Satellite-based Vegetation Index Research Articles

National forest carbon harvesting and allocation dataset for the period 2003 to 2018

Abstract. Forest harvesting is one of the anthropogenic activities that most significantly affect the carbon budget of forests. However, the absence of explicit spatial information on harvested carbon poses a huge challenge in assessing forest-harvesting impacts, as well as the forest carbon budget. This study utilized provincial-level statistical data on wood harvest, the tree cover loss (TCL) dataset, and a satellite-based vegetation index to develop a Long-term harvEst and Allocation of Forest Biomass (LEAF) dataset. The aim was to provide the spatial location of forest harvesting with a spatial resolution of 30 m and to quantify the post-harvest carbon dynamics. The validations against the surveyed forest harvesting in 133 cities and counties indicated a good performance of the LEAF dataset in capturing the spatial variation of harvested carbon, with a coefficient of determination (R2) of 0.83 between the identified and surveyed harvested carbon. The linear regression slope was up to 0.99. Averaged from 2003 to 2018, forest harvesting removed 68.3 ± 9.3 Mt C yr−1, of which more than 80 % was from selective logging. Of the harvested carbon, 19.6 ± 4.0 %, 2.1 ± 1.1 %, 35.5 ± 12.6 % 6.2 ± 0.3 %, 17.5 ± 0.9 %, and 19.1 ± 9.8 % entered the fuelwood, paper and paperboard, wood-based panels, solid wooden furniture, structural constructions, and residue pools, respectively. Direct combustion of fuelwood was the primary source of carbon emissions after wood harvest. However, carbon can be stored in wood products for a long time, and by 2100, almost 40 % of the carbon harvested during the study period will still be retained. This dataset is expected to provide a foundation and reference for estimating the forestry and national carbon budgets. The 30 m × 30 m harvested-carbon dataset from forests in China can be downloaded at https://doi.org/10.6084/m9.figshare.23641164.v2 (Wang et al., 2023).

Impact of climate variability and environmental policies on vegetation dynamics in the semi-arid Tigray

Anthropogenic and climate-related phenomena are among the main factors responsible for variations in vegetation structure and composition worldwide. However, studies that integrate the effects of human activities and climate variability in fragile tropical ecosystems, including the semi-arid Tigray region, are lacking. The objective of this study was to examine the effects of climate variability and environmental policy changes on the spatial distribution and pattern of vegetation cover in the semi-arid Tigray region of Ethiopia over the past four decades. We used satellite-based vegetation index (normalized difference vegetation index) and monthly rainfall data to analyze the relationship between vegetation cover and climatic variability. Residual analysis was also used to further disentangle the effects of climatic variability and environmental policy on vegetation cover. The regression analysis (r2 = 0.19) showed an insignificant causal relationship between vegetation dynamics and precipitation over the 41-years study period. This study also highlighted negative impact of the global rise in temperature on vegetation cover due to water stress caused by evapotranspiration. On the other hand, the residual analysis results (r = − 0.55, z-stat = − 11.58, p < 0.01) indicated a strong relationship between vegetation change and environmental policies implemented within the specified study period. Overall, the study revealed that environmental policies had a greater impact than climate variables on vegetation. Policymakers should, therefore, prioritize implementing effective environmental policies to restore degraded ecosystems and mitigate the effects of climate change.

