Greening Trend Research Articles

Sensitivity of Global Pasturelands to Climate Variation

AbstractPasturelands are globally extensive, sensitive to climate, and support livestock production systems that provide an essential source of food in many parts of the world. In this paper, we integrate information from remote sensing, global climate, and land use databases to improve understanding of the resilience and resistance of this ecologically vulnerable and societally critical land use. To characterize the effect of climate on pastureland productivity at global scale, we analyze the relationship between satellite‐derived enhanced vegetation index data from MODIS and gridded precipitation data from CHIRPS at 3‐ and 6‐month time lags. To account for the effects of different production systems, we stratify our analysis by agroecological zones and by rangeland versus mixed crop‐livestock systems. Results show that 14.5% of global pasturelands experienced statistically significant greening or browning trends over the 15‐year study period, with the majority of these locations showing greening. In arid ecosystems, precipitation and lagged vegetation index anomalies explain up to 69% of variation in vegetation productivity in both crop‐livestock and rangeland‐based production systems. Livestock production systems in Australia are least resistant to contemporaneous and short‐term precipitation anomalies, while arid livestock production systems in Latin America are least resilient to short‐term vegetation greenness anomalies. Because many arid regions of the world are projected to experience decreased total precipitation and increased precipitation variability in the coming decades, improved understanding regarding the sensitivity of pasturelands to the joint effects of climate change and livestock production systems is required to support sustainable land management in global pasturelands.

Arctic greening associated with lengthening growing seasons in Northern Alaska

Many studies have reported that the Arctic is greening; however, we lack an understanding of the detailed patterns and processes that are leading to this observed greening. The normalized difference vegetation index (NDVI) is used to quantify greening, which has had largely positive trends over the last few decades using low spatial resolution satellite imagery such as AVHRR or MODIS over the pan-Arctic region. However, substantial fine scale spatial heterogeneity in the Arctic makes this large-scale investigation hard to interpret in terms of vegetation and other environmental changes. Here we focus on one area of the northern Alaskan Arctic using high spatial resolution (4 m) multispectral satellite imagery from DigitalGlobe™ to analyze the greening trend near Utqiaġvik (formerly known as Barrow) over 14 years from 2002 to 2016. We found that tundra vegetation has been greening (τ = 0.65, p = 0.01, NDVI increase of 0.01 yr−1) despite no overall change in vegetation community composition. The greening is most closely correlated to the number of thawing degree days (R2 = 0.77, F = 21.5, p < 0.001) which increased in a similar linear trend over the 14 year study period (1.79 ± 0.50 days per year, p < 0.01, τ = −0.56). This suggests that in this Arctic ecosystem, greening is occurring due to a lengthening growing season that appears to stimulate plant productivity without any significant change in vegetation communities. We found that vegetation communities in wetter locations greened about twice as fast as those found in drier conditions supporting the hypothesis that these communities respond more strongly to warming. We suggest that in Arctic environments, vegetation productivity may continue to rise, particularly in wet areas.

Vegetation response to climatic variation and human activities on the Ordos Plateau from 2000 to 2016

Determination and analysis of the effects of climatic variation and human activities on vegetation changes since the implementation of intensive ecological projects in 2000 are critically important for the restoration of vegetation on the Ordos Plateau, one of the areas that suffer from the worst vegetation degradation, the most concentrated coal and gas exploitation, and the highest ecological risks in China. This study was performed to establish and validate a nonlinear regression model to express the relationships between the normalized difference vegetation index (NDVI), precipitation, and air temperature in areas with scarce human activities, as identified from the Global Human Influence Index Datasets. This model was then applied to the whole plateau with data on cumulative precipitation and average air temperature with the consideration of the delayed effects to simulate temporal NDVI changes induced by climatic variation. The residual trend was then computed as the slope of the difference between the actual NDVI and the simulated value to analyze the human impact on vegetation. The results show that the plateau’s vegetation had experienced a significant greening trend from 2000 to 2016 under a warmer and wetter climate and with the impact of human activities; compared to air temperature, precipitation played a leading role in vegetation greening in most parts of the plateau, and human activities had a significant positive impact in 22.0% of the plateau and a significant negative impact in 0.66% of the plateau (p < 0.1). More effective programs of ecological protection and restoration must still be conducted by the government and other organizations.

