Spatial patterns of vegetation phenology metrics and related climatic controls of eight contrasting forest types in India – analysis from remote sensing datasets

This study aims to improve the standard water balance evapotranspiration (WB ET) estimate, which is typically used as benchmark data for catchment-scale ET estimation, by accounting for net intercatchment groundwater flow in the ET calculation. Using the modified WB ET approach, we examine errors and shortcomings associated with the long-term annual mean (2002–2014) spatial patterns of three remote-sensing (RS) MODIS-based ET products from MODIS16, PML_V2, and TSEB algorithms at 1 km spatial resolution over Denmark, as a test case for small-scale, energy-limited regions. Our results indicate that the novel approach of adding groundwater net in water balance ET calculation results in a more trustworthy ET spatial pattern. This is especially relevant for smaller catchments where groundwater net can be a significant component of the catchment water balance. Nevertheless, large discrepancies are observed both amongst RS ET datasets and compared to modified water balance ET spatial pattern at the national scale; however, catchment-scale analysis highlights that difference in RS ET and WB ET decreases with increasing catchment size and that 90%, 87%, and 93% of all catchments have ∆ET < ±150 mm/year for MODIS16, PML_V2, and TSEB, respectively. In addition, Copula approach captures a nonlinear structure of the joint relationship with multiple densities amongst the RS/WB ET products, showing a complex dependence structure (correlation); however, among the three RS ET datasets, MODIS16 ET shows a closer spatial pattern to the modified WB ET, as identified by a principal component analysis also. This study will help improve the water balance approach by the addition of groundwater net in the ET estimation and contribute to better understand the true correlations amongst RS/WB ET products especially over energy-limited environments.

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Remote sensing of temporal and spatial patterns of suspended particle size in the Irish Sea in relation to the Kolmogorov microscale

Continental Shelf Research ◽

10.1016/j.csr.2009.01.016 ◽

2009 ◽

Vol 29 (9) ◽

pp. 1213-1225 ◽

Cited By ~ 34

Author(s):

E.M. van der Lee ◽

D.G. Bowers ◽

E. Kyte

Keyword(s):

Remote Sensing ◽

Particle Size ◽

Spatial Patterns ◽

Suspended Particle ◽

Irish Sea ◽

Temporal And Spatial Patterns ◽

Temporal And Spatial ◽

The Irish Sea ◽

Suspended Particle Size

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Reviews and syntheses: Australian vegetation phenology: new insights from satellite remote sensing and digital repeat photography

Biogeosciences ◽

10.5194/bg-13-5085-2016 ◽

2016 ◽

Vol 13 (17) ◽

pp. 5085-5102 ◽

Cited By ~ 39

Author(s):

Caitlin E. Moore ◽

Tim Brown ◽

Trevor F. Keenan ◽

Remko A. Duursma ◽

Albert I. J. M. van Dijk ◽

...

Keyword(s):

Remote Sensing ◽

Satellite Remote Sensing ◽

Tropical Rainforests ◽

Vegetation Phenology ◽

Repeat Photography ◽

Tropical Savannas ◽

Diverse Range ◽

Canopy Development ◽

Continental Scale ◽

Digital Repeat Photography

Abstract. Phenology is the study of periodic biological occurrences and can provide important insights into the influence of climatic variability and change on ecosystems. Understanding Australia's vegetation phenology is a challenge due to its diverse range of ecosystems, from savannas and tropical rainforests to temperate eucalypt woodlands, semi-arid scrublands, and alpine grasslands. These ecosystems exhibit marked differences in seasonal patterns of canopy development and plant life-cycle events, much of which deviates from the predictable seasonal phenological pulse of temperate deciduous and boreal biomes. Many Australian ecosystems are subject to irregular events (i.e. drought, flooding, cyclones, and fire) that can alter ecosystem composition, structure, and functioning just as much as seasonal change. We show how satellite remote sensing and ground-based digital repeat photography (i.e. phenocams) can be used to improve understanding of phenology in Australian ecosystems. First, we examine temporal variation in phenology on the continental scale using the enhanced vegetation index (EVI), calculated from MODerate resolution Imaging Spectroradiometer (MODIS) data. Spatial gradients are revealed, ranging from regions with pronounced seasonality in canopy development (i.e. tropical savannas) to regions where seasonal variation is minimal (i.e. tropical rainforests) or high but irregular (i.e. arid ecosystems). Next, we use time series colour information extracted from phenocam imagery to illustrate a range of phenological signals in four contrasting Australian ecosystems. These include greening and senescing events in tropical savannas and temperate eucalypt understorey, as well as strong seasonal dynamics of individual trees in a seemingly static evergreen rainforest. We also demonstrate how phenology links with ecosystem gross primary productivity (from eddy covariance) and discuss why these processes are linked in some ecosystems but not others. We conclude that phenocams have the potential to greatly improve the current understanding of Australian ecosystems. To facilitate the sharing of this information, we have formed the Australian Phenocam Network (http://phenocam.org.au/).

