Reforming livestock taxation for sustainable rangeland use: Macro-level analysis on Mongolia’s livestock tax law implementation

SummaryBackgroundExposure to natural vegetation, or greenness, might affect health through several pathways, including increased physical activity and social engagement, improved mental health, and reductions in exposure to air pollution, extreme temperatures, and noise. Few studies of the effects of greenness have focused on Asia, and, to the best of our knowledge, no study has assessed the effect on vulnerable oldest-old populations. We assessed the association between residential greenness and mortality in an older cohort in China.MethodsWe used five waves (February, 2000–October, 2014) of the China Longitudinal Healthy Longevity Survey (CLHLS), a prospective cohort representative of the general older population in China. We assessed exposure to greenness through satellite-derived Normalised Difference Vegetation Index (NDVI) values in the 250 m and 1250 m radius around the residential address for each individual included in the study. We calculated contemporaneous NDVI values, cumulative NDVI values, and changes in NDVI from the start of the study over time. The health outcome of the study was all-cause mortality, excluding accidental deaths. Mortality rate ratios were estimated with Cox proportional hazards models, adjusted for age, sex, ethnicity, marital status, geographical region, childhood and adult socioeconomic status, social and leisure activity, smoking status, alcohol consumption, and physical activity.FindingsAmong 23 754 individuals (mean age at baseline 93 years [SD 7·5]) totaling 80 001 person-years, we observed 18 948 deaths during 14 years of follow-up, between June, 2000, and December, 2014. Individuals in the highest quartile of contemporaneous NDVI values had 27% lower mortality than those in the lowest quartile for the 250 m radius (hazard ratio [HR] 0·73, 95% CI 0·70–0·76), and 30% lower mortality for the 1250 m radius (0·70, 0·67–0·74). No clear association was observed for cumulative NDVI measurements and mortality. We did not detect an association between area-level changes in NDVI and mortality.InterpretationOur research suggests that proximity to more green space is associated with increased longevity, which has policy implications for the national blueprint of ecological civilisation and preparation for an ageing society in China.FundingBill & Melinda Gates Foundation, US National Institute on Aging, US National Institute of Health, Natural Science Foundation of China, UN Population Fund, China Social Sciences Foundation, and Hong Kong Research Grants Council.

Soil Impact on Satellite Based Vegetation Monitoring in Sahelian Mali

Rainfall validates MODIS-derived NDVI as an index of spatio-temporal variation in green biomass across non-montane semi-arid and arid Central Asia

Similarities in ground- and satellite-based NDVI time series and their relationship to physiological activity of a Scots pine forest in Finland

Protected areas (PAs) are a prominent approach to maintaining and enhancing biodiversity and ecosystem services. A critical question for safeguarding these resources is how PA governance processes and management structures influence their effectiveness. We conduct an impact evaluation of 12 PAs in three Central American countries to assess how processes in management restrictions, management capacity, and decentralization affect the annual change in the satellite-derived Normalized Difference Vegetation Index (NDVI). NDVI varies with greenness that relates to plant production, biomass, and important ecosystem functions related to biodiversity and ecosystem services such as water quality and carbon storage. Any loss of vegetation cover in the form of deforestation or degradation would show up as a decrease in NDVI values over time and gains in vegetation cover and regeneration as an increase in NDVI values. Management restriction categories are based on international classifications of strict versus multiple-use PAs, and capacity and decentralization categories are based on key informant interviews of PA managers. We use matching to create a counterfactual of non-protected observations and a matching estimator and regression to estimate treatment effects of each sub-sample. On average, strict and multiple-use PAs have a significant and positive effect on NDVI compared to non-protected land uses. Both high and low decentralized PAs also positively affect NDVI. High capacity PAs have a positive and significant effect on NDVI, while low capacity PAs have a negative effect on NDVI. Our findings advance knowledge on how governance and management influence PA effectiveness and suggest that capacity may be more important than governance type or management restrictions in maintaining and enhancing NDVI. This paper also provides a guide for future studies to incorporate measures of PA governance and management into impact evaluations.

