Vegetation dynamics in Alpine glacier forelands tackled from space

Abstract Monitoring of plant succession in glacier forelands has so far been restricted to field sampling. In this study, in situ vegetation sampling along a chronosequence between Little Ice Age (LIA) maximum extent and the recent glacier terminus at Jamtalferner in the Austrian Alps is compared to time series of the Normalized Difference Vegetation Index (NDVI) calculated from 13 Landsat scenes (1985–2016). The glacier terminus positions at 16 dates between the LIA maximum and 2015 were analysed from historical maps, orthophotos and LiDAR images. We sampled plots of different ages since deglaciation, from very recent to approx. 150 years: after 100 years, roughly 80% of the ground is covered by plants and ground cover does not increase significantly thereafter. The number of species increases from 10–20 species on young sites to 40–50 species after 100 years. The NDVI increases with the time of exposure from a mean of 0.11 for 1985–1991 to 0.20 in 2009 and 0.27 in 2016. As the increase in ground cover is clearly reproduced by the NDVI (R² ground cover/NDVI 0.84) – even for sparsely vegetated areas –, we see a great potential of satellite-borne NDVI to perform regional characterizations of glacier forelands for hydrological, ecological and hazard management-related applications.

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Common Patterns and Diverging Trajectories in Primary Succession of Plants in Eastern Alpine Glacier Forelands

Diversity ◽

10.3390/d12050191 ◽

2020 ◽

Vol 12 (5) ◽

pp. 191 ◽

Cited By ~ 1

Author(s):

Thomas Fickert

Keyword(s):

Primary Succession ◽

Ground Cover ◽

Ice Age ◽

Central Alps ◽

Successional Stage ◽

Vegetation Development ◽

Alpine Glacier ◽

Glacier Terminus ◽

Ground Cover Plant ◽

Study Sites

This paper deals with the vegetation development in four glacier forelands, aligned along a distance of 250 km from West to East in the siliceous Eastern Central Alps. The study employs a chronosequence approach, which assumes a temporal sequence in vegetation development by spatially different sites regarding time since deglaciation. The chronosequences cover the area between Little Ice Age (LIA) maximum glacier extent around 1850, and the current glacier terminus. Despite some shortcomings, chronosequences allow the identification of general patterns of primary succession of plants as a function of site age and local environmental conditions, e.g., changes in species richness, ground cover, plant functional traits, and community structure. While there is no shortage of chronosequence studies in glacier forelands of the Alps, a straightforward comparison aimed at the deduction of general successional trajectories is tricky, due to different procedures of vegetation sampling and data analyses. The comparative examination by a standardized sampling and analyzing protocol of four glacier forelands in the Eastern Central Alps presented here proves the existence of several common patterns in primary succession, but also diverging successional trajectories from West to East. While the pioneer stage in all glacier forelands is similar both floristically and structurally, from the early successional stage onwards, differences increase, leading to different phases in the late successional stage, which is shrub dominated throughout in the westernmost study site, herb–grass–dwarfshrub dominated throughout in the easternmost study site, and divided into an earlier herb–grass–dwarfshrub phase and a later shrub phase in the two study sites in between.

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Evaluation of the AMSR2 L2 soil moisture product of JAXA on the Mongolian Plateau over seven years (2012–2018)

SN Applied Sciences ◽

10.1007/s42452-019-1488-y ◽

2019 ◽

Vol 1 (11) ◽

Cited By ~ 1

Author(s):

Ichirow Kaihotsu ◽

Jun Asanuma ◽

Kentaro Aida ◽

Dambaravjaa Oyunbaatar

Keyword(s):

Soil Moisture ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Surface Soil ◽

