Staff Publications

Staff Publications

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    'Staff publications' is the digital repository of Wageningen University & Research

    'Staff publications' contains references to publications authored by Wageningen University staff from 1976 onward.

    Publications authored by the staff of the Research Institutes are available from 1995 onwards.

    Full text documents are added when available. The database is updated daily and currently holds about 240,000 items, of which 72,000 in open access.

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    Monitoring forest cover loss using multiple data streams, a case study of a tropical dry forest in Bolivia
    Dutrieux, L.P. ; Verbesselt, J. ; Kooistra, L. ; Herold, M. - \ 2015
    ISPRS Journal of Photogrammetry and Remote Sensing 107 (2015). - ISSN 0924-2716 - p. 112 - 125.
    landsat time-series - structural-change - vegetation indexes - rainfall products - detecting trends - east-africa - amazon - disturbance - validation - modis
    Automatically detecting forest disturbances as they occur can be extremely challenging for certain types of environments, particularly those presenting strong natural variations. Here, we use a generic structural break detection framework (BFAST) to improve the monitoring of forest cover loss by combining multiple data streams. Forest change monitoring is performed using Landsat data in combination with MODIS or rainfall data to further improve the modelling and monitoring. We tested the use of the Normalized Difference Vegetation Index (NDVI) from the Moderate Resolution Imaging Spectroradiometer (MODIS) with varying spatial aggregation window sizes as well as a rainfall derived index as external regressors. The method was evaluated on a dry tropical forest area in lowland Bolivia where forest cover loss is known to occur, and we validated the results against a set of ground truth samples manually interpreted using the TimeSync environment. We found that the addition of an external regressor allows to take advantage of the difference in spatial extent between human induced and naturally induced variations and only detect the processes of interest. Of all configurations, we found the 13 by 13 km MODIS NDVI window to be the most successful, with an overall accuracy of 87%. Compared with a single pixel approach, the proposed method produced better time-series model fits resulting in increases of overall accuracy (from 82% to 87%), and decrease in omission and commission errors (from 33% to 24% and from 3% to 0% respectively). The presented approach seems particularly relevant for areas with high inter-annual natural variability, such as forests regularly experiencing exceptional drought events.
    Assessing global land cover reference datasets for different user communities
    Tsendbazar, N.E. ; Bruin, S. de; Herold, M. - \ 2015
    ISPRS Journal of Photogrammetry and Remote Sensing 103 (2015). - ISSN 0924-2716 - p. 93 - 114.
    classification accuracy assessment - thematic map accuracy - validation data set - igbp discover - design - products - modis - challenges - imagery - area
    Global land cover (GLC) maps and assessments of their accuracy provide important information for different user communities. To date, there are several GLC reference datasets which are used for assessing the accuracy of specific maps. Despite significant efforts put into generating them, their availability and role in applications outside their intended use have been very limited. This study analyses metadata information from 12 existing and forthcoming GLC reference datasets and assesses their characteristics and potential uses in the context of 4 GLC user groups, i.e., climate modellers requiring data on Essential Climate Variables (ECV), global forest change analysts, the GEO Community of Practice for Global Agricultural Monitoring and GLC map producers. We assessed user requirements with respect to the sampling scheme, thematic coverage, spatial and temporal detail and quality control of the GLC reference datasets. Suitability of the datasets is highly dependent upon specific applications by the user communities considered. The LC-CCI, GOFC-GOLD, FAO-FRA and Geo-Wiki datasets had the broadest applicability for multiple uses. The re-usability of the GLC reference datasets would be greatly enhanced by making them publicly available in an expert framework that guides users on how to use them for specific applications.
    El Nino-La Nina cycle and recent trends in continental evaporation
    Miralles, D.G. ; Berg, M.J. van den; Gash, J.H. ; Parinussa, R.M. ; Jeu, R.A.M. ; Beck, H.E. ; Holmes, T.R.H. ; Jimenez, C. ; Verhoest, N.E.C. ; Dorigo, W.A. ; Teuling, A.J. ; Dolman, A.J. - \ 2014
    Nature Climate Change 4 (2014)2. - ISSN 1758-678X - p. 122 - 126.
