Response Of NDVI Research Articles

A preliminary view of the CYGNSS soil moisture-vegetation activity linkage

IntroductionSoil moisture (SM) is crucial for regulating vegetation productivity and sustaining plant growth. Understanding the linkage between SM and vegetation activity is paramount in eco-hydrology modeling and meteorological applications. CYGNSS, one of the most commonly spaceborne GNSS-R missions with publicly available data, has the advantage of retrieving SM with high accuracy and high temporal resolution.MethodsThis paper describes the linkage between the CYGNSS SM and vegetation activity. The CYGNSS SM from 2019.01 to 2022.12 with system error and land surface calibration is first retrieved. The linkages between the CYGNSS SM and two key vegetation activity indexes, i.e., NDVI and the start of the growing season (SOS), are then investigated.ResultsThe findings and conclusions mainly include: (1) The CYGNSS SM with system error and land surface error calibration shows a good correlation with the SMAP SM, i.e., R = 0.693 vs. ubRMSE = 0.054 m3m−3. Long time-series CYGNSS SM can be useful data for large-scale terrestrial ecosystems and global change studies. (2) The NDVI shows a negative correlation with SM in most pan-tropical areas, whereas a positive correlation with SM in Africa. The response of NDVI to SM is more significant in shrublands and grasslands. (3) The link between the CYGNSS SM and SOS displays strong annual variations, and the SM has generally experienced a significant negative effect on SOS. SM advances the vegetation green-up in arid and semi-arid areas.

Shifting climatic responses of tree rings and NDVI along environmental gradients

Variations in the growth of aboveground biomass compartments such as tree stem and foliage significantly influence the carbon cycle of forest ecosystems. Yet the patterns of climate-driven responses of stem and foliage and their modulating factors remain poorly understood. In this study, we investigate the climatic response of Norway spruce (Picea abies) at 138 sites covering wide spatial and site fertility gradients in temperate forests in Central Europe. To characterize the annual growth rate of stem biomass and seasonal canopy vigor, we used tree-ring chronologies and time-series of NDVI derived from Landsat imagery. We calculated correlations of tree-ring width and NDVI with mean growing season temperature and standardized precipitation evapotranspiration index (SPEI). We evaluated how these climate responses varied with aridity index, soil category, stand age, and topographical factors. The results show that the climate-growth responses of tree rings shift from positive to negative for SPEI and from negative to positive for temperature from dry (warm) to wet (cold) areas. By contrast, NDVI revealed a negative response to temperature across the entire climatic gradient. The negative response of NDVI to temperature likely results from drought effects in warm areas and supporting effects of cloudy conditions on foliage greenness in wet areas. Contrary to NDVI, climate responses of tree rings differed according to stand age and were unaffected by local topographical features and soil conditions. Our findings demonstrate that the decoupling of stem and foliage climatic responses may result from their different climatic limitation along environmental gradients. These results imply that in temperate forest ecosystems, the canopy vigor may show different trends compared to stem growth under ongoing climate change.

Dynamic pattern-effect relationships between precipitation and vegetation in the semi-arid and semi-humid area of China

Explicating the relationship between vegetation and precipitation changes with different spatial dimensions is critical for clearly identifying the interaction mechanism between climate change and ecosystem health in the semi-arid and semi-humid (SASH) area, China. Due to investigating the dynamic pattern-effect relationships between precipitation and vegetation in the directions of longitude and latitude in the SASH area of China, the division method of SASH area, the identifying method of spatial distribution change of NDVI and precipitation, and the sensitivity index of NDVI to precipitation were developed in this study, based on the NDVI data and monthly precipitation data from 1991 to 2020. The research results showed that the NDVI was significantly related with precipitation in the whole SASH area. The relationship between NDVI and precipitation in the semi-arid area was higher than that in the semi-humid area between 1991 and 2020. The response of NDVI to precipitation (R(NDVI,Pre) = 0.90, P < 0.01) in the longitude direction was more than that (R(NDVI,Pre) = 0.55, P < 0.01)in the latitude direction. The sensitivity of 79% NDVI to precipitation was increased with increasing latitude within the same longitude range, and the sensitivity of 97% NDVI to precipitation was decreased with increasing longitude within the same latitude range. The NDVI was more sensitive to precipitation in the semi-arid area than that in the semi-humid area. In summary, the responses of NDVI to precipitation showed significant heterogeneity between 1991and 2020 in the whole SASH area. Thus, the planning of ecological protection should give more consideration to the precipitation condition in the SASH area in the future.

