, phenology, elevation, and NPP in Yunnan, China. The results are as follows. (1) NPP is positively correlated with NDVI, LOS, EOS, TEMP, PREC and SRAD, and negatively correlated with ELEV, NDBI and SOS. However, the main influencing factors leading to the variation of NPP differ with the region. (2) The spatial distribution of NPP and LOS in Yunnan Province is mainly influenced by the monsoon, showing a pattern of high in the southwest and low in the northeast, and the interaction between PREC and LOS is the main reason for the variation of NPP. (3) In the high-altitude region of northwest Yunnan, the interaction between altitude and PREC is the primary factor affecting NPP variation. (4) In the tropical monsoon forest region of southern Yunnan, the interaction between SRAD and PREC is the main cause of NPP changes. (5) Despite having a small effect on NPP, SOS was the only phenological factor that showed a significant linear relationship with NPP. This study reveals the complex and diverse interactions between climate, phenology, elevation, and NPP in different regions of Yunnan, and provides a new perspective for understanding the carbon cycle and ecological processes of terrestrial ecosystems under global warming.

Sensitivity of Vegetation Productivity to Extreme Droughts across the Yunnan Plateau, China

Extreme drought has negative impacts on the health of vegetation and the stability of ecosystems. In this study, the CASA model was employed to estimate the net primary productivity of vegetation over the Yunnan Plateau. The time-lag effects on vegetation were observed within a 0–6 month period of extreme droughts using the Pearson correlation coefficient. The resistance of vegetation during extreme droughts was quantified, and the recovery capability of vegetation following these events was analyzed using the ARIMA model. Moreover, the study investigated the response of vegetation to extreme droughts across diverse altitudinal gradients. The results showed that: (1) This round of extreme drought led to a decrease in the NPP of vegetation in the Yunnan Plateau. (2) Vegetation exhibits a 1–3-month lag period in response to extreme drought, with forests showing slower responses than grasslands and shrubs and higher resistance to the drought. Except for agricultural vegetation, most other vegetation types are able to recover their productivity within a year. (3) Vegetation above 3000 m is less susceptible to the impacts of extreme drought. With increasing elevation, forests exhibit an earlier lag period in response to extreme drought and an increase in resistance, but lower elevation vegetation demonstrates better recovery from extreme drought events. Shrub vegetation shows the highest resistance at elevations between 3000–4000 m, and shrubs at middle to high elevations have better recovery capacity than those at low elevations. Grassland vegetation exhibits increased resistance to extreme drought with higher elevation and shows better recovery. Agricultural vegetation demonstrates higher resistance at middle to high elevations, with no significant elevation differences in recovery capacity. Extreme drought events not only have a lag effect on the vegetation ecosystem, but also affect its stability and resilience to future drought events. To adapt to climate change, future research should emphasize the role of small-scale climate in vegetation’s response to drought.

Sustainability of Animal Stocks in Traditional Pastoral Systems of the High-Altitude Andean Altiplano

The main traditional livelihood in the high Central Andes Plateau, locally known as the Puna, is pastoralism. This system is based on llamas (Lama glama) and sheep (Ovis aries), which graze on natural vegetation (shrub steppe, grasslands, and peatlands). So, the livelihood of Andean communities is strongly influenced by the spatial and temporal dynamics of vegetation. Most of the existing models on herbivory used to estimate sustainable livestock loads require information on the nutritional values and primary production of vegetation, which is lacking for the Argentine Puna. This work aimed to fill the gap by implementing a carrying capacity model using data on annual vegetation growth and herbivore feeding strategies collected in the field in different plant communities. The hypothesis that guided this work was that environmental heterogeneity affects management decisions and should be taken into account to model sustainable animal stocks in Andean pastoralist systems. We estimated carrying capacity based on the MSY logistic model, considering biomass and aboveground primary production of vegetation communities, climatic features, and herbivore selective responses. We compared the estimated carrying capacity with real animal stocks in four production units within the study site in 2013, 2014, and 2017. The results indicate that the carrying capacity presents high spatial and temporal variability. In three of the production units assessed, the livestock—mixed herds of sheep and llamas—were above or near the estimated carrying capacity, even when the stocking rates were reduced during dry periods. The high stocking rates, along with evidence of land degradation, may indicate moderate to severe overgrazing. This work demonstrates the usefulness of estimating the carrying capacity to monitor the pastoral systems at fine space and time scales. These monitoring techniques can facilitate decision making among Puna herders in a biocultural context of knowledge dialog that may foster sustainability in pastoralist livelihoods.

