Bodies of water, particularly swimming pools, are land covers of high interest. Their maintenance involves energy costs that authorities must take into consideration. In addition, swimming pools are important water sources for firefighting. However, they also provide a habitat for mosquitoes to breed, potentially posing a serious health threat of mosquito-borne disease. This paper presents a novel semi-automatic method of detecting swimming pools in urban environments from aerial images and LIDAR data. A new index for detecting swimming pools is presented (Normalized Difference Swimming Pools Index) that is combined with three other decision indices using the Dempster–Shafer theory to determine the locations of swimming pools. The proposed method was tested in an urban area of the city of Alcalá de Henares in Madrid, Spain. The method detected all existing swimming pools in the studied area with an overall accuracy of 99.86%, similar to the results obtained by support vector machines (SVM) supervised classification. Full article

Irrigation-induced salinization is an important land degradation process that affects crop yield in the Brazilian semi-arid region, and gypsum has been used as a corrective measure for saline soils. Fluvent soil samples (180) were treated with increasing levels of salinization of NaCl, MgCl₂ and CaCl₂. The salinity was gauged using electrical conductivity (EC). Gypsum was added to one split of these samples before they were treated by the saline solutions. Laboratory reflectance spectra were measured at nadir under a controlled environment using a FieldSpec spectrometer, a 250-W halogen lamp and a Spectralon panel. Variations in spectral reflectance and brightness were evaluated using principal component analysis, as well as the continuum-removed absorption depths of major features at 1450, 1950, 1750 and 2200 nm for both the gypsum-treated (TG) and non-treated (NTG) air-dried soil samples as a function of EC. Pearson’s correlation coefficients of reflectance and the band depth with EC were also obtained to establish the relationships with salinity. Results showed that NTG samples presented a decrease in reflectance and brightness with increasing CaCl₂ and MgCl₂ salinization. The reverse was observed for NaCl. Gypsum increased the spectral reflectance of the soil. The best negative correlations between reflectance and EC were observed in the 1500–2400 nm range for CaCl₂ and MgCl₂, probably because these wavelengths are most affected by water absorption, as Ca and Mg are much more hygroscopic than Na. These decreased after chemical treatment with gypsum. The most prominent features were observed at 1450, 1950 and 1750 nm in salinized-soil spectra. The 2200-nm clay mineral absorption band depth was inversely correlated with salt concentration. From these features, only the 1750 and 2200 nm ones are within atmospheric absorption windows and can be more easily measured using hyperspectral sensors. Full article

To improve crop model performance for regional crop yield estimates, a new four-dimensional variational algorithm (POD4DVar) merging the Monte Carlo and proper orthogonal decomposition techniques was introduced to develop a data assimilation strategy using the Crop Environment Resource Synthesis (CERES)-Wheat model. Two winter wheat yield estimation procedures were conducted on a field plot and regional scale to test the feasibility and potential of the POD4DVar-based strategy. Winter wheat yield forecasts for the field plots showed a coefficient of determination (R²) of 0.73, a root mean square error (RMSE) of 319 kg/ha, and a relative error (RE) of 3.49%. An acceptable yield at the regional scale was estimated with an R² of 0.997, RMSE of 7346 tons, and RE of 3.81%. The POD4DVar-based strategy was more accurate and efficient than the EnKF-based strategy. In addition to crop yield, other critical crop variables such as the biomass, harvest index, evapotranspiration, and soil organic carbon may also be estimated. The present study thus introduces a promising approach for operationally monitoring regional crop growth and predicting yield. Successful application of this assimilation model at regional scales must focus on uncertainties derived from the crop model, model inputs, data assimilation algorithm, and assimilated observations. Full article

Plant species identification and mapping based on remotely-sensed spectral signatures is a challenging task with the potential to contribute enormously to ecological studies. Success in this task rests upon the appropriate collection and use of costly field-based training data, and researchers are in need of ways to improve collection efficiency based on quantitative evidence. Using imaging spectrometer data collected by the Carnegie Airborne Observatory for hundreds of field-identified tree crowns in Kruger National Park, South Africa, we developed woody plant species classification models and evaluated how classification accuracy increases with increasing numbers of training crowns. First, we show that classification accuracy must be estimated while respecting the crown as the basic unit of data; otherwise, accuracy will be overestimated and the amount of training data needed to perform successful classification will be underestimated. We found that classification accuracy and the number of training crowns needed to perform successful classification varied depending on the number and spectral separability of species in the model. We also used a modified Michaelis-Menten function to describe the empirical relationship between training crowns and model accuracy, and show how this function may be useful for predicting accuracy. This framework can assist researchers in designing field campaigns to maximize the efficiency of field data collection, and thus the amount of biodiversity information gained from remote species identification models. Full article

