Predicting forest structural parameters using the image texture derived from WorldView-2 multispectral imagery in a dryland forest,Israel

Estimation of forest structural parameters by field-based data collection methods is both expensive and time consuming. Satellite remote sensing is a low-cost alternative in modeling and mapping structural parameters in large forest areas. The current study investigates the potential of using WordView-2 multispectral satellite imagery for predicting forest structural parameters in a dryland plantation forest in Israel. The relationships between image texture features and the several structural parameters such as Number of Trees (NT), Basal Area (BA), Stem Volume (SV), Clark-Evans Index (CEI), Diameter Differentiation Index (DDI), Contagion Index (CI), Gini Coefficient (GC), and Standard Deviation of Diameters at Breast Heights (SDDBH) were examined using correlation analyses. These variables were obtained from 30 m × 30 m square-shaped plots. The Standard Deviation of Gray Levels (SDGL) as a first order texture feature and the second order texture variables based on Gray Level Co-occurrence Matrix (GLCM) were calculated for the pixels that corresponds to field plots. The results of the correlation analysis indicate that the forest structural parameters are significantly correlated with the image texture features. The highest correlation coefficients were calculated for the relationships between the SDDBH and the contrast of red band (r = 0.75, p < 0.01), the BA and the entropy of blue band (r = 0.73, p < 0.01), and the GC and the contrast of blue band (r = 0.71, p < 0.01). Each forest structural parameter was modeled as a function of texture measures derived from the satellite image using stepwise multi linear regression analyses. The determination coefficient (R²) and root mean square error (RMSE) values of the best fitting models, respectively, are 0.38 and 109.56 ha⁻¹ for the NT; 0.54 and 1.79 m² ha⁻¹ for the BA; 0.42 and 27.18 m³ ha⁻¹ for the SV; 0.23 and 0.16 for the CEI; 0.32 and 0.05 for the DDI; 0.25 and 0.06 for the CI; 0.50 and 0.05 for the GC; and 0.67 and 0.70 for the SDDBH. The leave-one-out cross-validation technique was applied for validation of the best-fitted models (R² > 0.50). In conclusion, cross-validated statistics confirmed that the structural parameters including the BA, SDDBH, and GC can be predicted and mapped with a reasonable accuracy using the texture features extracted from the spectral bands of WorldView-2 image.     

Generation and evaluation of gross primary productivity using Landsat data through blending with MODIS data

Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) has been used for the blending of Landsat and MODIS data. Specifically, the 30 m Landsat-7 ETM+ (Enhanced Thematic Mapper plus) surface reflectance was predicted for a period of 10 years (2000–2009) as the product of observed ETM+ and MODIS surface reflectance (MOD09A1) on the predicted and observed ETM+ dates. A pixel based analysis for six observed ETM+ dates covering winter and summer crops showed that the prediction method was more accurate for NIR band (mean r² = 0.71, p ≤ 0.01) compared to green band (mean r² = 0.53; p ≤ 0.01). A recently proposed chlorophyll index (CI), which involves NIR and green spectral bands, was used to retrieve gross primary productivity (GPP) as the product of CI and photosynthetic active radiation (PAR). The regression analysis of GPP derived from closet observed and synthetic ETM+ showed a good agreement (r² = 0.85, p ≤ 0.01 and r² = 0.86, p ≤ 0.01) for wheat and sugarcane crops, respectively. The difference between the GPP derived from synthetic and observed ETM+ (prediction residual) was compared with the difference in GPP values from observed ETM+ on the two dates (temporal residual). The prediction residuals (mean value of 1.97 g C/m² in 8 days) was found to be significantly lower than the temporal residuals (mean value of 4.46 g C/m² in 8 days) that correspondence to 12% and 27%, respectively, of GPP values (mean value of 16.53 g C/m² in 8 days) from observed ETM+ data, implying that the prediction method was better than temporal pixel substitution. Investigating the trend in synthetic ETM+ GPP values over a growing season revealed that phenological patterns were well captured for wheat and sugarcane crops. A direct comparison between the GPP values derived from MODIS and synthetic ETM+ data showed a good consistency of the temporal dynamics but a systematic error that can be read as bias (MODIS GPP over estimation). Further, the regression analysis between observed evapotranspiration and synthetic ETM+ GPP showed good agreement (r² = 0.66, p ≤ 0.01).     