    global water cycle - precipitation - climate - variability - ocean - intensification - avhrr - modis
    The hydrological cycle is expected to intensify in response to global warming(1-3). Yet, little unequivocal evidence of such an acceleration has been found on a global scale(4-6). This holds in particular for terrestrial evaporation, the crucial return flow of water from land to atmosphere(7). Here we use satellite observations to reveal that continental evaporation has increased in northern latitudes, at rates consistent with expectations derived from temperature trends. However, at the global scale, the dynamics of the El Nino/Southern Oscillation (ENSO) have dominated the multi-decadal variability. During El Nino, limitations in terrestrial moisture supply result in vegetation water stress and reduced evaporation in eastern and central Australia, southern Africa and eastern South America. The opposite situation occurs during La Nina. Our results suggest that recent multi-year declines in global average continental evaporation(8,9) reflect transitions to El Nino conditions, and are not the consequence of a persistent reorganization of the terrestrial water cycle. Future changes in continental evaporation will be determined by the response of ENSO to changes in global radiative forcing, which still remains highly uncertain(10,11).
    A cost-effective approach for improving the quality of soil sealing change detection from Landsat imagery
    Smiraglia, D. ; Rinaldo, S. ; Ceccarelli, T. ; Bajocco, S. ; Salvati, L. ; Ricotta, C. ; Perini, L. - \ 2014
    European Journal of Remote Sensing 47 (2014). - ISSN 2279-7254 - p. 805 - 819.
    urban - modis - tm - transformation - segmentation - phenology - sprawl - region - ndvi - area
    The aim of this study is to develop a cost-effective approach for soil sealing change detection integrating radiometric analysis, multi-resolution segmentation and object-based classifiers in two study areas in Italy: Campania region and Veneto region. The integrated approach uses multi-temporal satellite images and CORINE Land Cover (CLC) maps. A good overall accuracy was obtained for the soil sealing maps produced. The results show an improvement in terms of size of the minimum mapping unit and of the changed object (1,44 ha in both cases) in respect to the CLC. The approach proves to be cost-effective given the data which are provided at low or no cost and as well as the level of automation achievable.
    Application of remote sensing to understanding fire regimes and biomass burning emissions of the tropical Andes
    Oliveras Menor, I. ; Anderson, L.O. ; Malhi, Y. - \ 2014
    Global Biogeochemical Cycles 28 (2014)4. - ISSN 0886-6236 - p. 480 - 496.
    burned-area - amazonian forest - southern africa - carbon - modis - tree - variability - mortality - deforestation - biodiversity
    In the tropical Andes, there have been very few systematic studies aimed at understanding the biomass burning dynamics in the area. This paper seeks to advance on our understanding of burning regimes in this region, with the first detailed and comprehensive assessment of fire occurrence and the derived gross biomass burning emissions of an area of the Peruvian tropical Andes. We selected an area of 2.8 million hectares at altitudes over 2000¿m. We analyzed fire occurrence over a 12 year period with three types of satellite data. Fire dynamics showed a large intra-annual and interannual variability, with most fires occurring May–October (the period coinciding with the dry season). Total area burned decreased with increasing rainfall until a given rainfall threshold beyond which no relationship was found. The estimated fire return interval (FRI) for the area is 37¿years for grasslands, which is within the range reported for grasslands, and 65¿years for forests, which is remarkably shorter than other reported FRI in tropical moist forests. The greatest contribution (60–70%, depending on the data source) to biomass burning emissions came from burned montane cloud forests (4.5 million Mg CO2 over the study period), despite accounting for only 7.4–10% of the total burned area. Gross aboveground biomass emissions (7.55¿±¿2.14 Tg CO2; 0.43¿±¿0.04 Tg CO; 24,012¿±¿2685¿Mg CH4 for the study area) were larger than previously reported for the tropical Andes.