Responses of NDVI to Climate Change and LUCC along Large-Scale Transportation Projects in Fragile Karst Areas, SW China

The fragile karst habitat is extremely sensitive to human activities such as large-scale engineering construction. To explore the influence of the construction and operation of the GH (Guiyang-Huangguoshu) highway on the vegetation within a certain range and the response of NDVI to climate factors, Landsat data were used to synthesize annual NDVI maps using the maximum value compositing method. Trend, correlation, and coefficient of variation analyses were performed. The results demonstrate that: (1) During the construction and operation periods, NDVI showed an overall upward trend, and the NDVI value and growth rate in the contrast area were greater than those in the core area; (2) the correlation between temperature and vegetation cover along the GH highway was stronger than that between precipitation and vegetation; (3) construction of the GH highway has had a significant impact on the surrounding vegetation, with the impact on vegetation ecology along the road mainly concentrated within the 2 km range. The increase of artificial surfaces along the road has had a great impact on the NDVI, and the vegetation cover change in the core area is more significant than that in the contrast area; and (4) the overall disturbance of the GH highway project to the surrounding ecology was mainly observed in the form of low and medium fluctuations. This study aims to provide a reference for environmental assessment and management in karst areas.

Spatiotemporal Variation of NDVI in Anhui Province from 2001 to 2019 and Its Response to Climatic Factors

This paper intends to clarify that the spatial and temporal evolutionary patterns of regional vegetation and their relationship with climate form a premise of ecological conservation and environmental governance, and play an important role in maintaining regional ecosystem balance and promoting sustainable development. Based on measured data collected from NDVI remote sensing products and meteorological stations, NDVI variation in Anhui Province from 2001 to 2019 was determined through trend analysis and measurement methods involving coefficient of variation and Hurst index; in addition, the response to climatic factors was also explored. It was concluded that, firstly, in terms of spatiotemporal analysis, the interannual variation of NDVI in Anhui Province showed an increasing trend with a rate of 0.024/10 a, while the monthly variation showed a weak bimodal pattern, with the highest value in August and the lowest value in January. Furthermore, NDVI in Anhui Province showed significant spatial heterogeneity, with high values concentrated in mountainous regions in southern Anhui and Dabie Mountain region, and low values concentrated in the hilly areas of Jianghuai and areas along the Yangtze River. At the same time, the overall spatial variation of NDVI showed an increasing trend, and the areas with extremely significant and significant improvement in vegetation coverage accounted for 54.69% of the total area of Anhui Province. Secondly, in terms of the analysis on variation characteristics, the variation of NDVI in Anhui Province was generally stable, with an average CV coefficient of variation of 0.089, which, however, was quite different in different regions; meanwhile, the future trend of NDVI variation in the study areas was mostly in a random manner. Thirdly, the response of NDVI in Anhui Province to climatic factors showed significant spatial heterogeneity. NDVI was found to be positively correlated with precipitation and negatively correlated with temperature; in general, the impact of precipitation on NDVI was greater than that of temperature. In the 19 years studied, NDVI in Anhui Province showed an increasing trend; and climate, topography and human activities led to heterogeneous spatial distribution of vegetation. Therefore, in the future, the evolutionary trend of vegetation will be relatively random, and NDVI will be more greatly affected by temperature, than by precipitation.