Estimating vegetation parameter for soil erosion assessment in an alpine catchment by means of QuickBird imagery

Soil erosion rates in alpine regions are related to high spatial variability complicating assessment of risk and damages. A crucial parameter triggering soil erosion that can be derived from satellite imagery is fractional vegetation cover (FVC). The objective of this study is to assess the applicability of normalized differenced vegetation index (NDVI), linear spectral unmixing (LSU) and mixture tuned matched filtering (MTMF) in estimating abundance of vegetation cover in alpine terrain. To account for the small scale heterogeneity of the alpine landscape we used high resolved multispectral QuickBird imagery (pixel resolution = 2.4 m) of a site in the Urseren Valley, Central Swiss Alps (67 km²). A supervised land-cover classification was applied (total accuracy 93.3%) prior to the analysis in order to stratify the image. The regression between ground truth FVC assessment and NDVI as well as MTMF-derived vegetation abundance was significant (r² = 0.64, r² = 0.71, respectively). Best results were achieved for LSU (r² = 0.85). For both spectral unmixing approaches failed to estimate bare soil abundance (r² = 0.39 for LSU, r² = 0.28 for MTMF) due to the high spectral variability of bare soil at the study site and the low spectral resolution of the QuickBird imagery. The LSU-derived FVC map successfully identified erosion features (e.g. landslides) and areas prone to soil erosion. FVC represents an important but often neglected parameter for soil erosion risk assessment in alpine grasslands.     

Large-area rice yield forecasting using satellite imageries

Ability to make large-area yield prediction before harvest is important in many aspects of agricultural decision-making. In this study, canopy reflectance band ratios (NIR/RED, NIR/GRN) of paddy rice (Oryza sativa L.) at booting stage, from field measurements conducted from 1999 to 2005, were correlated with the corresponding yield data to derive regression-type yield prediction models for the first and second season crop, respectively. These yield models were then validated with ground truth measurements conducted in 2007 and 2008 at eight sites, of different soil properties, climatic conditions, and various treatments in cultivars planted and N application rates, using surface reflectance retrieved from atmospherically corrected SPOT imageries. These validation tests indicated that root mean square error of predicting grain yields per unit area by the proposed models were less than 0.7 T ha⁻¹ for both cropping seasons. Since village is the basic unit for national rice yield census statistics in Taiwan, the yield models were further used to forecast average regional yields for 14 selected villages and compared with officially reported data. Results indicate that the average yield per unit area at village scale can be forecasted with a root mean square error of 1.1 T ha⁻¹ provided no damaging weather occurred during the final month before actual harvest. The methodology can be applied to other optical sensors with similar spectral bands in the visible/near-infrared and to different geographical regions provided that the relation between yield and spectral index is established.     

Assessing land-use and carbon stock in slash-and-burn ecosystems in tropical mountain of Laos based on time-series satellite images