A methodology is presented for estimating the urban bias of surface shelter temperatures due to the effect of the urban heat island. Multiple regression techniques were used to predict surface shelter temperatures based on the time period 1986–89 using upper-air data from the European Centre for Medium-Range Weather Forecasts to represent the background climate, site-specific data to represent the local landscape, and satellite-derived data—the normalized difference vegetation index (NDVI) and the Defense Meteorological Satellite Program (DMSP) nighttime brightness data—to represent the urban and rural landscape. Local NDVI and DMSP values were calculated for each station using the mean NDVI and DMSP values from a 3 km × 3 km area centered over the given station. Regional NDVI and DMSP values were calculated to represent a typical rural value for each station using the mean NDVI and DMSP values from a 1° × 1° latitude–longitude area in which the given station was located. Models for the United States were then developed for monthly maximum, mean, and minimum temperatures using data from over 1000 stations in the U.S. Cooperative Network and for monthly mean temperatures with data from over 1150 stations in the Global Historical Climate Network. Local biases, or the differences between the model predictions using the observed NDVI and DMSP values, and the predictions using the background regional values were calculated and compared with the results of other research. The local or urban bias of U.S. temperatures, as derived from all U.S. stations (urban and rural) used in the models, averaged near 0.40°C for monthly minimum temperatures, near 0.25°C for monthly mean temperatures, and near 0.10°C for monthly maximum temperatures. The biases of monthly minimum temperatures for individual stations ranged from near −1.1°C for rural stations to 2.4°C for stations from the largest urban areas. There are some regions of the United States where a regional NDVI value based on a 1° × 1° latitude–longitude area will not represent a typical “rural” NDVI value for the given region, Thus, for some regions of the United States, the urban bias of this study may underestimate the actual current urban bias. The results of this study indicate minimal problems for global application once global NDVI and DMSP data become available. It is anticipated that results from global application will provide insights into the urban bias of the global temperature record.

Critical applications of satellite data products include monitoring vegetation dynamics and assessing vegetation health conditions. Some indicators like normalized difference vegetation index (NDVI) and land surface temperature (LST) are used to assess the status of vegetation growth and health. But one of the major problems with passive remote sensing satellite data products is cloud and shadow cover that leads to data gaps in the images. The present study proposes temporal aggregation of images over a short time span and developing short span harmonic analysis of time series (SS-HANTS) and pixel-wise multiple linear regression (PMLR) algorithms for retrieving cloud contaminated NDVI and LST information from Landsat-8 (L8) data products, respectively. The developed algorithms were applied in the northeastern part of Thailand to recover the missing NDVI and LST values from time series L8 images acquired in 2018. The predicted NDVI and LST values at artificially clouded locations were compared with the corresponding clear pixel values. Additionally, the model predicted LST and NDVI values were also compared with MODIS LST and NDVI datasets. The calculated root mean square (RMSE) values were ranging from 0.03 to 0.11 and 1.50 to 2.98°C for NDVI and LST variables, respectively. The validation statistics show that these models can be satisfactorily applied to retrieve NDVI and LST values from cloud-contaminated pixels of L8 images. Furthermore, a vegetation health index (VHI) computed from cloud retrieved continuous NDVI and LST images at province level shows that most of the western provinces have healthy vegetation condition than other provinces in the northeast of Thailand.

