Long-term Normalized Difference Vegetation Index Research Articles

Climate change and topographic differences influence grassland vegetation greening across environmental gradients

Despite the research on the responses of grassland vegetation to climate change and topography has advance worldwide, the large-scale importance of these parameters to grassland vegetation greening in arid regions across environmental gradients is unclear. To address this, in this study, we applied MODIS Normalized Difference Vegetation Index (NDVI) data and trend analysis methods to measure the spatial–temporal variation in grassland vegetation greening in central Eurasia. Multiple regression models and hierarchical partitioning were used to quantify the importance of climate [annual precipitation (AP), annual mean temperature (AMT), relative humidity (RH)] and topography [elevation (ELE), aspect (ASP), topographic position index (TPI)] to the NDVI. The results showed that there was a significant increasing trend in the NDVI of meadows, but not other grassland types, from 2000 to 2021 (3.3 × 10−3/year, p < 0.05). Additionally, the responses of the NDVI to climate and topography in deserts were positively correlated with RH, AP, and ELE. Meanwhile, the dependence of NDVI on climate and topography decreased with increasing RH. Under conditions of escalating AMT and AP, RH and ELE independently contributed to explaining the NDVI. However, RH may be the key determinant of long-term NDVI stabilization in arid grassland. These findings underscore the significance of vegetation–climate–topography feedback and can inform the development of more comprehensive and effective climate mitigation and adaptation strategies.

Applying deep-learning enhanced fusion methods for improved NDVI reconstruction and long-term vegetation cover study: A case of the Danjiang River Basin

The Normalized Difference Vegetation Index (NDVI) is an essential metric in vegetation monitoring for remote sensing applications. While there are numerous long-term low-resolution NDVI datasets available there remains an unmet need for high-resolution NDVI reconstructions over extended time frames. Existing research has not comprehensively assessed the efficacy of various temporal fusion techniques for NDVI reconstruction at large regional scales. Traditional Spatiotemporal Image Fusion (TSTIF) methods often suffer from limited fusion accuracy due to input data quality constraints. To address these limitations this study introduces an innovative Deep Learning-Enhanced Spatiotemporal Fusion Method. Deep learning algorithms are employed to refine the spatial resolution of input data thereby facilitating the separation of complex image elements located at feature boundaries. This improved approach significantly enhances fusion accuracy as validated against five established TSTIF techniques through empirical analysis. As a case study we generate long-term Fractional Vegetation Cover (FVC) datasets to investigate the ecological dynamics of the Danjiang River Basin. Our findings reveal substantial gains in NDVI reconstruction accuracy through the incorporation of deep learning into traditional fusion techniques. Among the methods tested the STRUM algorithm showed the greatest improvement with its R2 value increasing from 0.872 to 0.894 and a consistent reduction in Root Mean Square Error (RMSE). The FSDAF technique emerged as the most effective boasting an R2 value of 0.953 and an RMSE of 0.012. These enhanced NDVI datasets provide more accurate representations of the basin's vegetation cover thus enriching long-term observational archives and facilitating further quantitative remote sensing analyses. Moreover we report a 21-year mean FVC of 0.702 for the Danjiang River Basin characterized by a “north-high south-low” distribution that varies seasonally. Improvements in vegetation cover were observed in Xichuan and Neixiang counties while environmental degradation is evident in the broader Danjiang River watershed. Key factors influencing these spatial variations include elevation and soil composition in addition to climate variables such as rainfall and temperature. Infrastructure projects like the South-to-North Water Diversion Project and targeted ecological conservation policies have also positively impacted vegetation within the basin.

Spatiotemporally consistent global dataset of the GIMMS Normalized Difference Vegetation Index (PKU GIMMS NDVI) from 1982 to 2022