In the tropical mountains of Southeast Asia, slash-and-burn (S/B) agriculture is a widely practiced and important food production system. The ecosystem carbon stock in this land-use is linked not only to the carbon exchange with the atmosphere but also with food and resource security. The objective of this study was to provide quantitative information on the land-use and ecosystem carbon stock in the region as well as to infer the impacts of alternative land-use and ecosystem management scenarios on the carbon sequestration potential at a regional scale. The study area was selected in a typical slash-and-burn region in the northern part of Laos. The chrono-sequential changes of land-use such as the relative areas of community age and cropping (C) + fallow (F) patterns were derived from the analysis of time-series satellite images. The chrono-sequential analysis showed that a consistent increase of S/B area during the past three decades and a rapid increase after 1990. Approximately 37% of the whole area was with the community age of 1–5 years, whereas 10% for 6–10 years in 2004. The ecosystem carbon stock at a regional scale was estimated by synthesizing the land-use patterns and semi-empirical carbon stock model derived from in situ measurements where the community age was used as a clue to the linkage. The ecosystem carbon stock in the region was strongly affected by the land-use patterns; the temporal average of carbon stock in 1C + 10F cycles, for example, was greater by 33 MgC ha⁻¹ compared to that in 1C + 2F land-use pattern. The amount of carbon lost from the regional ecosystems during 1990–2004 periods was estimated to be 42 MgC ha⁻¹. The study approach proved to be useful especially in such regions with low data-availability and accessibility. This study revealed the dynamic change of land-use and ecosystem carbon stock in the tropical mountain of Laos as affected by land-use. Results suggest the significant potential of carbon sequestration through changing land-use and ecosystem management scenarios. These quantitative estimates would be useful to better understand and manage the land-use and ecosystem carbon stock towards higher sustainability and food security in similar ecosystems.     

Modeling biophysical properties of broad-leaved stands in the hyrcanian forests of Iran using fused airborne laser scanner data and ultraCam-D images

Inventories of mixed broad-leaved forests of Iran mainly rely on terrestrial measurements. Due to rapid changes and disturbances and great complexity of the silvicultural systems of these multilayer forests, frequent repetition of conventional ground-based plot surveys is often cost prohibitive. Airborne laser scanning (ALS) and multispectral data offer an alternative or supplement to conventional inventories in the Hyrcanian forests of Iran. In this study, the capability of a combination of ALS and UltraCam-D data to model stand volume, tree density, and basal area using random forest (RF) algorithm was evaluated. Systematic sampling was applied to collect field plot data on a 150 m × 200 m sampling grid within a 1100 ha study area located at 36°38′- 36°42′N and 54°24′–54°25′E. A total of 308 circular plots (0.1 ha) were measured for calculation of stand volume, tree density, and basal area per hectare. For each plot, a set of variables was extracted from both ALS and multispectral data. The RF algorithm was used for modeling of the biophysical properties using ALS and UltraCam-D data separately and combined. The results showed that combining the ALS data and UltraCam-D images provided a slight increase in prediction accuracy compared to separate modeling. The RMSE as percentage of the mean, the mean difference between observed and predicted values, and standard deviation of the differences using a combination of ALS data and UltraCam-D images in an independent validation at 0.1-ha plot level were 31.7%, 1.1%, and 84 m³ ha⁻¹ for stand volume; 27.2%, 0.86%, and 6.5 m² ha⁻¹ for basal area, and 35.8%, −4.6%, and 77.9 n ha⁻¹ for tree density, respectively. Based on the results, we conclude that fusion of ALS and UltraCam-D data may be useful for modeling of stand volume, basal area, and tree density and thus gain insights into structural characteristics in the complex Hyrcanian forests.     

Mapping beech (Fagus sylvatica L.) forest structure with airborne hyperspectral imagery

