30-Year Land Surface Trend from AVHRR-Based Global Vegetation Health Data

Urban forests contribute significantly to the ecological integrity of urban areas and the quality of life of urban dwellers through air quality control, energy conservation, improving urban hydrology, and regulation of land surface temperatures (LST). However, urban forests are under threat due to human activities, natural calamities, and bioinvasion continually decimating forest cover. Few studies have used fine-scaled Earth observation data to understand the dynamics of tree cover loss in urban forests and the sustainability of such forests in the face of increasing urban population. The aim of this work was to quantify the spatial and temporal changes in urban forest characteristics and to assess the potential drivers of such changes. We used data on tree cover, normalized difference vegetation index (NDVI), and land cover change to quantify tree cover loss and changes in vegetation health in urban forests within the Nairobi metropolitan area in Kenya. We also used land cover data to visualize the potential link between tree cover loss and changes in land use characteristics. From approximately 6600 hectares (ha) of forest land, 720 ha have been lost between 2000 and 2019, representing about 11% loss in 20 years. In six of the urban forests, the trend of loss was positive, indicating a continuing disturbance of urban forests around Nairobi. Conversely, there was a negative trend in the annual mean NDVI values for each of the forests, indicating a potential deterioration of the vegetation health in the forests. A preliminary, visual inspection of high-resolution imagery in sample areas of tree cover loss showed that the main drivers of loss are the conversion of forest lands to residential areas and farmlands, implementation of big infrastructure projects that pass through the forests, and extraction of timber and other resources to support urban developments. The outcome of this study reveals the value of Earth observation data in monitoring urban forest resources.

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The Jena Diversity-Dynamic Global Vegetation Model (JeDi-DGVM): a diverse approach to representing terrestrial biogeography and biogeochemistry based on plant functional trade-offs

Biogeosciences ◽

10.5194/bg-10-4137-2013 ◽

2013 ◽

Vol 10 (6) ◽

pp. 4137-4177 ◽

Cited By ~ 121

Author(s):

R. Pavlick ◽

D. T. Drewry ◽

K. Bohn ◽

B. Reu ◽

A. Kleidon

Keyword(s):

Functional Diversity ◽

Land Surface ◽

Grid Cell ◽

Surface Fluxes ◽

Growth Strategies ◽

Vegetation Model ◽

Terrestrial Biosphere ◽

Dynamic Global Vegetation Model ◽

Trade Offs ◽

Global Vegetation

Abstract. Terrestrial biosphere models typically abstract the immense diversity of vegetation forms and functioning into a relatively small set of predefined semi-empirical plant functional types (PFTs). There is growing evidence, however, from the field ecology community as well as from modelling studies that current PFT schemes may not adequately represent the observed variations in plant functional traits and their effect on ecosystem functioning. In this paper, we introduce the Jena Diversity-Dynamic Global Vegetation Model (JeDi-DGVM) as a new approach to terrestrial biosphere modelling with a richer representation of functional diversity than traditional modelling approaches based on a small number of fixed PFTs. JeDi-DGVM simulates the performance of a large number of randomly generated plant growth strategies, each defined by a set of 15 trait parameters which characterize various aspects of plant functioning including carbon allocation, ecophysiology and phenology. Each trait parameter is involved in one or more functional trade-offs. These trade-offs ultimately determine whether a strategy is able to survive under the climatic conditions in a given model grid cell and its performance relative to the other strategies. The biogeochemical fluxes and land surface properties of the individual strategies are aggregated to the grid-cell scale using a mass-based weighting scheme. We evaluate the simulated global biogeochemical patterns against a variety of field and satellite-based observations following a protocol established by the Carbon-Land Model Intercomparison Project. The land surface fluxes and vegetation structural properties are reasonably well simulated by JeDi-DGVM, and compare favourably with other state-of-the-art global vegetation models. We also evaluate the simulated patterns of functional diversity and the sensitivity of the JeDi-DGVM modelling approach to the number of sampled strategies. Altogether, the results demonstrate the parsimonious and flexible nature of a functional trade-off approach to global vegetation modelling, i.e. it can provide more types of testable outputs than standard PFT-based approaches and with fewer inputs. The approach implemented here in JeDi-DGVM sets the foundation for future applications that will explore the impacts of explicitly resolving diverse plant communities, allowing for a more flexible temporal and spatial representation of the structure and function of the terrestrial biosphere.

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Application of Vegetation Health Data and Products for Monitoring Food Security

Remote Sensing for Food Security - Sustainable Development Goals Series ◽

10.1007/978-3-319-96256-6_10 ◽

2018 ◽

pp. 225-241

Author(s):

Felix Kogan

Keyword(s):

Food Security ◽

Health Data ◽

Vegetation Health

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Changing sensitivity of global vegetation productivity to hydro-climate drivers

10.5194/egusphere-egu21-1783 ◽

2021 ◽

Author(s):

Wantong Li ◽

Matthias Forkel ◽

Mirco Migliavacca ◽

Markus Reichstein ◽

Sophia Walther ◽

...

