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.
Generation of spectral–temporal response surfaces by combining multispectral satellite and hyperspectral UAV imagery for precision agriculture applications
Gevaert, C. ; Suomalainen, J.M. ; Tang, J. ; Kooistra, L. - \ 2015
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 8 (2015)6. - ISSN 1939-1404 - p. 3140 - 3146.
leaf chlorophyll concentration - remote-sensing data - vegetation indexes - data fusion - reflectance - variability - prediction - landsat
Precision agriculture requires detailed crop status information at high spatial and temporal resolutions. Remote sensing can provide such information, but single sensor observations are often incapable of meeting all data requirements. Spectral–temporal response surfaces (STRSs) provide continuous reflectance spectra at high temporal intervals. This is the first study to combine multispectral satellite imagery (from Formosat-2) with hyperspectral imagery acquired with an unmanned aerial vehicle (UAV) to construct STRS. This study presents a novel STRS methodology which uses Bayesian theory to impute missing spectral information in the multispectral imagery and introduces observation uncertainties into the interpolations. This new method is compared to two earlier published methods for constructing STRS: a direct interpolation of the original data and a direct interpolation along the temporal dimension after imputation along the spectral dimension. The STRS derived through all three methods are compared to field measured reflectance spectra, leaf area index (LAI), and canopy chlorophyll of potato plants. The results indicate that the proposed Bayesian approach has the highest correlation (r = 0.953) and lowest RMSE (0.032) to field spectral reflectance measurements. Although the optimized soil-adjusted vegetation index (OSAVI) obtained from all methods have similar correlations to field data, the modified chlorophyll absorption in reflectance index (MCARI) obtained from the Bayesian STRS outperform the other two methods. A correlation of 0.83 with LAI and 0.77 with canopy chlorophyll measurements are obtained, compared to correlations of 0.27 and 0.09, respectively, for the directly interpolated STRS.
Detecting clear-cuts and decreases in forest vitality using MODIS NDVI time series
Lambert, J. ; Denux, J.P. ; Verbesselt, J. ; Balent, G. ; Cheret, V. - \ 2015
Remote Sensing 7 (2015)4. - ISSN 2072-4292 - p. 3588 - 3612.
coarse spatial-resolution - vegetation indexes - climate-change - landsat imagery - boreal forest - cover - trends - avhrr - disturbance - drought
This paper examines the potential of MODIS-NDVI time series for detecting clear-cuts in a coniferous forest stand in the south of France. The proposed approach forms part of a survey monitoring the status of forest health and evaluating the forest decline phenomena observed over the last few decades. One of the prerequisites for this survey was that a rapid and easily reproducible method had to be developed that differentiates between forest clear-cuts and changes in forest health induced by environmental factors such as summer droughts. The proposed approach is based on analysis of the breakpoints detected within NDVI time series, using the “Break for Additive Seasonal and Trend” (BFAST) algorithm. To overcome difficulties detecting small areas on the study site, we chose a probabilistic approach based on the use of a conditional inference tree. For model calibration, clear-cut reference data were produced at MODIS resolution (250 m). According to the magnitude of the detected breakpoints, probability classes for the presence of clear-cuts were defined, from greater than 90% to less than 3% probability of a clear-cut. One of the advantages of the probabilistic model is that it allows end users to choose an acceptable level of uncertainty depending on the application. In addition, the use of BFAST allows events to be dated, thus making it possible to perform a retrospective analysis of decreases in forest vitality in the study area.
Fast and nondestructive method for leaf level chlorophyll estimation using hyperspectral LiDAR
Nevalainen, O. ; Hakala, T. ; Suomalainen, J.M. ; Mäkipää, R. ; Peltoniemi, M. ; Krooks, A. ; Kaasalainen, S. - \ 2014
Agricultural and Forest Meteorology 198-199 (2014). - ISSN 0168-1923 - p. 250 - 258.