Using Continuous Change Detection and Classification of Landsat Data to Investigate Long-Term Mangrove Dynamics in the Sundarbans Region

Mangrove forests play a global role in providing ecosystem goods and services in addition to acting as carbon sinks, and are particularly vulnerable to climate change effects such as rising sea levels and increased salinity. For this reason, accurate long-term monitoring of mangrove ecosystems is vital. However, these ecosystems are extremely dynamic and data frequency is often reduced by cloud cover. The Continuous Change Detection and Classification (CCDC) method has the potential to overcome this by utilising every available observation on a per-pixel basis to build stable season-trend models of the underlying phenology. These models can then be used for land cover classification and to determine greening and browning trends. To demonstrate the utility of this approach, CCDC was applied to a 30-year time series of Landsat data covering an area of mangrove forest known as the Sundarbans. Spanning the delta formed by the confluence of the Ganges, Brahmaputra and Meghna river systems, the Sundarbans is the largest contiguous mangrove forest in the world. CCDC achieved an overall classification accuracy of 94.5% with a 99% confidence of being between 94.2% and 94.8%. Results showed that while mangrove extent in the Sundarbans has remained stable, around 25% of the area experienced an overall negative trend, probably due to the effect of die-back on Heritiera fomes. In addition, dates and magnitudes of change derived from CCDC were used to investigate damage and recovery from a major cyclone; 11% of the Sundarbans was found to have been affected by Cyclone Sidr in 2007, 47.6% of which had not recovered by mid-2018. The results indicate that while the Sundarbans forest is resilient to cyclone events, the long-term degrading effects of climate change could reduce this resilience to critical levels. The proposed methodology, while computationally expensive, also offers means by which the full Landsat archive can be analyzed and interpreted and should be considered for global application to mangrove monitoring.

Increasing interannual variability of global vegetation greenness

Despite the long-term greening trend in global vegetation identified in previous investigations, changes in the interannual variability (IAV) of vegetation greenness over time is still poorly understood. Using Global Inventory Modeling and Mapping Studies normalized difference vegetation index (NDVI) third generation data and corresponding meteorological data from 1982 to 2015, we studied the changes and drivers of the IAV of vegetation greenness as indicated by the coefficient of variation of vegetation greenness at a global scale. Dry and high-latitude areas exhibited high NDVI variability whereas humid areas exhibited relatively low NDVI variability. We detected an increase in the global IAV of vegetation greenness over time using a 15 year moving window. Spatially, we observed significant increases in the IAV of vegetation greenness in greater than 45% of vegetated areas globally and decreases in 21%. Our comparison of ecological models suggests good performance in terms of simulating spatial differences in vegetation variability, but relatively poor performance in terms of capturing changes in the IAV of vegetation greenness. Furthermore, the dominant climate variables controlling changes in the IAV of vegetation greenness were determined spatially using principal component regression and partial least squares regression. The two methods yielded similar patterns, revealing that temperature exerted the biggest influence on changes in the IAV of vegetation greenness, followed by solar radiation and precipitation. This study provides insights into global vegetation variability which should contribute to an understanding of vegetation dynamics in the context of climate change.

Spatiotemporal climate and vegetation greenness changes and their nexus\xa0for Dhidhessa River Basin, Ethiopia

BackgroundUnderstanding spatiotemporal climate and vegetation changes and their nexus is key for designing climate change adaptation strategies at a local scale. However, such a study is lacking in many basins of Ethiopia. The objectives of this study were (i) to analyze temperature, rainfall and vegetation greenness trends and (ii) determine the spatial relationship of climate variables and vegetation greenness, characterized using Normalized Difference in Vegetation Index (NDVI), for the Dhidhessa River Basin (DRB). Quality checked high spatial resolution satellite datasets were used for the study. Mann–Kendall test and Sen’s slope method were used for the trend analysis. The spatial relationship between climate change and NDVI was analyzed using geographically weighted regression (GWR) technique.ResultsAccording to the study, past and future climate trend analysis generally showed wetting and warming for the DRB where the degree of trends varies for the different time and spatial scales. A seasonal shift in rainfall was also observed for the basin. These findings informed that there will be a negative impact on rain-fed agriculture and water availability in the basin. Besides, NDVI trends analysis generally showed greening for most climatic zones for the annual and main rainy season timescales. However, no NDVI trends were observed in all timescales for cool sub-humid, tepid humid and warm humid climatic zones. The increasing NDVI trends could be attributed to agroforestry practices but do not necessarily indicate improved forest coverage for the basin. The change in NDVI was positively correlated to rainfall (r2 = 0.62) and negatively correlated to the minimum (r2 = 0.58) and maximum (r2 = 0.45) temperature. The study revealed a strong interaction between the climate variables and vegetation greenness for the basin that further influences the biophysical processes of the land surface like the hydrologic responses of a basin.ConclusionThe study concluded that the trend in climate and vegetation greenness varies spatiotemporally for the DRB. Besides, the climate change and its strong relationship with vegetation greenness observed in this study will further affect the biophysical and environmental processes in the study area; mostly negatively on agricultural and water resource sectors. Thus, this study provides helpful information to device climate change adaptation strategies at a local scale.