Surface Soil Moisture ◽

Mongolian Plateau ◽

Moisture Measurement ◽

Rainfall Condition ◽

Soil Moisture Measurement

Abstract This study evaluated the Advanced Microwave Scanning Radiometer 2 (AMSR2) L2 soil moisture product (ver. 3) using in situ hydrological observational data, acquired over 7 years (2012–2018), from a 50 × 50 km flat area of the Mongolian Plateau covered with bare soil, pasture and shrubs. Although AMSR2 slightly underestimated soil moisture content at 3-cm depth, satisfactory timing was observed in both the response patterns and the in situ soil moisture data, and the differences between these factors were not large. In terms of the relationship between AMSR2 soil moisture from descending orbits and in situ measured soil moisture at 3-cm depth, the values of the RMSE (m3/m3) and the bias (m3/m3) varied from 0.028 to 0.063 and from 0.011 to − 0.001 m3/m3, respectively. The values of the RMSE and bias depended on rainfall condition. The mean value of the RMSE for the 7-year period was 0.042 m3/m3, i.e., lower than the target accuracy 0.050 m3/m3. The validation results for descending orbits were found slightly better than for ascending orbits. Comparison of the Soil Moisture and Ocean Salinity (SMOS) soil moisture product with the AMSR2 L2 soil moisture product showed that AMSR2 could observe surface soil moisture with nearly same accuracy and stability. However, the bias of the AMSR2 soil moisture measurement was slightly negative and poorer than that of SMOS with deeper soil moisture measurement. It means that AMSR2 cannot effectively measure soil moisture at 3-cm depth. In situ soil temperature at 3-cm depth and surface vegetation (normalized difference vegetation index) did not influence the underestimation of AMSR2 soil moisture measurements. These results suggest that a possible cause of the underestimation of AMSR2 soil moisture measurements is the difference between the depth of the AMSR2 observations and in situ soil moisture measurements. Overall, this study proved the AMSR2 L2 soil moisture product has been useful for monitoring daily surface soil moisture over large grassland areas and it clearly demonstrated the high-performance capability of AMSR2 since 2012.

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Estimating Sahelian and East African soil moisture using the Normalized Difference Vegetation Index

Hydrology and Earth System Sciences Discussions ◽

10.5194/hessd-10-7963-2013 ◽

2013 ◽

Vol 10 (6) ◽

pp. 7963-7997 ◽

Cited By ~ 4

Author(s):

A. McNally ◽

C. Funk ◽

G. J. Husak ◽

J. Michaelsen ◽

B. Cappelaere ◽

...

Keyword(s):

Soil Moisture ◽

Early Warning ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Agricultural Drought ◽

Sub Saharan Africa ◽

Sub Saharan ◽

The Relationship ◽

African Sahel

Abstract. Rainfall gauge networks in Sub-Saharan Africa are inadequate for assessing Sahelian agricultural drought, hence satellite-based estimates of precipitation and vegetation indices such as the Normalized Difference Vegetation Index (NDVI) provide the main source of information for early warning systems. While it is common practice to translate precipitation into estimates of soil moisture, it is difficult to quantitatively compare precipitation and soil moisture estimates with variations in NDVI. In the context of agricultural drought early warning, this study quantitatively compares rainfall, soil moisture and NDVI using a simple statistical model to translate NDVI values into estimates of soil moisture. The model was calibrated using in-situ soil moisture observations from southwest Niger, and then used to estimate root zone soil moisture across the African Sahel from 2001–2012. We then used these NDVI-soil moisture estimates (NSM) to quantify agricultural drought, and compared our results with a precipitation-based estimate of soil moisture (the Antecedent Precipitation Index, API), calibrated to the same in-situ soil moisture observations. We also used in-situ soil moisture observations in Mali and Kenya to assess performance in other water-limited locations in sub Saharan Africa. The separate estimates of soil moisture were highly correlated across the semi-arid, West and Central African Sahel, where annual rainfall exhibits a uni-modal regime. We also found that seasonal API and NDVI-soil moisture showed high rank correlation with a crop water balance model, capturing known agricultural drought years in Niger, indicating that this new estimate of soil moisture can contribute to operational drought monitoring. In-situ soil moisture observations from Kenya highlighted how the rainfall-driven API needs to be recalibrated in locations with multiple rainy seasons (e.g., Ethiopia, Kenya, and Somalia). Our soil moisture estimates from NDVI, on the other hand, performed well in Niger, Mali and Kenya. This suggests that the NDVI-soil moisture relationship may be more robust across rainfall regimes than the API because the relationship between NDVI and plant available water is less reliant on local characteristics (e.g., infiltration, runoff, evaporation) than the relationship between rainfall and soil moisture.

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Disturbance Mapping in Arctic Tundra Improved by a Planning Workflow for Drone Studies: Advancing Tools for Future Ecosystem Monitoring

Remote Sensing ◽

10.3390/rs13214466 ◽

2021 ◽

Vol 13 (21) ◽

pp. 4466

Author(s):

Isabell Eischeid ◽

Eeva M. Soininen ◽

Jakob J. Assmann ◽

Rolf A. Ims ◽

Jesper Madsen ◽

...