Response of NDVI and SIF to Meteorological Drought in the Yellow River Basin from 2001 to 2020

Understanding the response of vegetation to drought is of great significance to the biodiversity protection of terrestrial ecosystem. Based on the MOD13A2 NDVI, GOSIF, and SPEI data of the Yellow River Basin from 2001 to 2020, this paper used the methods of Theil–Sen median trend analysis, Mann–Kendall significance test, and Pearson correlation analysis to analyze whether the vegetation change trends monitored by MODIS and GOSIF are consistent and their sensitivity to meteorological drought. The results showed that NDVI and SIF increased significantly (p &lt; 0.001) at the rate of 0.496 × 10−2 and 0.345 × 10−2, respectively. The significant improvement area of SIF (66.49%, p &lt; 0.05) is higher than NDVI (50.7%, p &lt; 0.05), and the spatial distribution trend of vegetation growth monitored by NDVI and SIF is consistent. The negative value of SPEI-12 accounts for 65.83%, with obvious periodic changes. The significant positive correlation areas of SIF-SPEI in spring, summer, and autumn (R &gt; 0, p &lt; 0.05) were 7.00%, 28.49%, and 2.28% respectively, which were higher than the significant positive correlation areas of NDVI-SPEI (spring: 1.79%; summer: 20.72%; autumn: 1.13%). SIF responded more strongly to SPEI in summer, and farmland SIF was significantly correlated with SPEI (0.3424, p &lt; 0.01). The results indicate that SIF is more responsive to drought than NDVI. Analyzing the response of vegetation to meteorological drought can provide constructive reference for ecological protection.

Responses of Vegetation NDVI to Climate Change and Land Use in Ordos City, North China

Ordos City lies in a semiarid-arid farm pastoral region in north China. In recent years, as an ecological security barrier and ecological vulnerable and sensitive region, the intensification of human activities and climate change in the foreseeable future have aroused people’s attention to ecological security issues. Based on the meteorological data of MOD13Q1 and the socioeconomic activity data in the same period, the variation characteristics of annual and seasonal vegetation NDVI, the response of vegetation NDVI to climate change, and the impact of human activities on vegetation NDVI in Ordos City from 2000 to 2020 were obtained by using trend analysis, sustainability analysis, and path analysis. The results showed that: (1) Ordos City vegetation NDVI showed an obvious growth trend, except for the interannual growth rate (0.0075/a), the growth rate in autumn increased faster than in other seasons (0.0067/a); (2) the future trend of vegetation NDVI in Ordos City is mainly anti-continuous improvement. If no effective measures are taken, the vegetation NDVI will degrade in the future; (3) precipitation was the main climate factor in the vegetation growth compared with temperature. An increase in precipitation promoted vegetation growth directly, while an increase in temperature showed a negative effect by reducing precipitation in the whole growing season; (4) the utilization pattern greatly affects the spatial distribution of vegetation NDVI, and stable land use can improve the utilization of vegetation.

Monitoring responses of NDVI and canopy temperature in a rice field to soil water and meteorological conditions

Aerial photography of the vegetation canopy is an effective approach for spatial monitoring and evaluating physiological plant conditions precisely and accurately. This study aimed to obtain the relationship between the response of NDVI and canopy temperature in the rice field to soil water (before and after the irrigation process) and meteorological variables. This study was conducted in Matsuyama, Japan, with a rice cultivar (Oryza sativa L. cv. Koshihikari) and focused on the heading and flowering stage (July 31 - August 10, 2020). Aerial photographs were successfully detected the spatial variation of NDVI and canopy temperature across the rice fields. Taken NDVI by the UAV camera was successfully correlated with the SPAD value at the same point (R2= 0.96), and thermal imagery by UAV also successfully correlated with the hand-held temperature measurement at the same point (R2=0.84). NDVI values in the wet soil condition of the rice field were higher than those in the dry condition. Moreover, the canopy temperature in the dry area was higher than in the wet area by an average difference of 0.63 °C. The yield with high soil water content was significantly higher (P=0.03) than those in the lower condition by 0.28 kg/m2.