FITOGÓRNICTWO PIERWIASTKÓW ZIEM RZADKICH JAKO METODA ZRÓWNOWAŻONEGO GOSPODAROWANIA NIEODNAWIALNYMI ZASOBAMI ZIEMI

Komercyjne wydobycie rud metali związane jest zwykle z surowcami mineralnymi zawierającymi znaczne ilości tych pierwiastków i wymaga dużych nakładów finansowych. Tak bogate rudy występują rzadko, a wzrastający popyt na surowce mineralne skutkuje potrzebą znalezienia alternatywnych metod pozyskiwania pierwiastków z rud mniej wzbogaconych. Naprzeciw temu wyzwaniu wychodzą dziś nowoczesne metody biotechnologiczne. Część z endemicznych gatunków roślin jest zdolna do gromadzenia w swoich tkankach bardzo wysokich stężeń pierwiastków śladowych i są one znane pod nazwą ,,hiperakumulatorów". Proces przyswajania pierwiastków z gleby przez hiperakumulatory nazywany jest ,,fitoekstrakcją" i może być stosowany w fitogórnictwie, polegającym na uprawie roślin na terenach wzbogaconych w pierwiastki oraz zebraniu i spalaniu biomasy w celu uzyskania cennej ekonomicznie ,,biorudy". Fitogórnictwo jest zgodne ze współczesnymi trendami ekologicznymi i zasadami zrównoważonego rozwoju. Metoda ta jest stosowana od lat 80-tych ubiegłego stulecia, początkowo jedynie w celu pozyskiwania niklu. Obecnie używana jest w szerszym zakresie pierwiastków (m.in. Tl, Co, Ag, Au), w tym pierwiastków ziem rzadkich (ang. rare earth elements – REE). REE to grupa metali od La do Lu uznawana za kluczową dla rozwoju nowoczesnej gospodarki. W poniższym artykule zostaną zaprezentowane najważniejsze aspekty fitogórnictwa REE, jego opłacalność, wady i zalety oraz perspektywy rozwoju w niedalekiej przyszłości.

Climatic Drivers of Greening Trends in the Alps

Since the 1980s, vegetated lands have experienced widespread greening at the global scale. Numerous studies have focused on spatial patterns and mechanisms of this phenomenon, especially in the Arctic and sub-Arctic regions. Greening trends in the European Alps have received less attention, although this region has experienced strong climate and land-use changes during recent decades. We studied the rates and spatial patterns of greening in an inner-alpine region of the Western Alps. We used MODIS-derived normalized difference vegetation index (NDVI) at 8-day temporal and 250 m spatial resolution, for the period 2000–2018, and removed areas with disturbances in order to consider the trends of undisturbed vegetation. The objectives of this study were to (i) quantify trends of greening in a representative area of the Western Alps; and (ii) examine mechanisms and causes of spatial patterns of greening across different plant types. We show that 63% of vegetated areas experienced significant trends during the 2000–2018 period, of which only 8% were negative. We identify (i) a climatic control on spring and autumn phenology with contrasting effects depending on plant type and elevation, and (ii) land-use change dynamics, such as shrub encroachment on abandoned pastures and colonization of new surfaces at high elevation. Below 1500 m, warming temperatures promote incremental greening in the transition from spring to summer, but not in fall, suggesting either photoperiod or water limitation. In the alpine and sub-alpine belts (&gt;1800 m asl), snow prevents vegetation development until late spring, despite favorable temperatures. Instead, at high elevation greening acts both in summer and autumn. However, photoperiod limitation likely prevents forested ecosystems from fully exploiting warmer autumn conditions. We furthermore illustrate two emblematic cases of prominent greening: recent colonization of previously glaciated/non vegetated areas, as well as shrub/tree encroachment due to the abandonment of agricultural practices. Our results demonstrate the interplay of climate and land-use change in controlling greening dynamics in the Western Alps.