supercontinuum laser source - vegetation indexes - reflectance spectra - precision agriculture - canopy reflectance - red edge - airborne - model - spectroscopy - validation
We propose an empirical method for nondestructive estimation of chlorophyll in tree canopies. The first prototype of a full waveform hyperspectral LiDAR instrument has been developed by the Finnish Geodetic Institute (FGI). The instrument efficiently combines the benefits of passive and active remote sensing sensors. It is able to produce 3D point clouds with spectral information included for every point, which offers great potential in the field of environmental remote sensing. The investigation was conducted by using chlorophyll sensitive vegetation indices applied to hyperspectral LiDAR data and testing their performance in chlorophyll estimation. The amount of chlorophyll in vegetation is an important indicator of photosynthetic capacity and stress, and thus important for monitoring of forest condition and carbon sequestration on Earth. Performance of chlorophyll estimation was evaluated for 27 published vegetation indices applied to waveform LiDAR collected from ten Scots pine shoots. Reference data were collected by laboratory chlorophyll concentration analysis. The performance of the indices in chlorophyll estimation was determined by linear regression and leave-one-out cross-validation. The chlorophyll estimates derived from hyperspectral LiDAR linearly correlate with the laboratory analyzed chlorophyll concentrations, and they are able to represent a range of chlorophyll concentrations in Scots pine shoots (R2 = 0.88, RMSE = 0.10 mg/g). Furthermore, they are insensitive to measurement scale as nearly the same values of vegetation indices were measured in natural setting while scanning the whole canopy and from clipped shoots re-measured with hyperspectral LiDAR in laboratory. The results indicate that the hyperspectral LiDAR instrument has the potential to estimate vegetation biochemical parameters such as the chlorophyll concentration. The instrument holds much potential in various environmental applications and provides a significant improvement over single wavelength LiDAR or passive optical systems for environmental remote sensing.
Trait estimation in herbaceous plant assemblages from in situ canopy spectra
Roelofsen, H.D. ; Bodegom, P.M. van; Kooistra, L. ; Witte, J.M. - \ 2013
Remote Sensing 5 (2013)12. - ISSN 2072-4292 - p. 6323 - 6345.
least-squares regression - hyperspectral data - economics spectrum - vegetation indexes - indicator values - nitrogen-content - national-park - chlorophyll - reflectance - model
Estimating plant traits in herbaceous plant assemblages from spectral reflectance data requires aggregation of small scale trait variations to a canopy mean value that is ecologically meaningful and corresponds to the trait content that affects the canopy spectral signal. We investigated estimation capacities of plant traits in a herbaceous setting and how different trait-aggregation methods influence estimation accuracies. Canopy reflectance of 40 herbaceous plant assemblages was measured in situ and biomass was analysed for N, P and C concentration, chlorophyll, lignin, phenol, tannin and specific water concentration, expressed on a mass basis (mg·g-1). Using Specific Leaf Area (SLA) and Leaf Area Index (LAI), traits were aggregated to two additional expressions: mass per leaf surface (mg·m-2) and mass per canopy surface (mg·m-2). All traits were related to reflectance using partial least squares regression. Accuracy of trait estimation varied between traits but was mainly influenced by the trait expression. Chlorophyll and traits expressed on canopy surface were least accurately estimated. Results are attributed to damping or enhancement of the trait signal upon conversion from mass based trait values to leaf and canopy surface expressions. A priori determination of the most appropriate trait expression is viable by considering plant growing strategies
Shrimp pond effluent dominates foliar nitrogen in disturbed mangroves as mapped using hyperspectral imagery
Fauzi, A. ; Skidmore, A.K. ; Gils, H. ; Schlerf, M. ; Heitkonig, I.M.A. - \ 2013
Marine Pollution Bulletin 76 (2013)1-2. - ISSN 0025-326X - p. 42 - 51.
leaf-area index - species discrimination - absorption features - chlorophyll content - squares regression - vegetation indexes - avicennia-marina - canopy nitrogen - reflectance - forest
Conversion of mangroves to shrimp ponds creates fragmentation and eutrophication. Detection of the spatial variation of foliar nitrogen is essential for understanding the effect of eutrophication on mangroves. We aim (i) to estimate nitrogen variability across mangrove landscapes of the Mahakam delta using airborne hyperspectral remote sensing (HyMap) and (ii) to investigate links between the variation of foliar nitrogen mapped and local environmental variables. In this study, multivariate prediction models achieved a higher level of accuracy than narrow-band vegetation indices, making multivariate modeling the best choice for mapping. The variation of foliar nitrogen concentration in mangroves was significantly influenced by the local environment: (1) position of mangroves (seaward/landward), (2) distance to the shrimp ponds, and (3) predominant mangrove species. The findings suggest that anthropogenic disturbances, in this case shrimp ponds, influence nitrogen variation in mangroves. Mangroves closer to the shrimp ponds had higher foliar nitrogen concentrations.