While there have been various studies on global vegetation dynamics, limited studies have been conducted to understand vegetation changes in arid areas. Vegetation distribution patterns can be affected by multiple factors, so understanding their interactions can help improve the capability of predicting future vegetation dynamics. This study, therefore, analyzed the dynamic vegetation changes in Alxa League, China, using the Normalized Difference Vegetation Index (NDVI) dataset (2000–2019), with the consideration of land cover types, digital elevation model, air temperature, precipitation, soil moisture, total evaporation, and air quality. The results show that the NDVI in Alxa League is small. Before 2012, the NDVI value fluctuated, while after 2012, the NDVI value dropped sharply and then slowly recovered after 2015. High NDVI values were found in areas with high and frequent human activities (city centers). The NDVI in the northwest region showed a slight degradation trend, and the southeast showed a slight improvement trend. According to the land cover type analysis, the NDVI value was the largest when the land cover type was tree cover, and the NDVI value was the smallest when the land cover type was bare/sparse vegetation. Alxa League was dominated by a bare/sparse vegetation distribution. The terrain analysis indicates that when the height was between 1800 and 3492 m, the NDVI value was the highest, and high NDVI values were mainly distributed in the area with a slope > 25°. When the slope was flat, the NDVI value was the smallest. Considering climate factors, the NDVI was negatively correlated with air temperature, precipitation, soil moisture, and total evaporation in space, and only precipitation and soil moisture were positively correlated in time. Moreover, the population size has a strong positive correlation with the NDVI in this area. The monthly variation of the NDVI and absorbable particulate matter (PM10) was negatively correlated, i.e., strongly negatively correlated in spring, summer, and autumn, but only weakly positively correlated in winter. The seasonal variation of the NDVI was as follows: summer > autumn > spring > winter, and the seasonal variation of PM10 was spring > winter > summer > autumn. The interannual variation of the NDVI and PM10 was positively correlated. This suggests that the absorbable particulate matter (PM10) may be an essential factor for the normalized vegetation index to underestimate the dynamic changes of vegetation in arid regions. This study provides a theoretical basis for the dynamic changes of vegetation in the dry Alxa League.

This study aimed to increase satellite-derived Normalized Difference Vegetation Index (NDVI) sensitivity to biophysical parameters changes with aid of a forest age-based adjustment factor. This factor is defined as a ratio between stand age and age of rotation, which value multiplied by Landsat-5/TM-derived NDVI generated the so-called adjusted index NDVI_a. Soil Adjusted Vegetation Index (SAVI) was also calculated. The relationship between these vegetation indices (VI) with Eucalyptus and Pinus stands’ wood volume was investigated. The adjustment factor caused an increase in NDVI dynamic range values, since older stands tended to be assigned with highest NDVI values, while younger ones tended to be forced to assume lower NDVI values. As a result, direct and significant relationship between NDVI_a and wood volume could be maintained for wider ranges of wood volume. However, it was observed that NDVI_a was only statistically superior to NDVI and SAVI when a detailed age dataset is available. It is conclude that, the stand age has potential to improve NDVI sensitivity to biophysical parameters allowing that quantitative estimates could be made since young to adult stands.

To effectively manage and conserve ecosystems, it is crucial to understand how vegetation changes over time and space and what drives these changes. The Normalized Difference Vegetation Index (NDVI) is a key measure of plant growth that is highly sensitive to climate variations. Despite its importance, there has been limited research on vegetation changes in the upper sections of the Irtysh River. In our study, we combined various datasets, including NDVI, temperature, precipitation, soil moisture, elevation, and land cover. We conducted several analyses, such as Theil–Sen median trend analysis, Mann–Kendall trend and mutation tests, partial correlation analysis, the geographical detector model, and wavelet analysis, to reveal the region’s pronounced warming and moistening trend in recent years, the response relationship between NDVI and the climate, and the primary drivers influencing NDVI variations. We also delved into the spatiotemporal evolution of NDVI and identified key factors driving these changes by analyzing atmospheric circulation patterns. Our main findings are as follows: (1) Between 1901 and 2022, the area’s temperature rose by 0.018 °C/a, with a noticeable increase in the rate of warming around 1990; precipitation increased by 0.292 mm/a. From 1950 to 2022, soil moisture exhibited a steady increase of 0.0002 m3 m−3/a. Spatial trend distributions indicated that increasing trends in temperature and precipitation were evident across the entire region, while trends in soil moisture showed significant spatial variation. (2) During 1982 to 2022, the vegetation greening trend was 0.002/10a, indicating a gradual improvement in vegetation growth in the study area. The spatial distribution of monthly average NDVI values revealed that the main growing season of vegetation spanned April to November, with peak NDVI values occurring in June–August. Combined with serial partial correlation and spatial partial correlation analysis, temperatures during April to May effectively promoted the germination and growth of vegetation, while soil moisture accumulation from June to August (or January to August) effectively met the water demand of vegetation during its growth process, with a significant promoting effect. Geographical detector results demonstrate that temperature exhibits the strongest explanatory power for NDVI variation, whereas land cover has the weakest. The synergistic promotional effect of multiple climatic factors is highly pronounced. (3) Wavelet analysis revealed that the periodic characteristics of NDVI and climate variables over a 2–15-year timescale may have been associated with the impacts of atmospheric circulation. Taking NDVI and climatic factors from June to August as an example, before 2000, temperature was the dominant influencing factor, followed by precipitation and soil moisture; after 2000, precipitation and soil moisture became the primary drivers. The North Atlantic Oscillation (NAO) and Arctic Oscillation (AO) were the primary atmospheric circulation patterns influencing vegetation variability in the region. Their effects were reflected in the inverse relationship observed between NAO/AO indices and NDVI, with typical phases of high and low NDVI closely corresponding to shifts in NAO and AO activity. This study helps us to understand how plants have been changing in the upper parts of the Irtysh River. These insights are critical for guiding efforts to develop the area in a way that is sustainable and beneficial for the environment.