Response of NDVI of Natural Vegetation to Climate Changes and Drought in China

Temporal and spatial changes in vegetation and their influencing factors are of great significance for the assessment of climate change and sustainable development of ecosystems. This study applied the Asymmetric Gaussians (AG) fitting method, Mann-Kendall test, and correlation analysis to the Global Inventory Monitoring and Modeling System (GIMMS) third-generation Normalized Difference Vegetation Index and gridded climate and drought data for 1982–2015. The temporal and spatial changes to NDVI for natural grassland and forest during the growing season were analyzed. Relationships among NDVI, climate change, and droughts were also analyzed to reveal the influence of vegetation change. The results showed that: (1) Land use/cover change (LUCC) in China was mainly represented by increases in agricultural land (Agrl) and urban and rural land (Uril), and decreases in unutilized land (Bald), grassland, forest, and permanent glacier and snow (Snga). The increase in agricultural land was mainly distributed in the western northwest arid area (WNW) and northern North China (NNC), whereas regions with severe human activities such as southern South China (SNC), western South China (WSC), and eastern South China (ESC) showed significant decreases in agricultural land due to conversion to urban and rural land. (2) The start of the growing season (SOS) was advanced in WNW, SNC, WSC, and ESC, and the end of growing season (EOS) was delayed in WNW, NNC, and SNC. The growing season length (GSL) of natural vegetation in China has been extended by eight days over the last 34 years. However, the phenology of the Qinghai-Tibet Plateau (TP) was opposite to that of the other regions and the GSL showed an insignificant decreasing trend. (3) The NDVI increased significantly, particularly in the SNC, WSC, ESC, and the grassland of the WNW. Precipitation was found to mainly control the growth of vegetation in the arid and semi-arid regions of northwest China (WNW and ENW), and precipitation had a much greater impact on grassland than on forests. Temperature had an impact on the growth of vegetation throughout China, particularly in SNC, ESC, and WSC. (4) The Standardized Precipitation Evapotranspiration Index (SPEI) showed a downward trend, indicating an aridification trend in China, particularly in ENW, NNC, and WNW. Similar to precipitation, the main areas affected by drought were WNW and ENW and grassland was found to be more sensitive to drought than forest. The results of this study are of great significance for predicting the response of ecosystem productivity to climate change under future climate change scenarios.

NDVI Variation and Yield Prediction in Growing Season: A Case Study with Tea in Tanuyen Vietnam

Tea is one of the most significant cash crops and plays an important role in economic development and poverty reduction. On the other hand, tea is an optimal choice in the extreme weather conditions of Tanuyen Laichau, Vietnam. In our study, the NDVI variation of tea in the growing season from 2009 to 2018 was showed by calculating NDVI trend and the Mann-Kendall analysis to assess trends in the time series. Support Vector Machine (SVM) and Random Forest (RF) model were used for predicting tea yield. The NDVI of tea showed an increasing trend with a slope from −0.001–0.001 (88.9% of the total area), a slope from 0.001–0.002 (11.1% of the total area) and a growing rate of 0.00075/year. The response of tea NDVI to almost climatic factor in a one-month time lag is higher than the current month. The tea yield was estimated with higher accuracy in the RF model. Among the input variables, we detected that the role of Tmean and NDVI is stronger than other variables when squared with each of the independent variables into input data.

On the suitability of using vegetation indices to monitor the response of Africa's terrestrial ecoregions to drought