Non-linear partial least square regression increases the estimation accuracy of grass nitrogen and phosphorus using in situ hyperspectral and environmental data
Ramoelo, A. ; Skidmore, A.K. ; Cho, M.A. ; Mathieu, R. ; Heitkonig, I.M.A. ; Dudeni-Tlhone, N. ; Schlerf, M. ; Prins, H.H.T. - \ 2013
ISPRS Journal of Photogrammetry and Remote Sensing 82 (2013). - ISSN 0924-2716 - p. 27 - 40.
kruger-national-park - multiple linear-regression - band-depth analysis - vegetation indexes - south-africa - chlorophyll estimation - imaging spectroscopy - absorption features - biochemical content - mineral-nutrition
Grass nitrogen (N) and phosphorus (P) concentrations are direct indicators of rangeland quality and provide imperative information for sound management of wildlife and livestock. It is challenging to estimate grass N and P concentrations using remote sensing in the savanna ecosystems. These areas are diverse and heterogeneous in soil and plant moisture, soil nutrients, grazing pressures, and human activities. The objective of the study is to test the performance of non-linear partial least squares regression (PLSR) for predicting grass N and P concentrations through integrating in situ hyperspectral remote sensing and environmental variables (climatic, edaphic and topographic). Data were collected along a land use gradient in the greater Kruger National Park region. The data consisted of: (i) in situ-measured hyperspectral spectra, (ii) environmental variables and measured grass N and P concentrations. The hyperspectral variables included published starch, N and protein spectral absorption features, red edge position, narrow-band indices such as simple ratio (SR) and normalized difference vegetation index (NDVI). The results of the non-linear PLSR were compared to those of conventional linear PLSR. Using non-linear PLSR, integrating in situ hyperspectral and environmental variables yielded the highest grass N and P estimation accuracy (R2 = 0.81, root mean square error (RMSE) = 0.08, and R2 = 0.80, RMSE = 0.03, respectively) as compared to using remote sensing variables only, and conventional PLSR. The study demonstrates the importance of an integrated modeling approach for estimating grass quality which is a crucial effort towards effective management and planning of protected and communal savanna ecosystems.
Predicting foliar biochemistry of tea (Camellia sinensis) using reflectance spectra measured at powder, leaf and canopy levels
Bian, B.M. ; Skidmore, A.K. ; Schlerf, M. ; Wang, T. ; Liu, X. ; Zeng, R. ; Fei, T. - \ 2013
ISPRS Journal of Photogrammetry and Remote Sensing 78 (2013). - ISSN 0924-2716 - p. 148 - 156.
near-infrared spectroscopy - green tea - chlorophyll content - squares regression - amino-acids - hyperspectral measurements - imaging spectrometry - vegetation indexes - continuum removal - forest canopy
Some biochemical compounds are closely related with the quality of tea (Camellia sinensis (L.)). In this study, the concentration of these compounds including total tea polyphenols, free amino acids and soluble sugars were estimated using reflectance spectroscopy at three different levels: powder, leaf and canopy, with partial least squares regression. The focus of this study is to systematically compare the accuracy of tea quality estimations based on spectroscopy at three different levels. At the powder level, the average r2 between predictions and observations was 0.89 for polyphenols, 0.81 for amino acids and 0.78 for sugars, with relative root mean square errors (RMSE/mean) of 5.47%, 5.50% and 2.75%, respectively; at the leaf level, the average r2 decreased to 0.46-0.81 and the relative RMSE increased to 4.46-7.09%. Compared to the results yielded at the leaf level, the results from canopy spectra were slightly more accurate, yielding average r2 values of 0.83, 0.77 and 0.56 and relative RMSE of 6.79%, 5.73% and 4.03% for polyphenols, amino acids and sugars, respectively. We further identified wavelength channels that influenced the prediction model. For powder and leaves, some bands identified can be linked to the absorption features of chemicals of interest (1648nm for phenolic, 1510nm for amino acids, 2080nm and 2270nm for sugars), while more indirectly related wavelengths were found to be important at the canopy level for predictions of chemical compounds. Overall, the prediction accuracies achieved at canopy level in this study are encouraging for future study on tea quality estimated at the landscape scale using airborne and space-borne sensors.