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Summaries of certain spatial patterns retrieved from multidate remote-sensing data

Discrete Dynamics in Nature and Society ◽

10.1155/s1026022604402027 ◽

2004 ◽

Vol 2004 (2) ◽

pp. 287-300

Author(s):

Hema Nair

Keyword(s):

Remote Sensing ◽

Genetic Algorithms ◽

Fuzzy Logic ◽

Spatial Patterns ◽

Satellite Images ◽

Remote Sensing Data ◽

Geographic Area ◽

Image Mining ◽

Sensing Data

This paper presents an approach to describe patterns in remote-sensed images utilising fuzzy logic. The truth of a linguistic proposition such as Y isF can be determined for each pattern characterised by a tuple in the database, where Y is the pattern andFis a summary that applies to that pattern. This proposition is formulated in terms of primary quantitative measures, such as area, length, perimeter, and so forth, of the pattern. Fuzzy descriptions of linguistic summaries help to evaluate the degree to which a summary describes a pattern or object in the database. Techniques, such as clustering and genetic algorithms, are used to mine images. Image mining is a relatively new area of research. It is used to extract patterns from multidated satellite images of a geographic area.

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Spatial pattern evaluation of a calibrated national hydrological model – a remote sensing based diagnostic approach

10.5194/hess-2017-233 ◽

2017 ◽

Cited By ~ 2

Author(s):

Gorka Mendiguren ◽

Julian Koch ◽

Simon Stisen

Keyword(s):

Remote Sensing ◽

Spatial Pattern ◽

Spatial Patterns ◽

Hydrological Model ◽

Remote Sensing Data ◽

Driving Mechanism ◽

Distributed Hydrological Model ◽

Eof Analysis ◽

Sensing Data ◽

Spatio Temporal

Abstract. Distributed hydrological models are traditionally evaluated against discharge stations, emphasizing the temporal and neglecting the spatial component of a model. The present study widens the traditional paradigm by highlighting spatial patterns of evapotranspiration (ET), a key variable at the land-atmosphere interface, obtained from two different approaches at the national scale of Denmark. The first approach is based on a national water resources model (DK-model), using the MIKE-SHE model code, and the second approach utilizes a two source energy balance model (TSEB) driven mainly by satellite remote sensing data. The main hypothesis of the study is that while both approaches are essentially estimates, the spatial patterns of the remote sensing based approach are explicitly driven by observed land surface temperature and therefore represent the most direct spatial pattern information of ET; enabling its use for distributed hydrological model evaluation. Ideally the hydrological model simulation and remote sensing based approach should present similar spatial patterns and driving mechanism of ET. However, the spatial comparison showed that the differences are significant and indicating insufficient spatial pattern performance of the hydrological model. The differences in spatial patterns can partly be explained by the fact that the hydrological model is configured to run in 6 domains that are calibrated independently from each other, as it is often the case for large scale multi-basin calibrations. Furthermore, the model incorporates predefined temporal dynamics of Leaf Area Index (LAI), root depth (RD) and Crop coefficient (Kc) for each land cover type. This zonal approach of model parametrization ignores the spatio-temporal complexity of the natural system. To overcome this limitation, the study features a modified version of the DK-Model in which LAI, RD, and KC are empirically derived using remote sensing data and detailed soil property maps in order to generate a higher degree of spatio-temporal variability and spatial consistency between the 6 domains. The effects of these changes are analyzed by using the empirical orthogonal functions (EOF) analysis to evaluate spatial patterns. The EOF-analysis shows that including remote sensing derived LAI, RD and KC in the distributed hydrological model adds spatial features found in the spatial pattern of remote sensing based ET.