Abstract. Global products of remote sensing Normalized Difference Vegetation Index (NDVI) are critical to assessing the vegetation dynamic and its impacts and feedbacks on climate change from local to global scales. The previous versions of the Global Inventory Modeling and Mapping Studies (GIMMS) NDVI product derived from the Advanced Very High Resolution Radiometer (AVHRR) provide global biweekly NDVI data starting from the 1980s, being a reliable long-term NDVI time series that has been widely applied in Earth and environmental sciences. However, the GIMMS NDVI products have several limitations (e.g., orbital drift and sensor degradation) and cannot provide continuous data for the future. In this study, we presented a machine learning model that employed massive high-quality global Landsat NDVI samples and a data consolidation method to generate a new version of the GIMMS NDVI product, i.e., PKU GIMMS NDVI (1982–2022), based on AVHRR and Moderate-Resolution Imaging Spectroradiometer (MODIS) data. A total of 3.6 million Landsat NDVI samples that were well spread across the globe were extracted for vegetation biomes in all seasons. The PKU GIMMS NDVI exhibits higher accuracy than its predecessor (GIMMS NDVI3g) in terms of R2 (0.97 over 0.94), root mean squared error (RMSE: 0.05 over 0.09), mean absolute error (MAE: 0.03 over 0.07), and mean absolute percentage error (MAPE: 9 % over 20 %). Notably, PKU GIMMS NDVI effectively eliminates the evident orbital drift and sensor degradation effects in tropical areas. The consolidated PKU GIMMS NDVI has a high consistency with MODIS NDVI in terms of pixel value (R2 = 0.956, RMSE = 0.048, MAE = 0.034, and MAPE = 6.0 %) and global vegetation trend (0.9×10-3 yr−1). The PKU GIMMS NDVI product can potentially provide a more solid data basis for global change studies. The theoretical framework that employs Landsat data samples can facilitate the generation of remote sensing products for other land surface parameters. The PKU GIMMS NDVI product is open access and available under a Creative Commons Attribution 4.0 License at https://doi.org/10.5281/zenodo.8253971 (Li et al., 2023).

The Effects of Precipitation Event Characteristics and Afforestation on the Greening in Arid Grasslands, China

Global greening and its relationship with climate change remain the hot topics in recent years, and are of critical importance for understanding the interactions between the terrestrial ecosystem carbon cycle and the climate system. China, especially north China, has contributed a lot to global greening during the past few decades. As a water-limited ecosystem, human activities, not precipitation amount, were thought as the main contributor to the greening of north China. Considering the importance of precipitation event characteristics (PEC) in the altered precipitation regimes, we integrated long-term normalized difference vegetation index (NDVI) and meteorological datasets to reveal the role of precipitation regimes, especially PECs, on vegetation growth across temperate grasslands in north China. Accompanied with a significantly decreased growing season precipitation (GSP), NDVI increased significantly in the largest area of the temperate grasslands during 1982–2015, i.e., greening. We found that 28.44% of the area was explained by PECs, including more heavy or extreme precipitation events, alleviated extreme drought, and fewer light events, while only 0.92% of the area was associated with GSP. NDVI did not always increase over the 30 years and there was a decrease during 1996–2005. Taking afforestation projects in desertified lands into account, we found that precipitation, mainly PECs, explained more the increase and decline of NDVI during 1982–1995 and 1996–2005, respectively, while an equivalent explanatory power of precipitation and afforestation projects to the increase in NDVI after 2005. Our study indicates a possible higher productivity under future precipitation regime scenario (e.g., fewer but larger precipitation events) or intensive afforestation activity, implying more carbon sequestration or livestock production of temperate steppe in the future.

Unravelling geological controls on groundwater flow and surface water-groundwater interaction in mountain systems: A multi-disciplinary approach

Mountain water resources are considered to be the world’s water towers. Still, despite their importance for downstream societies and ecosystems and their vulnerability to climate change, they remain poorly understood – It is the case in particular of mountain groundwater systems. Their complexity makes them difficult to conceptualize, while their remoteness makes them difficult to study, both observationally and instrumentally. Understanding mountain hydrogeological systems is mostly limited by the lack of characterization of the subsurface geologic framework and by the limited understanding of the role of geological structures on groundwater flow and on surface water-groundwater interaction. Removing methodological barriers is therefore a necessary step for improving the understanding of mountain hydrogeological systems.To tackle this problem, we develop a comprehensive multi-disciplinary approach to gain insights into the hydrogeological role of geological structures in ungauged mountain catchments. The methodology consists of several complementary methods: (1) geological mapping at multiple scales; (2) a geophysical study including on-ground Electrical Resistivity Tomography (ERT) and, gravimetry transects, and a UAV-based magnetic survey; (3) hydraulic data, including a 9 km long transect of streamflow measurements in the recession period, the long-term Normalized Difference Vegetation Index (NDVI), and varied hydric markers (e.g., a thermal spring and a puddle). The methodology is tested in the Parque Nacional del Río Clarillo, an ungauged catchment in the Andes Mountains (≈130 km2) that is illustrative of the complexity of mountain hydrosystems featuring fault zones, weathered zones, intrusive rocks, and volcano-sedimentary successions.An increase of approximately 50% in the streamflow is observed over a short distance of 1 km. Such a localized and significant increase in the baseflow is not related to any superficial supply and can only be explained by groundwater exfiltration. Based on the multiscale geological mapping and geophysical survey, a regional N-S fault and a secondary set of E-W local faults are identified in the vicinity of the resurgence area, which conjointly are likely to export groundwater from a neighbouring subcatchment up to the resurgence area. Downstream of the resurgence area, no significant change in the baseflow is observed, corresponding to the presence of an impermeable granitic pluton identified by the geological and geophysical mapping. Finally, a fractured zone in the Andean foothills is identified in the volcanic unit, which coincides with a perennial thermal spring, indicating upwelling flow and hydrogeological connectivity between the mountain block and the alluvial basin.The results strongly support the ability of the proposed methodology to identify geological structures that substantially impact the evolution of the baseflow through the catchment. The complementary multi-disciplinary methods are used innovatively to infer the link between geological and hydrogeological structures. The methodology does not aim to fully characterize the geological framework of the catchment but pragmatically focuses on hydrogeologically pertinent structures that may impact baseflow and consequently catchment management.