With 298 heterogeneous soil samples from Yixing (Jiangsu Province), Zhongxiang and Honghu (Hubei Province), this study aimed to combine a successive projections algorithm (SPA) with a support vector machine regression (SVMR) model (SPA-SVMR model) to improve the estimation accuracy of soil organic carbon (SOC) contents using the laboratory-based visible and near-infrared (VIS/NIR, 350−2500 nm) spectroscopy of soils. The effects of eight spectra pre-processing methods, i.e., Log (1/R), Log (1/R) coupled with Savitzky-Golay (SG) smoothing (Log (1/R) + SG), first derivative with SG smoothing (FD), second derivative with SG smoothing (SD), SG, standard normal variate (SNV), mean center (MC) and multiplicative scatter correction (MSC), on SPA-based informative wavelength selection were explored. The SVMR model (i.e., SVMR without SPA) and SPA-PLSR model (i.e., SPA combined with partial least squares regression (PLSR)) were developed and compared with the SPA-SVMR model in order to evaluate the performance of SPA-SVMR. The results indicated that the variables selected by SPA and their distributions were strongly affected by different pre-processing methods, and SG was the optimal pre-processing method for SPA-SVMR model development; the SPA-SVMR model using SG pre-processing and 28 SPA-selected wavelengths obtained a better result (R²_V = 0.73, RMSE_V = 2.78 g∙kg⁻¹ and RPD_V = 1.89) and outperformed the SVMR model (R²_V = 0.72, RMSE_V = 2.83 g∙kg⁻¹ and RPD_V = 1.86) and the SPA-PLSR model (R²_V = 0.62, RMSE_V = 3.23 g∙kg⁻¹ and RPD_V = 1.63). Most of the spectral bands used by the SPA-SVMR model over the near-infrared region were important wavelengths for SOC content estimation. This study demonstrated that the combination of SPA and SVMR is feasible and reliable for estimating SOC content from the VIS/NIR spectra of soils in regions with multiple soil and land-use types. Full article

Seasonal cycles, interannual variations and decadal trends of Sea-viewing Wide Field-of-view Sensor (SeaWiFS)-retrieved chlorophyll-a concentration (Chl-a) in the Strait of Malacca (SM) were investigated with reconstructed, cloud-free SeaWiFS Chl-a during the period of the SeaWiFS full mission (September 1997 to December 2010). Pixel-based non-parametric correlations of SeaWiFS Chl-a on environmental variables were used to identify the probable causes of the observed spatio-temporal variations of SeaWiFS Chl-a in northern, middle and southern regions of the SM. Chl-a was high (low) during the northeast (southwest) monsoon. The principal causes of the seasonality were wind-driven vertical mixing in the northern region and wind-driven coastal upwelling and possibly river discharges in the middle region. Among the three regions, the southern region showed the largest interannual variations of Chl-a. These variations were associated with the El Niño/Southern Oscillation (ENSO) and river runoff. Interannual variations of Chl-a in the middle and northern regions were more responsive to the Indian Ocean Dipole and ENSO, respectively, with atmospheric deposition being the most important driver. The most significant decadal-scale trend of increasing Chl-a was in the southern region; the trend was moderate in the middle region. This increasing trend was probably caused by environmental changes unrelated to the variables investigated in this study. Full article

The availability of ZY-3 satellite data provides additional potential for surveying, mapping, and quantitative studies. Topographic correction, which eliminates the terrain effect caused by the topographic relief, is one of the fundamental steps in data preprocessing for quantitative analysis of vegetation. In this paper, we rectified ZY-3 satellite data using five commonly used topographic correction models and investigate their impact on the regression estimation of shrub forest leaf biomass obtained from sample plots in the study area. All the corrections were assessed by means of: (1) visual inspection (2) reduction of the standard deviation (SD) at different terrain slopes (3) correlation analysis of different correction results. Best results were obtained from the Minnaert+SCS correction, based on the non-Lambertian reflection assumption. Additional analysis showed that the coefficient correlation of the biomass fitting result was improved after the Minnaert+SCS correction, as well as the fitting precision. The R² has increased by 0.113 to reach 0.869, while the SD (standard deviation) of the biomass dropped by 21.2%. Therefore, based on the facts, we conclude that in the region with large topographic relief, the topographical correction is essential to the estimation of the biomass. Full article