Remote sensing of evapotranspiration for irrigated crops at Yuma, Arizona, USA

A satellite-based vegetation index model that tracks daily crop growth and evapotranspiration (ETc) is developed, tested, and validated over irrigated farms in Yuma irrigation districts of Arizona and California. Model inputs are remotely sensed normalized difference vegetation index (NDVI) images, crop type maps, and local weather. The utility and novelty of the model is a more accurate assessment of ETc than currently provided by the US Bureau of Reclamation’s evapotranspiration modeling system. The model analyzes NDVI time series data from the European Space Agency’s Sentinel-2 satellites using the Google Earth Engine, constructs FAO-56 style crop growth stages from NDVI, and then estimates daily ETc using pre-defined crop coefficients (Kc) and grass reference evapotranspiration (ETos). Four crops were selected to test and evaluate model performance: short-season broccoli, mid-season cotton and wheat, and perennial alfalfa. Comparison of model results showed that Reclamation reports overestimate alfalfa and wheat ETc by 21–25%, cotton ETc by 6%, and underestimate broccoli ETc by 21%. Variability resolved by the model ranged 6–18% of median total ETc. Comparison of model results with those obtained from 13 eddy covariance sites showed validation discrepancies ranging 1–14%: average total actual ETc differences were 12, − 14, 78, and 87 mm/season, respectively, for alfalfa, broccoli, cotton, and wheat. The wide availability of Sentinel-2 data, collected every 5 days or less, and the rapid processing via Google Earth Engine make the vegetation index model implementation fast and practical. Its accuracy and ability to resolve ETc for every field would benefit the Reclamation water accounting system and provide valuable consumptive water use data for any Colorado River stakeholder.

Development of machine learning models for estimating wheat biophysical variables using satellite-based vegetation indices

Monitoring wheat biophysical variables during the growing season is essential for achieving precision agriculture's important goals. This study estimated plant height, leaf development stage, leaf area index, dry matter, and plant nitrogen content in wheat from Landsat-7 images in North Iran using machine learning algorithms. Forty points were selected from ten wheat fields (270 ha), and the agronomic parameters were recorded during the 2011–2012 growing season. The machine learning models applied in this study were Artificial Neural Network (ANN), Support Vector Machine (SVM), and Deep Neural Network (DNN). Results suggest that the DNN model was the best for predicting all the parameters, except for the nitrogen percentage, in which SVM and DNN have the same accuracy. The DNN model R2 was 0.82, 0.95, 0.95, and 0.90 RMSE (Root Mean Square Error) 9.61, 0.46, 0.47 and 1.2 and MAE (Mean Absolute Error) 26.4, 0.34, 0.28, and 0.84 for estimating plant height, leaf area index, leaf development stage and dry matter biomass, respectively. The DNN and SVM algorithms can effectively predict plant nitrogen percentage with identical R2, RMSE, and MAE values of 0.83–0.84, 0.44–0.46, and 0.33–0.36, respectively. These results show that machine learning models have great potential to provide timely and accurate data for better wheat management under field conditions from satellite imagery.

Confounding effects of snow cover on remotely sensed vegetation indices of evergreen and deciduous trees: An experimental study.

Located at northern latitudes and subject to large seasonal temperature fluctuations, boreal forests are sensitive to the changing climate, with evidence for both increasing and decreasing productivity, depending upon conditions. Optical remote sensing of vegetation indices based on spectral reflectance offers a means of monitoring vegetation photosynthetic activity and provides a powerful tool for observing how boreal forests respond to changing environmental conditions. Reflectance-based remotely sensed optical signals at northern latitude or high-altitude regions are readily confounded by snow coverage, hampering applications of satellite-based vegetation indices in tracking vegetation productivity at large scales. Unraveling the effects of snow can be challenging from satellite data, particularly when validation data are lacking. In this study, we established an experimental system in Alberta, Canada including six boreal tree species, both evergreen and deciduous, to evaluate the confounding effects of snow on three vegetation indices: the normalized difference vegetation index (NDVI), the photochemical reflectance index (PRI), and the chlorophyll/carotenoid index (CCI), all used in tracking vegetation productivity for boreal forests. Our results revealed substantial impacts of snow on canopy reflectance and vegetation indices, expressed as increased albedo, decreased NDVI values and increased PRI and CCI values. These effects varied among species and functional groups (evergreen and deciduous) and different vegetation indices were affected differently, indicating contradictory, confounding effects of snow on these indices. In addition to snow effects, we evaluated the contribution of deciduous trees to vegetation indices in mixed stands of evergreen and deciduous species, which contribute to the observed relationship between greenness-based indices and ecosystem productivity of many evergreen-dominated forests that contain a deciduous component. Our results demonstrate confounding and interacting effects of snow and vegetation type on vegetation indices and illustrate the importance of explicitly considering snow effects in any global-scale photosynthesis monitoring efforts using remotely sensed vegetation indices.

