Climate Drivers Linked to Changing Seasonality of Alaska Coastal Tundra Vegetation Productivity

Abstract The mechanisms driving trends and variability of the normalized difference vegetation index (NDVI) for tundra in Alaska along the Beaufort, east Chukchi, and east Bering Seas for 1982–2013 are evaluated in the context of remote sensing, reanalysis, and meteorological station data as well as regional modeling. Over the entire season the tundra vegetation continues to green; however, biweekly NDVI has declined during the early part of the growing season in all of the Alaskan tundra domains. These springtime declines coincide with increased snow depth in spring documented in northern Alaska. The tundra region generally has warmed over the summer but intraseasonal analysis shows a decline in midsummer land surface temperatures. The midsummer cooling is consistent with recent large-scale circulation changes characterized by lower sea level pressures, which favor increased cloud cover. In northern Alaska, the sea-breeze circulation is strengthened with an increase in atmospheric moisture/cloudiness inland when the land surface is warmed in a regional model, suggesting the potential for increased vegetation to feedback onto the atmospheric circulation that could reduce midsummer temperatures. This study shows that both large- and local-scale climate drivers likely play a role in the observed seasonality of NDVI trends.

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NDVI, biomass, and landscape evolution of glaciated terrain in northern Alaska

Polar Record ◽

10.1017/s003224740001367x ◽

1995 ◽

Vol 31 (177) ◽

pp. 169-178 ◽

Cited By ~ 62

Author(s):

D.A. Walker ◽

N.A. Auerbach ◽

M.M. Shippert

Keyword(s):

Large Scale ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Ground Level ◽

Digital Data ◽

Middle Pleistocene ◽

Tussock Tundra ◽

Vegetation Maps ◽

Circumpolar Arctic ◽

Northern Alaska

AbstractThe patterns of the normalized difference vegetation index (NDVI) on three glacial surfaces of different ages in the vicinity of Toolik Lake, Alaska, were examined. NDVI was derived from SPOT multispectral digital data, and the images were stratified according to boundaries on glacial geology and vegetation maps. Ground-level measurements of NDVI from common vegetation types were also collected, using a portable spectrometer. Late Pleistocene glacial surfaces have lower image-NDVI than older Middle Pleistocene surfaces, and the mean NDVI is correlated with approximate time since deglaciation. The trends are related to differences in NDVI associated with vegetation growing on mineral vs peaty substrates. Nonacidic mineral substrates are more common on the younger landscapes, and acidic peaty soils are more common on the older surfaces. The field-NDVIs of acidic dry, moist, and wet tundra are consistently higher than those of corresponding nonacidic tundra types. These same trends are seen when the SPOT NDVI image is stratified according to vegetation boundaries appearing on two detailed vegetation maps in the region. Above-ground biomass of moist and wet acidic tundra is significantly greater than corresponding nonacidic types. Vegetation species composition was examined along two transects on the oldest and youngest glacial surfaces. Shrub cover is the most important factor affecting the spectral signatures and biomass. Older surfaces have greater cover of shrub-rich tussock tundra and shrub-filled water tracks, and the younger surfaces have more dry, well-drained sites with low biomass and relatively barren nonsorted circles and stripes. These trends are related to paludification and modification of the terrain by geomorphic and geochemical processes. Similar patterns of spectral reflectance have been noted in association with a variety of large-scale natural disturbances in northern Alaska. However, extrapolation of these results to much broader regions of the circumpolar Arctic will require the use of sensors covering larger areas, such as the AVHRR aboard the NOAA satellites.

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Do natural landscapes contribute to reducing Land Surface Temperature (LST)? A case study from Muthurajawela wetland, Sri Lanka

Journal of Degraded and Mining Lands Management ◽

10.15243/jdmlm.2022.092.3329 ◽

2022 ◽

Vol 9 (2) ◽

pp. 3329-3339

Author(s):

Harsha Dahanayake ◽

Deepthi Wickramasinghe ◽

DDGL Dahanayaka

Keyword(s):

Water Body ◽

Land Surface ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Atmospheric Temperature ◽