Estimating forest structural attributes using multispectral remote sensing is challenging because of the saturation of multispectral indices at high canopy cover. The objective of this study was to assess the utility of hyperspectral data in estimating and mapping forest structural parameters including mean diameter-at-breast height (DBH), mean tree height and tree density of a closed canopy beech forest (Fagus sylvatica L.). Airborne HyMap images and data on forest structural attributes were collected from the Majella National Park, Italy in July 2004. The predictive performances of vegetation indices (VI) derived from all possible two-band combinations (VI_(i,j) = (R_i − R_j)/(R_i + R_j), where R_i and R_j = reflectance in any two bands) were evaluated using calibration (n = 33) and test (n = 20) data sets. The potential of partial least squares (PLS) regression, a multivariate technique involving several bands was also assessed. New VIs based on the contrast between reflectance in the red-edge shoulder (756–820 nm) and the water absorption feature centred at 1200 nm (1172–1320 nm) were found to show higher correlations with the forest structural parameters than standard VIs derived from NIR and visible reflectance (i.e. the normalised difference vegetation index, NDVI). PLS regression showed a slight improvement in estimating the beech forest structural attributes (prediction errors of 27.6%, 32.6% and 46.4% for mean DBH, height and tree density, respectively) compared to VIs using linear regression models (prediction errors of 27.8%, 35.8% and 48.3% for mean DBH, height and tree density, respectively). Mean DBH was the best predicted variable among the stand parameters (calibration R² = 0.62 for an exponential model fit and standard error of prediction = 5.12 cm, i.e. 25% of the mean). The predicted map of mean DBH revealed high heterogeneity in the beech forest structure in the study area. The spatial variability of mean DBH occurs at less than 450 m. The DBH map could be useful to forest management in many ways, e.g. thinning of coppice to promote diameter growth, to assess the effects of management on forest structure or to detect changes in the forest structure caused by anthropogenic and natural factors.     

Exploring hyperspectral bands and estimation indices for leaf nitrogen accumulation in wheat

Hyperspectral sensing can provide an effective means for fast and non-destructive estimation of leaf nitrogen (N) status in crop plants. The objectives of this study were to design a new method to extract hyperspectral spectrum information, to explore sensitive spectral bands, suitable bandwidth and best vegetation indices based on precise analysis of ground-based hyperspectral information, and to develop regression models for estimating leaf N accumulation per unit soil area (LNA, g N m⁻²) in winter wheat (Triticum aestivum L.). Three field experiments were conducted with different N rates and cultivar types in three consecutive growing seasons, and time-course measurements were taken on canopy hyperspectral reflectance and LNA under the various treatments. Then, normalized difference spectral indices (NDSI) and ratio spectral indices (RSI) based on the original spectrum and the first derivative spectrum were constructed within the range of 350–2500 nm, and their relationships with LNA were quantified. The results showed that both LNA and canopy hyperspectral reflectance in wheat changed with varied N rates, with consistent patterns across different cultivars and seasons. The sensitive spectral bands for LNA existed mainly within visible and near infrared regions. The best spectral indices for estimating LNA in wheat were found to be NDSI (R₈₆₀, R₇₂₀), RSI (R₉₉₀, R₇₂₀), NDSI (FD₇₃₆, FD₅₂₆) and RSI (FD₇₂₅, FD₅₁₆), and the regression models based on the above four spectral indices were formulated as Y = 26.34x^1.887, Y = 5.095x − 6.040, Y = 0.609 e^3.008x and Y = 0.388x^1.260, respectively, with R² greater than 0.81. Furthermore, expanding the bandwidth of NDSI (R₈₆₀, R₇₂₀) and RSI (R₉₉₀, R₇₂₀) from 1 nm to 100 nm at 1 nm interval produced the LNA monitoring models with similar performance within about 33 nm and 23 nm bandwidth, respectively, over which the statistical parameters of the models became less stable. From testing of the derived equations, the model for LNA estimation on NDSI (R₈₆₀, R₇₂₀), RSI (R₉₉₀, R₇₂₀), NDSI (FD₇₃₆, FD₅₂₆) and RSI (FD₇₂₅, FD₅₁₆) gave R² over 0.79 with more satisfactory performance than previously reported models and physical models in wheat. It can be concluded that the present hyperspectral parameters of NDSI (R₈₆₀, R₇₂₀), RSI (R₉₉₀, R₇₂₀), NDSI (FD₇₃₆, FD₅₂₆) and RSI (FD₇₂₅, FD₅₁₆) can be reliably used for estimating LNA in winter wheat.     