    Global cropland monthly gross primary production in the year 2000
    Chen, T. ; Werf, G.R. van der; Gobron, N. ; Moors, E.J. ; Dolman, A.J. - \ 2014
    Biogeosciences 11 (2014). - ISSN 1726-4170 - p. 3871 - 3880.
    net primary production - light-use efficiency - ecosystem exchange - constant fraction - terrestrial gross - model - forest - modis - respiration - climate
    Croplands cover about 12% of the ice-free terrestrial land surface. Compared with natural ecosystems, croplands have distinct characteristics due to anthropogenic influences. Their global gross primary production (GPP) is not well constrained and estimates vary between 8.2 and 14.2 Pg C yr-1. We quantified global cropland GPP using a light use efficiency (LUE) model, employing satellite observations and survey data of crop types and distribution. A novel step in our analysis was to assign a maximum light use efficiency estimate (¿*GPP) to each of the 26 different crop types, instead of taking a uniform value as done in the past. These ¿*GPP values were calculated based on flux tower CO2 exchange measurements and a literature survey of field studies, and ranged from 1.20 to 2.96 g C MJ-1. Global cropland GPP was estimated to be 11.05 Pg C yr-1 in the year 2000. Maize contributed most to this (1.55 Pg C yr-1), and the continent of Asia contributed most with 38.9% of global cropland GPP. In the continental United States, annual cropland GPP (1.28 Pg C yr-1) was close to values reported previously (1.24 Pg C yr-1) constrained by harvest records, but our estimates of ¿*GPP values were considerably higher. Our results are sensitive to satellite information and survey data on crop type and extent, but provide a consistent and data-driven approach to generate a look-up table of ¿*GPP for the 26 crop types for potential use in other vegetation models.
    Validating gap-filling of Landsat ETM+ satellite images in the Golestan Province, Iran
    Mohammdy, M. ; Moradi, H.R. ; Zeinivand, H. ; Temme, A.J.A.M. ; Pourghasemi, H.R. ; Alizadeh, H. - \ 2014
    Arabian Journal of Geosciences 7 (2014)9. - ISSN 1866-7511 - p. 3633 - 3638.
    slc-off images - modis - cover
    The Landsat series of satellites provides a valuable data source for land surface mapping and monitoring. Unfortunately, the scan line corrector (SLC) of the Landsat7 Enhanced Thematic Mapper plus (ETM+) sensor failed on May 13, 2003. This problem resulted in about 22 % of the pixels per scene not being scanned and has seriously limited the scientific applications of ETM+ data. A number of methods have been developed to fill the gaps in the incorrect images. Most of these methods have problems in heterogeneous landscapes. We applied and validated a simple and effective gap-fill algorithm developed by the US Geological Survey to a study area in the Golestan Province in the north of Iran. This algorithm operates under the assumption that the same-class neighboring pixels around the unscanned pixels have similar spectral characteristics, and that these neighboring and unscanned pixels share patterns of spectral differences between dates. For validation, unsupervised land use classification was performed on both gap-filled SLC-off data and the original “sound” data set. Classification results and accuracies were very comparable
    Sensitivity of the agro-ecosystem in the Ganges Basin to inter-annual rainfall variability and associated changes in land use
    Siderius, C. ; Hellegers, P.J.G.J. ; Mishra, A. ; Ierland, E.C. van; Kabat, P. - \ 2014
    International Journal of Climatology 34 (2014)10. - ISSN 0899-8418 - p. 3066 - 3077.
    climate variability - food security - ndvi data - india - vegetation - modis - dynamics - dataset - areas
    The rate of growth in agricultural production has been decreasing in several regions of the world in recent years. The availability of water, which is one of the main inputs, is becoming limiting and more variable. In this article, we study the sensitivity of the agroecosystem to rainfall variability in order to identify vulnerable areas. We applied a longitudinal assessment of remote sensing time-series data, using the correlation between inter-annual rainfall anomalies and anomalies in Normalized Difference Vegetation Index (NDVI), a proxy for crop production. With a novel approach, we then identified whether the sensitivity results from a variation in crop growth or from a deliberate adjustment in the cropping pattern, reflecting a coping strategy. In our case study area, the Ganges basin, 25% of the basin area showed a significant correlation (p¿
    Trend change detection in NDVI time series: Effects of inter-annual variability and methodology
    Forkel, M. ; Carvalhais, N. ; Verbesselt, J. ; Mahecha, M.D. ; Neigh, C. ; Reichstein, M. - \ 2013
    Remote Sensing 5 (2013)5. - ISSN 2072-4292 - p. 2113 - 2144.
    spectral vegetation indexes - satellite data - north-america - boreal forest - el-nino - alaska - modis - climate - disturbance - accuracy
    Changing trends in ecosystem productivity can be quantified using satellite observations of Normalized Difference Vegetation Index (NDVI). However, the estimation of trends from NDVI time series differs substantially depending on analyzed satellite datase
    Linear trends in seasonal vegetation time series and the modifiable temporal unit problem
    Jong, R. de; Bruin, S. de - \ 2012
    Biogeosciences 9 (2012). - ISSN 1726-4170 - p. 71 - 77.