Air Temperatures ◽

Heating Effects ◽

Natural Land ◽

Land Surface Temperatures ◽

Cooling Effects

Microclimate regulation is one of the most significant ecosystem services provided by wetlands. The present study attempted to investigate the cooling effect provided by Muthurajawela, a coastal Ramsar wetland using Remote Sensing and GIS. The variation of Land Surface Temperatures (LST) over different land use categories of natural (water bodies, marsh, thick vegetation, grassland) and anthropogenic (built-up areas, coconut cultivations and bare lands) areas in 2015 and 2020. Parameters including Satellite Brightness Temperature, Normalized Difference Vegetation Index, Proportion of Vegetation and Land Surface Emissivity were calculated along eight transects starting from the center of the water body and extending up to 5 km from the boundary of the wetland. The results revealed that LST over areas under natural land cover (2015 - mean 25.040C, 2020 - mean 23.360C) were significantly lower than that of areas under anthropogenic influence (2015 - mean 26.520C and 2020 - mean 26.220C). The lowest increase of LST was over the water body and the highest was over the built-up areas indicating the buffering capacity of wetlands. As air temperatures are highly linked to LST, our findings suggest that wetlands contribute to lower atmospheric temperature and offer cooling effects during dry months. Acknowledging the importance of wetlands in reducing temperature, at least in a local scale, justifies the need of conserving these ecosystems, as seeking mitigatory measures for climate change driven frequent heating effects is challenging.

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Algorithms and Applications in Grass Growth Monitoring

Abstract and Applied Analysis ◽

10.1155/2013/508315 ◽

2013 ◽

Vol 2013 ◽

pp. 1-7 ◽

Cited By ~ 4

Author(s):

Jun Liu ◽

Xi Yang ◽

Hao Long Liu ◽

Zhi Qiao

Keyword(s):

Land Surface ◽

Large Scale ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Logistic Function ◽

Growth Monitoring ◽

Function Fitting ◽

Fitting Algorithm ◽

Moderate Resolution ◽

Grass Growth

Monitoring vegetation phonology using satellite data has been an area of growing research interest in recent decades. Validation is an essential issue in land surface phenology study at large scale. In this paper, double logistic function-fitting algorithm was used to retrieve phenophases for grassland in North China from a consistently processed Moderate Resolution Spectrodiometer (MODIS) dataset. Then, the accuracy of the satellite-based estimates was assessed using field phenology observations. Results show that the method is valid to identify vegetation phenology with good success. The phenophases derived from satellite and observed on ground are generally similar. Greenup onset dates identified by Normalized Difference Vegetation Index (NDVI) and in situ observed dates showed general agreement. There is an excellent agreement between the dates of maturity onset determined by MODIS and the field observations. The satellite-derived length of vegetation growing season is generally consistent with the surface observation.

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GPP and NEE estimation for global forests based on a deep convolutional neural network

10.5194/egusphere-egu2020-2135 ◽

2020 ◽

Author(s):

Wenjin Wu

Keyword(s):

Neural Network ◽

Surface Temperature ◽

Land Surface Temperature ◽

Land Surface ◽

Large Scale ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Forest Type ◽

Google Earth ◽

New Model

To generate FluxNet-consistent annual forest GPP and NEE, we have developed a deep neural network that can retrieve estimations globally. Seven parameters considering different aspects of forest ecological and climatic features which include the Normalized Difference Vegetation Index (NDVI), the Enhanced Vegetation Index (EVI), Evapotranspiration (ET), Land Surface Temperature during Daytime (LSTD), Land Surface Temperature at Night (LSTN), precipitation, and forest type were selected as the input. All these datasets can be acquired from the Google earth engine platform to ensure rapid large-scale analysis. The model has three favorable traits: (1) Based on a multidimensional convolutional block, this model arranges all temporal variables into a two-dimensional feature map to consider phenology and inter-parameter relationships. The model can thus obtain the estimation with encoded meaningful patterns instead of raw input variables. (2) In contrast to filling data gaps with historical values or smoothing methods, the new model is developed and trained to catch signals with certain levels of occlusions; therefore, it can tolerate a relativly large portion of missing data. (3) The model is data-driven and interpretable. Therefore, it can potentially discover unknown mechanisms of forest carbon absorption by showing us how these mechanisms work to make correct estimations. The model was compared to three traditional machine learning models and presented superior performances. With this new model, global forest GPP and NEE in 2003 and 2018 were obtained. Variations of the carbon flux during the 16 years in between were analyzed.

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Modified Shuttleworth-Wallace model for monitoring evapotranspiration over complex surface: Relationship between the surface resistances and remotely sensed stress indexes

10.5194/egusphere-egu2020-8614 ◽

2020 ◽

Author(s):

Jamal Elfarkh ◽

Salah Er-Raki ◽

Jamal Ezzahar ◽

Abdelghani Chehbouni ◽

Bouchra Aithssaine ◽

...