Drought remains one of the world's most devastating phenomena, exhibiting impacts in both magnitude and frequency. African vegetation remains highly vulnerable to drought impacts and this is heightened by a changing climate. In this study, we evaluated the suitability of vegetation indices to monitor the response of Africa's terrestrial ecoregions to drought. Here, we used the SPEI, a global drought index to investigate the spatiotemporal characteristics of drought on vegetation. In addition, TVDI, TCI, VCI, NVSWI, VSWI and DSI, which are remotely sensed derived drought indices were also used to characterize drought. For the vegetation indices, we used the optical satellite calculated NDVI; VOD, a passive microwave remote sensing product; and derived Nvod as proxies for vegetation. The climatology of climate and vegetation data was calculated, and the trend of the variables was examined. Additionally, comparisons were performed between the SPEI and the other drought indices. Subsequently, we computed the correlations between the SPEI and vegetation indices spatially, temporally and seasonally.Our results show that VOD and the NDVI have similar spatial distribution, with higher values of the indices recorded over the Democratic Republic of Congo (DRC) and Central African Republic (C.A.R) compared to the rest of the region. Furthermore, we also found that the indices have similar seasonal patterns as precipitation and an inverse relationship with temperature. The study also reveals that there is a declining long-term trend of precipitation over evergreen needleleaf forest, evergreen broadleaf forest, and woody savanna; and an increasing trend of VOD and NDVI over Africa's ecoregions. Furthermore, the results show a high SPEI – VOD correlations (r2 = 0.8) in southern Africa and the Horn of Africa, and a weak response in the Sahelian region. While the response of NDVI is similar to a spatial distribution as VOD, the magnitudes of response are generally weaker in the NDVI, and the magnitudes and distribution of response by Nvod are similar to VOD. Also, the response of Nvod is the weakest across all the timescales although its magnitudes vary significantly from year – year, with the timescale of occurrence mostly shorter for JJA but largely longer for MAM. However, the magnitudes of the response of vegetation indices are different for remotely sensed derived drought indices. In addition, the mean and trend of the response of VOD are consistently stronger in evergreen needleleaf forest and open shrublands but weaker over the evergreen broadleaf forest. Our study has presented insights on methods by which the impacts of droughts on plant activities and functions may be monitored.

Linking data of ENSO, NDVI-MODIS and crops yield as a base of an early warning system for agriculture in Córdoba, Argentina

By influencing interannual rainfall variability in the Province of Córdoba, Argentina, El Niño Southern Oscillation (ENSO) also impact on corn and soybean yields at the main rain-fed agricultural production departments. Spectral NDVI data not only capture the environmental impact on vegetation directly, but also can expose it spread all over a region at the same time. As support of an early warning system and decision-making for agriculture, the impact of ENSO on corn and soybean yields anomaly (YA) was analyzed between 2000 and 2017, by using different indicators: SOI, MEI and ONI over the 12 major production departments of Córdoba. Also, NDVI-MODIS data was assessed all over Río Segundo (RS) territory in the center of Córdoba as a vegetation proxy to depict the agricultural activity in a broad sense. To check signal persistency, the relationship was assessed first using the indicators from July to October prior to the sowing date; and then once the growing season has finished, to show the effective ENSO impact on crops productivity.Persistence of ENSO signal was verified, so that the ENSO influence during the growing season quantified remains or intensifies when is compared to information in advance to the start of crop season. While for SOIGS and MEIGS the correlation with corn yield is significant (p < 0.05) in all Córdoba departments, it is always a little lower for ONIGS. Instead, for soybean only 4 departments show significant relationships when using ONIGS, 5 in the case of MEIGS and it increases to 6 when using SOIGS. The particular response of NDVI anomaly (AND) to ENSO was located and quantified for different indices and periods during the crop season. A consistent relationship was found between ENSO and the NDVI as an indicator of agricultural activity in RS territory but, in any case, the area fraction with a significant condition is quite small.The relationship with NDVI is stronger for SOI particularly when a more extended period is considered that include NDVI data of April and May, at the end of the rainy season. While before the beginning of the growing season SOIA-S and SOIS-O reach a significant relationship with the NDVI accumulated since November to May in 11% and 10% of Río Segundo territory, respectively, the percentage increases to 19% when SOIGS is used. Finally, all this information was integrated in an unsupervised cartographic scheme that recognizes different responses of crops yield and NDVI to ENSO in the RS territory. Multiple regression models for corn and soybean yield prediction were developed from NDVI and ENSO indicators for each one of the identified productive sectors. Crops yield can be estimated before harvest with good accuracy, making use of the maximum NDVI value and SOI, both before (SOIA-S) and during the growing season (SOIA-S-O-N-D). As NDVI data capture the ENSO influence on agriculture as well as over crop productivity, its incorporation into a climate monitoring protocol must conforms the basis of an early warning system for a sustainable agriculture in the region.