    proxy global assessment - land degradation - sahel - environment - america - modis - ndvi
    Time series of vegetation indices (VI) derived from satellite imagery provide a consistent monitoring system for terrestrial plant productivity. They enable detection and quantification of gradual changes within the time frame covered, which are of crucial importance in global change studies, for example. However, VI time series typically contain a strong seasonal signal which complicates change detection. Commonly, trends are quantified using linear regression methods, while the effect of serial autocorrelation is remediated by temporal aggregation over bins having a fixed width. Aggregating the data in this way produces temporal units which are modifiable. Analogous to the well-known Modifiable Area Unit Problem (MAUP), the way in which these temporal units are defined may influence the fitted model parameters and therefore the amount of change detected. This paper illustrates the effect of this Modifiable Temporal Unit Problem (MTUP) on a synthetic data set and a real VI data set. Large variation in detected changes was found for aggregation over bins that mismatched full lengths of vegetative cycles, which demonstrates that aperiodicity in the data may influence model results. Using 26 yr of VI data and aggregation over full-length periods, deviations in VI gains of less than 1% were found for annual periods (with respect to seasonally adjusted data), while deviations increased up to 24% for aggregation windows of 5 yr. This demonstrates that temporal aggregation needs to be carried out with care in order to avoid spurious model results.
    Integration of multi-sensor data to assess grassland dynamics in a Yellow River sub-watershed
    Ouyang, W. ; Hao, F. ; Skidmore, A.K. ; Groen, T.A. ; Toxopeus, A.G. ; Wang, T. - \ 2012
    Ecological Indicators 18 (2012)1. - ISSN 1470-160X - p. 163 - 170.
    qinghai-xizang plateau - time-series - land-cover - west-africa - vegetation - modis - variability - patterns - imagery - china
    Grasslands form the dominant land cover in the upper reaches of the Yellow River and provide a reliable indicator by being strongly correlated with regional terrestrial ecological status. Remote sensing can provide information useful for vegetation quality assessments, but no single sensor can meet the needs for the high temporal-spatial resolution required for such assessments on a watershed scale. To observe long-term grassland dynamics in the Longliu Watershed located in the upper reaches of the Yellow River, Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat images were integrated to obtain Normalized Difference Vegetation Index (NDVI) data. The MODIS images were used to identify patterns of monthly variation. With the temporal dynamics of NDVI provided by the MODIS images, an exponential regression model was obtained that described the relationship between Julian day and NDVI. Four time-series data sets from multi-spectral sensors were constructed to obtain regional grassland NDVI information from 1977 to 2006 in the Longliu Watershed. Using the daily NDVI correlation coefficient, NDVI values for different days were obtained from Landsat series images, standardised to the same day and integrated into TM format by using NDVI coefficients between the four different sensors. Thus, the NDVI data obtained from multi-sensors on different days were integrated into a comparable format. A regression analysis correlating the NDVI data from two sensors with fresh grass biomass showed that the integration procedure was reliable.
    Vegetation NDVI Linked to Temperature and Precipitation in the Upper Catchments of Yellow River
    Hao, F. ; Zhang, X. ; Ouyang, W. ; Skidmore, A.K. ; Toxopeus, A.G. - \ 2012
    Environmental Modeling and Assessment 17 (2012)4. - ISSN 1420-2026 - p. 389 - 398.
    land-cover changes - qinghai-xizang plateau - net primary production - central new-mexico - tibetan plateau - modis - china - soil - climate - basin
    Vegetation in the upper catchment of Yellow River is critical for the ecological stability of the whole watershed. The dominant vegetation cover types in this region are grassland and forest, which can strongly influence the eco-environmental status of the whole watershed. The normalized difference vegetation index (NDVI) for grassland and forest has been calculated and its daily correlation models were deduced by Moderate Resolution Imaging Spectroradiometer products on 12 dates in 2000, 2003, and 2006. The responses of the NDVI values with the inter-annual grassland and forest to three climatic indices (i.e., yearly precipitation and highest and lowest temperature) were analyzed showing that, except for the lowest temperature, the yearly precipitation and highest temperature had close correlations with the NDVI values of the two vegetation communities. The value of correlation coefficients ranged from 0.815 to 0.951 (p <0.01). Furthermore, the interactions of NDVI values of vegetation with the climatic indicators at monthly interval were analyzed. The NDVI of vegetation and three climatic indices had strong positive correlations (larger than 0.733, p <0.01). The monthly correlations also provided the threshold values for the three climatic indictors, to be used for simulating vegetation growth grassland under different climate features, which is essential for the assessment of the vegetation growth and for regional environmental management.