Spatiotemporal Variation of Hourly Scale Extreme Rainstorms in the Huang-Huai-Hai Plain and Its Impact on NDVI

This paper utilizes high-resolution ERA5 hourly data from 1980 to 2020 and long-term normalized difference vegetation index (NDVI) time series obtained from remote sensing and applies trend analysis, correlation analysis, lag analysis, and other methods to study the spatiotemporal characteristics of extreme rainfall at daily and hourly scales in the Huang-Huai-Hai Plain. The paper explores the NDVI’s variability and its relationship with extreme hourly precipitation and analyzes the main factors affecting it. The study made the following observations: (1) The extreme daily precipitation in the Huang-Huai-Hai Plain shows a decreasing trend, with a 13.6 mm/yr reduction rate. In contrast, the proportion of extreme rainfall to total precipitation generally exceeds 20%, and the intensity of extreme rain has gradually increased. The spatial distribution pattern of extreme rainfall follows the distribution pattern of China’s rain belts, with the terrain being an important influencing factor. The high-incidence areas for extreme rainfall are the Huaihe River region and the Shandong Peninsula. (2) The observed significant increase in hourly extreme precipitation events in the Shandong and Henan provinces of the Huang-Huai-Hai Plain has led to an increased risk of flooding, while the corresponding events in the northwest region of the Plain have exhibited a gradual weakening trend over time. (3) The extreme hourly precipitation in the Huang-Huai-Hai plain shows a frequent and scattered pattern, with decreasing intensity over time. Extreme precipitation mainly occurs in the first half of the night, especially between 19:00 and 21:00, with extreme hourly rainfall intensity fluctuating between 0.2 and 0.25 and the proportion of rainfall to total precipitation reaching as high as 10%. The spatial distribution of extreme hourly rainstorms during the peak period (19:00–21:00) exhibits a high rainfall volume, intensity, and frequency pattern in the eastern region, while the western part exhibits low rainfall volume, intensity, and frequency. (4) The incidence of extremely heavy rainfall in an hour has exhibited a more significant increase compared to extreme daily events in the Huang-Huai-Hai Plain, primarily in the form of backward-type precipitation. Hourly extreme precipitation events in the Huang-Huai-Hai Plain are affected by terrain and land use/cover change (LUCC), with the micro-topography of hilly areas leading to a concentrated distribution of precipitation and LUCC suppressing extreme precipitation events in arid climates. (5) At the ten-day scale, the spatial distribution of the NDVI shows a gradually increasing trend from northwest to southeast, with the highest NDVI value reaching up to 0.6 in the southern part of the study area. For extreme hourly precipitation, there is no significant change observed at the multi-year ten-day scale; while the NDVI in the northern and central parts of the Huang-Huai-Hai Plain shows a significant decreasing trend, in contrast, it presents a significant increasing trend in the southern region. (6) Finally, the correlation between NDVI at the ten-day scale and extreme hourly precipitation exhibits a decreasing pattern from north to south, with a correlation coefficient decreasing from 0.48 to 0.08. The lagged correlation analysis of extreme hourly rainfall and NDVI for one, two, and three ten-day periods shows that the lagged effect of extreme hourly precipitation on NDVI is negligible. Analyzing the correlation between extreme hourly rainfall and NDVI for different months, the impact of extreme hourly precipitation on NDVI is predominantly negative, except for June, which shows a positive correlation (0.35), passing the significance test. This study offers a scientific foundation for enhancing disaster warning accuracy and timeliness and strengthening the research on disaster reduction techniques.

