Drone data reveal heterogeneity in tundra greenness and phenology not captured by satellites

Data across scales are required to monitor ecosystem responses to rapid warming in the Arctic and to interpret tundra greening trends. Here, we tested the correspondence among satellite- and drone-derived seasonal change in tundra greenness to identify optimal spatial scales for vegetation monitoring on Qikiqtaruk - Herschel Island in the Yukon Territory, Canada. We combined time-series of the Normalised Difference Vegetation Index (NDVI) from multispectral drone imagery and satellite data (Sentinel-2, Landsat 8 and MODIS) with ground-based observations for two growing seasons (2016 and 2017). We found high cross-season correspondence in plot mean greenness (drone-satellite Spearman’s ⍴ 0.67-0.87) and pixel-by-pixel greenness (drone-satellite R2 0.58-0.69) for eight one-hectare plots, with drones capturing lower NDVI values relative to the satellites. We identified a plateau in the spatial variation of tundra greenness at distances of around half a metre in the plots, suggesting that these grain sizes are optimal for monitoring such variation in the two most common vegetation types on the island. We further observed a notable loss of seasonal variation in the spatial heterogeneity of landscape greenness (46.2 - 63.9%) when aggregating from ultra-fine-grain drone pixels (approx. 0.05 m) to the size of medium-grain satellite pixels (10 – 30 m). Finally, seasonal changes in drone-derived greenness were highly correlated with measurements of leaf-growth in the ground-validation plots (mean Spearman’s ⍴ 0.70). These findings indicate that multispectral drone measurements can capture temporal plant growth dynamics across tundra landscapes. Overall, our results demonstrate that novel technologies such as drone platforms and compact multispectral sensors allow us to study ecological systems at previously inaccessible scales and fill gaps in our understanding of tundra ecosystem processes. Capturing fine-scale variation across tundra landscapes will improve predictions of the ecological impacts and climate feedbacks of environmental change in the Arctic.

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Temporal Influences of Vegetation Cover (C) Dynamism on MUSLE Sediment Yield Estimates: NDVI Evaluation

Water ◽

10.3390/w13192707 ◽

2021 ◽

Vol 13 (19) ◽

pp. 2707

Author(s):

David Gwapedza ◽

Denis Arthur Hughes ◽

Andrew Robert Slaughter ◽

Sukhmani Kaur Mantel

Keyword(s):

Sediment Yield ◽

Vegetation Cover ◽

Vegetation Index ◽

Scale Validation ◽

Spatial Scales ◽

Temporal Variations ◽

Early Summer ◽

Landsat 8 ◽

Temporal Scales ◽

Normalised Difference Vegetation Index

Vegetation cover is an important factor controlling erosion and sediment yield. Therefore, its effect is accounted for in both experimental and modelling studies of erosion and sediment yield. Numerous studies have been conducted to account for the effects of vegetation cover on erosion across spatial scales; however, little has been conducted across temporal scales. This study investigates changes in vegetation cover across multiple temporal scales in Eastern Cape, South Africa and how this affects erosion and sediment yield modelling in the Tsitsa River catchment. Earth observation analysis and sediment yield modelling are integrated within this study. Landsat 8 imagery was processed, and Normalised Difference Vegetation Index (NDVI) values were extracted and applied to parameterise the Modified Universal Soil Loss Equation (MUSLE) vegetation (C) factor. Imagery data from 2013–2018 were analysed for an inter-annual trend based on reference summer (March) images, while monthly imagery for the years 2016–2017 was analysed for intra-annual trends. The results indicate that the C exhibits more variation across the monthly timescale than the yearly timescale. Therefore, using a single month to represent the annual C factor increases uncertainty. The modelling shows that accounting for temporal variations in vegetation cover reduces cumulative simulated sediment by up to 85% across the inter-annual and 30% for the intra-annual scale. Validation with observed data confirmed that accounting for temporal variations brought cumulative sediment outputs closer to observations. Over-simulations are high in late autumn and early summer, when estimated C values are high. Accordingly, uncertainties are high in winter when low NDVI leads to high C, whereas dry organic matter provides some protection from erosion. The results of this study highlight the need to account for temporal variations in vegetation cover in sediment yield estimation but indicate the uncertainties associated with using NDVI to estimate C factor.