Causality of Biodiversity Loss: Climate, Vegetation, and Urbanization in China and America.

Essential for directing conservation resources is to identify threatened vertebrate regions and diagnose the underlying causalities. Through relating vertebrates and threatened vertebrates to the rainfall-runoff chain, to the food chain, and to the human impact of urbanization, the following relationships are noticed: (i) The Earth’s vertebrates generally show increasing abundance and decreasing threatened species indicator (threatened species number/species abundance) for a higher Normalized Difference Vegetation Index (NDVI) or larger city-size. (ii) Regional vertebrates reveal a notable ‘U-shape profile’ (‘step-like jump’) of threatened species indicator occurs in the moderate (high) NDVI regions in China (America). (iii) Positive/green city states emerge in China and are characterized by the lowest threatened species indicators in areas of low to moderate greenness, where the greenness trend of change during the last 30 years is about three times higher in the urbanized areas than over land. (iv) Negative/brown city states emerge in America revealing high threatened species indicators for greenness exceeding NDVI > 0.2, where similar greenness trends are of both urbanized and land areas. The occurrence of green and brown city states suggests a biodiversity change pattern characterized by the threatened species indicator declining from city regimes with high to those with low indicator values for increasing ratio of the city-over-land NDVI trends.

Grease the Green Wheels: A Framework for Expediting the Green Building Movement in the Arab World

It has been over three decades since the term “sustainable development” was coined in Brundtland’s report in 1987, and 28 years have passed since the world’s first sustainability assessment method for buildings was founded by the Building Research Establishment in UK in 1990. During these three decades, many sustainability standards, codes, and rating systems were created and used to help in designing, constructing, maintaining, rating, and labeling buildings with attaining the principles of sustainability. Yet by looking at the Arab world at the beginning of 2019, one can argue that, although the Arab countries have dedicated the effort and budget to save energy, water, and natural resources, the region as a whole is still struggling to shift the paradigm of the building industry from conventional to sustainable. This struggle raises some questions; are there any challenges that Arab countries must overcome to leap forward to a prosperous sustainable building design and construction practices? Why are existing green building rating systems such as Estidama in United Arab Emirates, global sustainability assessment system (GSAS) in Qatar, and ARZ in Lebanon lagging behind the trends of green building rating systems in the developed countries? What are the coordinated steps needed to expedite this movement across the region? The current study explores the limits and potentials of the green building industry in the Arab world through analysis of the green building initiatives, academic scholarship activities in architecture and engineering sectors, and feedback from green building professionals across the Arab world. This article introduces a theoretical framework to expedite the green building movement in the Arab region; the framework is shaped by the environmental, social, and economic factors that are crucial to the transformation of the building industry from conventional to sustainable. The study seeks to support a line of research that could help governments in the Arab world catch up with the global green building trends.

Assessing Spatiotemporal Variations of Landsat Land Surface Temperature and Multispectral Indices in the Arctic Mackenzie Delta Region between 1985 and 2018