Drought Assessment across Erbil Using Satellite Products

In this article, meteorological and agricultural droughts across the Erbil province, Iraq, were assessed using remote sensing data and satellite products. To this end, the long-term (2000–2022) Standardized Precipitation Evapotranspiration index (SPEI) at 1- and 3-month accumulation periods (SPEI-1 and SPEI-3) as well as the Normalized Difference Vegetation Index (NDVI) across Erbil were utilized. While the former was retrieved from the global SPEI data repository, the latter was derived from Moderate Resolution Imaging Spectroradiometer (MODIS) products. The spatiotemporal variations in the SPEI indices indicated that two to nine extreme drought events occurred in the province with an increasing northward pattern. An increasing trend in the long-term NDVI series was also detected, having more diversity in vegetation coverage in the northern part of the province. The relationship between the SPEI and MODIS-NDVI was found to be positive but insignificant. Thus, we concluded that short-term meteorological droughts were not the only reason for the agricultural droughts in Erbil. Furthermore, the climate characteristics related to the cumulative water balance over a previous season is not an important trigger for the spatial variation in vegetation coverage across the province.

Trends in normalized difference vegetation index time series in differently regulated cascade reservoirs in Wujiang catchment, China

Dam construction is economically and ecologically important for power generation and water capture. However, the effects of cascade dams with various regulation types (i.e., daily, weekly, seasonal, yearly) on vegetation coverage are still need to be clarified. In the current study, we investigated the effects of dam construction on vegetation coverage and predicted the dynamic trends of vegetation at the sub-catchment (i.e., the catchment removing the overlapping catchment of the upstream reservoir) and buffer scale of each reservoir. MODIS Terra normalized difference vegetation index (NDVI) composites (MOD13Q1, 2000–2019) were used to study spatial and temporal changes in riparian vegetation coverage. At the sub-catchment scale, the long-term mean NDVI increased significantly from upstream to downstream from 2000 to 2019, and the NDVI mutation point (i.e., turning point) was consistent with the construction year of the corresponding dam. At the temporal scale, the vegetation trend predictions indicated a greater degree of degradation (23 %–61 %) than in the past (2000–2019; 3 %–32 %). Our findings also showed that reservoir vegetation degradation was associated with regulation type, i.e., daily reservoirs experienced a higher degree of vegetation degradation. Each cascade reservoir exhibited higher past vegetation degradation within the 0–1 km buffer zone compared to the sub-catchment. However, future degradation was predicted to be much higher in the sub-catchment than in the 0–1 km buffer zone, indicating the potential impact of damming on vegetation from adjacent areas (i.e., 0–1 km buffer) with gradual expansion to more distant areas (i.e., sub-catchment) over time. A potential cascade effect of upstream dams on sub-catchment vegetation of downstream dams was also observed. This study provides a reference for watershed management and ecological civilization construction. Relevant laws and policies should be established in areas surrounding reservoirs to help mitigate dam-induced degradation of vegetation communities, especially in reservoirs with short retention times.

An L1-regularized variational approach for NDVI time-series reconstruction considering inter-annual seasonal similarity

Long-term normalized difference vegetation index (NDVI) data are extensively applied in environmental and ecological researches. However, cloud-induced interference and contamination seriously affects the quality of the current NDVI products, bringing huge uncertainty for subsequent applications. Although plenty of temporal methods have been developed to reconstruct NDVI time series, they still struggle to solve the challenge of temporally continuous gaps, due to the insufficient utilization of the prior characteristics in the time series. This paper develops a variational-based method to reconstruct multi-year NDVI time series by jointly regularizing local smoothness and inter-annual Seasonal similarity using the L1-norm (termed the SeasonL1 method). The proposed method innovatively introduces the information from adjacent years combined with assistance from temporal neighbors, and the L1-norm is imposed to establish the two corresponding regularization terms, to better characterize their statistical distributions. Simulated and real-data experiments were conducted in the Yangtze River Economic Belt of China using Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI data, to validate the performance of the SeasonL1 method by comparing with five classic methods. The results demonstrate that the proposed SeasonL1 method can achieve a satisfactory performance with an acceptable time cost in terms of both the quantitative indicators and spatio-temporal visual effect. In particular, the SeasonL1 method shown obvious advantages in recovering temporally continuous missing values and preventing over-smoothing in the inflection points of vegetation growth. We expect that the SeasonL1 method will become a useful filtering approach for obtaining high-quality NDVI time-series data in large-scale applications.

