The potential contribution of satellite remote sensing to the understanding of arid lands processes

Verstraete, M. M.; Pinty, B.

doi:10.1007/bf00036048

Cited by 33 publications

(11 citation statements)

References 20 publications

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“…In support of these applications, many remote sensing approaches have been developed to estimate LAI at spatial scales ranging from plot (< 10 m 2 ) to large regions (> 100 km 2 ) (e.g. Asrar et al ., 1986; Verstraete & Pinty, 1991; Gobron et al ., 1997). These efforts require calibration and evaluation information, and this LAI database could contribute in this way.…”

Section: Resultsmentioning

confidence: 99%

Global synthesis of leaf area index observations: implications for ecological and remote sensing studies

Asner

Scurlock

Hicke

2003

Global Ecology and Biogeography

768

457

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Aim We present the first global synthesis of plant canopy leaf area index (LAI) measurements from more than 1000 published estimates representing ∼400 unique field sites. LAI is a key variable for regional and global models of biosphere‐atmosphere exchanges of energy, carbon dioxide, water vapour, and other materials. Location The location is global, geographically distributed. Results Biomes with LAI values well represented in the literature included croplands, forests and plantations. Biomes not well represented were deserts, shrublands, tundra and wetlands. Nearly 40% of the records in the database were published in the past 10 years (1991–2000), with a further 20% collected between 1981 and 1990. Mean (± SD) LAI, distributed between 15 biome classes, ranged from 1.3 ± 0.9 for deserts to 8.7 ± 4.3 for tree plantations, with temperate evergreen forests (needleleaf and broadleaf) displaying the highest average LAI (5.1–6.7) among the natural terrestrial vegetation classes. Following a statistical outlier analysis, the global mean (± SD) LAI decreased from 5.2 (4.1) to 4.5 (2.5), with a maximum LAI of 18. Biomes with the highest LAI values were plantations > temperate evergreen forests > wetlands. Those with the lowest LAI values were deserts < grasslands < tundra. Mean LAI values for all biomes did not differ statistically by the methodology employed. Direct and indirect measurement approaches produced similar LAI results. Mean LAI values for all biomes combined decreased significantly in the 1990s, a period of substantially more studies and improved methodologies. Main conclusions Applications of the LAI database span a wide range of ecological, biogeochemical, physical, and climate research areas. The data provide input to terrestrial ecosystem and land‐surface models, for evaluation of global remote sensing products, for comparisons to field studies, and other applications. Example uses of the database for global plant productivity, fractional energy absorption, and remote sensing studies are highlighted.

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Section: Resultsmentioning

confidence: 99%

Global synthesis of leaf area index observations: implications for ecological and remote sensing studies

Asner

Scurlock

Hicke

2003

Global Ecology and Biogeography

768

457

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“…The standard error of the y-axis predicted by the x-axis is also much lower at 0)44. The NDVI has been shown in the literature to relate strongly to changes in green vegetation cover or biomass, but not to dry vegetation or vegetation with a low green cover (Boutton & Tieszen, 1983;Huete & Jackson, 1987;Beck et al, 1990;Verstraete & Pinty, 1991). The raw and averaged data sets in this study incorporated data from the period before and after the rainy season.…”

Section: Resultsmentioning

confidence: 99%

Linear regression relationships between NDVI, vegetation and rainfall in Etosha National Park, Namibia

Plessis

1999

Journal of Arid Environments

129

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“…Remote sensing has contributed greatly to our understanding of vegetation structure in drylands (e.g. Verstraete & Pinty, 1991; Myneni et al , 1996; Prince et al , 1998). However, differential changes in vegetation cover and phenology – resulting from climate variation, desertification, or both – are difficult to determine from traditional remote‐sensing observations.…”

Section: Introductionmentioning

confidence: 99%

Desertification alters regional ecosystem–climate interactions

Asner

Heidebrecht

2004

Global Change Biology

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We studied the spatial patterns and temporal dynamics of vegetation structural responses to precipitation variation in grassland, transitional, and desertified-shrubland ecosystems in an 800 km 2 region of Northern Chihuahua, USA. Airborne high-fidelity imaging spectroscopy data collected from 1997 to 2001 provided spatially detailed measurements of photosynthetic and senescent canopy cover and bare soil extent. The observations were made following wintertime and summer monsoonal rains, which varied in magnitude by 4300% over the study period, allowing an assessment of ecosystem responses to climate variation in the context of desertification.Desertification caused a persistent increase in both photosynthetic vegetation (PV) and bare soil cover, and a lasting decrease in nonphotosynthetic vegetation (NPV). We did not observe a change in the spatial variability of PV cover, but its temporal variation decreased substantially. In contrast, desertification caused the spatial variability of NPV to increase markedly, while its temporal variation did not change. Both the spatial and temporal variation of exposed bare surfaces decreased with desertification. Desertification appeared to be linked to a shift in seasonal precipitation use by vegetation from mainly summer to winter inputs, resulting in an apparent decoupling of vegetation responses to inter-annual monsoonal variation. Higher winter rainfall led to decreased springtime spatial variability in the PV cover of desertified areas. Higher summer rainfall resulted in decreased PV cover variation in grassland, transition and desertifiedshrubland regions. The effects of desertification on NPV dynamics were more than three times greater than on PV or bare soil dynamics. Using remotely sensed PV and NPV as proxies for net primary production (NPP) and litter dynamics, respectively, we estimated that desertification decreases the temporal variability of NPP and increases spatial variation of litter production and loss. Quantitative studies of surface biological materials and ecosystem processes can now be measured with high 'structural' detail using imaging spectroscopy and shortwave-infrared spectral mixture analysis.

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The potential contribution of satellite remote sensing to the understanding of arid lands processes

Cited by 33 publications

References 20 publications

Global synthesis of leaf area index observations: implications for ecological and remote sensing studies

Global synthesis of leaf area index observations: implications for ecological and remote sensing studies

Linear regression relationships between NDVI, vegetation and rainfall in Etosha National Park, Namibia

Desertification alters regional ecosystem–climate interactions

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