Terrestrial laser scanning to estimate plot-level forest canopy fuel properties

This paper evaluates the potential of a terrestrial laser scanner (TLS) to characterize forest canopy fuel characteristics at plot level. Several canopy properties, namely canopy height, canopy cover, canopy base height and fuel strata gap were estimated. Different approaches were tested to avoid the effect of canopy shadowing on canopy height estimation caused by deployment of the TLS below the canopy. Estimation of canopy height using a grid approach provided a coefficient of determination of R² = 0.81 and an RMSE of 2.47 m. A similar RMSE was obtained using the 99th percentile of the height distribution of the highest points, representing the 1% of the data, although the coefficient of determination was lower (R² = 0.70). Canopy cover (CC) was estimated as a function of the occupied cells of a grid superimposed upon the TLS point clouds. It was found that CC estimates were dependent on the cell size selected, with 3 cm being the optimum resolution for this study. The effect of the zenith view angle on CC estimates was also analyzed. A simple method was developed to estimate canopy base height from the vegetation vertical profiles derived from an occupied/non-occupied voxels approach. Canopy base height was estimated with an RMSE of 3.09 m and an R² = 0.86. Terrestrial laser scanning also provides a unique opportunity to estimate the fuel strata gap (FSG), which has not been previously derived from remotely sensed data. The FSG was also derived from the vegetation vertical profile with an RMSE of 1.53 m and an R² = 0.87.     

Spectral mixture analysis to monitor defoliation in mixed-aged Eucalyptus globulus Labill plantations in southern Australia using Landsat5-TM and EO-1Hyperion data

Defoliation is a key parameter of forest health and is associated with reduced productivity and tree mortality. Assessing the health of forests requires regular observations over large areas. Satellite remote sensing provides a cost-effective alternative to traditional ground-based assessment of forest health, but assessing defoliation can be difficult due to mixed pixels where vegetation cover is low or fragmented. In this study we apply a novel spectral unmixing technique, referred to as weighted Multiple Endmember Spectral Mixture Analysis (wMESMA), to Landsat 5-TM and EO-1 Hyperion data acquired over a Eucalyptus globulus (Labill.) plantation in southern Australia. This technique combines an iterative mixture analysis cycle allowing endmembers to vary on a per pixel basis (MESMA) and a weighting algorithm that prioritizes wavebands based on their robustness against endmember variability. Spectral mixture analysis provides an estimate of the physically interpretable canopy cover, which is not necessarily correlated with defoliation in mixed-aged plantations due to natural variation in canopy cover as stands age. There is considerable variability in the degree of defoliation as well as in stand age among sites and in this study we found that results were significantly improved by the inclusion of an age correction algorithm for both the multi-spectral (R²_{no age correction} = 0.55 vs R²_{age correction} = 0.73 for Landsat) and hyperspectral (R²_{no age correction} = 0.12 vs R²_{age correction} = 0.50 for Hyperion) image data. The improved accuracy obtained from Landsat compared to the Hyperion data illustrates the potential of applying SMA techniques for analysis of multi-spectral datasets such as MODIS and SPOT-VEGETATION.     

Applications of AnnAGNPS model for soil loss estimation and nutrient loading for Malaysian conditions

The study evaluated the performance and suitability of AnnAGNPS model in assessing runoff, sediment loading and nutrient loading under Malaysian conditions. The watershed of River Kuala Tasik in Malaysia, a combination of two sub-watersheds, was selected as the area of study. The data for the year 2004 was used to calibrate the model and the data for the year 2005 was used for validation purposes. Several input parameters were computed using methods suggested by other researchers and studies carried out in Malaysia. The study shows that runoff was predicted well with an overall R² value of 0.90 and E value of 0.70. Sediment loading was able to produce a moderate result of R² = 0.66 and E = 0.49, nitrogen loading predictions were slightly better with R² = 0.68 and E = 0.53, and phosphorus loading performance was slightly poor with an R² = 0.63 and E = 0.33. The erosion map developed was in agreement with the erosion risk map produced by the Department of Agriculture, Malaysia. Rubber estates and urban areas were found to be the main contributors to soil erosion. The simulation results showed that AnnAGNPS has the potential to be used as a valuable tool for planning and management of watersheds under Malaysian conditions.     