Drought-related cumulative and time-lag effects on vegetation dynamics across the Yellow River Basin, China

• Accumulated (lagged) drought significantly affected 50% (60%) of the vegetated area in the YRB. • Vegetation in the arid zone tended to be more sensitive and resistant to drought. • Grasslands and crops were more vulnerable to accumulated and lagged drought than forests. • Cumulative effect was stronger than lag effect independent of climate zone and vegetation type. • Cumulative and lag effects were significantly correlated with annual water availability. Yellow River Basin (YRB), a climate-sensitive and ecologically compromised area in China, is increasingly affected by extreme climate events (especially droughts) resulting from climate change and frequent human activity. Vegetation responds asymmetrically to drought with cumulative and time-lag effects, whereas response across various climatic zones and diverse vegetation types in the YRB remains unclear. To address this deficiency, we examined the spatiotemporal patterns of accumulated and lagged drought effects on vegetation dynamics for the period 1982 to 2015. The examination was based on the long-term Normalized Difference Vegetation Index (NDVI) and multiscale dataset of the Standardized Precipitation Evapotranspiration Index (SPEI). Cumulative (time-lag) effects were determined via the maximum correlation between the NDVI and the one- to 12-month timescale SPEI (one-month timescale SPEI), as well as the corresponding months of optimal response to drought. The main findings were as follows: (1) Accumulated and lagged drought significantly affected approximately 50% and 60% of the vegetated area in the YRB, respectively, with the strongest effects and the corresponding optimal months varying across climatic zones and vegetation types. (2) In general, vegetation in the arid zone tended to be more sensitive and resistant to drought, as evidenced by the occurrence of lagged and accumulated drought effects mostly in the short-term (one–three months) and medium-term (six–eight months), respectively. This finding may be related to the vegetation’s strategy for coping with water deficits. (3) The biome-level effects of drought on grassland and cultivated vegetation were stronger than those on forests, which may be associated with differences in the functional characteristics of root systems. (4) Annual water availability significantly affected the spatiotemporal patterns by which the NDVI responded to droughts of multiple timescales, with the correlation coefficients and corresponding months of response decreasing with increasing average annual SPEI. These results indicate that vegetated areas with low water availability were more susceptible to cumulative (time-lag) droughts. (5) Independent of the climate zone or vegetation type, drought cumulatively affected vegetation more than time-lag effects. The study improves the knowledge of climate–vegetation relationships in the YRB and provides theoretical support for addressing drought risk in a changing climate.

A Global 250-m Downscaled NDVI Product from 1982 to 2018

Satellite-based normalized difference vegetation index (NDVI) time series data are useful for monitoring the changes in vegetation ecosystems in the context of global climate change. However, most of the current NDVI products cannot effectively reconcile high spatial resolution and continuous observations in time. Here, to produce a global-scale, long-term, and high-resolution NDVI database, we developed a simple and new data downscaling approach. The downscaling algorithm considers the pixel-wise ratios of the coefficient of variation (CV) between the coarse- and fine-resolution NDVI data and relative changes in the NDVI against a baseline period. The algorithm successfully created a worldwide monthly NDVI database with 250 m resolution from 1982 to 2018 by translating the fine spatial information from MODIS (Moderate-resolution Imaging Spectroradiometer) data and the long-term temporal information from AVHRR (Advanced Very High Resolution Radiometer) data. We employed the evaluation indices of root mean square error (RMSE), mean absolute error (MAE), and Pearson’s correlation coefficient (Pearson’s R) to assess the accuracy of the downscaled data against the MODIS NDVI. Both the RMSE and MAE values at the regional and global scales are typically between 0 and 0.2, whereas the Pearson’s R values are mostly above 0.7, which implies that the downscaled NDVI product is similar to the MODIS NDVI product. We then used the downscaled data to monitor the NDVI changes in different plant types and places with significant vegetation heterogeneity, as well as to investigate global vegetation trends over the last four decades. The Google Earth Engine platform was used for all the data downscaling processes, and here we provide a code for users to easily acquire data corresponding to any part of the world. The downscaled global-scale NDVI time series has high potential for the monitoring of the long-term temporal and spatial dynamics of terrestrial ecosystems under changing environments.

Time Series of Remote Sensing Data for Interaction Analysis of the Vegetation Coverage and Dust Activity in the Middle East

Motivated by the lack of research on land cover and dust activity in the Middle East, this study seeks to increase the understanding of the sensitivity of dust centers to climatic and surface conditions in this specific region. In this regard, we explore vegetation cover and dust emission interactions using 16-day long-term Normalized Difference Vegetation Index (NDVI) data and daily Aerosol Optical Depth (AOD) data from Moderate Resolution Imaging Spectroradiometer (MODIS) and conduct spatiotemporal and statistical analyses. Eight major dust hotspots were identified based on long-term AOD data (2000–2019). Despite the relatively uniform climate conditions prevailing throughout the region during the study period, there is considerable spatial variability in interannual relationships between AOD and NDVI. Three subsets of periods (2000–2006, 2007–2013, 2014–2019) were examined to assess periodic spatiotemporal changes. In the second period (2007–2013), AOD increased significantly (6% to 32%) across the studied hotspots, simultaneously with a decrease in NDVI (−0.9% to −14.3%) except in Yemen−Oman. Interannual changes over 20 years showed a strong relationship between reduced vegetation cover and increased dust intensity. The correlation between NDVI and AOD (−0.63) for the cumulative region confirms the significant effect of vegetation canopy on annual dust fluctuations. According to the results, changes in vegetation cover have an essential role in dust storm fluctuations. Therefore, this factor must be regarded along with wind speed and other climate factors in Middle East dust hotspots related to research and management efforts.