A Deep Learning-Based Approach to Predict Large-Scale Dynamics of Normalized Difference Vegetation Index for the Monitoring of Vegetation Activities and Stresses Using Meteorological Data

Vegetation activities and stresses are crucial for vegetation health assessment. Changes in an environment such as drought do not always result in vegetation drought stress as vegetation responses to the climate involve complex processes. Satellite-based vegetation indices such as the Normalized Difference Vegetation Index (NDVI) have been widely used to monitor vegetation activities. As satellites only carry information for understanding past and current vegetation conditions, there is a need to model vegetation dynamics to make future predictions. Although many other factors are related, we attempt to predict the vegetation activities and stresses via simulating NDVI, based on only meteorological data and using a deep learning method (bidirectional long short-term memory model, BiLSTM). The BiLSTM is a sequence processing model that can predict NDVI by establishing the relationship between meteorological variables and vegetation activities. Experimental results show that the predicted NDVI is consistent with the reference data (R2 = 0.69 ± 0.28). The best accuracy was achieved in the deciduous forest (R2 = 0.87 ± 0.16). The vegetation condition index (VCI) calculated from the BiLSTM-predicted NDVI also agreed with the satellite-based ones (R2 = 0.70 ± 0.28). Both the monitored and predicted VCI indicated an upward but insignificant trend of vegetation activity in the past decade and increased vegetation stresses in the early growing season over northern China. Based on meteorological data, the deep learning-based solution shows the potential for not only retrospective analysis, but also future prediction of vegetation activities and stresses under varied climate conditions as compared with remote sensing data.

Developing machine learning models for wheat yield prediction using ground-based data, satellite-based actual evapotranspiration and vegetation indices

Timely and accurate crop yield estimation is important for adjusting agronomic management and enseuring agricultural sustainability. Machine learning (ML) algorithms provide new opportunities to integrate agronomic information with ground-based and satellite data and develop flexible yield predictive models. In particular, satellite-based vegetation indices and evapotranspiration provide robust proxies for crop yield estimations in the absence of measurements; nevertheless, most prior model development efforts have focused on using only vegetation indices due to the simplicity of the process. Additionally, the contribution of input categories (i.e., field, meteorological, and satellite data) and the use of appropriate proxies, aligned with the crop growth stages, in developing yield predictive models have not been adequately investigated. To address these challenges, we employed two ML techniques, Random Forest (RF) and extreme gradient boosting algorithm (XGB), to estimate wheat yield using meteorological variables, satellite-driven actual evapotranspiration (ETa), and vegetation indices (VIs). ETa was separately computed using the surface energy balance concept and the METRIC model. The models were first trained and tested in the study area using three input combinations: i) meteorological variables, ii) satellite data, and iii) an ensemble of meteorological and satellite data. Then, the best-performing model was further evaluated using two independent datasets. We found ETa to be particularly important in improving the accuracy of the model predictions. Among the vegetation indices, EVI, EVI2, and NDVI during May, and among the meteorological data, growing degree days during the grain filling stage plus minimum temperature in the stem elongation stage had the highest contributions to yield predictions. Both ML algorithms generated relatively accurate results, where XGB was marginally more accurate than RF, considering an average mean absolute error of 0.39 t ha-1 for XGB and 0.50 t ha-1 for RF. Normalized root-mean-square errors of the ensemble, satellite-derived and meteorological-derived models in XGB were 0.05, 0.07, and 0.10, respectively. Nevertheless, both algorithms’ performances deteriorated in predicting the yield values beyond the range of the training set, though XGB could handle the extrapolation process more efficiently than RF.