Air temperatures in the Arctic have increased substantially over the last decades, which has extensively altered the properties of the land surface. Capturing the state and dynamics of Land Surface Temperatures (LSTs) at high spatial detail is of high interest as LST is dependent on a variety of surficial properties and characterizes the land–atmosphere exchange of energy. Accordingly, this study analyses the influence of different physical surface properties on the long-term mean of the summer LST in the Arctic Mackenzie Delta Region (MDR) using Landsat 30 m-resolution imagery between 1985 and 2018 by taking advantage of the cloud computing capabilities of the Google Earth Engine. Multispectral indices, including the Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI) and Tasseled Cap greenness (TCG), brightness (TCB), and wetness (TCW) as well as topographic features derived from the TanDEM-X digital elevation model are used in correlation and multiple linear regression analyses to reveal their influence on the LST. Furthermore, surface alteration trends of the LST, NDVI, and NDWI are revealed using the Theil-Sen (T-S) regression method. The results indicate that the mean summer LST appears to be mostly influenced by the topographic exposition as well as the prevalent moisture regime where higher evapotranspiration rates increase the latent heat flux and cause a cooling of the surface, as the variance is best explained by the TCW and northness of the terrain. However, fairly diverse model outcomes for different regions of the MDR (R2 from 0.31 to 0.74 and RMSE from 0.51 °C to 1.73 °C) highlight the heterogeneity of the landscape in terms of influential factors and suggests accounting for a broad spectrum of different factors when modeling mean LSTs. The T-S analysis revealed large-scale wetting and greening trends with a mean decadal increase of the NDVI/NDWI of approximately +0.03 between 1985 and 2018, which was mostly accompanied by a cooling of the land surface given the inverse relationship between mean LSTs and vegetation and moisture conditions. Disturbance through wildfires intensifies the surface alterations locally and lead to significantly cooler LSTs in the long-term compared to the undisturbed surroundings.

Vegetation change in response to climate factors and human activities on the Mongolian Plateau.

BackgroundVegetation in the Mongolian Plateau is very sensitive to climate change, which has a significant impact on the regulation of terrestrial carbon cycle.MethodsWe analyzed spatio-temporal changes of both growing season and the seasonal Normalized Difference Vegetation Index (NDVI) using simple linear trend analysis. Besides, correlation analysis was applied to explore the climate factors’ effects on vegetation growth at temporal and spatial scale. Potential effects of human factors on vegetation growth were also explored by residual trend analysis.ResultsThe results indicated that vegetation growth showed a greening trend in the Mongolian Plateau over the past 30 years. At the temporal scale, the growing season NDVI showed an insignificant increasing trend (at a rate of 0.0003 yr−1). At the spatial scale, a large region (53.8% of the whole Mongolian Plateau) with an increasing growing season NDVI, was primarily located in the southern and northern parts of the plateau. The correlation analysis suggested that temperature and precipitation were the main limiting factors that affected vegetation growth in spring and the growing season, respectively. The residual trend analysis showed that human activities primarily stimulated the growth of grasslands and shrublands, while croplands displayed a decreasing trend due to human disturbances, implying that anthropogenic factors may lead to croplands abandonment in favor of grasslands restoration. Our results provided detailed spatial and temporal changes of vegetation growth, and explored how climate and human factors affected vegetation growth, which may offer baseline data and scientific suggestions for local land and resources management, and facilitate the sustainable development of the terrestrial ecosystems.

Green Marketing: Recent Trends and Challenges in India

Green marketing is the marketing of products that are presumed to be eco-friendly. Green marketing means re-marketing of products, which are adhere to certain environmental guidelines. Green marketing also known as sustainable marketing is an effort made by any business organization to develop or promote the products which are environmentally safe. Green advertising is a wonder which has created specific significance in the present scenario and has risen in India as well as in different parts of the world and is seen as an essential procedure of encouraging practical advancement. The main aim of this paper is to find out the challenges of green marketing and latest trends of green marketing.

The impact of eco-label to consumer attitude and green purchase intention in agricultural products: A theoretical approach

Environmental pollution and green consumption trends are changing the intention of consumers' choices and purchases. Ecological labels form the role of stimulating acts of purchasing environmentally friendly products and transmitting green product messages. Based on synthetic of intentional behavioral models and theory, the paper proposes an analytical framework using four elements (ecological label knowledge, belief, value perception, environmental concern) to analyze the effect of eco-labels on consumer attitudes and green purchase intention of agricultural products.

An Improved Conceptual Model Quantifying the Effect of Climate Change and Anthropogenic Activities on Vegetation Change in Arid Regions