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Mapping of Cotton Fields Within-Season Using Phenology-Based Metrics Derived from a Time Series of Landsat Imagery

Remote Sensing ◽

10.3390/rs12183038 ◽

2020 ◽

Vol 12 (18) ◽

pp. 3038

Author(s):

Dhahi Al-Shammari ◽

Ignacio Fuentes ◽

Brett M. Whelan ◽

Patrick Filippi ◽

Thomas F. A. Bishop

Keyword(s):

Time Series ◽

Vegetation Index ◽

Landsat Imagery ◽

Google Earth ◽

Growth Stages ◽

Landsat 8 ◽

Crop Type ◽

Eastern Australia ◽

Normalised Difference Vegetation Index ◽

Cotton Fields

A phenology-based crop type mapping approach was carried out to map cotton fields throughout the cotton-growing areas of eastern Australia. The workflow was implemented in the Google Earth Engine (GEE) platform, as it is time efficient and does not require processing in multiple platforms to complete the classification steps. A time series of Normalised Difference Vegetation Index (NDVI) imagery were generated from Landsat 8 Surface Reflectance Tier 1 (L8SR) and processed using Fourier transformation. This was used to produce the harmonised-NDVI (H-NDVI) from the original NDVI, and then phase and amplitude values were generated from the H-NDVI to visualise active cotton in the targeted fields. Random Forest (RF) models were built to classify cotton at early, mid and late growth stages to assess the ability of the model to classify cotton as the season progresses, with phase, amplitude and other individual bands as predictors. Results obtained from leave-one-season-out cross validation (LOSOCV) indicated that Overall Accuracy (OA), Kappa, Producer’s Accuracies (PA) and User’s Accuracy (UA), increased significantly when adding amplitude and phase as predictor variables to the model, than prediction using H-NDVI or raw bands only. Commission and omission errors were reduced significantly as the season progressed and more in-season imagery was available. The methodology proposed in this study can map cotton crops accurately based on the reconstruction of the unique cotton reflectance trajectory through time. This study confirms the importance of phenological metrics in improving in-season cotton fields mapping across eastern Australia. This model can be used in conjunction with other datasets to forecast yield based on the mapped crop type for improved decision making related to supply chain logistics and seasonal outlooks for production.

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Comparison of satellite-based evapotranspiration estimates over the Tibetan Plateau

Hydrology and Earth System Sciences ◽

10.5194/hess-20-3167-2016 ◽

2016 ◽

Vol 20 (8) ◽

pp. 3167-3182 ◽

Cited By ~ 17

Author(s):

Jian Peng ◽

Alexander Loew ◽

Xuelong Chen ◽

Yaoming Ma ◽

Zhongbo Su

Keyword(s):

Tibetan Plateau ◽

Vegetation Index ◽

Water Cycle ◽

Global Climate ◽

Spatial Scales ◽

Accurate Estimation ◽

The Tibetan Plateau ◽

North West ◽

Comparison Results ◽

Normalised Difference Vegetation Index

Abstract. The Tibetan Plateau (TP) plays a major role in regional and global climate. The understanding of latent heat (LE) flux can help to better describe the complex mechanisms and interactions between land and atmosphere. Despite its importance, accurate estimation of evapotranspiration (ET) over the TP remains challenging. Satellite observations allow for ET estimation at high temporal and spatial scales. The purpose of this paper is to provide a detailed cross-comparison of existing ET products over the TP. Six available ET products based on different approaches are included for comparison. Results show that all products capture the seasonal variability well with minimum ET in the winter and maximum ET in the summer. Regarding the spatial pattern, the High resOlution Land Atmosphere surface Parameters from Space (HOLAPS) ET demonstrator dataset is very similar to the LandFlux-EVAL dataset (a benchmark ET product from the Global Energy and Water Cycle Experiment), with decreasing ET from the south-east to north-west over the TP. Further comparison against the LandFlux-EVAL over different sub-regions that are decided by different intervals of normalised difference vegetation index (NDVI), precipitation, and elevation reveals that HOLAPS agrees best with LandFlux-EVAL having the highest correlation coefficient (R) and the lowest root mean square difference (RMSD). These results indicate the potential for the application of the HOLAPS demonstrator dataset in understanding the land–atmosphere–biosphere interactions over the TP. In order to provide more accurate ET over the TP, model calibration, high accuracy forcing dataset, appropriate in situ measurements as well as other hydrological data such as runoff measurements are still needed.