    Pan-arctic land cover mapping and fire assessment for the ESA Data User Element Permafrost
    Urban, M. ; Hese, S. ; Herold, M. ; Pöcking, S. ; Schmullius, C. - \ 2010
    Photogrammetrie, Fernerkundung, Geoinformation 2010 (2010)4. - ISSN 1432-8364 - p. 283 - 293.
    carbon-cycle - vegetation - climate - avhrr - validation - emissions - products - wildfire - siberia - modis
    The paper presents first results of a pan-boreal scale land cover harmonization and classification. A methodology is presented that combines global and regional vegetation datasets to extract percentage cover information for different vegetation physiognomy and barren for the pan-arctic region within the ESA Data User Element Permafrost. Based on the legend description of each land cover product the datasets are harmonized into four LCCS (Land Cover Classification System) classifiers which are linked to the MODIS Vegetation Continuous Field (VCF) product. Harmonized land cover and Vegetation Continuous Fields products are combined to derive a best estimate of percentage cover information for trees, shrubs, herbaceous and barren areas for Russia. Future work will concentrate on the expansion of the developed methodology to the pan-arctic scale. Since the vegetation builds an isolation layer, which protects the permafrost from heat and cold temperatures, a degradation of this layer due to fire strongly influences the frozen conditions in the soil. Fire is an important disturbance factor which affects vast processes and dynamics in ecosystems (e.g. biomass, biodiversity, hydrology, etc.). Especially in North Eurasia the fire occupancy has dramatically increased in the last 50 years and has doubled in the 1990s with respect to the last five decades. A comparison of global and regional fire products has shown discrepancies between the amounts of burn scars detected by different algorithms and satellite data
    Phenological change detection while accounting for abrupt and gradual trends in satellite image time series
    Verbesselt, J. ; Hyndman, R. ; Zeileis, A. ; Culvenor, D. - \ 2010
    Remote Sensing of Environment 114 (2010)12. - ISSN 0034-4257 - p. 2970 - 2980.
    land-surface phenology - structural-change models - avhrr ndvi data - fourier-analysis - spatial-resolution - plant phenology - climate data - modis - variability - ecosystems
    A challenge in phenology studies is understanding what constitutes phenological change amidst background variation. The majority of phenological studies have focused on extracting critical points in the seasonal growth cycle, without exploiting the full temporal detail. The high degree of phenological variability between years demonstrates the necessity of distinguishing long-term phenological change from temporal variability. Here, we demonstrate the phenological change detection ability of a method for detecting change within time series. BFAST, Breaks For Additive Seasonal and Trend, integrates the decomposition of time series into trend, seasonal, and remainder components with methods for detecting change. We tested BFAST by simulating 16-day NDVI time series with varying amounts of seasonal amplitude and noise, containing abrupt disturbances (e.g. fires) and long-term phenological changes. This revealed that the method is able to detect the timing of phenological changes within time series while accounting for abrupt disturbances and noise. Results showed that the phenological change detection is influenced by the signal-to-noise ratio of the time series. Between different land cover types the seasonal amplitude varies and determines the signal-to-noise ratio, and as such the capacity to differentiate phenological changes from noise. Application of the method on 16-day NDVI MODIS images from 2000 until 2009 for a forested study area in south eastern Australia confirmed these results. It was shown that a minimum seasonal amplitude of 0.1 NDVI is required to detect phenological change within cleaned MODIS NDVI time series using the quality flags. BFAST identifies phenological change independent of phenological metrics by exploiting the full time series. The method is globally applicable since it analyzes each pixel individually without the setting of thresholds to detect change within a time series. Long-term phenological changes can be detected within NDVI time series of a large range of land cover types (e.g. grassland, woodlands and deciduous forests) having a seasonal amplitude larger than the noise level. The method can be applied to any time series data and it is not necessarily limited to NDVI
    Detecting trend and seasonal changes in satellite image time series
    Verbesselt, J. ; Hyndman, R. ; Newnham, G. ; Culvenor, D. - \ 2010
    Remote Sensing of Environment 114 (2010)1. - ISSN 0034-4257 - p. 106 - 115.