Remote estimation of crop and grass chlorophyll and nitrogen content using red-edge bands on Sentinel-2 and -3
Clevers, J.G.P.W. ; Gitelson, A.A. - \ 2013
International Journal of applied Earth Observation and Geoinformation 23 (2013). - ISSN 0303-2434 - p. 344 - 351.
higher-plant leaves - spectral reflectance - vegetation indexes - canopy chlorophyll - leaf reflectance - mission - meris - wheat - lai
Sentinel-2 is planned for launch in 2014 by the European Space Agency and it is equipped with the Multi Spectral Instrument (MSI), which will provide images with high spatial, spectral and temporal resolution. It covers the VNIR/SWIR spectral region in 13 bands and incorporates two new spectral bands in the red-edge region, which can be used to derive vegetation indices using red-edge bands in their formulation. These are particularly suitable for estimating canopy chlorophyll and nitrogen (N) content. This band setting is important for vegetation studies and is very similar to the ones of the Ocean and Land Colour Instrument (OLCI) on the planned Sentinel-3 satellite and the Medium Resolution Imaging Spectrometer (MERIS) on Envisat, which operated from 2002 to early 2012. This paper focuses on the potential of Sentinel-2 and Sentinel-3 in estimating total crop and grass chlorophyll and N content by studying in situ crop variables and spectroradiometer measurements obtained for four different test sites. In particular, the red-edge chlorophyll index (CIred-edge), the green chlorophyll index (CIgreen) and the MERIS terrestrial chlorophyll index (MTCI) were found to be accurate and linear estimators of canopy chlorophyll and N content and the Sentinel-2 and -3 bands are well positioned for deriving these indices. Results confirm the importance of the red-edge bands on particularly Sentinel-2 for agricultural applications, because of the combination with its high spatial resolution of 20 m
Savanna grass nitrogen to phosphorous ratio estimation using field spectroscopy and the potential for estimation with imaging spectroscopy
Ramoelo, A. ; Skidmore, A.K. ; Schlerf, M. ; Heitkonig, I.M.A. ; Mathieu, R. ; Cho, M.A. - \ 2013
International Journal of applied Earth Observation and Geoinformation 23 (2013). - ISSN 0303-2434 - p. 334 - 343.
least-squares regression - band-depth analysis - red edge position - n-p ratios - nutrient limitation - reflectance spectra - absorption features - vegetation indexes - mineral-nutrition - continuum removal
Determining the foliar N:P ratio provides a tool for understanding nutrient limitation on plant production and consequently for the feeding patterns of herbivores. In order to understand the nutrient limitation at landscape scale, remote sensing techniques offer that opportunity. The objective of this study is to investigate the utility of field spectroscopy and a potential of hyperspectral mapper (HyMap) spectra to estimate foliar N:P ratio. Field spectral measurements were undertaken, and grass samples were collected for foliar N and P extraction. The foliar N:P ratio prediction models were developed using partial least square regression (PLSR) with original spectra and transformed spectra for field and the resampled field spectra to HyMap. Spectral transformations included the continuum removal (CR), water removal (WR), first difference derivative (FD) and log transformation (Log(1/R)). The results showed that CR and WR spectra in combination with PLSR predicted foliar N:P ratio with higher accuracy as compared to FD and R, using field spectra. For HyMap spectral analysis, addition to CR and WR, FD achieved higher estimation accuracy. The performance of FD, CR and WR spectra were attributed to their ability to minimize sensor and water effects on the fresh leaf spectra, respectively. The study demonstrated a potential to predict foliar N:P ratio using field and HyMap simulated spectra and shortwave infrared (SWIR) found to be highly sensitive to foliar N:P ratio. The study recommends the prediction of foliar N:P ratio at landscape level using airborne hyperspectral data and could be used by the resource managers, park managers, farmers and ecologists to understand the feeding patterns, resource selection and distribution of herbivores (i.e. wild and livestock).