Keyword(s):

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Predictive Ability ◽

Ground Cover ◽

Arctic Tundra ◽

The Arctic ◽

Ecosystem Monitoring ◽

Selection Of Variables ◽

State Changes ◽

Study Sites

The Arctic is under great pressure due to climate change. Drones are increasingly used as a tool in ecology and may be especially valuable in rapidly changing and remote landscapes, as can be found in the Arctic. For effective applications of drones, decisions of both ecological and technical character are needed. Here, we provide our method planning workflow for generating ground-cover maps with drones for ecological monitoring purposes. The workflow includes the selection of variables, layer resolutions, ground-cover classes and the development and validation of models. We implemented this workflow in a case study of the Arctic tundra to develop vegetation maps, including disturbed vegetation, at three study sites in Svalbard. For each site, we generated a high-resolution map of tundra vegetation using supervised random forest (RF) classifiers based on four spectral bands, the normalized difference vegetation index (NDVI) and three types of terrain variables—all derived from drone imagery. Our classifiers distinguished up to 15 different ground-cover classes, including two classes that identify vegetation state changes due to disturbance caused by herbivory (i.e., goose grubbing) and winter damage (i.e., ‘rain-on-snow’ and thaw-freeze). Areas classified as goose grubbing or winter damage had lower NDVI values than their undisturbed counterparts. The predictive ability of site-specific RF models was good (macro-F1 scores between 83% and 85%), but the area of the grubbing class was overestimated in parts of the moss tundra. A direct transfer of the models between study sites was not possible (macro-F1 scores under 50%). We show that drone image analysis can be an asset for studying future vegetation state changes on local scales in Arctic tundra ecosystems and encourage ecologists to use our tailored workflow to integrate drone mapping into long-term monitoring programs.

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Assessment of Soil Moisture Anomaly Sensitivity to Detect Drought Spatio-Temporal Variability in Romania

Sensors ◽

10.3390/s21248371 ◽

2021 ◽

Vol 21 (24) ◽

pp. 8371

Author(s):

Irina Ontel ◽

Anisoara Irimescu ◽

George Boldeanu ◽

Denis Mihailescu ◽

Claudiu-Valeriu Angearu ◽

...

Keyword(s):

Soil Moisture ◽

Land Surface ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Root Zone ◽

Standardized Precipitation Index ◽

In Situ Measurements ◽

Near Surface ◽

Soil Moisture Anomaly

This paper will assess the sensitivity of soil moisture anomaly (SMA) obtained from the Soil water index (SWI) product Metop ASCAT, to identify drought in Romania. The SWI data were converted from relative values (%) to absolute values (m3 m−3) using the soil porosity method. The conversion results (SM) were validated using soil moisture in situ measurements from ISMN at 5 cm depths (2015–2020). The SMA was computed based on a 10 day SWI product, between 2007 and 2020. The analysis was performed for the depths of 5 cm (near surface), 40 cm (sub surface), and 100 cm (root zone). The standardized precipitation index (SPI), land surface temperature anomaly (LST anomaly), and normalized difference vegetation index anomaly (NDVI anomaly) were computed in order to compare the extent and intensity of drought events. The best correlations between SM and in situ measurements are for the stations located in the Getic Plateau (Bacles (r = 0.797) and Slatina (r = 0.672)), in the Western Plain (Oradea (r = 0.693)), and in the Moldavian Plateau (Iasi (r = 0.608)). The RMSE were between 0.05 and 0.184. Furthermore, the correlations between the SMA and SPI, the LST anomaly, and the NDVI anomaly were significantly registered in the second half of the warm season (July–September). Due to the predominantly agricultural use of the land, the results can be useful for the management of water resources and irrigation in regions frequently affected by drought.

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The Impact of Non-Photosynthetic Vegetation on LAI Estimation by NDVI in Mixed Grassland

Remote Sensing ◽

10.3390/rs12121979 ◽

2020 ◽

Vol 12 (12) ◽

pp. 1979

Author(s):

Dandan Xu ◽

Deshuai An ◽

Xulin Guo

Keyword(s):

Land Surface ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Time Series Data ◽

Vegetation Indices ◽

Series Data ◽

Area Index ◽

Plant Area Index ◽

The Impact

Leaf area index (LAI) is widely used for algorithms and modelling in the field of ecology and land surface processes. At a global scale, normalized difference vegetation index (NDVI) products generated by different remote sensing satellites, have provided more than 40 years of time series data for LAI estimation. NDVI saturation issues are reported in agriculture and forest ecosystems at high LAI values, creating a challenge when using NDVI to estimate LAI. However, NDVI saturation is not reported on LAI estimation in grasslands. Previous research implies that non-photosynthetic vegetation (NPV) reduces the accuracy of LAI estimation from NDVI and other vegetation indices. A question arises: is the absence of NDVI saturation in grasslands a result of low LAI value, or is it caused by NPV? This study aims to explore whether there is an NDVI saturation issue in mixed grassland, and how NPV may influence LAI estimation by NDVI. In addition, in-situ measured plant area index (PAI) by sensors that detect light interception through the vegetation canopy (e.g., Li-cor LAI-2000), the most widely used field LAI collection method, might create bias in LAI estimation or validation using NDVI. Thus, this study also aims to quantify the contribution of green vegetation (GV) and NPV on in-situ measured PAI. The results indicate that NDVI saturation (using the portion of NDVI only contributed by GV) exists in grassland at high LAI (LAI threshold is much lower than that reported for other ecosystems in the literature), and that the presence of NPV can override the saturation effects of NDVI used to estimate green LAI. The results also show that GV and NPV in mixed grassland explain, respectively, the 60.33% and 39.67% variation of in-situ measured PAI by LAI-2000.