Identification of the Spring Green-Up Date Derived from Satellite-Based Vegetation Index over a Heterogeneous Ecoregion

Multiple methods have been developed to identify the transition threshold from the reconstructed satellite-derived normalized difference vegetation indices (NDVI) time series and to determine the inflection point corresponding to a certain phenology phase (e.g., the spring green-up date (GUD)). We address an issue that large uncertainties might occur in the inflection point identification of spring GUD using the traditional satellite-based methods since different vegetation types exhibit asynchronous phenological phases over a heterogeneous ecoregion. We tentatively developed a Maximum-derivative-based (MDB) method and provided inter-comparisons with two traditional methods to detect the turning points by the reconstructed time-series data of NDVI for identifying the GUD against long-term observations from the sites covered by a mixture of deciduous forest and herbages in the Pan European Phenology network. Results showed that higher annual mean temperature would advance the spring GUD, but the sensitive magnitudes differed depending on the vegetation type. Therefore, the asynchronization of phenological phases among different vegetation types would be more pronounced in the context of global warming. We found that the MDB method outperforms two other traditional methods (the 0.5-threshold-based method and the maximum-ratio-based method) in predicting the GUD of the subsequent-green-up vegetation type when compared with ground observation, especially at sites with observed GUD of herbages earlier than deciduous forest, while the Maximum-ratio-based method showed better performance for identifying GUDs of the foremost-green-up vegetation type. Although the new method improved in our study is not universally applicable on a global scale, our results, however, highlight the limitation of current inflection point identify algorithms in predicting the GUD derived from satellite-based vegetation indices datasets in an ecoregion with heterogeneous vegetation types and asynchronous phenological phases, which makes it helpful for us to better predict plant phenology on an ecoregion-scale under future ongoing climate warming.

Monitoring the Spring 2021 Drought Event in Taiwan Using Multiple Satellite-Based Vegetation and Water Indices

The monitoring of droughts is practically important yet challenging due to the complexity of the phenomena. The occurrence of drought involves changes in meteorological conditions, vegetation coverage and soil moisture. To advance the techniques for detecting and monitoring droughts, this study explores the usage of a suite of vegetation and water indices derived from high-resolution images produced by geostationary satellite Himawari-8. The technique is tested on the detection of the drought event in Spring 2021 across Taiwan due to deficit of precipitation in that season. It is found that the time series analysis of green chlorophyll index (CIgreen) and normalized difference vegetation index (NDVI) helps detect the initiation of drought before its severity intensifies. The vegetation condition index (VCI) and vegetation health index (VHI) derived from GIgreen and NDVI are similarly useful for the early warning of a drought event. In addition to vegetation indices, the normalized difference water index (NDWI) is adopted for quantifying the deficit in precipitation. It is found that NDWI provides a better early warning system of drought compared to the vegetation indices. Combining the vegetation and water indices allows a more complete description of the evolution of drought for the Spring 2021 event. The potential for using the new framework for the early warning of future drought events is discussed.

Object-oriented Model to Predict Crop Yield Using Satellite-based Vegetation Index

Different models had been developed to predict crop yields based on remotely sensed data. Most approaches were based on developing empirical relationships between the Satellite based Normalized difference vegetation index NDVI data and the crop yield. This article proposes to introduce methodological framework for constructing an object-oriented yield prediction model using satellite data based on the two-level regression models; with taken into consideration the trend caused by the influence of technological improvements. Regression models for the wheat and barley crop yield predictions have been developed for twenty-seven typical districts in the Voronezh region. The two-level regression model, including FSR technique firstly to select the set of features that reflect the spatial variations in crops, soil, agriculture management within districts, after carry out the steps of exploratory data analysis EDA, objects creation and zonal average of each object. Whereas the second level consist of yield prediction with multiple linear regression MLR, Least Absolute Shrinkage and Selection Operator Lasso, Support vector machines SVM techniques. The absolute error between the predicted and observed yields of 2011-2014 years was not exceed 19(16) centner/ha for wheat(barley) at worst case. In spite of that the SVM technique outperforms rest techniques by average RMSE of 5.59(4.51) for wheat(barley).