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Competing Vegetation Structure Indices for Estimating Spatial Constrains in Carabid Abundance Patterns in Chinese Grasslands Reveal Complex Scale and Habitat Patterns

Insects ◽

10.3390/insects11040249 ◽

2020 ◽

Vol 11 (4) ◽

pp. 249 ◽

Cited By ~ 2

Author(s):

Noelline Tsafack ◽

Simone Fattorini ◽

Camila Benavides Frias ◽

Yingzhong Xie ◽

Xinpu Wang ◽

...

Keyword(s):

Land Cover ◽

Vegetation Index ◽

Spatial Scales ◽

Landsat 8 ◽

Remotely Sensed Data ◽

Insect Communities ◽

Landscape Characteristics ◽

Species Abundances ◽

Negative Impacts ◽

Structure Indices

Carabid communities are influenced by landscape features. Chinese steppes are subject to increasing desertification processes that are changing land-cover characteristics with negative impacts on insect communities. Despite those warnings, how land-cover characteristics influence carabid communities in steppe ecosystems remains unknown. The aim of this study is to investigate how landscape characteristics drive carabid abundance in different steppes (desert, typical, and meadow steppes) at different spatial scales. Carabid abundances were estimated using pitfall traps. Various landscape indices were derived from Landsat 8 Operational Land Imager (OLI) images. Indices expressing moisture and productivity were, in general, those with the highest correlations. Different indices capture landscape aspects that influence carabid abundance at different scales, in which the patchiness of desert vegetation plays a major role. Carabid abundance correlations with landscape characteristics rely on the type of grassland, on the vegetation index, and on the scale considered. Proper scales and indices are steppe type-specific, highlighting the need of considering various scales and indices to explain species abundances from remotely sensed data.

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Biophysical remote sensing of arctic environments

Progress in Physical Geography Earth and Environment ◽

10.1191/0309133303pp358ra ◽

2003 ◽

Vol 27 (1) ◽

pp. 44-68 ◽

Cited By ~ 33

Author(s):

Gita J. Laidler ◽

Paul Treitz

Keyword(s):

Remote Sensing ◽

Satellite Data ◽

Spatial Scales ◽

Temporal Distribution ◽

Remote Sensing Data ◽

Vegetation Mapping ◽

General Purpose ◽

The Arctic ◽

Natural Systems ◽

Tundra Ecosystem

Various remote sensing studies have been conducted to investigate methods and applications of vegetation mapping and analysis in arctic environments. The general purpose of these studies is to extract information on the spatial and temporal distribution of vegetation as required for tundra ecosystem and climate change studies. Because of the recent emphasis on understanding natural systems at large spatial scales, there has been an increasing interest in deriving biophysical variables from satellite data. Satellite remote sensing offers potential for extrapolating, or ‘scaling up’ biophysical measures derived from local sites, to landscape and even regional scales. The most common investigations include mapping spatial vegetation patterns or assessing biophysical tundra characteristics, using medium resolution satellite data. For instance, Landsat TM data have been shown to be useful for broad vegetation mapping and analysis, but not accurately representative of smaller vegetation communities or local spatial variation. It is anticipated, that high spatial resolution remote sensing data, now available from commercial remote sensing satellites, will provide the necessary sampling scale to link field data to remotely sensed reflectance data. As a result, it is expected that these data will improve the representation of biophysical variables over sparsely vegetated regions of the Arctic.

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Predicting Soil Respiration from Plant Productivity (NDVI) in a Sub-Arctic Tundra Ecosystem

Remote Sensing ◽

10.3390/rs13132571 ◽

2021 ◽

Vol 13 (13) ◽

pp. 2571

Author(s):

Olivia Azevedo ◽

Thomas C. Parker ◽

Matthias B. Siewert ◽

Jens-Arne Subke

Keyword(s):

Soil Respiration ◽

Vegetation Index ◽

Vegetation Type ◽

Net Ecosystem Exchange ◽

Plant Productivity ◽

Ecosystem Monitoring ◽

Vegetation Communities ◽

Direct Measurements ◽

Tundra Ecosystem ◽

Normalised Difference Vegetation Index

Soils represent the largest store of carbon in the biosphere with soils at high latitudes containing twice as much carbon (C) than the atmosphere. High latitude tundra vegetation communities show increases in the relative abundance and cover of deciduous shrubs which may influence net ecosystem exchange of CO2 from this C-rich ecosystem. Monitoring soil respiration (Rs) as a crucial component of the ecosystem carbon balance at regional scales is difficult given the remoteness of these ecosystems and the intensiveness of measurements that is required. Here we use direct measurements of Rs from contrasting tundra plant communities combined with direct measurements of aboveground plant productivity via Normalised Difference Vegetation Index (NDVI) to predict soil respiration across four key vegetation communities in a tundra ecosystem. Soil respiration exhibited a nonlinear relationship with NDVI (y = 0.202e3.508 x, p < 0.001). Our results further suggest that NDVI and soil temperature can help predict Rs if vegetation type is taken into consideration. We observed, however, that NDVI is not a relevant explanatory variable in the estimation of SOC in a single-study analysis.