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Assessing the Effect of Real Spatial Resolution of In Situ UAV Multispectral Images on Seedling Rapeseed Growth Monitoring

Remote Sensing ◽

10.3390/rs12071207 ◽

2020 ◽

Vol 12 (7) ◽

pp. 1207 ◽

Cited By ~ 1

Author(s):

Jian Zhang ◽

Chufeng Wang ◽

Chenghai Yang ◽

Tianjin Xie ◽

Zhao Jiang ◽

...

Keyword(s):

Spatial Resolution ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Vegetation Indices ◽

Crop Growth ◽

Growth Monitoring ◽

Multispectral Images ◽

Area Index ◽

Uav Images

The spatial resolution of in situ unmanned aerial vehicle (UAV) multispectral images has a crucial effect on crop growth monitoring and image acquisition efficiency. However, existing studies about optimal spatial resolution for crop monitoring are mainly based on resampled images. Therefore, the resampled spatial resolution in these studies might not be applicable to in situ UAV images. In order to obtain optimal spatial resolution of in situ UAV multispectral images for crop growth monitoring, a RedEdge Micasense 3 camera was installed onto a DJI M600 UAV flying at different heights of 22, 29, 44, 88, and 176m to capture images of seedling rapeseed with ground sampling distances (GSD) of 1.35, 1.69, 2.61, 5.73, and 11.61 cm, respectively. Meanwhile, the normalized difference vegetation index (NDVI) measured by a GreenSeeker (GS-NDVI) and leaf area index (LAI) were collected to evaluate the performance of nine vegetation indices (VIs) and VI*plant height (PH) at different GSDs for rapeseed growth monitoring. The results showed that the normalized difference red edge index (NDRE) had a better performance for estimating GS-NDVI (R2 = 0.812) and LAI (R2 = 0.717), compared with other VIs. Moreover, when GSD was less than 2.61 cm, the NDRE*PH derived from in situ UAV images outperformed the NDRE for LAI estimation (R2 = 0.757). At oversized GSD (≥5.73 cm), imprecise PH information and a large heterogeneity within the pixel (revealed by semi-variogram analysis) resulted in a large random error for LAI estimation by NDRE*PH. Furthermore, the image collection and processing time at 1.35 cm GSD was about three times as long as that at 2.61 cm. The result of this study suggested that NDRE*PH from UAV multispectral images with a spatial resolution around 2.61 cm could be a preferential selection for seedling rapeseed growth monitoring, while NDRE alone might have a better performance for low spatial resolution images.

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Estimation of leaf area index using an angular vegetation index based on in situ measurements and CHRIS/PROBA data

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xli-b7-121-2016 ◽

2016 ◽

Vol XLI-B7 ◽

pp. 121-128

Author(s):

Lijuan Wang ◽

Guimin Zhang ◽

Hui Lin ◽

Liang Liang ◽

Zheng Niu

Keyword(s):

Leaf Area Index ◽

Leaf Area ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Vegetation Indices ◽

Dark Spot ◽

Canopy Reflectance ◽

In Situ Experiment ◽

Area Index

The Normalized Difference Vegetation Index (NDVI) is widely used for Leaf Area Index (LAI) estimation. It is well documented that the NDVI is extremely subject to the saturation problem when LAI reaches a high value. A new multi-angular vegetation index, the Hotspot-darkspot Difference Vegetation Index (HDVI) is proposed to estimate the high density LAI. The HDVI, defined as the difference between the hot and dark spot NDVI, relative to the dark spot NDVI, was proposed based on the Analytical two-layer Canopy Reflectance Model (ACRM) model outputs. This index is validated using both in situ experimental data in wheat and data from the multi-angular optical Compact High-Resolution Imaging Spectrometer (CHRIS) satellite. Both indices, the Hotspot-Darkspot Index (HDS) and the NDVI were also selected to analyze the relationship with LAI, and were compared with new index HDVI. The results show that HDVI is an appropriate proxy of LAI with higher determination coefficients (R2) for both the data from the in situ experiment (R2=0.7342, RMSE=0.0205) and the CHRIS data (R2=0.7749, RMSE=0.1013). Our results demonstrate that HDVI can make better the occurrence of saturation limits with the information of multi-angular observation, and is more appropriate for estimating LAI than either HDS or NDVI at high LAI values. Although the new index needs further evaluation, it also has the potential under the condition of dense canopies. It provides the effective improvement to the NDVI and other vegetation indices that are based on the red and NIR spectral bands.