Vegetation shows a greening trend on the global scale in the past decades, which has an important effect on the hydrological cycle, and thus quantitative interpretation of the causes for vegetation change is of great benefit to understanding changes in ecology, climate, and hydrology. Although the Donohue13 model, a simple conceptual model based on gas exchange theory, provides an effective tool to interpret the greening trend, it cannot be used to evaluate the impact from land use and land cover change (LULCC) on the regional scale, whose importance to vegetation change has been demonstrated in a large number of studies. Hence, we have improved the Donohue13 model by taking into account the change in vegetation cover ratio due to LULCC, and applied this model to the Yarkand Oasis in the arid region of northwest China. The estimated change trend in leaf area index (LAI) is 1.20%/year from 2001 to 2017, which accounts for approximately half of the observed (2.31%/year) by the moderate resolution imaging spectroradiometer (MODIS). Regarding the causes for vegetation greening, the contributions of: (1) LULCC; (2) atmospheric CO2 concentration; and (3) vapor pressure deficit were: (1) 88.3%; (2) 40.0%; and (3) −28.3%, respectively, which reveals that the largest contribution was from LULCC, which is probably driven by increased total water availability in whole oasis with a constant transpiration in vegetation area. The improved Donohue13 model, a simple but physics-based model, can partially explain the impact of factors related to climate change and anthropogenic activity on vegetation change in arid regions. It can be further combined with the Budyko hypothesis to establish a framework for quantifying the changes in coupled response of vegetation and hydrological processes to environment changes.

Time Series of Landsat Imagery Shows Vegetation Recovery in Two Fragile Karst Watersheds in Southwest China from 1988 to 2016

Since the implementation of China’s afforestation and conservation projects during recent decades, an increasing number of studies have reported greening trends in the karst regions of southwest China using coarse-resolution satellite imagery, but small-scale changes in the heterogenous landscapes remain largely unknown. Focusing on two typical karst regions in the Nandong and Xiaojiang watersheds in Yunnan province, we processed 2,497 Landsat scenes from 1988 to 2016 using the Google Earth Engine cloud platform and analyzed vegetation trends and associated drivers. We found that both watersheds experienced significant increasing trends in annual fractional vegetation cover, at a rate of 0.0027 year−1 and 0.0020 year−1, respectively. Notably, the greening trends have been intensifying during the conservation period (2001–2016) even under unfavorable climate conditions. Human-induced ecological engineering was the primary factor for the increased greenness. Moreover, vegetation change responded differently to variations in topographic gradients and lithological types. Relatively more vegetation recovery was found in regions with moderate slopes and elevation, and pure limestone, limestone and dolomite interbedded layer as well as impure carbonate rocks than non-karst rocks. Partial correlation analysis of vegetation trends and temperature and precipitation trends suggested that climate change played a minor role in vegetation recovery. Our findings contribute to an improved understanding of the mechanisms behind vegetation changes in karst areas and may provide scientific supports for local afforestation and conservation policies.

Slower vegetation greening faced faster social development on the landscape of the Belt and Road region

The thinking of landscape sustainability discussed the win-win goal of landscape management for both ecological conservation and social development. The China-proposed Belt and Road Initiative has a pathway of green development towards sustainability, which could be benefited from a corporation on transnational landscape management. Although previous remote sensing observations showed that the ecosystem improvement and social development can coexist in most of the Belt and Road region, whether the change tendencies can be sustained have been neglected. Based on the continuous vegetation index, nighttime light and landcover datasets between 1980s–2010s, a detailed observation of the landscape evolution was obtained in this study. Depending on the identifications of temporal breakpoints, the time series was separated to identify the former and latter tendencies, and the evolution score were evaluated. The results showed that the vegetation greening trend weakened in nearly half of the region, and the arid region mostly experienced vegetation browning lately. The evolution patterns of ecological landscape and social landscape were coincided distributed in China and India, and in Southeast Asia was not scored high for both ecological conservation and social development. Consequently, we should not be overly optimistic about the synergy between ecosystem improvement and social development.

Evaluation of the MODIS collections 5 and 6 for change analysis of vegetation and land surface temperature dynamics in North and South America