Photosynthetic bark: use of chlorophyll absorption continuum index to estimate Boswellia papyrifera bark chlorophyll content
Girma, A. ; Skidmore, A.K. ; Bie, C.A.J.M. de; Bongers, F. ; Schlerf, M. - \ 2013
International Journal of applied Earth Observation and Geoinformation 23 (2013)August. - ISSN 0303-2434 - p. 71 - 80.
least-squares regression - red edge position - hyperspectral measurements - spectral reflectance - vegetation indexes - leaves - stems - frankincense - performance - prospect
Quantification of chlorophyll content provides useful insight into the physiological performance of plants. Several leaf chlorophyll estimation techniques, using hyperspectral instruments, are available. However, to our knowledge, a non-destructive bark chlorophyll estimation technique is not available. We set out to assess Boswellia papyrifera tree bark chlorophyll content and to provide an appropriate bark chlorophyll estimation technique using hyperspectral remote sensing techniques. In contrast to the leaves, the bark of B. papyrifera has several outer layers masking the inner photosynthetic bark layer. Thus, our interest includes understanding how much light energy is transmitted to the photosynthetic inner bark and to what extent the inner photosynthetic bark chlorophyll activity could be remotely sensed during both the wet and the dry season. In this study, chlorophyll estimation using the chlorophyll absorption continuum index (CACI) yielded a higher R2 (0.87) than others indices and methods, such as the use of single band, simple ratios, normalized differences, and conventional red edge position (REP) based estimation techniques. The chlorophyll absorption continuum index approach considers the increase or widening in area of the chlorophyll absorption region, attributed to high concentrations of chlorophyll causing spectral shifts in both the yellow and the red edge. During the wet season B. papyrifera trees contain more bark layers than during the dry season. Having less bark layers during the dry season (leaf off condition) is an advantage for the plants as then their inner photosynthetic bark is more exposed to light, enabling them to trap light energy. It is concluded that B. papyrifera bark chlorophyll content can be reliably estimated using the chlorophyll absorption continuum index analysis. Further research on the use of bark signatures is recommended, in order to discriminate the deciduous B. papyrifera from other species during the dry season.
Estimation of grassland biomass and nitrogen using MERIS data
Ullah, G. ; Si, Y. ; Schlerf, M. ; Skidmore, A.K. ; Shafique, M. ; Iqbal, I.A. - \ 2012
International Journal of applied Earth Observation and Geoinformation 19 (2012)1. - ISSN 0303-2434 - p. 196 - 204.
terrestrial chlorophyll index - band-depth analysis - red edge position - vegetation indexes - aboveground biomass - hyperspectral data - branta-leucopsis - broad-band - canopy - quality
This study aimed to investigate the potential of MERIS in estimating the quantity and quality of a grassland using various vegetation indices (NDVI, SAVI, TSAVI, REIP, MTCI and band depth analysis parameters) at a regional scale. Green biomass was best predicted by NBDI (normalised band depth index) and yielded a calibration R2 of 0.73 and a Root Mean Square Error (RMSE) of 136.2 g m-2 (using an independent validation dataset, n = 30) compared to a much higher RMSE obtained from soil adjusted vegetation index SAVI (444.6 g m-2). Nitrogen density was also best predicted by NBDI and yielded a calibration R2 of 0.51 and a RMSE of 4.2 g m-2 compared to a relatively higher RMSE obtained from MERIS terrestrial chlorophyll index MTCI (6.6 g m-2). For the estimation of nitrogen concentration (%), band depth analysis parameters showed poor R2 of 0.21 and the results of MTCI and REIP were statistically non-significant (P > 0.05). It is concluded that band depth analysis parameters consistently showed higher accuracy than vegetation indices, suggesting that band depth analysis parameters could be used to monitor grassland condition over time at regional scale.