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Are vegetation indices useful in the Arctic?

Polar Record ◽

10.1017/s0032247400026036 ◽

1998 ◽

Vol 34 (191) ◽

pp. 333-336 ◽

Cited By ~ 12

Author(s):

W. G. Rees ◽

E. I. Golubeva ◽

M. Williams

Keyword(s):

Boreal Forests ◽

Vegetation Index ◽

Preliminary Investigation ◽

Vegetation Indices ◽

Ground Cover ◽

Field Measurements ◽

The Arctic ◽

Tundra Vegetation ◽

Normalised Difference Vegetation Index ◽

Extent Of Damage

AbstractThis paper describes a preliminary investigation of the extent to which the normalised difference vegetation index (NDVI), derived from satellite optical imagery, can indicate the extent of damage to upland tundra (fruticose lichen and dwarf shrub) vegetation. We combine the results of a previously reported classification of Landsat multispectral scanner imagery from Kol'skiy Poluostrov, Russia, with field measurements of the biomass and spectral reflectance of tundra vegetation. The results show that the NDVI is not strongly influenced by biomass, but that differences in species composition and ground cover are significant. Other workers have concluded that vegetation indices are not useful for boreal forests. It is therefore suggested that the use of the NDVI by itself as an indicator of the state of disturbed vegetation in Arctic regions is not recommended.

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Landscape Representation by a Permanent Forest Plot and Alternative Plot Designs in a Typhoon Hotspot, Fushan, Taiwan

Remote Sensing ◽

10.3390/rs12040660 ◽

2020 ◽

Vol 12 (4) ◽

pp. 660 ◽

Cited By ~ 2

Author(s):

Jonathan Peereman ◽

James Aaron Hogan ◽

Teng-Chiu Lin

Keyword(s):

Forest Dynamics ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Landscape Heterogeneity ◽

Spatial Scales ◽

Vegetation Indices ◽

Permanent Plots ◽

Forest Plot ◽

Landsat 8 ◽

Four Square

Permanent forest dynamics plots have provided valuable insights into many aspects of forest ecology. The evaluation of their representativeness within the landscape is necessary to understanding the limitations of findings from permanent plots at larger spatial scales. Studies on the representativeness of forest plots with respect to landscape heterogeneity and disturbance effect have already been carried out, but knowledge of how multiple disturbances affect plot representativeness is lacking—particularly in sites where several disturbances can occur between forest plot censuses. This study explores the effects of five typhoon disturbances on the Fushan Forest Dynamics Plot (FFDP) and its surrounding landscape, the Fushan Experimental Forest (FEF), in Taiwan where typhoons occur annually. The representativeness of the FFDP for the FEF was studied using four topographical variables derived from a digital elevation model and two vegetation indices (VIs), Normalized Difference Vegetation Index (NDVI) and Normalized Difference Infrared Index (NDII), calculated from Landsat-5 TM, Landsat-7 ETM+, and Landsat-8 OLI data. Representativeness of four alternative plot designs were tested by dividing the FFDP into subplots over wider elevational ranges. Results showed that the FFDP neither represents landscape elevational range (<10%) nor vegetation cover (<7% of the interquartile range, IQR). Although disturbance effects (i.e., ΔVIs) were also different between the FFDP and the FEF, comparisons showed no under- or over-exposure to typhoon damage frequency or intensity within the FFDP. In addition, the ΔVIs were of the same magnitudes in the plots and the reserve, and the plot covered 30% to 75.9% of IQRs of the reserve ΔVIs. Unexpectedly, the alternative plot designs did not lead to increased representation of damage for 3 out of the 4 tested typhoons and they did not suggest higher representativeness of rectangular vs. square plots. Based on the comparison of mean Euclidian distances, two rectangular plots had smaller distances than four square or four rectangular plots of the same area. Therefore, this study suggests that the current FFDP provides a better representation of its landscape disturbances than alternatives, which contained wider topographical variation and would be more difficult to conduct ground surveys. However, upscaling needs to be done with caution as, in the case of the FEF, plot representativeness varied among typhoons.