    forest disturbance detection - structural-change models - land-cover change - temporal-resolution - spatial-resolution - vegetation indexes - tasseled cap - modis - ndvi - ecosystems
    A wealth of remotely sensed image time series covering large areas is now available to the earth science community. Change detection methods are often not capable of detecting land cover changes within time series that are heavily influenced by seasonal climatic variations. Detecting change within the trend and seasonal components of time series enables the classification of different types of changes. Changes occurring in the trend component often indicate disturbances (e.g. fires, insect attacks), while changes occurring in the seasonal component indicate phenological changes (e.g. change in land cover type). A generic change detection approach is proposed for time series by detecting and characterizing Breaks For Additive Seasonal and Trend (BFAST). BFAST integrates the decomposition of time series into trend, seasonal, and remainder components with methods for detecting change within time series. BFAST iteratively estimates the time and number of changes, and characterizes change by its magnitude and direction. We tested BFAST by simulating 16-day Normalized Difference Vegetation Index (NDVI) time series with varying amounts of seasonality and noise, and by adding abrupt changes at different times and magnitudes. This revealed that BFAST can robustly detect change with different magnitudes (> 0.1 NDVI) within time series with different noise levels (0.01–0.07 s) and seasonal amplitudes (0.1–0.5 NDVI). Additionally, BFAST was applied to 16-day NDVI Moderate Resolution Imaging Spectroradiometer (MODIS) composites for a forested study area in south eastern Australia. This showed that BFAST is able to detect and characterize spatial and temporal changes in a forested landscape. BFAST is not specific to a particular data type and can be applied to time series without the need to normalize for land cover types, select a reference period, or change trajectory. The method can be integrated within monitoring frameworks and used as an alarm system to flag when and where changes occur
    Recent land degradation and improvement in China
    Bai, Z.G. ; Dent, D.L. - \ 2009
    Ambio 38 (2009)3. - ISSN 0044-7447 - p. 150 - 156.
    ndvi time-series - modis - avhrr - gpp - africa
    Land degradation is a global development and environment issue that afflicts China more than most countries in terms of the extent, economic impact, and number of people affected. Up-to-date, quantitative information is needed to support policy and action for food and water security, economic development, and environmental integrity. Data for a defined, recent period enable us to distinguish the legacy of historical land degradation from what is happening now. We define land degradation as long-term decline in ecosystem function and productivity and measure it by remote sensing of the normalized difference vegetation index (NDVI), the greenness index. NDVI may be translated to net primary productivity (NPP). Deviation from the norm serves as a proxy assessment of land degradation and improvement—if other factors that may be responsible are taken into account. These other factors include climate, which may be assessed by rain-use efficiency and energy-use efficiency. Analysis of the 23-year Global Inventory Modeling and Mapping Studies (GIMMS) NDVI data reveals that, in China over the period 1981–2003, NPP increased overall, but areas of declining climate-adjusted NPP comprise 23% of the country, mainly in south China. About 35% of China's population (457 million out of 1 317 million) depend on the degrading land. Degrading areas suffered a loss of NPP of 12 kgC ha-1 y-1, amounting to almost 60 million tC not fixed from the atmosphere; loss of soil organic carbon from these areas is likely to be orders of magnitude greater. There is no correlation between land degradation and dry lands; it is more of an issue in cropland and forest: 21% of degrading land is cropland and 40% is forest, 24% of the arable and 44% of the forest, respectively. There is no simple statistical relationship between land degradation and rural population density or poverty. Most identified land degradation is in the south and east, driven by unprecedented land-use change
    Climate regulation of fire emissions and deforestation in equatorial Asia
    Werf, G.R. van der; Dempewolf, J. ; Trigg, S.N. ; Randerson, J.T. ; Peters, W. - \ 2008
    Proceedings of the National Academy of Sciences of the United States of America 105 (2008)51. - ISSN 0027-8424 - p. 20350 - 20355.