Multi-temporal Landsat-derived NDVI for vegetation cover degradation for the period 1984-2018 in part of the Arganeraie Biosphere Reserve (Morocco)

Biosphere Reserves (BRs) are especial zones established to conserve the biodiversity (ecosystem, species or genetic). Therefore, evaluating and analyzing the ecosystem degradation in these zones are crucial for protecting them. This study attempts to assess and analyze the vegetation cover degradation during the last three decades (period between 1984 and 2018) in part of the Arganeraie Biosphere Reserve (ABR) of Morocco. The study aims to describe the ecosystem degradation scenario by using multi-temporal Landsat-derived NDVI (Normalized Difference Vegetation Index), and to highlight the reasonable factors causing its degradation. Long-term and short-term NDVI changes were investigated using image difference change detection technique and IsoData classification. The obtained results confirm that about 3644.64 ha of the vegetation cover was degraded during last decade. Field investigations confirm that the anthropogenic activities caused vegetation cover degradation that last a long time (2007–2018) as confirmed by long-term analysis. Forest fire, due to charcoal production, caused degradation of about 2989.59 ha of the vegetation cover during the period 2010–2018. This study has highlighted the importance to use long-term NDVI change detection to map anthropogenic-induced vegetation cover degradation and to use short-term NDVI change detection for best chronological analysis of ecosystem degradation. In addition, the easiest proposed method offers a user-friendly tool to monitor ecosystem degradation.

水热条件分别控制了中国温带草地NDVI的年际变化和增长趋势

温带草地区是我国植被对气候变化响应的敏感区和陆地生态系统的生态脆弱区, 是组成草地资源的重要部分。由于全球变暖, 气候变化带来的极端气候影响越来越大, 探究我国温带草地植被覆盖时空变化规律和水热条件变化对其生长的影响, 对区域生态的环境保护, 实现畜牧业的可持续发展和更加合理有效地利用草地资源具有重要的现实意义。基于1982—2015年长时间序列的气象(降水、温度、太阳辐射)数据和GIMMS NDVI 3g数据, 采用去趋势分析法和相关性分析法, 探究近34年中国温带地区生长季草地归一化植被指数(Normalized difference vegetation index, NDVI)和气候因子变化格局, 以及水热条件对NDVI的同步影响和滞后影响。研究结果表明：(1)1982—2015年中国温带草地生长季平均温度和月平均太阳辐射呈增长趋势, 降水量为下降趋势, 温带草地气候逐渐呈现“暖干化”；(2)1982—2015年中国温带草地生长季NDVI的年际变化由降水因子主导, 特别是在1999年之后, 降水的对于中国温带草地生长季NDVI年际变化的影响更为显著; (3)1982—2015年中国温带草地生长季NDVI为增长趋势, 生长季热条件(温度和太阳辐射)的显著上升控制着NDVI的增长趋势; (4)1982—2015年中国温带草地生长季NDVI对水热因子在年际变化上存在明显的滞后响应, 生长季前期NDVI对温度滞后响应最明显, 生长季中后期降水则成为植被生长滞后影响的主要控制因子。;The temperate grassland is the sensitive to climate change and is the ecologically fragile area of the terrestrial ecosystem in China, and it is an important part of the grassland resources. Due to global warming, the extreme climate impact brought by climate change is getting bigger, so exploring the temporal and spatial changes of vegetation coverage in the temperate grasslands in China and the effects of changes in water and heat conditions on vegetation growth are of greatly practical significance to the environmental protection of regional ecology, the sustainable development of animal husbandry, and more rational and effective use of grassland resources. Based on the long-term meteorological data (precipitation, temperature and radiation) and GIMMS NDVI 3g data from 1982—2015, this study used trend analysis and correlation analysis to explore the changes of grassland NDVI (Normalized difference vegetation index) in the growing season in the temperate regions of China, and the pattern of climate change, as well as the simultaneous and lagging effects of hydrothermal conditions on the NDVI. The results showed that: (1) From 1982 to 2015, the average temperature and total solar radiation in China's temperate grassland growing season showed an increasing trend, the total amount of precipitation was a downward trend, and the temperate grassland climate gradually became warm and dry. (2) From 1982 to 2015, the inter-annual variation of NDVI in the growing season of temperate grassland in China was dominated by precipitation factors. Especially after 1999, the influence of precipitation on the inter-annual variation of NDVI in the growing season of temperate grassland in China was more significant. (3) From 1982 to 2015, the NDVI of China's temperate grassland growing season was with an increasing trend, and it was positively correlated with water and heat factors. The significant increase in thermal conditions (temperature and solar radiation) during the growing season controlled the growth trend of the NDVI. (4) From 1982 to 2015, the NDVI of China's temperate grassland growing season had an obvious lagging response to the inter-annual variation of water and heat factor. The NDVI responded most obviously to the temperature lag in the early stage of the growing season, and precipitation became the main controlling factor for the lagging influence on vegetation growth in the middle and late growing seasons.