Keyword(s):

Soil Moisture ◽

Land Surface ◽

Root Zone ◽

Growing Season ◽

Climatic Conditions ◽

Terrestrial Vegetation ◽

Spatial And Temporal Patterns ◽

Vegetation Productivity ◽

Area Index ◽

Global Vegetation

<p>Terrestrial vegetation couples&#160;the global water, energy and carbon exchange between the atmosphere and the land surface. Thereby, vegetation productivity is determined by a multitude of energy- and water-related variables. While the emergent sensitivity of productivity to these variables has been inferred from Earth observations, its temporal evolution&#160;during the last decades is unclear, as well as potential changes in response to trends in hydro-climatic conditions.&#160;In this study, we analyze the changing sensitivity of global vegetation productivity to hydro-climate&#160;conditions by using satellite-observed vegetation indices&#160;(i.e. NDVI)&#160;at the monthly timescale from 1982&#8211;2015. Further, we&#160;repeat the analysis&#160;with simulated leaf area index and gross primary productivity from the TRENDY vegetation models, and contrast the findings with the observation-based results. We train a&#160;random forest model to predict anomalies of productivity from&#160;a comprehensive set of hydro-meteorological variables&#160;(temperature, solar radiation, vapor pressure deficit, surface and root-zone soil moisture and precipitation), and to infer the sensitivity to each of these variables. By&#160;training models from&#160;temporal independent subsets of the data we detect the evolution of sensitivity&#160;across time. Results based on observations&#160;show that vegetation sensitivity to energy- and water-related variables has significantly changed&#160;in many regions across the globe. In particular we find decreased (increased) sensitivity to temperature in very warm (cold) regions. Thereby, the magnitude of the sensitivity tends to differ between the early and late growing seasons. Likewise, we find changing sensitivity&#160;to root-zone soil moisture with increases predominantly in the early growing season and decreases in the late growing season.&#160;For better understanding the mechanisms behind the sensitivity changes, we analyse land-cover changes, hydro-climatic trends, and abrupt disturbances&#160;(e.g. drought, heatwave events or fires could result in breaking points of sensitivity evolution in the local interpretation). In summary, this study sheds light on how and where vegetation productivity changes&#160;its response to&#160;the drivers under&#160;climate change, which can help to understand possibly resulting&#160;changes in spatial and temporal patterns of land carbon uptake.</p>

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A process-based <i>Sphagnum</i> plant-functional-type model for implementation in the TRIFFID Dynamic Global Vegetation Model

10.5194/gmd-2019-51 ◽

2019 ◽

Cited By ~ 1

Author(s):

Richard Coppell ◽

Emanuel Gloor ◽

Joseph Holden

Keyword(s):

Water Table ◽

Primary Productivity ◽

Photosynthetically Active Radiation ◽

Land Surface ◽

Net Primary Productivity ◽

Type Model ◽

Surface Model ◽

Dynamic Global Vegetation Model ◽

Active Radiation ◽

Global Vegetation

Abstract. Peatlands are important carbon stores and Sphagnum moss represents a critical peatland genus contributing to carbon exchange and storage. However, gas fluxes in Sphagnum-dominated systems are poorly represented in Dynamic Global Vegetation Models (DGVMs) which simulate, via incorporation of Plant Functional Types (PFTs), biogeochemical and energy fluxes between vegetation, the land surface and the atmosphere. Mechanisms characterised by PFTs within DGVMs include photosynthesis, respiration and competition and, in more recent DGVMs, sub-daily gas-exchange processes regulated by leaf 10 stomata. However, Sphagnum, like all mosses, are non-vascular plants and do not exhibit stomatal regulation. In order to achieve a level of process detail consistent with existing vascular vegetation PFTs within DGVMs, this paper describes a new process-based non-vascular-PFT model that is implemented within the TRIFFID DGVM used by the JULES land surface model. The new PFT model was tested against extant published field and laboratory studies of peat assemblage-net primary productivity, assemblage-gross primary productivity, assemblage respiration, water-table position, incoming 15 photosynthetically active radiation, temperature, and canopy dark respiration. The PFT model’s parameters were roughly tuned and the PFT model easily produced curves of the correct shape for peat assemblage-net primary productivity against water-table position, incoming photosynthetically active radiation and temperature, suggesting that it replicates the internal productivity mechanism of Sphagnum for the first time. Minor modifications should also allow it to be used across a range of other bryophytes enabling this non-vascular PFT model to have enhanced functionality.