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Impact of Varying Light and Dew on Ground Cover Estimates from Active NDVI, RGB, and LiDAR

Plant Phenomics ◽

10.34133/2021/9842178 ◽

2021 ◽

Vol 2021 ◽

pp. 1-14

Author(s):

David M. Deery ◽

David J. Smith ◽

Robert Davy ◽

Jose A. Jimenez-Berni ◽

Greg J. Rebetzke ◽

...

Keyword(s):

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

High Reliability ◽

Stem Elongation ◽

Digital Camera ◽

Ground Cover ◽

Growth Stages ◽

Rgb Images ◽

High Degree ◽

Pearson Correlations

Canopy ground cover (GC) is an important agronomic measure for evaluating crop establishment and early growth. This study evaluates the reliability of GC estimates, in the presence of varying light and dew on leaves, from three different ground-based sensors: (1) normalized difference vegetation index (NDVI) from the commercially available GreenSeeker®; (2) RGB images from a digital camera, where GC was determined as the portion of pixels from each image meeting a greenness criterion (i.e., Green−Red/Green+Red>0); and (3) LiDAR using two separate approaches: (a) GC from LiDAR red reflectance (whereby red reflectance less than five was classified as vegetation) and (b) GC from LiDAR height (whereby height greater than 10 cm was classified as vegetation). Hourly measurements were made early in the season at two different growth stages (tillering and stem elongation), among wheat genotypes highly diverse for canopy characteristics. The active NDVI showed the least variation through time and was particularly stable, regardless of the available light or the presence of dew. In addition, between-sample-time Pearson correlations for NDVI were consistently high and significant (P<0.0001), ranging from 0.89 to 0.98. In comparison, GC from LiDAR and RGB showed greater variation across sampling times, and LiDAR red reflectance was strongly influenced by the presence of dew. Excluding times when the light was exceedingly low, correlations between GC from RGB and NDVI were consistently high (ranging from 0.79 to 0.92). The high reliability of the active NDVI sensor potentially affords a high degree of flexibility for users by enabling sampling across a broad range of acceptable light conditions.

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Estimación de la evapotranspiración de maíz forrajero apoyada con sensores remotos y mediciones in situ.

REVISTA TERRA LATINOAMERICANA ◽

10.28940/terra.v37i3.485 ◽

2019 ◽

Vol 37 (3) ◽

pp. 279 ◽

Cited By ~ 1

Author(s):

Arturo Reyes González ◽

David Guadalupe Reta Sánchez ◽

Juan Isidro Sánchez Duarte ◽

Esmeralda Ochoa Martínez ◽

Karla Rodríguez Hernández ◽

...

Keyword(s):

Remote Sensing ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Irrigation System ◽

Vegetation Indices ◽

Irrigation Scheduling ◽

Irrigated Agriculture ◽

Crop Development ◽

Subsurface Drip

Irrigated agriculture requires better estimates of crop water demand. The aim of this study was to estimate the evapotranspiration (ETc) in forage corn through vegetation indices obtained in situ and estimated with remote sensing in the Comarca Lagunera, Mexico. The research was carried out in 2011 and 2012 in four 900 m2 plots irrigated with a subsurface drip irrigation system. Normalized Difference Vegetation Index (NDVI) and crop coeff icient (Kc) during crop development were determined. The initial, maximum and f inal NDVI values were 0.13, 0.79 and 0.63 for both methods and in both cycles. The maximum Kc values were obtained 54 and 48 days after sowing (DDS) with GreenSeeker, and at 61 and 59 DDS with satellite images in 2011 and 2012, respectively. The results showed a good relationship between ETc estimated in situ and ETc estimated with remote sensing (r = 0.98) for both years. Although the variation of ETc using both methods was 1.2 mm day‑1, early in the cycle and 7.4 mm day-1 to flowering start-milky grains. Water needs of forage corn were estimated with similar precision using remote sensing and in situ measurements. Therefore, both methods can be used to improve irrigation scheduling and preserve water resources in agriculture.

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