The latest collection (C6) of MODIS data provides several algorithmic improvements and calibration adjustments that correct for sensor degradation, theoretically making the C6 MODIS products more accurate compared to previous collections. C6 adjustments also introduce several improvements in the vegetation index (VI) retrieval algorithms. With these improvements, we expect only minor differences between data from Terra and Aqua, but significantly different results between C5 and C6. In this paper, we investigate three different MODIS products to determine the extent that improvements made to C6 influence the overall trend results for time series between 2001 and 2017. We focus on these three products specifically, both to allow for a comparison of vegetation index products—NDVI and EVI from MOD13C1, and NDVI and EVI calculated based on surface reflectance from MCD43C4—and also to gain an understanding of the improvements on an entirely different product from the same sensor, namely Land Surface Temperature (LST) from MOD11C2. For the MCD43C4 dataset, we find that 17.9% and 16.4% of EVI and NDVI pixels, respectively, display trend discordance between C5 and C6. For the MOD13C1 vegetation indices, we found comparable rates of trend discordance between C5 and C6: 18.5% and 17.4% for the EVI and NDVI pixels, respectively. For both products the greatest changes between C5 and C6 are an overall increase in pixels exhibiting a significant greening trend and an overall decline in pixels exhibiting a significant browning trend. Moreover, the largest differences between C5 and C6 for the NDVI and EVI data appear in cropland areas and in regions with relatively little human influence. In the Land Surface Temperature product (MOD11C2), the discordance between C5 and C6 is much lower: only 3.2% of day and 5.0% of night LST trends exhibited discordance between C5 and C6. We analyze the complementary results of vegetation index and land surface temperature trends and demonstrate that combining the results from different products observed at different portions of the electromagnetic spectrum—but linked through the biogeophysical processes of surface energy balance—allows us to portray change with more confidence than when relying on vegetation index data alone.

Detecting Vegetation Variations and Main Drivers over the Agropastoral Ecotone of Northern China through the Ensemble Empirical Mode Decomposition Method

Vegetation is the major component of the terrestrial ecosystem. Understanding both climate change and anthropogenically induced vegetation variation is essential for ecosystem management. In this study, we used an ensemble empirical mode decomposition (EEMD) method and a linear regression model to investigate spatiotemporal variations in the normalized difference vegetation index (NDVI) over the agropastoral ecotone of northern China (APENC) during the 1982–2015 period. A quantitative approach was proposed based on the residual trend (RESTREND) method to distinguish the effects of climatic (i.e., temperature (TEM), precipitation (PRE), total downward solar radiation (RAD), and near surface wind speed (SWS)) and anthropogenic effects on vegetation variations. The results showed that the NDVI exhibited a significant greening trend of 0.002 year−1 over the entire study period of 1982–2015 and that areas with monotonous greening dominated the entire APENC, occupying 40.97% of the region. A browning trend was also found in the central and northern parts of the APENC. PRE presented the highest spatial correlation with the NDVI and climate factors, suggesting that PRE was the most important factor affecting NDVI changes in the study area. In addition, the RESTREND results indicated that anthropogenic contributions dominated the vegetation variations in the APENC. Therefore, reusing farmland for grass and tree planting made a positive contribution to vegetation restoration, while deforestation, overgrazing, and the reclamation of grasslands were the opposite. In addition, with the continuous implementation of national ecological engineering programs such as the Grain to Green Program, positive human activity contributions to vegetation greening significantly increased. These results will support decision- and policy-making in the assessment and rehabilitation of ecosystems in the study region.

No Proportional Increase of Terrestrial Gross Carbon Sequestration From the Greening Earth

AbstractTerrestrial vegetation, as the key component of the biosphere, has a greening trend since the beginning of this century. However, how this substantial greening translated to global gross carbon sequestration or gross primary production (GPP) is not clear. Here we investigated terrestrial GPP dynamics and the respective contributions of climate change and vegetation cover change (VCC) from 2000 to 2015. We adopted a remote sensing based data‐driven model, which was calibrated based on the global eddy flux data set (FLUXNET2015) and Moderate Resolution Imaging Spectroradiometer vegetation index data (Collection 6). A series of simulation experiments were conducted to disaggregate the effects of climate and VCC. We found a much weaker increase in global GPP (0.08%/year; P = 0.07) when compared with the global greening rate (0.23%/year; P &lt; 0.001). The positive effect of VCC on GPP was reduced by 53% due to climate stress. Enhanced global GPP were largely contributed by nonforests, especially croplands. However, tropical forests, once a major driver of the global GPP increase, negatively contributed to global GPP trend due to warming‐induced moisture stress and deforestation. Given the limited potential of cropland carbon storage due to harvest and consumption, the contrasting GPP changes (i.e., cropland GPP increase vs. forest GPP reduction) may have shifted the distribution of the land carbon sink. Our study highlights the potential vulnerability of terrestrial gross carbon sequestration under climate and land use changes and has important implications in the global carbon cycle and climate warming mitigation.