Response of vegetation to El Niño-Southern Oscillation (ENSO) via compound dry and hot events in southern Africa

El Niño-Southern Oscillation (ENSO) events are well-known to be related to dry and hot extremes and have considerable impacts on the terrestrial ecosystem in southern Africa. However, the impact pathway from ENSO to vegetation (i.e., relationship among ENSO, climate anomalies, and vegetation) is still unclear in southern Africa. In this study, we investigated the impact of ENSO on vegetation (represented by the Normalized Difference Vegetation Index, NDVI) through its impacts on compound dry and hot extremes in austral summers (December-January-February, DJF) from 1982 to 2015 in southern Africa. The Standardized Precipitation Index (SPI) and Standardized Temperature Index (STI) are used as dry and hot indicators, respectively. The response of NDVI to ENSO during DJF is assessed based on correlation analysis and negative correlation coefficients are present for 87.3% of southern Africa. In addition, the response of NDVI anomaly (NDVIA) to both dry and hot conditions are also investigated, which shows positive NDVIA-SPI correlations and negative NDVIA-STI correlations for 83.2% and 76.5% of the area, respectively. The area percentage with significant impacts of ENSO on concurrent dry and hot events is estimated as 45.29%. Finally, we also evaluated lag effects between vegetation and dry/hot conditions and between dry/hot conditions and ENSO. Vegetation changes from September to December 2015 illustrate the connection among ENSO, compound dry and hot events, and vegetation decline in southern Africa. This study can aid our understanding of ENSO induced climate extremes and their impacts on vegetation in southern Africa. • Quantify ENSO's impacts on vegetation in southern Africa. • Investigate impact pathway from ENSO to vegetation in southern Africa. • Evaluate vegetation response to compound dry and hot events with lag effects.

The environmental drivers of annual variation in forest greenness are variable in the northern Intermountain West, USA

AbstractPeak snowpack in the western USA has decreased 21% since the mid‐20th century, and these trends are forecasted to continue over the next century. In water‐limited systems, forest productivity during the growing season is assumed to be linked with snowpack during the previous winter, but this linkage has proven not to be universal in studies across varying ecosystems. We compared peak Normalized Difference Vegetation Index (NDVI, a proxy for vegetation productivity) at relatively fine scales (30‐m pixel) to point measurements of snow water equivalent (SWE) at 169 locations scattered among the northern Intermountain West for the 25‐yr period between 1993 and 2017. We further compared NDVI to additional environmental variables including topographical variables (elevation, aspect, and slope), and climatic variables (Palmer Drought Severity Index, snowmelt date, and snowmelt length). We hypothesized that peak SWE would be a strong predictor of peak NDVI, but the strength of the SWE–NDVI relationships would vary spatially in our topographically complex study area. We observed weak relationships between snowpack and vegetation productivity, with only 10% of the locations displaying statistically significant correlations between annual peak SWE and peak NDVI. Furthermore, we determined that the response of NDVI to snowpack and environmental variables differed among subregions of our study area. For example, we observed strong SWE–NDVI associations in the continental‐most portion of our study area and weak correlations in other subregions of our study area. Our results highlight that in regions characterized by complex topography, (1) environmental drivers of NDVI are highly variable and (2) fine‐resolution studies more accurately capture the spatial heterogeneity of NDVI compared to course‐resolution studies. However, one consequence of fine‐resolution studies may be that microclimate effects at each site swamp the signal of predictor‐ecological response relationships, whereas these relationships may be more discernable in larger scale remote sensing studies.

Vegetation Response to Recent Trends in Climate and Landuse Dynamics in a Typical Humid and Dry Tropical Region under Global Change