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Selecting appropriate variables for detecting grassland to cropland changes using high resolution satellite data

PeerJ ◽

10.7717/peerj.5487 ◽

2018 ◽

Vol 6 ◽

pp. e5487 ◽

Cited By ~ 3

Author(s):

Tomáš Klouček ◽

David Moravec ◽

Jan Komárek ◽

Ondřej Lagner ◽

Přemysl Štych

Keyword(s):

High Resolution ◽

Land Cover ◽

Change Detection ◽

Spatial Data ◽

Vegetation Index ◽

Identification System ◽

Landsat 8 ◽

Economic Point ◽

Normalised Difference Vegetation Index ◽

Cropland Change

Grassland is one of the most represented, while at the same time, ecologically endangered, land cover categories in the European Union. In view of the global climate change, detecting its change is growing in importance from both an environmental and a socio-economic point of view. A well-recognised tool for Land Use and Land Cover (LULC) Change Detection (CD), including grassland changes, is Remote Sensing (RS). An important aspect affecting the accuracy of change detection is finding the optimal indicators of LULC changes (i.e., variables). Inappropriately selected variables can produce inaccurate results burdened with a number of uncertainties. The aim of our study is to find the most suitable variables for the detection of grassland to cropland change, based on a pair of high resolution images acquired by the Landsat 8 satellite and from the vector database Land Parcel Identification System (LPIS). In total, 59 variables were used to create models using Generalised Linear Models (GLM), the quality of which was verified through multi-temporal object-based change detection. Satisfactory accuracy for the detection of grassland to cropland change was achieved using all of the statistically identified models. However, a three-variable model can be recommended for practical use, namely by combining the Normalised Difference Vegetation Index (NDVI), Wetness and Fifth components of Tasselled Cap. Increasing number of variables did not significantly improve the accuracy of detection, but rather complicated the interpretation of the results and was less accurate than detection based on the original Landsat 8 images. The results obtained using these three variables are applicable in landscape management, agriculture, subsidy policy, or in updating existing LULC databases. Further research implementing these variables in combination with spatial data obtained by other RS techniques is needed.

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Assessing the susceptibility of semiarid rangelands to wildfires using Terra MODIS and Landsat Thematic Mapper data

International Journal of Wildland Fire ◽

10.1071/wf10001 ◽

2011 ◽

Vol 20 (5) ◽

pp. 690 ◽

Cited By ~ 4

Author(s):

Fang Chen ◽

Keith T. Weber ◽

Jamey Anderson ◽

Bhushan Gokhal

Keyword(s):

Vegetation Change ◽

Vegetation Index ◽

Spatial Scales ◽

Late Summer ◽

Thematic Mapper ◽

Fuel Load ◽

Landsat Thematic Mapper ◽

Change Analysis ◽

Active Growth ◽

Normalised Difference Vegetation Index

In order to monitor wildfires at broad spatial scales and with frequent periodicity, satellite remote sensing techniques have been used in many studies. Rangeland susceptibility to wildfires closely relates to accumulated fuel load. The normalised difference vegetation index (NDVI) and fraction of photosynthetically active radiation (fPAR) are key variables used by many ecological models to estimate biomass and vegetation productivity. Subsequently, both NDVI and fPAR data have become an indirect means of deriving fuel load information. For these reasons, NDVI and fPAR, derived from the Moderate Resolution Imaging Spectroradiometer on-board Terra and Landsat Thematic Mapper imagery, were used to represent prefire vegetation changes in fuel load preceding the Millennial and Crystal Fires of 2000 and 2006 in the rangelands of south-east Idaho respectively. NDVI and fPAR change maps were calculated between active growth and late-summer senescence periods and compared with precipitation, temperature, forage biomass and percentage ground cover data. The results indicate that NDVI and fPAR value changes 2 years before the fire were greater than those 1 year before fire as an abundance of grasses existed 2 years before each wildfire based on field forage biomass sampling. NDVI and fPAR have direct implication for the assessment of prefire vegetation change. Therefore, rangeland susceptibility to wildfire may be estimated using NDVI and fPAR change analysis. Furthermore, fPAR change data may be included as an input source for early fire warning models, and may increase the accuracy and efficiency of fire and fuel load management in semiarid rangelands.

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