Remote detection of bare soil moisture using a surface-temperature-based soil evaporation transfer coefficient

An approach for estimating soil moisture is presented and tested by using surface-temperature-based soil evaporation transfer coefficient (h_a), a coefficient recently proposed through the equation h_a = (T_s − T_a)/(T_sd − T_a), where T_s, T_sd, and T_a are land surface temperature (LST), reference soil (dry soil without evaporation) surface temperature, and air temperature respectively. Our analysis and controllable experiment indicated that h_a closely related to soil moisture, and therefore, a relationship between field soil moisture and h_a could be developed for soil moisture estimation. Field experiments were carried out to test the relationship between h_a and soil moisture. Time series Aqua-MODIS images were acquired between 11 Sep. 2006 and 1 Nov. 2007. Then, MODIS derived h_a and simultaneous measured soil moisture for different soil depths were used to establish the relations between the two variables. Results showed that there was a logarithmic relationship between soil moisture and h_a (P < 0.01). These logarithmic models were further validated by introducing another ground-truth data gathered from 46 meteorological stations in Hebei Province. Good agreement was observed between the measured and estimated soil moisture with RMSE of 0.0374 cm³/cm³ and 0.0503 cm³/cm³ for surface energy balance method at two soil depths (10 cm and 20 cm), with RMSE of 0.0467 cm³/cm³ and 0.0581 cm³/cm³ for maximum temperature method at two soil depths. For vegetated surfaces, the ratio of h_a and NDVI suggested to be considered. The proposed approach has a great potential for soil moisture and drought evaluation by remote sensing.     

Forecast of wheat yield throughout the agricultural season using optical and radar satellite images

The aim of this study is to estimate the capabilities of forecasting the yield of wheat using an artificial neural network combined with multi-temporal satellite data acquired at high spatial resolution throughout the agricultural season in the optical and/or microwave domains. Reflectance (acquired by Formosat-2, and Spot 4–5 in the green, red, and near infrared wavelength) and multi-configuration backscattering coefficients (acquired by TerraSAR-X and Radarsat-2 in the X- and C-bands, at co- (abbreviated HH and VV) and cross-polarization states (abbreviated HV and VH)) constitute the input variable of the artificial neural networks, which are trained and validated on the successively acquired images, providing yield forecast in near real-time conditions. The study is based on data collected over 32 fields of wheat distributed over a study area located in southwestern France, near Toulouse. Among the tested sensor configurations, several satellite data appear useful for the yield forecasting throughout the agricultural season (showing coefficient of determination (R²) larger than 0.60 and a root mean square error (RMSE) lower than 9.1 quintals by hectare (q ha⁻¹)): C_VH, C_HV, or the combined used of X_HH and C_HH, C_HH and C_HV, or green reflectance and C_HH. Nevertheless, the best accurate forecast (R² = 0.76 and RMSE = 7.0 q ha⁻¹) is obtained longtime before the harvest (on day 98, during the elongation of stems) using the combination of co- and cross-polarized backscattering coefficients acquired in the C-band (C_VV and C_VH). These results highlight the high interest of using synthetic aperture radar (SAR) data instead of optical ones to early forecast the yield before the harvest of wheat.     

Estimating sagebrush cover in semi-arid environments using Landsat Thematic Mapper data

Sagebrush ecosystems of the western US provide important habitat for several ungulate and vertebrate species. As a consequence of energy development, these ecosystems in Wyoming have been subjected to a variety of anthropogenic disturbances. Land managers require methodology that will allow them to consistently catalog sagebrush ecosystems and evaluate potential impact of proposed anthropogenic activities. This study addresses the utility of remotely sensed and ancillary geospatial data to estimate sagebrush cover using ordinal logistic regression. We demonstrate statistically significant prediction of ordinal sagebrush cover categories using spectral (χ² = 113; p < 0.0001) and transformed indices (χ² = 117; p < 0.0001). Both Landsat spectral bands (c-value = 0.88) and transformed indices (c-value = 0.89) can distinguish sites with closed, moderate and open cover sagebrush cover categories from no cover. The techniques described in this study can be used for estimating categories of sagebrush cover in arid ecosystems.     