The influence of global change on vegetation cover and processes has drawn increasing attention in the past few decades. In this study, we used remotely sensed rainfall and land surface temperature to investigate the spatiotemporal pattern and trend in vegetation condition using NDVI as proxy from 2001 to 2017 in a humid and dry tropical region. We also determined the partial correlation coefficient of temperature and rainfall with NDVI and the response of NDVI to changes in landcover categories due to human activities. We found that the mean annual maximum NDVI was 0.42, decreasing at a rate of 0.06 per decade. About 27.4% of the area was found to have experienced a significant negative trend in vegetation cover, while only 0.34% exhibited significant increasing vegetation vigour. Land surface temperature increased at a mean rate of 0.75°C/decade, with higher rates in agriculture, savanna, settlements, woodlands, and riparian vegetation than in forest and mangrove vegetations. Precipitation also reduced at a mean rate of 58.69 mm/decade, with higher rates in agriculture savanna and riparian vegetation than in sahelian grasslands, mangrove, forest, and woodlands. NDVI was negatively correlated with temperature in savanna, settlements, degraded forest, and sahelian grasslands providing confirmation of ongoing land degradation. It was concluded that vegetation vigour will continue to decline under rainfall and increasing temperature conditions especially in dryer regions. The use of land surface temperature in this study is particularly valuable in highlighting areas where changes in NDVI occurred as a result of synergistic action between climate and human-induced landcover changes. Our findings underscore the importance of landuse policies that account for spatial variation in synergistic relationships between the nexus of climate and land conversion processes that influence vegetation cover change in different landcover types in tropical regions.

Investigating the spatially heterogeneous relationships between climate factors and NDVI in China during 1982 to 2013

Climate change is a major driver of vegetation activity, and thus their complex processes become a frontier and difficulty in global change research. To understand this relationship between climate change and vegetation activity, the spatial distribution and dynamic characteristics of the response of NDVI to climate change were investigated by the geographically weighted regression (GWR) model during 1982 to 2013 in China. This model was run based on the combined datasets of satellite vegetation index (NDVI) and climate observation (temperature and moisture) from meteorological stations nationwide. The results showed that the spatial non-stationary relationship between NDVI and surface temperature has appeared in China: the significant negative temperature-vegetation relationship was located in Northeast, Northwest and Southeast China, while the positive correlation was more concentrated from southwest to northeast. By comparing the normalized regression coefficients from GWR model for different climate factors, it presented the regions with moisture dominants for NDVI were in North China and the Tibetan Plateau, and the areas of temperature dominants were distributed in East, Central and Southwest China, where the annual mean maximum temperature accounted for the largest areas. In addition, regression coefficients from GWR model between NDVI dynamics and climate variability indicated that the higher warming rate could result in the weakened vegetation activity through some mechanisms such as enhanced drought, while the moisture variability could mediate the hydrothermal conditions for the variation of vegetation activity. When the increasing rate of photosynthesis exceeded that of respiration, the positive correlation between vegetation dynamics and climate variability was reflected. However, the continuous and dynamic process of vegetation activity response to climate change will be determined by spatially heterogeneous conditions in climate change and vegetation cover. Furthermore, the dynamic description of climate-induced vegetation activity from its rise to decline in different regions is expected to provide a scientific basis for initiating ecosystem-based adaptation strategies in response to global climate change.

Quantifying Grazing Intensity Using Remote Sensing in Alpine Meadows on Qinghai-Tibetan Plateau

Remote sensing data have been widely used in the study of large-scale vegetation activities, which have important significance in estimating grassland yields, determining grassland carrying capacity, and strengthening the scientific management of grasslands. Remote sensing data are also used for estimating grazing intensity. Unfortunately, the spatial distribution of grazing-induced degradation remains undocumented by field observation, and most previous studies on grazing intensity have been qualitative. In our study, we tried to quantify grazing intensity using remote sensing techniques. To achieve this goal, we conducted field experiments at Gansu Province, China, which included a meadow steppe and a semi-arid region. The correlation between a vegetation index and grazing intensity was simulated, and the results demonstrated that there was a significant negative correlation between NDVI and relative grazing intensity (p &lt; 0.05). The relative grazing intensity increased with a decrease in NDVI, and when the relative grazing intensity reached a certain level, the response of NDVI to relative grazing intensity was no longer sensitive. This study shows that the NDVI model can illustrate the feasibility of using a vegetation index to monitor the grazing intensity of livestock in free-grazing mode. Notably, it is feasible to use the remote sensing vegetation index to obtain the thresholds of livestock grazing intensity.