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Effect of Disturbance Regimes on Spatial Patterns of Tree Species in Three Sites in a Tropical Evergreen Forest in Vietnam

International Journal of Forestry Research ◽

10.1155/2016/4903749 ◽

2016 ◽

Vol 2016 ◽

pp. 1-16 ◽

Cited By ~ 1

Author(s):

Do Thi Ngoc Le ◽

Nguyen Van Thinh ◽

Nguyen The Dung ◽

Ralph Mitlöhner

Keyword(s):

Spatial Patterns ◽

Tree Species ◽

Interspecific Interactions ◽

Abundant Species ◽

Evergreen Forest ◽

Small Scale ◽

Forest Types ◽

Disturbance Regimes ◽

Tropical Evergreen Forest ◽

Disturbed Forest

The effects of disturbance regimes on the spatial patterns of the five most abundant species were investigated in three sites in a tropical forest at Xuan Nha Nature Reserve, Vietnam. Three permanent one-ha plots were established in undisturbed forest (UDF), lightly disturbed forest (LDF), and highly disturbed forest (HDF). All trees ≥5 cm DBH were measured in twenty-five 20 m × 20 m subplots. A total of 57 tree species belonging to 26 families were identified in the three forest types. The UDF had the highest basal area (30 m2 ha−1), followed by the LDF (17 m2 ha−1) and the HDF (13.0 m2 ha−1). The UDF also had the highest tree density (751 individuals ha−1) while the HDF held the lowest (478 individuals ha−1). Across all species, there were 417 “juveniles,” 267 “subadults,” and 67 “adults” in the UDF, while 274 “juveniles,” 230 “subadults,” and 36 “adults” were recorded in the LDF. 238 “juveniles,” 227 “subadults,” and 13 “adults” were obtained in the HDF. The univariate and bivariate data with pair- and mark-correlation functions of intra- and interspecific interactions of the five most abundant species changed in the three forest types. Most species indicated clumping or regular distributions at small scale, but a high ratio of negative interspecific small-scale associations was recorded in both the LDF and HDF sites. These were, however, rare in the UDF.

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Estimates of current status of forest types in Kolli Hill using remote sensing

Journal of the Indian Society of Remote Sensing ◽

10.1007/bf02989898 ◽

2000 ◽

Vol 28 (2-3) ◽

pp. 141-151 ◽

Cited By ~ 3

Author(s):

S Jayakumar ◽

D I Arockiasamy ◽

S John Britto

Keyword(s):

Remote Sensing ◽

Current Status ◽

Forest Types

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No growth stimulation of Canada’s boreal forest under half-century of combined warming and CO2 fertilization

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.1610156113 ◽

2016 ◽

Vol 113 (52) ◽

pp. E8406-E8414 ◽

Cited By ~ 111

Author(s):

Martin P. Girardin ◽

Olivier Bouriaud ◽

Edward H. Hogg ◽

Werner Kurz ◽

Niklaus E. Zimmermann ◽

...

Keyword(s):

Remote Sensing ◽

Boreal Forest ◽

Spatial Patterns ◽

Tree Growth ◽

Hydrological Cycle ◽

Forest Growth ◽

Spatiotemporal Variability ◽

Late 20Th Century ◽

Growth Trends ◽

Growth Variability

Considerable evidence exists that current global temperatures are higher than at any time during the past millennium. However, the long-term impacts of rising temperatures and associated shifts in the hydrological cycle on the productivity of ecosystems remain poorly understood for mid to high northern latitudes. Here, we quantify species-specific spatiotemporal variability in terrestrial aboveground biomass stem growth across Canada’s boreal forests from 1950 to the present. We use 873 newly developed tree-ring chronologies from Canada’s National Forest Inventory, representing an unprecedented degree of sampling standardization for a large-scale dendrochronological study. We find significant regional- and species-related trends in growth, but the positive and negative trends compensate each other to yield no strong overall trend in forest growth when averaged across the Canadian boreal forest. The spatial patterns of growth trends identified in our analysis were to some extent coherent with trends estimated by remote sensing, but there are wide areas where remote-sensing information did not match the forest growth trends. Quantifications of tree growth variability as a function of climate factors and atmospheric CO2 concentration reveal strong negative temperature and positive moisture controls on spatial patterns of tree growth rates, emphasizing the ecological sensitivity to regime shifts in the hydrological cycle. An enhanced dependence of forest growth on soil moisture during the late-20th century coincides with a rapid rise in summer temperatures and occurs despite potential compensating effects from increased atmospheric CO2 concentration.