Keyword(s):

Land Surface ◽

Large Scale ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Complex Surface ◽

Balance Model ◽

Large Area ◽

Exponential Relationship ◽

New Approach ◽

Input Variables

The main goal of this work was to evaluate the potential of the Shuttleworth-Wallace (SW) model for mapping actual crop evapotranspiration (ET) over complex terrain located within the foothill of the Atlas Mountain (Morocco). This model needs many input variables to compute soil (rss) and vegetation (rsv) resistances, which are often difficult to estimate at large scale particularly soil moisture. In this study, a new approach to spatialize rss and rsv based on two thermal-based proxy variables is proposed. Land Surface Temperature (LST) and Normalized Difference Vegetation Index (NDVI) derived from LANDSAT data were combined with the endmember temperatures&#160; for soil (Tsmin and Tsmax) and vegetation (Tvmin and Tvmax), which are simulated by a surface energy balance model, to compute the temperature of the two components, namely the soil (Ts) and the vegetation (Tv). Based on these temperatures, two thermal proxies (SIss for soil and SIsv for vegetation) were calculated and related to rss and rsv, with an empirical exponential relationship (with a correlation coefficient (R) of about 0,6 and 0,5 for soil and vegetation, respectively). The proposed approach was firstly evaluated at a local scale, by comparing the results to observations by an eddy covariance system installed over an area planted with olive trees intercropped with wheat. In a second step, the new approach was applied over a large area which contains a mixed vegetation (tall and short vegetation) crossed by a river to derive rss and rsv, and thereafter to estimate ET. A Large aperture scintillometer (LAS) installed over a transect of 1.4 km and spanning the total area is used to validate the obtained ET. The comparison confirms the ability of the proposed approach to provide satisfactory ET maps with an RMSE and R2 equal to 52.51 W/m2 and 0.80, respectively.

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A Fused Variable by Night Light Images and MODIS Products for Improving Urban Built-Up Area Extraction

Technologies ◽

10.3390/technologies9020040 ◽

2021 ◽

Vol 9 (2) ◽

pp. 40

Author(s):

Guang Yang ◽

Yuntao Ma ◽

Jiaqi Hu

Keyword(s):

Surface Temperature ◽

Land Surface ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Extraction Methods ◽

Average Error ◽

Geographic Coordinate System ◽

Optimal Thresholds ◽

Moderate Resolution Imaging Spectroradiometer ◽

Spatial Locations

The boundary of urban built-up areas is the baseline data of a city. Rapid and accurate monitoring of urban built-up areas is the prerequisite for the boundary control and the layout of urban spaces. In recent years, the night light satellite sensors have been employed in urban built-up area extraction. However, the existing extraction methods have not fully considered the properties that directly reflect the urban built-up areas, like the land surface temperature. This research first converted multi-source data into a uniform projection, geographic coordinate system and resampling size. Then, a fused variable that integrated the Defense Meteorological Satellite Program/Operational Linescan System (DMSP/OLS) night light images, the Moderate-resolution Imaging Spectroradiometer (MODIS) surface temperature product and the normalized difference vegetation index (NDVI) product was designed to extract the built-up areas. The fusion results showed that the values of the proposed index presented a sharper gradient within a smaller spatial range, compared with the only night light images. The extraction results were tested in both the area sizes and the spatial locations. The proposed index performed better in both accuracies (average error rate 1.10%) and visual perspective. We further discussed the regularity of the optimal thresholds in the final boundary determination. The optimal thresholds of the proposed index were more stable in different cases on the premise of higher accuracies.

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Start of the Green Season and Normalized Difference Vegetation Index in Alaska’s Arctic National Parks

Remote Sensing ◽

10.3390/rs13132554 ◽

2021 ◽

Vol 13 (13) ◽

pp. 2554

Author(s):

David K. Swanson

Keyword(s):

National Parks ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Free Surfaces ◽

Modis Ndvi ◽

Curve Fit ◽

Winter Snow ◽

Systematic Sample ◽

Low Levels ◽

Northern Alaska

Daily Normalized Difference Vegetation Index (NDVI) values from the MODIS Aqua and Terra satellites were compared with on-the-ground camera observations at five locations in northern Alaska. Over half of the spring rise in NDVI was due to the transition from the snow-covered landscape to the snow-free surface prior to the deciduous leaf-out. In the fall after the green season, NDVI fluctuated between an intermediate level representing senesced vegetation and lower values representing clouds and intermittent snow, and then dropped to constant low levels after establishment of the permanent winter snow cover. The NDVI value of snow-free surfaces after fall leaf senescence was estimated from multi-year data using a 90th percentile smoothing spline curve fit to a plot of daily NDVI values vs. ordinal date. This curve typically showed a flat region of intermediate NDVI values in the fall that represent cloud- and snow-free days with senesced vegetation. This “fall plateau” was readily identified in a large systematic sample of MODIS NDVI values across the study area, in typical tundra, shrub, and boreal forest environments. The NDVI level of the fall plateau can be extrapolated to the spring rising leg of the annual NDVI curve to approximate the true start of green season.