Residential greenness and mortality in oldest-old women and men in China: a prospective cohort study

Normalized difference vegetation index (NDVI) has been correlated with various vegetation parameters using different preprocessing methods, corrections and sampling time based on the aim of the study. In yield estimation studies, maximum NDVI value of the season and the same day of the year NDVI value, which are based on chronological sampling time, are used within different techniques from statistical analysis to machine learning. However, analysis of biological systems based on their chronological timing results in an error increase at data extraction phase due to the non-linearity among phenological stages, representing plant development and its variability. In this study, a phenology based optimum NDVI sampling time is determined and proposed as a replacement of chronologically sampled NDVI time for yield estimation analysis. It may not be possible to have or acquire satellite images for the desired NDVI date due to the temporal resolution of existing remote sensing satellites and meteorological limitations. Therefore, a compensation process based on Adaptive Savitzky-Golay filter and using the existing images is proposed to constitute a new NDVI value for the desired day of the season. The study area is situated in the Southeastern Anatolia region of Turkey within the Fertile Crescent where wheat was first cultivated 10000 years ago. The region has the highest durum wheat production, supplying %46 of the whole production in Turkey. 8-day interval, Landsat-7 and Landsat-8 NDVI time-series are analyzed for seasonal vegetation development with TIMESAT software for the 2014–2016 period. Ground-based ancillary data was obtained within the Turkish Agricultural and Environmental Informatics Research and Application Center (TARBIL) project. Trend analysis of NDVI time-series was performed using Adaptive Savitzky-Golay filter, form of a moving average, adapting to the upper envelope of the data points. Two different sampling methods representing chronological and phenological approaches in addition to the max NDVI value are used to determine the optimum NDVI day. Phenological sampling is carried out as 10-day intervals starting from the emergence phase indicating the start of the season whereas 15 April, representing the long-term annual mean peak NDVI date of the study area was used for chronological sampling. Adaptive Savitzky-Golay filtering and different sampling combinations were used to perform correlation analysis with annual yield data. Best sampling method along with the optimum NDVI sampling day of the season was determined based on the correlation analysis. It is observed that the combinations with phenological sampling corresponding to the first node stage according to Food and Agriculture Organization (FAO) guidelines have the highest correlation. Regression analysis between agrometeorological data with and without compensated NDVI and yield variables showed that the usage of compensated NDVI had higher correlation for wheat yield estimation. The results showed that, in comparison with the conventional approaches, the usage of phenology based compensated NDVI, enhanced the yield estimation percentage. Along with the possibility of producing ancillary data from remote sensing images, this approach will minimize the need for ground-based observations that are time and money consuming.