    modis - vegetation - indonesia - peat - algorithm - dataset - borneo - policy
    Drainage of peatlands and deforestation have led to large-scale fires in equatorial Asia, affecting regional air quality and global concentrations of greenhouse gases. Here we used several sources of satellite data with biogeochemical and atmospheric modeling to better understand and constrain fire emissions from Indonesia, Malaysia, and Papua New Guinea during 2000¿2006. We found that average fire emissions from this region [128 ± 51 (1¿) Tg carbon (C) year¿1, T = 1012] were comparable to fossil fuel emissions. In Borneo, carbon emissions from fires were highly variable, fluxes during the moderate 2006 El Niño more than 30 times greater than those during the 2000 La Niña (and with a 2000¿2006 mean of 74 ± 33 Tg C yr¿1). Higher rates of forest loss and larger areas of peatland becoming vulnerable to fire in drought years caused a strong nonlinear relation between drought and fire emissions in southern Borneo. Fire emissions from Sumatra showed a positive linear trend, increasing at a rate of 8 Tg C year¿2 (approximately doubling during 2000¿2006). These results highlight the importance of including deforestation in future climate agreements. They also imply that land manager responses to expected shifts in tropical precipitation may critically determine the strength of climate¿carbon cycle feedbacks during the 21st century.
    Canopy spectral invariants for remote sensing and model applications
    Huang, D. ; Knyazikhin, Y. ; Dickinson, R.E. ; Rautiainen, M. ; Stenberg, P. ; Disney, M. ; Lewis, P. ; Cescatti, A. ; Tian, Y. ; Verhoef, W. ; Martonchik, J.V. ; Myneni, R.B. - \ 2007
    Remote Sensing of Environment 106 (2007)1. - ISSN 0034-4257 - p. 106 - 122.
    community climate model - vegetation leaf-area - radiative-transfer - conifer needles - solar-radiation - simulations - modis - satellite - algorithm - fraction
    The concept of canopy spectral invariants expresses the observation that simple algebraic combinations of leaf and canopy spectral transmittance and reflectance become wavelength independent and determine a small set of canopy structure specific variables. This set includes the canopy interceptance, the recollision and the escape probabilities. These variables specify an accurate relationship between the spectral response of a vegetation canopy to the incident solar radiation at the leaf and the canopy scale and allow for a simple and accurate parameterization for the partitioning of the incoming radiation into canopy transmission, reflection and absorption at any wavelength in the solar spectrum. This paper presents a solid theoretical basis for spectral invariant relationships reported in literature with an emphasis on their accuracies in describing the shortwave radiative properties of the three-dimensional vegetation canopies. The analysis of data on leaf and canopy spectral transmittance and reflectance collected during the international field campaign in Flakaliden, Sweden, June 25¿July 4, 2002 supports the proposed theory. The results presented here are essential to both modeling and remote sensing communities because they allow the separation of the structural and radiometric components of the measured/modeled signal. The canopy spectral invariants offer a simple and accurate parameterization for the shortwave radiation block in many global models of climate, hydrology, biogeochemistry, and ecology. In remote sensing applications, the information content of hyperspectral data can be fully exploited if the wavelength-independent variables can be retrieved, for they can be more directly related to structural characteristics of the three-dimensional vegetation canopy.
    The utility of satellite fire product accuracy information - Perspectives and recommendations from the southern Africa fire network
    Roy, D.P. ; Trigg, S.N. ; Bhima, R. ; Brockett, B.H. ; Mutanga, O. ; Virgilo, S. - \ 2006
    IEEE Transactions on Geoscience and Remote Sensing 44 (2006)7. - ISSN 0196-2892 - p. 1928 - 1930.
    validation - modis - algorithm - surface
    This correspondence gives Southern Africa Fire Network (SAFNet) perspectives on the utility of satellite fire product accuracy information, drawing on two main sources: insights gained during SAFNet's six years of working together, and relevant findings from a SAFNet focus group study that explored factors that promote and constrain the use of the MODIS fire products. In giving this perspective, we comment on the approach and findings of recent fire product validation articles, including the two contained in this special issue. We recommend five ways that validation activities might be made more relevant to users and better connect producers of remotely sensed products to users in order to communicate satellite fire product accuracy information more effectively.
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