Space-Borne Sun-Induced Fluorescence:An Advanced Probe to Monitor Seasonality of Dry and Moist Tropical Forest Sites

Space-borne sun-induced fluorescence (SIF) is the latest breakthrough in remote sensing of physiological response of plants. We studied the seasonality of sal ( Shorea robusta ) forest canopies analysing space-borne SIF and reflectance data collected over moist and dry sites in central India. Results indicate that the monthly response of OCO-2 SIF, MODIS NDVI and GPP differs significantly across the wet and dry forest sites. SIF explained higher seasonal variations and was also better correlated to rainfall across sites compared to NDVI.

Analysis on spatio-temporal trends and drivers in vegetation growth during recent decades in Xinjiang, China

Vegetation plays an important role in regulating the terrestrial carbon balance and the climate system, and also overwhelmingly dominates the provisioning of ecosystem services. In this study, a non-stationary 1982–2012 AVHRR NDVI3g time series, the newest dataset, were used to evaluate spatio-temporal patterns of seasonal vegetation changes in Xinjiang province of China at regional, biome and pixel scales over progressively longer periods from 18 to 31 years, starting in 1982, and their linkages to climatic factors and human activities were analyzed. At regional scale, the increases were statistically significant for autumn NDVI during fourteen periods, for growing season and summer NDVI during the most periods, and for spring only during the first four periods. The rates of NDVI increase in growing season and all seasons significantly decreased over fourteen periods. At pixel scale, areas with significant browning rapidly increased over fourteen periods for growing season and all seasons, and these areas were mainly concentrated in northern desert of Xinjiang. Vegetation growth in Xinjiang was regulated by both moisture and thermal conditions: the response of NDVI in spring and autumn was more sensitive to thermal factors, such as temperature and potential evapotranspiration, and correlations between NDVI and precipitation and between NDVI and humidity index were stronger in summer and growing season. Extensive use of fertilizers and expanded farmland irrigated area increased vegetation growth for cropland. However, the rapid increase in the proportion of cotton cultivation and use of drip irrigation may reduce spring NDVI in the part of farmlands. Trend analysis during the multiple nested time series may contribute to a better and deep understanding of NDVI dynamic and foreseeing changes in the future. Accordingly, NDVI in Xinjiang will continuously increase at regional scale and the areas showing significant browning will also furthermore grow.

Productivity responses of desert vegetation to precipitation patterns across a rainfall gradient.

The influences of previous-year precipitation and episodic rainfall events on dryland plants and communities are poorly quantified in the temperate desert region of Northwest China. To evaluate the thresholds and lags in the response of aboveground net primary productivity (ANPP) to variability in rainfall pulses and seasonal precipitation along the precipitation-productivity gradient in three desert ecosystems with different precipitation regimes, we collected precipitation data from 2000 to 2012 in Shandan (SD), Linze (LZ) and Jiuquan (JQ) in northwestern China. Further, we extracted the corresponding MODIS Normalized Difference Vegetation Index (NDVI, a proxy for ANPP) datasets at 250 m spatial resolution. We then evaluated different desert ecosystems responses using statistical analysis, and a threshold-delay model (TDM). TDM is an integrative framework for analysis of plant growth, precipitation thresholds, and plant functional type strategies that capture the nonlinear nature of plant responses to rainfall pulses. Our results showed that: (1) the growing season NDVIINT (INT stands for time-integrated) was largely correlated with the warm season (spring/summer) at our mildly-arid desert ecosystem (SD). The arid ecosystem (LZ) exhibited a different response, and the growing season NDVIINT depended highly on the previous year's fall/winter precipitation and ANPP. At the extremely arid site (JQ), the variability of growing season NDVIINT was equally correlated with the cool- and warm-season precipitation; (2) some parameters of threshold-delay differed among the three sites: while the response of NDVI to rainfall pulses began at about 5 mm for all the sites, the maximum thresholds in SD, LZ, and JQ were about 55, 35 and 30 mm respectively, increasing with an increase in mean annual precipitation. By and large, more previous year's fall/winter precipitation, and large rainfall events, significantly enhanced the growth of desert vegetation, and desert ecosystems should be much more adaptive under likely future scenarios of increasing fall/winter precipitation and large rainfall events. These results highlight the inherent complexity in predicting how desert ecosystems will respond to future fluctuations in precipitation.