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Contributions of Vegetation Greening and Climate Change to Evapotranspiration Trend after Large-Scale Vegetation Restoration on the Loess Plateau, China

Water ◽

10.3390/w13131755 ◽

2021 ◽

Vol 13 (13) ◽

pp. 1755

Author(s):

Shuo Wang ◽

Chenfeng Cui ◽

Qin Dai

Keyword(s):

Climate Change ◽

Loess Plateau ◽

Large Scale ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Potential Evapotranspiration ◽

Development Trend ◽

Future Trend ◽

Natural Ecosystem ◽

The Loess Plateau

Since the early 2000s, the vegetation cover of the Loess Plateau (LP) has increased significantly, which has been fully recorded. However, the effects on relevant eco-hydrological processes are still unclear. Here, we made an investigation on the changes of actual evapotranspiration (ETa) during 2000–2018 and connected them with vegetation greening and climate change in the LP, based on the remote sensing data with correlation and attribution analysis. Results identified that the average annual ETa on the LP exhibited an obvious increasing trend with the value of 9.11 mm yr−1, and the annual ETa trend was dominated by the changes of ETa in the third quarter (July, August, and September). The future trend of ETa was predicted by the Hurst exponent. Partial correlation analysis indicated that annual ETa variations in 87.8% regions of the LP were controlled by vegetation greening. Multiple regression analysis suggested that the relative contributions of potential evapotranspiration (ETp), precipitation, and normalized difference vegetation index (NDVI), to the trend of ETa were 5.7%, −26.3%, and 61.4%, separately. Vegetation greening has a close relationship with the Grain for Green (GFG) project and acts as an essential driver for the long-term development trend of water consumption on the LP. In this research, the potential conflicts of water demanding between the natural ecosystem and social-economic system in the LP were highlighted, which were caused by the fast vegetation expansion.

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Modeling the Radial Stem Growth of the Pine (Pinus sylvestris L.) Forests Using the Satellite-Derived NDVI and LST (MODIS/AQUA) Data

Atmosphere ◽

10.3390/atmos12010012 ◽

2020 ◽

Vol 12 (1) ◽

pp. 12

Author(s):

Yulia Ivanova ◽

Anton Kovalev ◽

Vlad Soukhovolsky

Keyword(s):

Land Surface ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Principal Component ◽

Stem Growth ◽

Forest Growth ◽

Parabolic Approximation ◽

New Approach ◽

The Relationship ◽

Tree Increment

The paper considers a new approach to modeling the relationship between the increase in woody phytomass in the pine forest and satellite-derived Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST) (MODIS/AQUA) data. The developed model combines the phenological and forest growth processes. For the analysis, NDVI and LST (MODIS) satellite data were used together with the measurements of tree-ring widths (TRW). NDVI data contain features of each growing season. The models include parameters of parabolic approximation of NDVI and LST time series transformed using principal component analysis. The study shows that the current rate of TRW is determined by the total values of principal components of the satellite indices over the season and the rate of tree increment in the preceding year.

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A Simple Method for Converting 1-km Resolution Daily Clear-Sky LST into Real LST

Remote Sensing ◽

10.3390/rs12101641 ◽

2020 ◽

Vol 12 (10) ◽

pp. 1641

Author(s):

Yunfei Zhang ◽

Yunhao Chen ◽

Jing Li ◽

Xi Chen

Keyword(s):

Land Surface ◽

Missing Values ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Shortwave Radiation ◽

Simple Method ◽

The Real ◽

Independent Variables ◽

Clear Sky ◽

Reconstruction Methods

Land-surface temperature (LST) plays a key role in the physical processes of surface energy and water balance from local through global scales. The widely used one kilometre resolution daily Moderate Resolution Imaging Spectroradiometer (MODIS) LST product has missing values due to the influence of clouds. Therefore, a large number of clear-sky LST reconstruction methods have been developed to obtain spatially continuous LST datasets. However, the clear-sky LST is a theoretical value that is often an overestimate of the real value. In fact, the real LST (also known as cloudy-sky LST) is more necessary and more widely used. The existing cloudy-sky LST algorithms are usually somewhat complicated, and the accuracy needs to be improved. It is necessary to convert the clear-sky LST obtained by the currently better-developed methods into cloudy-sky LST. We took the clear-sky LST, cloud-cover duration, downward shortwave radiation, albedo and normalized difference vegetation index (NDVI) as five independent variables and the real LST at the ground stations as the dependent variable to perform multiple linear regression. The mean absolute error (MAE) of the cloudy-sky LST retrieved by this method ranged from 3.5–3.9 K. We further analyzed different cases of the method, and the results suggested that this method has good flexibility. When we chose fewer independent variables, different clear-sky algorithms, or different regression tools, we also achieved good results. In addition, the method calculation process was relatively simple and can be applied to other research areas. This study preliminarily explored the influencing factors of the real LST and can provide a possible option for researchers who want to obtain cloudy-sky LST through clear-sky LST, that is, a convenient conversion method. This article lays the foundation for subsequent research in various fields that require real LST.

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