Emerging signals of declining forest resilience under climate change

Forest ecosystems depend on their capacity to withstand and recover from natural and anthropogenic perturbations (that is, their resilience)1. Experimental evidence of sudden increases in tree mortality is raising concerns about variation in forest resilience2, yet little is known about how it is evolving in response to climate change. Here we integrate satellite-based vegetation indices with machine learning to show how forest resilience, quantified in terms of critical slowing down indicators3–5, has changed during the period 2000–2020. We show that tropical, arid and temperate forests are experiencing a significant decline in resilience, probably related to increased water limitations and climate variability. By contrast, boreal forests show divergent local patterns with an average increasing trend in resilience, probably benefiting from warming and CO2 fertilization, which may outweigh the adverse effects of climate change. These patterns emerge consistently in both managed and intact forests, corroborating the existence of common large-scale climate drivers. Reductions in resilience are statistically linked to abrupt declines in forest primary productivity, occurring in response to slow drifting towards a critical resilience threshold. Approximately 23% of intact undisturbed forests, corresponding to 3.32 Pg C of gross primary productivity, have already reached a critical threshold and are experiencing a further degradation in resilience. Together, these signals reveal a widespread decline in the capacity of forests to withstand perturbation that should be accounted for in the design of land-based mitigation and adaptation plans.

Improving WRF-Fire Wildfire Simulation Accuracy Using SAR and Time Series of Satellite-Based Vegetation Indices

Wildfire simulations depend on fuel representation. Present fuel models are mainly based on the density and properties of different vegetation types. This study aims to improve the accuracy of WRF-Fire wildfire simulations, by using synthetic-aperture radar (SAR) data to estimate the fuel load and the trend of vegetation index to estimate the dryness of woody vegetation. We updated the chaparral and timber standard woody fuel classes in the WRF-Fire fuel settings. We used the ESA global above-ground biomass (AGB) based on SAR data to estimate the fuel load, and the Landsat normalized difference vegetation index (NDVI) trends of woody vegetation to estimate the fuel moisture content. These fuel sub-parameters represent the dynamic changes and spatial variability of woody fuel. We simulated two wildfires in Israel while using three different fuel models: the original 13 Anderson Fire Behavior fuel model, and two modified fuel models introducing AGB alone, and AGB and dryness. The updated fuel model (the basic fuel model plus the AGB and dryness) improved the simulation results significantly, i.e., the Jaccard similarity coefficient increased by 283% on average. Our results demonstrate the potential of combining satellite SAR data and Landsat NDVI trends to improve WRF-Fire wildfire simulations.

Divergent Climate Sensitivities of the Alpine Grasslands to Early Growing Season Precipitation on the Tibetan Plateau