Mapping spatio-temporal variation of grassland quantity and quality using MERIS data and the PROSAIL model
Si, Y. ; Schlerf, M. ; Zurita-Milla, R. ; Skidmore, A.K. ; Wang, T. - \ 2012
Remote Sensing of Environment 121 (2012). - ISSN 0034-4257 - p. 415 - 425.
radiative-transfer models - remote-sensing data - chlorophyll content - vegetation indexes - reflectance data - hyperspectral measurements - heterogeneous grassland - canopy reflectance - leaf - lai
Accurate estimates of the quantity and quality of grasslands, as they vary in space and time and from regional to global scales, furthers our understanding of grassland ecosystems. The Medium Resolution Imaging Spectrometer (MERIS) is a promising sensor for measuring and monitoring grasslands due to its high spectral resolution, medium spatial resolution and a two- to three-day repeat cycle. However, thus far the multi-biome MERIS land products have limited consistency with in-situ measurements of leaf area index (LAI), while the multi-biome canopy chlorophyll content (CCC) has not been validated yet with in-situ data. This study proposes a single-biome approach to estimate grassland LAI (a surrogate of grass quantity) and leaf chlorophyll content (LCC) and CCC (surrogates of grass quality) using the inversion of the PROSAIL model and MERIS reflectance. Both multi-biome and single-biome approaches were validated using two-season in-situ data sets and the temporal consistency was analyzed using time-series of MERIS data. The single-biome approach showed a consistently better performance for estimating LAI (R 2=0.70, root mean square error (RMSE)=1.02, normalized RMSE (NRMSE)=16%) and CCC (R 2=0.61, RMSE=0.36, NRMSE=23%) compared with the multi-biome approach (LAI: R 2=0.36, RMSE=1.77, NRMSE=28%; CCC: R 2=0.47, RMSE=1.33, NRMSE=84%). However, both single-biome and multi-biome approaches failed to retrieve LCC. The multi-biome LAI was overestimated at lower LAI values (
Trend changes in global greening and browning: Contribution of short-term trends to longer-term change
Jong, R. de; Verbesselt, J. ; Schaepman, M.E. ; Bruin, S. de - \ 2012
Global Change Biology 18 (2012)2. - ISSN 1354-1013 - p. 642 - 655.
net primary production - drought-induced reduction - structural-change models - image time-series - land-cover data - terrestrial ecosystems - photosynthetic trends - environmental-change - phenological change - vegetation indexes
Field observations and time series of vegetation greenness data from satellites provide evidence of changes in terrestrial vegetation activity over the past decades for several regions in the world. Changes in vegetation greenness over time may consist of an alternating sequence of greening and/or browning periods. This study examined this effect using detection of trend changes in normalized difference vegetation index (NDVI) satellite data between 1982 and 2008. Time series of 648 fortnightly images were analyzed using a trend breaks analysis (BFAST) procedure. Both abrupt and gradual changes were detected in large parts of the world, especially in (semi-arid) shrubland and grassland biomes where abrupt greening was often followed by gradual browning. Many abrupt changes were found around large-scale natural influences like the Mt Pinatubo eruption in 1991 and the strong 1997/98 El Niño event. The net global figure – considered over the full length of the time series – showed greening since the 1980s. This is in line with previous studies, but the change rates for individual short-term segments were found to be up to five times higher. Temporal analysis indicated that the area with browning trends increased over time while the area with greening trends decreased. The Southern Hemisphere showed the strongest evidence of browning. Here, periods of gradual browning were generally longer than periods of gradual greening. Net greening was detected in all biomes, most conspicuously in croplands and least conspicuously in needleleaf forests. For 15% of the global land area, trends were found to change between greening and browning within the analysis period. This demonstrates the importance of accounting for trend changes when analyzing long-term NDVI time series.