Retrieval of chlorophyll and nitrogen in Norway spruce (Picea abies L. Karst.) using imaging spectroscopy

The research evaluated the information content of spectral reflectance (laboratory and airborne data) for the estimation of needle chlorophyll (C_AB) and nitrogen (C_N) concentration in Norway spruce (Picea abies L. Karst.) needles. To identify reliable predictive models different types of spectral transformations were systematically compared regarding the accuracy of prediction. The results of the cross-validated analysis showed that C_AB can be well estimated from laboratory and canopy reflectance data. The best predictive model to estimate C_AB was achieved from laboratory spectra using continuum-removal transformed data (R²cv = 0.83 and a relative RMSEcv of 8.1%, n = 78) and from hyperspectral HyMap data using band-depth normalised spectra (R²cv = 0.90, relative RMSEcv = 2.8%, n = 13). Concerning the nitrogen concentration, we observed somewhat weaker relations, with however still acceptable accuracies (at canopy level: R²cv = 0.57, relative RMSEcv = 4.6%). The wavebands selected in the regression models to estimate C_AB were typically located in the red edge region and near the green reflectance peak. For C_N, additional wavebands related to a known protein absorption feature at 2350 nm were selected. The portion of selected wavebands attributable to known absorption features strongly depends on the type of spectral transformation applied. A method called “water removal” (WR) produced for canopy spectra the largest percentage of wavebands directly or indirectly related to known absorption features. The derived chlorophyll and nitrogen maps may support the detection and the monitoring of environmental stressors and are also important inputs to many bio-geochemical process models.     

Tree survey and allometric models for tiger bush in northern Senegal and comparison with tree parameters derived from high resolution satellite data

A tree survey and an analysis of high resolution satellite data were performed to characterise the woody vegetation within a 10 × 10 km² area around a site located close to the town of Dahra in the semi-arid northern part of Senegal. The surveyed parameters were tree species, height, tree crown radius, and diameter at breast height (DBH), for which allometric models were determined. An object-based classification method was used to determine tree crown cover (TCC) from Quickbird data. The average TCC from the tree survey and the respective TCC from remote sensing were both about 3.0%. For areas beyond the surveyed areas TCC varied between 3.0% and 4.5%. Furthermore, an empirical correction factor for tree clumping was obtained, which considerably improved the estimated number of trees and the estimated average tree crown area and radius. An allometric model linking TCC to tree stem crosssectional area (CSA) was developed, which allows to estimate tree biomass from remote sensing. The allometric models for the three main tree species found performed well and had r²-values of about 0.7–0.8.     

Monitoring global land surface drought based on a hybrid evapotranspiration model

The latent heat of evapotranspiration (ET) plays an important role in the assessment of drought severity as one sensitive indicator of land drought status. A simple and accurate method of estimating global ET for the monitoring of global land surface droughts from remote sensing data is essential. The objective of this research is to develop a hybrid ET model by introducing empirical coefficients based on a simple linear two-source land ET model, and to then use this model to calculate the Evaporative Drought Index (EDI) based on the actual estimated ET and the potential ET in order to characterize global surface drought conditions. This is done using the Global Energy and Water Cycle Experiment (GEWEX) Surface Radiation Budget (SRB) products, AVHRR-NDVI products from the Global Inventory Modeling and Mapping Studies (GIMMS) group, and National Centers for Environmental Prediction Reanalysis-2 (NCEP-2) datasets. We randomly divided 22 flux towers into two groups and performed a series of cross-validations using ground measurements collected from the corresponding flux towers. The validation results from the second group of flux towers using the data from the first group for calibration show that the daily bias varies from −6.72 W/m² to 12.95 W/m² and the average monthly bias is −1.73 W/m². Similarly, the validation results of the first group of flux towers using data from second group for calibration show that the daily bias varies from −12.91 W/m² to 10.26 W/m² and the average monthly bias is −3.59 W/m². To evaluate the reliability of the hybrid ET model on a global scale, we compared the estimated ET from the GEWEX, AVHRR-GIMMS-NDVI, and NECP-2 datasets with the latent heat flux from the Global Soil Wetness Project-2 (GSWP-2) datasets. We found both of them to be in good agreement, which further supports the validity of our model's global ET estimation. Significantly, the patterns of monthly EDI anomalies have a good spatial and temporal correlation with the Palmer Drought Severity Index (PDSI) anomalies from January 1984 to December 2002, which indicates that the method can be used to accurately monitor long-term global land surface drought.     