Warming is expected to intensify hydrological processes and reshape precipitation regimes, which is closely related to water availability for terrestrial ecosystems. Effects of the inter-annual precipitation changes on plant growth are widely concerned. However, it is not well-known how plant growth responds to intra-annual precipitation regime changes. Here, we compiled reanalysis climate data (ERA5) and four satellite-based vegetation indices, including the Normalized Difference Vegetation Index (NDVI), the Enhanced Vegetation Index (EVI), the Solar-induced Chlorophyll Fluorescence (SIF), and the Modified Triangular Vegetation Index (MTVI2), to evaluate the response of alpine grasslands (including alpine meadow and alpine steppe) to the change of precipitation regimes, especially to the intra-annual precipitation regimes on the Tibetan Plateau. We found monthly precipitation over the alpine steppe significantly increased in the growing season (May–September), but precipitation over the alpine meadow significantly increased only in the early growing season (May–June) (MJP) during the past four decades (1979–2019). The inter-annual plant growth (vegetation indices changes) on the alpine meadow was dominated by temperature, but it was driven by precipitation for the alpine steppe. On the intra-annual scale, the temperature sensitivity of the vegetation indices generally decreased but precipitation sensitivity increased during the growing season for both the alpine meadow and steppe. In response to the increase in MJP, we found the temperature sensitivity of the vegetation indices during the mid-growing season (July–August) (MGNDVI, MGEVI, MGSIF, and MGMTVI2) in the alpine meadow significantly increased (p &lt; 0.01) while its precipitation sensitivity significantly decreased (p &lt; 0.01). We infer that more MJP over the meadow may be the result of enhanced evapotranspiration, which is at the expense of soil moisture and even induces soil “drought” in the early growing season. This may be to elevate community water acquisition capacity through altering root mass allocation and community composition, consequently regulating the divergent climate sensitivities of vegetation growth in the mid-growing season. Our findings highlight that it is inadequate to regard precipitation as an indicator of water availability conditions for plant growth, which may limit our understanding of the response and acclimatization of plants to climate change.

A generalized model to predict large-scale crop yields integrating satellite-based vegetation index time series and phenology metrics

Convenient and reliable large-scale crop yield prediction is needed when formulating administrative plans and ensuring food security, especially under changing climate and international conditions. In this study, we explored Moderate Resolution Imaging Spectroradiometer (MODIS) vegetation indices- and phenology-based yield prediction generalization model taking the US Corn Belt as an example. We calculated the normalized difference vegetation index (NDVI) and 2-band enhanced vegetation index (EVI2) time series, which were adjusted using greenup dates derived from the Land Cover Dynamics product MCD12Q2. Based on the adjusted VI (NDVI, EVI2) time series, the VI change rate (dVI) time series was calculated, which represents crop growth rate. The first step was to cluster the adjusted VI and dVI time series, called ‘greenup groups’, according to corresponding greenup dates with a five-day interval. Then in different greenup groups, we constructed empirical univariate models with VI having the maximum correlation with crop yield, and multivariate models with VI and dVI, which were also used to construct the generalized model. After clustering, the days with maximum VI correlation gradually decreased as greenup days increasing, and the univariate VI model and multivariate VI and dVI model performances in different groups improved. The generalized models with specific VI and dVI variables in each group predicted yields of corn and soybean with R2 values mainly ranging from 0.55 to 0.75 and 0.55 to 0.70, while RMSE mainly ranging from 1000 to 1500 kg/ha and 300 to 400 kg/ha for both NDVI and EVI2 from 2008 to 2018 with leave-one-year-out cross-validation for all groups. The model using MODIS data was convenient and scalable with limited data requirements and date-determined variables after greenup, and offered a generalized method to predict crop yields at a large scale before harvest with good performance.

Developing machine learning models with multi-source environmental data to predict wheat yield in China

Crop yield is controlled by different environmental factors. Multi-source data for site-specific soils, climates, and remotely sensed vegetation indices are essential for yield prediction. Algorithms of data-model fusion for crop growth monitoring and yield prediction are complicated and need to be optimized to deal with model uncertainty. This study integrated multi-source environmental variables (e.g., satellite-based vegetation indices, climate data, and soil properties) into random forest (RF) and support vector machine (SVM) models for wheat yield prediction in China. The performance of both RF and SVM models was investigated using different types of vegetation indices associated with other predictors. Relative importance and partial dependence analyses were used to identify the main predictors and their relationships with wheat yield. We found that using remotely sensed vegetation indices improved our model precision, and that near-infrared reflectance of terrestrial vegetation (NIRv) was slightly better than normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI) in predicting yield. NIRv was better in detecting climate stress on crops, and could capture more information regarding crop growth and yield formation. Compared with the SVM model, the RF model with NIRv and other covariates had better performance in wheat yield prediction, with R2 and RMSE being 0.74 and 758 kg/ha respectively. We also found that NIRv from jointing to heading was the most important predictor in determining yield, followed by solar radiation (especially during tillering–heading), relative humidity (during planting–tillering), soil organic carbon, and wind speed (throughout the growing season). In addition, wheat yield exhibited threshold-like responses to most factors based on our RF model. These threshold values can help to better understand how different environmental factors limit wheat yield, which will provide useful information for climate-adaptive crop management. Our findings demonstrated the potential of using NIRv for yield prediction. This approach is broadly applicable to other regions globally using publicly available data.