Advanced very high resolution radiometer (AVHRR) data provide the longest available time series of global satellite observations and have been extensively used. The Land Long-Term Data Record (LTDR) project has generated daily surface reflectance and normalized difference vegetation index (NDVI) products from AVHRR. However, residual cloud and aerosol contamination in the LTDR AVHRR surface reflectance and NDVI products significantly limits their applications and results in temporal and spatial inconsistencies in subsequent downstream products. Based on the LTDR AVHRR surface reflectance, a temporally continuous vegetation indices-based land-surface reflectance reconstruction (VIRR) method was refined in this study to generate Global LAnd Surface Satellite (GLASS) AVHRR NDVI and surface reflectance products from 1982 to 2015. The daily LTDR AVHRR surface reflectance data were first aggregated into eight-day intervals. The aggregated surface reflectance data were used to calculate NDVI, and a robust smoothing algorithm was used to reconstruct continuous and smooth NDVI upper envelopes, which were used to identify cloud-contaminated surface reflectance values. Then the surface reflectance time series was reconstructed from cloud-free surface reflectance values by incorporating the upper envelopes of the NDVI time series as constraints. The results show that the refined VIRR method successfully removes NDVI and surface reflectance values contaminated by clouds and can reconstruct temporally continuous NDVI and land-surface reflectance time series. Comparison of the GLASS AVHRR NDVI product with the third-generation Global Inventory Monitoring and Modeling System (GIMMS3g) and the moderate resolution imaging spectroradiometer (MODIS) NDVI products indicates that these NDVI products exhibit similar spatial patterns, but the GIMMS3g NDVI values were clearly higher than the GLASS AVHRR and MODIS NDVI values in tropical forest regions and the 50°N−60°N latitude band, particularly in July. Comparisons with the MODIS NDVI values over the BELMANIP (Benchmark Land Multisite Analysis and Intercomparison of Products) sites demonstrate that the GLASS AVHRR NDVI product provides better performance (RMSE = 0.1007 and Bias = 0.0518) than the GIMMS3g NDVI product (RMSE = 0.1288 and Bias = 0.0852). The temporal profiles of all these NDVI products exhibited consistent seasonal variations, but the temporal smoothness of the GLASS AVHRR NDVI product was superior to that of the GIMMS3g and MODIS NDVI products. The GLASS AVHRR and GIMMS3g NDVI products show consistent trends in most situations, but the trends of the GLASS AVHRR NDVI product were slightly more pronounced than those of the GIMMS3g NDVI product for each biome type. Comparison of the GLASS AVHRR surface reflectance product with MODIS surface reflectance product indicates the GLASS AVHRR and MODIS surface reflectance showed similar seasonal and interannual variations and the GLASS AVHRR surface reflectance was in good agreement with the MODIS surface reflectance, especially in the red band.

Seasonal and interannual changes in vegetation activity of tropical forests in Southeast Asia

The Normalized Difference Vegetation Index (NDVI) is the most commonly used index for assessing vegetation. However, significant differences among various satellite datasets, complex terrain, and the impact of clouds on optical sensors limit vegetation change assessment based on NDVI. To address these issues, this study utilizes multi-source satellite data (GIMMS3g NDVI, CDR AVHRR NDVI, SPOT NDVI, and MODIS NDVI) to monitor vegetation dynamics at different time scales from 1990 to 2020 in Sichuan Province, China. The results indicate that over time, NDVI values from the four NDVI products in Sichuan Province have shown an upward trend. There are certain differences in the spatial distribution and spatial heterogeneity of the change rate of NDVI values among the four NDVI products at different time scales, and the differences are mainly concentrated in the Sichuan Basin (SB) and the Western Sichuan alpine plateau region (WS). Compared with the other three NDVI products, GIMMS NDVI has the highest value but the smallest increase during the study period. The SPOT NDVI value is the smallest, but the increase is relatively large. However, within the overlapping period of the four NDVI datasets, only the annual average of CDR AVHRR NDVI showed a downward trend (slope2000–2013 = −0.0001·a−1). The annual fluctuation of CDR AVHRR NDVI is the smallest, and compared to other NDVI datasets, its correlation with climate factors shows significantly weaker spatial variability. Moreover, the ability of CDR AVHRR NDVI to distinguish different vegetation land cover types is significantly poor (STD = 0.045). The findings of this study will provide a reference for further research on vegetation changes in Sichuan Province and NDVI reconstruction in cloudy areas.