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Fire Recurrence and Normalized Difference Vegetation Index (NDVI) Dynamics in Brazilian Savanna

Fire ◽

10.3390/fire2010001 ◽

2018 ◽

Vol 2 (1) ◽

pp. 1 ◽

Cited By ~ 1

Author(s):

Níckolas Santana

Keyword(s):

Remote Sensing ◽

Time Series ◽

Trend Analysis ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Management Policy ◽

Brazilian Savanna ◽

Vegetation Phenology ◽

Modis Ndvi ◽

Fire Recurrence

Fire is one of the main modeling agents of savanna ecosystems, affecting their distribution, physiognomy and species diversity. Changes in the natural fire regime on savannas cause disturbances in the structural characteristics of vegetation. Theses disturbances can be effectively monitored by time series of remote sensing data in different terrestrial ecosystems such as savannas. This study used trend analysis in NDVI (Normalized Difference Vegetation Index)–MODIS (Moderate Resolution Imaging Spectroradiometer) time series to evaluate the influence of different fire recurrences on vegetation phenology of the Brazilian savanna in the period from 2001 to 2016. The trend analysis indicated several factors responsible for changes in vegetation: (a) The absence of fire in savanna phytophysiognomies causes a constant increase in MODIS–NDVI, ranging from 0.001 to 0.002 per year, the moderate presence of fire in these areas does not cause significant changes, while the high recurrence results in decreases of MODIS–NDVI, ranging from −0.002 to −0.008 per year; (b) Forest areas showed a high decrease in NDVI, reaching up to −0.009 MODIS–NDVI per year, but not related to fire recurrence, indicating the high degradation of these phytophysiognomies; (c) Changes in vegetation are highly connected to the protection status of the area, such as areas of integral protection or sustainable use, and consequently their conservation status. Areas with greater vegetation conservation had more than 70% of positive changes in pixels with significant tendencies. Absence or presence of fire are the main agents of vegetation change in areas with lower anthropic influence. These results reinforce the need for a suitable fire management policy for the different types of Cerrado phytophysiognomies, in addition to highlighting the efficiency of remote sensing time series for evaluation of vegetation phenology.

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The Vegetation Remote Sensing Phenology of Qinling Mountains Based on the NDVI and the Response of Temperature to it

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.700.394 ◽

2014 ◽

Vol 700 ◽

pp. 394-399 ◽

Cited By ~ 1

Author(s):

Xin Ping Ma ◽

Hong Ying Bai ◽

Ying Na He ◽

Shu Heng Li

Keyword(s):

Remote Sensing ◽

Time Series ◽

Vegetation Phenology ◽

Qinling Mountains ◽

Threshold Method ◽

Climate Change Research ◽

Phenological Data ◽

Growing Period ◽

Macro Scale ◽

Ndvi Time Series

The acquisition vegetation phenology information by using time series of satellite data is an important aspect of the application of remote sensing and climate change research . Based on the MODOS NDVI time series of images in 2000-2010, Dynamic threshold method and GIS tools were used to extract the vegetation phenology parameters of Qinling Mountains in 2000-2010 , the accuracy of remote sensing phenology results was verified combined with the measured phenological data, And analyzed the characteristis of phenological variation and the relationship between temperature changes and the phenology of Qinling region,and quantified the extent of temperature change on vegetation phenology in a macro scale. Calculated :the trend of vegetation phenology variation based on the NDVI and the results of phenological data are consistent. Results show that NDVI has good revealed effect on vegetation phenology; From 2000 to 2010,it ahead of 1.8 days at the beginning period of vegetation phenology and late back 1.2 days at the end period ; The start phenology NDVI was generally greater than the late phenology on spatial distribution; The effective temperatures and the temperature in spring, growing period had a maximum influence on NDVI at beginning phenology period,the temperatures in summer and autumn had greater impact on the final NDVI .

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