May-July NDVI variation for the middle Qinling Mountains over the past 194 years indicated by tree rings of Pinus tabuliformis

Due to the short-term observation record of the normalized difference vegetation index (NDVI), the research on long-term NDVI changes is scarce, which limits our understanding of the impacts of NDVI changes in the context of global warming. In this study, a regional tree-ring chronology was developed based on the tree-ring samples of Pinus tabuliformis in the middle Qinling Mountains. The results showed that tree-ring width of P. tabuliformis was significantly positively correlated with May-July NDVI (r=0.624, P<0.01, n=34). The Sig-Free tree-ring width chronology was used to reconstruct May-July NDVI during the period 1825-2018, which explained 38.9% of the total NDVI variance. Results of spatial analysis showed that the reconstructed series could better represent the NDVI changes in the study area. There were six high NDVI periods and five low NDVI periods in the past 194 years. The vegetation grew best in 2006-2018, indicating vegetation cove-rage in the middle of Qinling Mountains had been improved during the warming hiatus. Low NDVI periods in the reconstruction series were consistent with drought over much of study area. Results of wavelet analysis indicated the existence of 2-4 years and 12-16 years cycles in the reconstruction series. SEA analysis showed that the reconstruction series decreased significantly in the El Nino year, while increased significantly in the first to third years after the La Nina event. The growth of P. tabuliformis was predicted to increase slightly under the SSP2-4.5, SSP3-7.0 and SSP5-8.5 scenarios.

Long time-series NDVI reconstruction in cloud-prone regions via spatio-temporal tensor completion

The applications of Normalized Difference Vegetation Index (NDVI) time-series data are inevitably hampered by cloud-induced gaps and noise. Although numerous reconstruction methods have been developed, they have not effectively addressed the issues associated with large gaps in the time series over cloudy and rainy regions, due to the insufficient utilization of the spatial, temporal and periodical correlations. In this paper, an adaptive Spatio-Temporal Tensor Completion method (termed ST-Tensor) method is proposed to reconstruct long-term NDVI time series in cloud-prone regions, by making full use of the multi-dimensional spatio-temporal information simultaneously. For this purpose, a highly-correlated tensor is built by considering the correlations among the spatial neighbors, inter-annual variations, and periodic characteristics, in order to reconstruct the missing information via an adaptive-weighted low-rank tensor completion model. An iterative ℓ1 trend filtering method is then implemented to eliminate the residual temporal noise. This new method was tested using MODIS 16-day composite NDVI products from 2001 to 2018 obtained in Mainland Southeast Asia, where the rainy climate commonly induces large gaps and noise in the data. The qualitative and quantitative results indicate that the ST-Tensor method is more effective than the five previous methods in addressing the different missing data problems, especially the temporally continuous gaps and spatio-temporally continuous gaps. It is also shown that the ST-Tensor method performs better than the other methods in tracking NDVI seasonal trajectories, and is therefore a superior option for generating high-quality long-term NDVI time series for cloud-prone regions.

Recent vegetation browning and its drivers on Tianshan Mountain, Central Asia

The strong signal of vegetation increases since the 1980s is considered as reliable evidence of anthropogenic climate change. However, some studies have alerted us to the recent stalling or even reversal of vegetation greening. To determine whether vegetation browning exists on Tianshan Mountain, we investigated the changes in the Normalized Difference Vegetation Index (NDVI) based on long-term satellite-derived NDVI data series from 1982 to 2015. Although the trend of growing season NDVI was statistically significant throughout the study period (0.0006a−1, p < 0.01), two different periods with opposite trends were evident around 1998. The NDVI showed a significant increase before 1998 but a reversal after 1998, when vegetation browning began to appear. The strong correlation between the interannual variability of vegetation and growing season temperature only existed in wet years, indicating that vegetation on Tianshan Mountain is extremely vulnerable and sensitive to water deficits. Our results also suggest that relative to high vapor pressure deficit, soil moisture deficit played a greater role in the recent browning of vegetation on Tianshan Mountain. This study is of scientific value in understanding the response of vegetation growth and carbon cycling to environmental changes and predicting future developments.