Effects of eastern vs. central Pacific El Niño on Northern Hemisphere photosynthetic seasonality

The El Niño–Southern Oscillation (ENSO) affects many climatic controls on vegetation activity, driving interannual variation in timing (phenology) and magnitude of terrestrial carbon uptake. However, the climatic effects of ENSO can differ for sea surface temperature (SST) anomalies primarily centered in the eastern vs. central Pacific (EP and CP, respectively). Here, we examine the extent to which EP and CP SST anomalies affect Northern Hemisphere photosynthetic seasonality and whether their effects differ. Using two decades of satellite near-infrared reflectance of vegetation (NIRv) and FLUXNET2015 gross primary production, we estimated annual 0.05° start and end of growing season timing corresponding to the onset and offset of photosynthetic activity, as well as peak NIRv magnitude as a proxy for peak growing season productivity. We find that correlations between Northern Hemisphere photosynthetic timing/magnitude and ENSO differ for EP- and CP-centered SST anomalies, though in many regions the effects and differences between them are quite small. Warmer SSTs generally led to an earlier start of the photosynthetic season, especially in North America and parts of Eurasia. However, the magnitude (and even direction) of the relationships between start of season and SST differed for CP- and EP-dominated events. Correlations of both peak NIRv magnitude and end of season timing with ENSO tended to be smaller in magnitude and more regionally idiosyncratic, though with strong evidence of different effects of CP and EP SSTs. In southern North America, CP SSTs (but not EP SSTs) were positively associated with peak NIRv, while in boreal regions of North America and Eurasia, CP SSTs were negatively associated with peak NIRv (despite small positive associations with EP SSTs). Differences between the effects of EP and CP SST anomalies suggest that short-term vegetation forecasts based on aggregate ENSO indices could be improved by separately considering the EP and CP components.

A High Spatiotemporal Enhancement Method of Forest Vegetation Leaf Area Index Based on Landsat8 OLI and GF-1 WFV Data

The leaf area index (LAI) is a crucial parameter for analyzing terrestrial ecosystem carbon cycles and global climate change. Obtaining high spatiotemporal resolution forest stand vegetation LAI products over large areas is essential for an accurate understanding of forest ecosystems. This study takes the northwestern part of the Inner Mongolia Autonomous Region (the northern section of the Greater Khingan Mountains) in northern China as the research area. It also generates the LAI time series product of the 8-day and 30 m forest stand vegetation growth period from 2013 to 2017 (from the 121st to the 305th day of each year). The Simulated Annealing-Back Propagation Neural Network (SA-BPNN) model was used to estimate LAI from Landsat8 OLI, and the multi-period GaoFen-1 WideField-View satellite images (GF-1 WFV) and the spatiotemporal adaptive reflectance fusion mode (STARFM) was used to predict high spatiotemporal resolution LAI by combining inversion LAI and Global LAnd Surface Satellite-derived vegetation LAI (GLASS LAI) products. The results showed the following: (1) The SA-BPNN estimation model has relatively high accuracy, with R2 = 0.75 and RMSE = 0.38 for the 2013 LAI estimation model, and R2 = 0.74 and RMSE = 0.17 for the 2016 LAI estimation model. (2) The fused 30 m LAI product has a good correlation with the LAI verification of the measured sample site (R2 = 0.8775) and a high similarity with the GLASS LAI product. (3) The fused 30 m LAI product has a high similarity with the GLASS LAI product, and compared with the GLASS LAI interannual trend line, it accords with the growth trend of plants in the seasons. This study provides a theoretical and technical reference for forest stand vegetation growth period LAI spatiotemporal fusion research based on high-score data, and has an important role in exploring vegetation primary productivity and carbon cycle changes in the future.