A 30 m Resolution Distribution Map of Maize for China Based on Landsat and Sentinel Images

As the second largest producer of maize, China contributes 23% of global maize production and plays an important role in guaranteeing maize markets stability. In spite of its importance, there is no 30 m spatial resolution distribution map of maize for all of China. This study used a time-weighted dynamic time warping method to identify planting areas of maize by comparing the similarity of time series of a satellite-based vegetation index at each pixel with a standard time series derived from known maize fields and mapped maize distribution from 2016 to 2020 over 22 provinces accounting for more than 99% of the maize planting area in China. Based on 18800 field-surveyed pixels at 30-meter spatial resolution, the distribution map yields 76.15% and 81.59% of producer’s and user’s accuracies averaged over the entire investigated provinces, respectively. Municipality- and county-level census data also show a good performance in reproducing the spatial distribution of maize. This study provides an approach to mapping maize over large areas based on a small volume of field survey data.

Climate-related inter-annual variability and long-term influence on wheat yield across canal-irrigated areas of Punjab, Pakistan

The association of climatic variables and climatic oscillations with wheat yield across 26 canal command areas (CCAs) in Punjab, Pakistan, is studied, for recent 34 years (1982–2015). Climatic variables include precipitation, temperature, dry/wet spell lengths, hot/cold spell lengths, wet/dry spell ratio along with climatic oscillations, and satellite-based vegetation index for the months of January to April (Rabi season). During Rabi season, temperature increase rate was 0.14 °C decade−1, whereas precipitation trends were spatially inhomogeneous and varied between – 2.7 mm decade−1 and + 5.4 mm decade−1. The precipitation, temperature, cold spell length, smoothed NDVI (ND), and standard deviation of ND (SDND) displayed significant correlations (0.31–0.96) with wheat yield at varying time scales. The inter-annual wheat yield variability displayed a gain of 415.7 kg ha−1 under present climatic conditions for all CCAs. Worse to extreme climatic conditions based on percentiles obtained from present climate displayed a reduction of wheat yield by 252.0 to 954.9 kg ha−1, respectively. The good to best climate in comparison to present climatic conditions added up 108.1 to 251.9 kg ha−1, respectively. The 11-year period of 1993–2003 was the most drought prone period with consecutive five years of drought (1998–2002), having decreased precipitation, small wet/dry spell ratio and increased temperature, and long dry/hot spell length during wheat growing season. The wheat yield is predicted based on multiple regression models with sizable skill (having an explained variance of 0.79 and a root mean squared error of 121.3 kg ha−1).

Spatial patterns of arctic tundra vegetation properties on different soils along the Eurasia Arctic Transect, and insights for a changing Arctic