Quantitative contributions of climate change and human activities to vegetation changes over multiple time scales on the Loess Plateau

Vegetation is a crucial component of terrestrial ecosystems, and its changes are driven mainly by a combination of climate change and human activities. This paper aims to reveal the relationship between vegetation and climate change by using the normalized difference vegetation index (NDVI) and standardized precipitation evapotranspiration index (SPEI), and to find the cause of vegetation change by performing residual analysis on the Loess Plateau during the period from 2000 to 2016. The results showed that the NDVI on the Loess Plateau exhibited an increase of 0.086 per decade, and an increasing trend was observed across 94.86% of the total area. The relationship between the NDVI and SPEI was mainly positive, and the correlation increased as the time scale of the SPEI lengthened, indicating that long-term water availability was the major climate factor affecting vegetation growth. Residual analysis indicated that climate change was responsible for 45.78% of NDVI variation, while human activities were responsible for 54.22%. In areas with degraded vegetation, the relative roles of climate change and human activities were 28.11% and 72.89%, respectively. In addition, the relative role of climate change increased with an increase in the time scales, implying that the long-term NDVI trend was more sensitive to climate change then the short-term trend. The results of this study are expected to enhance our understanding of vegetation changes under climate change and human activities and provide a scientific basis for future ecological restoration in arid regions.

Farmers’ drought experience, risk perceptions, and behavioural intentions for adaptation: evidence from Ethiopia

ABSTRACT This paper examines farmers' cognitive perceptions of risk and the behavioural intentions to implement specific drought risk reduction measures using the Protection Motivation Theory (PMT) model. We follow an innovative route by extending a PMT model with a drought experience variable, which, we hypothesize, will influence risk perceptions and the take-up of adaptation measures. In order to do so, we investigated detailed historical drought patterns by looking at the spatial and temporal aspects of drought conditions during crop growing season at the village level. In our extended PMT model, drought experience, as represented by a long-term Normalized Difference Vegetation Index (NDVI), plays a significant role in predicting farmers' intention to take up adaptation measures. The result reveals that drier conditions significantly increase farmers' behavioural intention to implement adaptation measures against drought risk, which is the key finding of the study. Moreover, our findings show that perceived vulnerability and severity, self-efficacy, and response efficacy are positively and significantly associated with the number of drought risk reduction measures implemented. The findings of our empirical analysis contribute to the effect of cognitive perceptions through a new lens of farmer's personal experience with drought shocks, represented by the state of vegetation or physical environment.

Improved mapping and change detection of the start of the crop growing season in the US Corn Belt from long-term AVHRR NDVI

Long-term monitoring of crop phenology is a critical process to understand changes in cropland vegetation dynamics related to climate and human management. During the last four decades, seasonal changes in vegetation have been measured by satellite instruments, the Advanced Very High Resolution Radiometers (AVHRRs). However, an acceptable detection of crop phenology has been prevented due to its coarse spatial resolution (~8 km) pixels in which croplands are frequently mixed with non-cropland. This study introduces a novel method for detecting the start of the growing season in cropland (SOSC) from the AVHRR Normalized Difference Vegetation Index (NDVI) to examine the long-term changes in SOSC at the 8-km pixel level across the core cropping regions of the Midwestern United States (the so-called ‘the Corn Belt’) for 1982–2015. The AVHRR-based SOSC was compared with the SOSC retrieved from the pixels covering mostly croplands of the 250-m Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI. These two satellite-based SOSC shows reasonable correspondence in spatial patterns so that the annual values of the root mean square difference ranges from 3.6 to 7.4 days during the overlapping period of 2008–2015. Also, an acceptable correspondence was found between the interannual variations in the AVHRR SOSCs and in the state-level survey of crop stages. For sowing stages, correlation coefficients ranged from 0.43 to 0.68; for flowering stages those ranged from 0.51 to 0.83. By analyzing long-term SOSC changes with climate factors, we found a strong tendency that warmer pre-season condition is accompanied with earlier SOSC. In addition, larger pre-season precipitation tends to postpone SOSC as strongly as the pre-season temperature affects SOSC in a considerable portion of the core cropland. This study suggests that the postponing of sowing due to larger precipitation, which has rarely been highlighted in previous studies, requires more attentions in order for better understanding of vegetation dynamics in agriculture-climate studies.