Application of satellite remote sensing for mapping wind erosion risk and dusk emission-deposition in Inner Mongolia grassland, China
Reiche, M. ; Funk, R. ; Zhang, Z. ; Hoffmann, C. ; Reiche, J. ; Wehrhan, M. ; Li, Y. ; Sommer, M. - \ 2012
Grassland Science 58 (2012)1. - ISSN 1744-6961 - p. 8 - 19.
vegetation indexes - northern china - landsat tm - degradation - variability - topography - forest - cover - hills - aster
Intensive grazing leads to land degradation and desertification of grassland ecosystems followed by serious environmental and social problems. The Xilingol steppe grassland in Inner Mongolia, China, which has been a sink area for dust for centuries, is strongly affected by the negative effects of overgrazing and wind erosion. The aim of this study is the provision of a wind erosion risk map with a spatial high resolution of 25 m to identify actual source and sink areas. In an integrative approach, field measurements of vegetation features and surface roughness length z0 were combined with Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) image data for a land use classification. To determine the characteristics of the different land use classes, a field observation (ground truth) was performed in April 2009. The correlation of vegetation height and z0 (R2 = 0.8, n = 55) provided the basis for a separation of three main classes, “grassland”, “non-vegetation” and “other”. The integration of the soil-adjusted vegetation index (SAVI) and the spectral information from the atmospheric corrected ASTER bands 1, 2 and 3 (visible to near-infrared) led to a classification of the overall accuracy (OA) of 0.79 with a kappa () statistic of 0.74, respectively. Additionally, a digital elevation model (DEM) was used to identify topographical effects in relation to the main wind direction, which enabled a qualitative estimation of potential dust deposition areas. The generated maps result in a significantly higher description of the spatial variability in the Xilingol steppe grassland reflecting the different land use intensities on the current state of the grassland – less, moderately and highly degraded. The wind erosion risk map enables the identification of characteristic mineral dust sources, sinks and transition zones.
Near real-time disturbance detection using satellite image time series
Verbesselt, J.P. ; Zeileis, A. ; Herold, M. - \ 2012
Remote Sensing of Environment 123 (2012). - ISSN 0034-4257 - p. 98 - 108.
land-surface phenology - monitoring structural-change - terrestrial ecosystems - vegetation indexes - ndvi - dynamics - patterns - exchange - models
Near real-time monitoring of ecosystem disturbances is critical for rapidly assessing and addressing impacts on carbon dynamics, biodiversity, and socio-ecological processes. Satellite remote sensing enables cost-effective and accurate monitoring at frequent time steps over large areas. Yet, generic methods to detect disturbances within newly captured satellite images are lacking. We propose a multi-purpose time-series-based disturbance detection approach that identifies and models stable historical variation to enable change detection within newly acquired data. Satellite image time series of vegetation greenness provide a global record of terrestrial vegetation productivity over the past decades. Here, we assess and demonstrate the method by applying it to (1) simulated time series of vegetation greenness data from satellite data, (2) real-world satellite greenness image time series between February 2000 and July 2011 covering Somalia to detect drought-related vegetation disturbances. First, simulation results illustrate that disturbances are successfully detected in near real-time while being robust to seasonality and noise. Second, major drought-related disturbance corresponding with most drought-stressed regions in Somalia are detected from mid-2010 onwards. The method can analyse in-situ or satellite data time series of biophysical indicators from local to global scale since it is fast, does not depend on thresholds and does not require time series gap filling. While the data and methods used are appropriate for proof-of-concept development of global scale disturbance monitoring, specific applications (e.g., drought or deforestation monitoring) mandate integration within an operational monitoring framework
|Evaluation of spectral reflectance of seven Iranian rice varieties canopies
Darvishsefat, A.A. ; Abbasi, M. ; Schaepman, M.E. - \ 2011
Journal of Agricultural Science and Technology (JAST) 13 (2011)Supplement. - ISSN 1680-7073 - p. 1091 - 1104.