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Modifying a dynamic global vegetation model for simulating large spatial scale land surface water balances

Hydrology and Earth System Sciences ◽

10.5194/hess-16-2547-2012 ◽

2012 ◽

Vol 16 (8) ◽

pp. 2547-2565 ◽

Cited By ~ 4

Author(s):

G. Tang ◽

P. J. Bartlein

Keyword(s):

Soil Moisture ◽

Surface Water ◽

Surface Runoff ◽

Land Surface ◽

Surface Hydrology ◽

Large Spatial Scale ◽

Land Surface Water ◽

Land Surface Hydrology ◽

Water Balances ◽

Global Vegetation

Abstract. Satellite-based data, such as vegetation type and fractional vegetation cover, are widely used in hydrologic models to prescribe the vegetation state in a study region. Dynamic global vegetation models (DGVM) simulate land surface hydrology. Incorporation of satellite-based data into a DGVM may enhance a model's ability to simulate land surface hydrology by reducing the task of model parameterization and providing distributed information on land characteristics. The objectives of this study are to (i) modify a DGVM for simulating land surface water balances; (ii) evaluate the modified model in simulating actual evapotranspiration (ET), soil moisture, and surface runoff at regional or watershed scales; and (iii) gain insight into the ability of both the original and modified model to simulate large spatial scale land surface hydrology. To achieve these objectives, we introduce the "LPJ-hydrology" (LH) model which incorporates satellite-based data into the Lund-Potsdam-Jena (LPJ) DGVM. To evaluate the model we ran LH using historical (1981–2006) climate data and satellite-based land covers at 2.5 arc-min grid cells for the conterminous US and for the entire world using coarser climate and land cover data. We evaluated the simulated ET, soil moisture, and surface runoff using a set of observed or simulated data at different spatial scales. Our results demonstrate that spatial patterns of LH-simulated annual ET and surface runoff are in accordance with previously published data for the US; LH-modeled monthly stream flow for 12 major rivers in the US was consistent with observed values respectively during the years 1981–2006 (R2 > 0.46, p < 0.01; Nash-Sutcliffe Coefficient > 0.52). The modeled mean annual discharges for 10 major rivers worldwide also agreed well (differences < 15%) with observed values for these rivers. Compared to a degree-day method for snowmelt computation, the addition of the solar radiation effect on snowmelt enabled LH to better simulate monthly stream flow in winter and early spring for rivers located at mid-to-high latitudes. In addition, LH-modeled monthly soil moisture for the state of Illinois (US) agreed well (R2 = 0.79, p < 0.01) with observed data for the years 1984–2001. Overall, this study justifies both the feasibility of incorporating satellite-based land covers into a DGVM and the reliability of LH to simulate land-surface water balances. To better estimate surface/river runoff at mid-to-high latitudes, we recommended that LPJ-DGVM considers the effects of solar radiation on snowmelt.

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Observational Evidence of Irrigation Influence on Vegetation Health and Land Surface Temperature in India

Geophysical Research Letters ◽

10.1029/2019gl084367 ◽

2019 ◽

Vol 46 (22) ◽

pp. 13441-13451 ◽

Cited By ~ 4

Author(s):

Anukesh Krishnankutty Ambika ◽

Vimal Mishra

Keyword(s):

Surface Temperature ◽

Land Surface Temperature ◽

Land Surface ◽

Observational Evidence ◽

Vegetation Health

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Mapping Periodic Patterns of Global Vegetation Based on Spectral Analysis of NDVI Time Series

Remote Sensing ◽

10.3390/rs11212497 ◽

2019 ◽

Vol 11 (21) ◽

pp. 2497

Author(s):

Laura Recuero ◽

Javier Litago ◽

Jorge E. Pinzón ◽

Margarita Huesca ◽

Maria C. Moyano ◽

...

Keyword(s):

Time Series ◽

Spectral Analysis ◽

Land Surface ◽

Arid Regions ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Periodic Patterns ◽

Annual Cycles ◽

Ndvi Time Series ◽

Global Vegetation

Vegetation seasonality assessment through remote sensing data is crucial to understand ecosystem responses to climatic variations and human activities at large-scales. Whereas the study of the timing of phenological events showed significant advances, their recurrence patterns at different periodicities has not been widely study, especially at global scale. In this work, we describe vegetation oscillations by a novel quantitative approach based on the spectral analysis of Normalized Difference Vegetation Index (NDVI) time series. A new set of global periodicity indicators permitted to identify different seasonal patterns regarding the intra-annual cycles (the number, amplitude, and stability) and to evaluate the existence of pluri-annual cycles, even in those regions with noisy or low NDVI. Most of vegetated land surface (93.18%) showed one intra-annual cycle whereas double and triple cycles were found in 5.58% of the land surface, mainly in tropical and arid regions along with agricultural areas. In only 1.24% of the pixels, the seasonality was not statistically significant. The highest values of amplitude and stability were found at high latitudes in the northern hemisphere whereas lowest values corresponded to tropical and arid regions, with the latter showing more pluri-annual cycles. The indicator maps compiled in this work provide highly relevant and practical information to advance in assessing global vegetation dynamics in the context of global change.

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