Evaluating Impacts of Opencast Stone Mining on Vegetation Primary Production and Transpiration over Rajmahal Hills

Opencast mining has significantly triggered vegetation degradation in many ecologically sensitive regions across the globe. The detailed spatio-temporal information on mining-induced vegetation degradation and associated primary production loss are crucial inputs to authorities and policymakers to frame and implement sustainable development programs in the mining regions to conserve vegetation ecology. Thus, the present study aimed to decipher the mining-induced vegetation cover and subsequent productivity losses over the Rajmahal Hills in Jharkhand (India). The Gross Primary Productivity (GPP), Net Primary Productivity (NPP), and Vegetation Transpiration (VT) datasets were used for analyzing the mines-induced losses in vegetation cover and associated productivity. The key findings indicated a loss of vegetation cover by ~340 km2 and an expansion of the mining area by ~54 km2 over the Rajmahal Hills during 1990–2020. The change detection analysis at the decadal period revealed that ~3.06 km2, 8.10 km2, and 22.29 km2 of vegetation cover were lost only due to the mining activity during 2000–2010, 2010–2020, and 2000–2020, respectively. The replacement of vegetation cover by mining area has caused GPP loss of 0.01 tonnes carbon (tC) per day, 0.04 tC/day, and 0.09 tC/day; NPP loss of ~1.25 tC, 2.77 tC, and 7.27 tC; VT loss of 5200 mm/day, 13,630 mm/day, and 30,190 mm/day during 2000–2010, 2010–2020, and 2000–2020, respectively. Hence, the present study revealed that the mining-induced vegetation losses have caused an alteration of carbon sequestration, carbon stock, and VT over the Rajmahal Hills.

Temporal-spatial monitoring of population growth with RS-GIS technology at Wasit province of Iraq

In this study, the growth rate of the population in a spatial-temporal manner has been modeled and explored by relying on the data from remote sensing and using the analytical functions of the geographic information system in Wasit province of Iraq during the period from 2000 to 2020. WorldPop layers were used to prepare population maps and forecast it in 2025 with the help of artificial neural networks. The predictive model was highly accurate (R 2 = 0.98). The next step was to measure the relationship between the rate of population growth and the changes in water areas and the primary production of vegetation with the help of time series of satellite data, which showed the distribution of the population. It is denser in the central, northwestern and southeastern areas of Wasit province. Low density populations have higher frequency. While from 2015 onwards, the frequency of some population classes with higher density increased compared to lower densities. In general, the trend of changes in the average population in the study area has been completely linear. But the maximum annual population density changes were upward and nonlinear. Changes in the area of ​​deciles representing denser areas have increased and the area of ​​deciles representing less densely populated areas has faced a decrease. The results and findings of this study showed that NPP changes were less predictable compared to population density changes because it is a complex function of climatic and human factors. In the estimated population density map in 2025, dense population cores have been formed in the northeastern areas of Wasit province in the vicinity of the border areas with Iran.

Habitat heterogeneity promotes bird diversity in agricultural landscapes: Insights from remote sensing data

Understanding the main drivers of biodiversity loss in Europe's agricultural landscapes has been a research priority in the last decades. One of the most important factors promoting biodiversity in farmed landscapes is habitat heterogeneity, which has often proved crucial for avian species and communities. Birds are highly sensitive to environmental changes and make use of a broad range of ecological niches, thus being exceptionally sensitive to the loss of habitat heterogeneity. Remote sensing data are particularly suited to quantify habitat heterogeneity at fine scales over relatively large extents, allowing to consider how different measures of heterogeneity may affect biotic communities at a regional scale. Here, we used airborne LiDAR (Light Detection And Ranging) and satellite multispectral data to derive vegetation canopy height and primary productivity for 118 sites in complex agricultural landscapes in a region in the Central Alps. We computed different bird diversity indices and classified bird species into guilds according to specific traits to analyse the relationship between avian communities and different facets of habitat heterogeneity. Results confirmed that habitat heterogeneity is essential in shaping rich and diverse bird communities, and it is particularly important for several farmland birds. By comparing the effects of canopy height, primary productivity, and specific vegetation features (e.g., cover of grassland, shrub, and tree layers), we showed how different habitat characteristics as well as landscape heterogeneity affected bird richness, diversity, functional entropy, and trait patterns. Landscape and height heterogeneity, estimated by NDVI and LiDAR Rao's Q indices, strongly influenced all response variables, for example, high NDVI values promoted species diversity and ground-understory nesters, and shrub layer was important for ground-understory nesters and forest specialists. Finally, we provide recommendations for conservation practices and mitigation measures to improve bird diversity in agricultural landscapes.