Understanding the 2007-2008 eruption of Anak Krakatau Volcano by combining remote sensing technique and seismic data

One of the most violent volcanic eruptions in recorded history is the Krakatau eruption on August 27, 1883. This caldera-forming eruption destroyed two thirds of the Krakatau volcanic island in the Sunda Strait resulting in the remaining three small islands later known as the Krakatau complex. From 1927 to 1929, eruptions in the center of Krakatau complex have produced a new volcano named Anak Krakatau, which continuously builds its body through eruptions until now. One eruption event took place between 2007 and 2008 with several eruptions that lasted in total from the end of October 2007 to August 2008. Eruptions were characterized by Strombolian activity with ash columns 1 km high, as well as pyroclastic and lava flows. We monitored the ground deformation of Anak Krakatau Volcano by interfering PALSAR data from June 2007 to February 2009. The result of InSAR technique shows a complex pattern of ground deformation. Inflation up to 4 cm, together with subsidence around the crater, was measured for almost three months before the eruption with a volume increase of approximately 1 × 10⁶ m³. After the eruption, the southwest side of the volcanic cone subsided by 18 cm, whereas the northeast side of the cone uplifted 12 cm in almost two years. The observed ground deformation after the eruption can be explained by 4 m of tensile opening along a dipping rectangular tensile dislocation buried in an elastic half-space, approximately 400 m below sea level.     

Estimating land-surface temperature under clouds using MSG/SEVIRI observations

The retrieval of land-surface temperature (LST) from thermal infrared satellite sensor observations is known to suffer from cloud contamination. Hence few studies focus on LST retrieval under cloudy conditions. In this paper a temporal neighboring-pixel approach is presented that reconstructs the diurnal cycle of LST by exploiting the temporal domain offered by geo-stationary satellite observations (i.e. MSG/SEVIRI), and yields LST estimates even for overcast moments when satellite sensor can only record cloud-top temperatures. Contrasting to the neighboring pixel approach as presented by Jin and Dickinson (2002), our approach naturally satisfies all sorts of spatial homogeneity assumptions and is hence more suited for earth surfaces characterized by scattered land-use practices. Validation is performed against in situ measurements of infrared land-surface temperature obtained at two validation sites in Africa. Results vary and show a bias of −3.68 K and a RMSE of 5.55 K for the validation site in Kenya, while results obtained over the site in Burkina Faso are more encouraging with a bias of 0.37 K and RMSE of 5.11 K. Error analysis reveals that uncertainty of the estimation of cloudy sky LST is attributed to errors in estimation of the underlying clear sky LST, all-sky global radiation, and inaccuracies inherent to the ‘neighboring pixel’ scheme itself. An error propagation model applied for the proposed temporal neighboring-pixel approach reveals that the absolute error of the obtained cloudy sky LST is less than 1.5 K in the best case scenario, and the uncertainty increases linearly with the absolute error of clear sky LST. Despite this uncertainty, the proposed method is practical for retrieving the LST under a cloudy sky condition, and it is promising to reconstruct diurnal LST cycles from geo-stationary satellite observations.