leaf chlorophyll content - photosynthetic efficiency - hyperspectral reflectance - vegetation indexes - nitrogen status - area index - paddy rice - leaves - water - parameters
Rice cultivated areas and yield information is indispensable for sustainable management and economic policy making for this strategic food crop. Introduction of high spectral and special resolution satellite data has enabled production of such information in a timely and accurate manner. Knowledge of the spectral reflectance of various land covers is a prerequisite for their identification and study. Evaluation of the spectral reflectance of plants using field spectroradiometry provides the possibility to identify and map different rice varieties especially while using hyperspectral remote sensing. This paper reports the results of the first attempt to evaluate spectral signatures of seven north Iranian rice varieties (Fajr, Hybrid, Khazar, Nemat, Neda, Shiroudi and Tarom plots) in the experimental station of the Iranian Rice Research Institute (main station in Amol, Mazanderan Province). Measurements were carried out using a field spectroradiometer in the range of 350-2,500 nm under natural light and environmental conditions. In order to eliminate erroneous data and also experimental errors in spectral reflectance curves, all curves were individually quality controlled. A set of important vegetation indices sensitive to canopy chlorophyll content, photosynthesis intensity, nitrogen and water content were employed to enhance probable differences in spectral reflectance among various rice varieties. Analysis of variance and Tukey's paired test were then used to compare rice varieties. Using Datt and PRI1 indices, significant differences (a=0.01) were found among rice varieties reflectances in 19 out of 21 cases. This promises the possibility of accurate mapping of rice varieties cultivated areas based on hyperspectral remotely sensed data.
Ground-Based Optical Measurements at European Flux Sites:
Balzarolo, M. ; Anderson, K. ; Nichol, C. ; Elbers, J.A. ; Rossini, M. ; Vescovo, L. - \ 2011
Sensors 11 (2011). - ISSN 1424-8220 - p. 7954 - 7981.
vegetation indexes - spectral reflectance - co2 fluxes - photosynthetic efficiency - ndvi measurements - resolution ndvi - carbon-dioxide - ecosystem - validation - footprint
This paper reviews the currently available optical sensors, their limitations and opportunities for deployment at Eddy Covariance (EC) sites in Europe. This review is based on the results obtained from an online survey designed and disseminated by the Co-cooperation in Science and Technology (COST) Action ESO903—“Spectral Sampling Tools for Vegetation Biophysical Parameters and Flux Measurements in Europe” that provided a complete view on spectral sampling activities carried out within the different research teams in European countries. The results have highlighted that a wide variety of optical sensors are in use at flux sites across Europe, and responses further demonstrated that users were not always fully aware of the key issues underpinning repeatability and the reproducibility of their spectral measurements. The key findings of this survey point towards the need for greater awareness of the need for standardisation and development of a common protocol of optical sampling at the European EC sites
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
Merging the Minnaert- k parameter with spectral unmixing to map forest heterogeneity with CHRIS/PROBA data
Verrelst, J. ; Schaepman, M.E. ; Clevers, J.G.P.W. - \ 2010
IEEE Transactions on Geoscience and Remote Sensing 48 (2010)11. - ISSN 0196-2892 - p. 4014 - 4022.
vegetation indexes - hyperspectral data - rpv model - canopy - reflectance - surface - cover - land - misr - classification
The Compact High Resolution Imaging Spectrometer (CHRIS) mounted onboard the Project for Onboard Autonomy (PROBA) spacecraft is capable of sampling reflected radiation at five viewing angles over the visible and near-infrared regions of the solar spectrum with high spatial resolution. We combined the spectral domain with the angular domain of CHRIS data in order to map the surface heterogeneity of an Alpine coniferous forest during winter. In the spectral domain, linear spectral unmixing of the nadir image resulted in a canopy cover map. In the angular domain, pixelwise inversion of the Rahman-Pinty-Verstraete (RPV) model at a single wavelength at the red edge (722 nm) yielded a map of the Minnaert-k parameter that provided information on surface heterogeneity at a subpixel scale. However, the interpretation of the Minnaert-k parameter is not always straightforward because fully vegetated targets typically produce the same type of reflectance anisotropy as non-vegetated targets. Merging both maps resulted in a forest cover heterogeneity map, which contains more detailed information on canopy heterogeneity at the CHRIS subpixel scale than is possible to realize from a single-source optical data set.