Vegetation properties of arctic tundra vary dramatically across its full latitudinal extent, yet few studies have quantified tundra ecosystem properties across latitudinal gradients with field-based observations that can be related to remotely sensed proxies. Here we present data from field sampling of six locations along the Eurasia Arctic Transect in northwestern Siberia. We collected data on the aboveground vegetation biomass, the normalized difference vegetation index (NDVI), and the leaf area index (LAI) for both sandy and loamy soil types, and analyzed their spatial patterns. Aboveground biomass, NDVI, and LAI all increased with increasing summer warmth index (SWI—sum of monthly mean temperatures > 0 °C), although functions differed, as did sandy vs. loamy sites. Shrub biomass increased non-linearly with SWI, although shrub type biomass diverged with soil texture in the southernmost locations, with greater evergreen shrub biomass on sandy sites, and greater deciduous shrub biomass on loamy sites. Moss biomass peaked in the center of the gradient, whereas lichen biomass generally increased with SWI. Total aboveground biomass varied by two orders of magnitude, and shrubs increased from 0 g m−2 at the northernmost sites to >500 g m−2 at the forest-tundra ecotone. Current observations and estimates of increases in total aboveground and shrub biomass with climate warming in the Arctic fall short of what would represent a ‘subzonal shift’ based on our spatial data. Non-vascular (moss and lichen) biomass is a dominant component (>90% of the photosynthetic biomass) of the vegetation across the full extent of arctic tundra, and should continue to be recognized as crucial for Earth system modeling. This study is one of only a few that present data on tundra vegetation across the temperature extent of the biome, providing (a) key links to satellite-based vegetation indices, (b) baseline field-data for ecosystem change studies, and (c) context for the ongoing changes in arctic tundra vegetation.

MAKING SMART URBAN DECISIONS: THE NICHE OF A PARAMETRIC SPATIAL MODEL TO BALANCE THE NEEDS OF URBAN STORMWATER MANAGEMENT AND HUMAN WELLBEING

Abstract. Rapid urbanization has resulted in high-density construction, more impervious area and with increasing threats of urban stormwater, drought and negative influences on human wellbeing. Although there has been progress in sustainable stormwater management and the promotion of human wellbeing with green space, the research has been conducted from a mono-disciplinary perspective and has brought potential conflicts in the utilisation of green space in an urban area. Since smart city proposed, researchers are exploring informational and integrated urban management to face this type of development conflicts and achieve a harmonious and sustainable future. This research proposes a parametric spatial model to integrate stormwater management and human recreation needs based on the understanding of the relationship between them on the same urban green space. We expect that this integrated parametric spatial model will help residents to interact with functional green space and provide options to organize the urban green space smarter and more effectively. This paper presents: (1) A review of the main sustainable stormwater management measures with principles, models and facilities to reveal an overlooked but important relationship between space and stormwater management. (2) Investigation of the research on the benefits of green space on human wellbeing. The study argues that the participation of green space cannot be effectively and accurately analysed with the current main analysis measures which relying on satellite-based vegetation indices or land-use database. This research is intended to set smarter decision making on urban green spaces which connects sustainable stormwater management with human wellbeing via visualised parametric spatial model.

Primary productivity of managed and pristine forests in Sweden

Land use is affecting 70% of global ecosystems and their functioning. Forest management is a regionally dominant land use and affects forest ecosystems by changing both structure and functioning, but its impact on primary productivity is not well known. Here we investigated the effect of forest management on primary productivity by comparing managed secondary forests with relatively pristine unmanaged primary forests in Sweden. As proxy for primary productivity we used the satellite-based vegetation index NIRv which has been shown to be closely and linearly related to primary productivity. We produced a digital map of 390 primary forests across Sweden, and extracted NIRv over these and surrounding secondary forests forming spatially proximate pairs. By comparing the primary and secondary forests NIRv in the pairs we found that secondary forests on average show higher NIRv, but the highest values were found in primary forests. The difference in NIRv between pairs is related to their difference in mean stand age, and at equal stand age the NIRv of primary forests is higher than in their paired secondary forests. Overall, management leads to increased NIRv through regeneration of forests stands that reduce their mean age. However, primary forests show higher NIRv when controlling for age, despite being found on higher altitudes and on steeper slopes with lower soil moisture, which suggests that forest management other than regeneration is not increasing primary productivity of Swedish forests.