The ability to classify asphalt surfaces is an important goal for the selection of suitable non-variant targets as pseudo-invariant targets during the calibration/validation of remotely-sensed images. In addition, the possibility to recognize different types of asphalt surfaces on the images can help optimize road network management. This paper presents a multi-resolution study to improve asphalt surface differentiation using field spectroradiometric data, laboratory analysis and remote sensing imagery. Multispectral Infrared and Visible Imaging Spectrometer (MIVIS) airborne data and multispectral images, such as Quickbird and Ikonos, were used. From scatter plots obtained by field data using λ = 460 and 740 nm, referring to MIVIS Bands 2 and 16 and Quickbird and Ikonos Bands 1 and 4, pixels corresponding to asphalt covering were identified, and the slope of their interpolation lines, assumed as asphalt lines, was calculated. These slopes, used as threshold values in the Spectral Angle Mapper (SAM) classifier, obtained an overall accuracy of 95% for Ikonos, 98% for Quickbird and 93% for MIVIS. Laboratory investigations confirm the existence of the asphalt line also for new asphalts, too. Full article

Forests contain a majority of the aboveground carbon (C) found in ecosystems, and understanding biomass lost from disturbance is essential to improve our C-cycle knowledge. Our study region in the Wisconsin and Minnesota Laurentian Forest had a strong decline in Normalized Difference Vegetation Index (NDVI) from 1982 to 2007, observed with the National Ocean and Atmospheric Administration’s (NOAA) series of Advanced Very High Resolution Radiometer (AVHRR). To understand the potential role of disturbances in the terrestrial C-cycle, we developed an algorithm to map forest disturbances from either harvest or insect outbreak for Landsat time-series stacks. We merged two image analysis approaches into one algorithm to monitor forest change that included: (1) multiple disturbance index thresholds to capture clear-cut harvest; and (2) a spectral trajectory-based image analysis with multiple confidence interval thresholds to map insect outbreak. We produced 20 maps and evaluated classification accuracy with air-photos and insect air-survey data to understand the performance of our algorithm. We achieved overall accuracies ranging from 65% to 75%, with an average accuracy of 72%. The producer’s and user’s accuracy ranged from a maximum of 32% to 70% for insect disturbance, 60% to 76% for insect mortality and 82% to 88% for harvested forest, which was the dominant disturbance agent. Forest disturbances accounted for 22% of total forested area (7349 km²). Our algorithm provides a basic approach to map disturbance history where large impacts to forest stands have occurred and highlights the limited spectral sensitivity of Landsat time-series to outbreaks of defoliating insects. We found that only harvest and insect mortality events can be mapped with adequate accuracy with a non-annual Landsat time-series. This limited our land cover understanding of NDVI decline drivers. We demonstrate that to capture more subtle disturbances with spectral trajectories, future observations must be temporally dense to distinguish between type and frequency in heterogeneous landscapes. Full article

The latest CERES FM-5 instrument launched onboard the S-NPP spacecraft will use the VIIRS visible radiances from the NASA Land Product Evaluation and Analysis Tool Elements (PEATE) product for retrieving the cloud properties associated with its TOA flux measurement. In order for CERES to provide climate quality TOA flux datasets, the retrieved cloud properties must be consistent throughout the record, which is dependent on the calibration stability of the VIIRS imager. This paper assesses the NASA calibration stability of the VIIRS reflective solar bands using the Libya-4 desert and deep convective clouds (DCC). The invariant targets are first evaluated for temporal natural variability. It is found for visible (VIS) bands that DCC targets have half of the variability of Libya-4. For the shortwave infrared (SWIR) bands, the desert has less variability. The brief VIIRS record and target variability inhibits high confidence in identifying any trends that are less than ±0.6%/yr for most VIS bands, and ±2.5%/yr for SWIR bands. None of the observed invariant target reflective solar band trends exceeded these trend thresholds. Initial assessment results show that the VIIRS data have been consistently calibrated and that the VIIRS instrument stability is similar to or better than the MODIS instrument. Full article

Remotely sensed vegetation indices have been extensively used to quantify plant and soil characteristics. The objectives of this study were to: (i) compare vegetation indices developed at different scales for measuring canopy N content (g∙N∙m⁻²) and concentration (%); and (ii) evaluate the effects of soil background reflectance, cultivar, illumination and atmospheric conditions on the ability of vegetation indices to estimate canopy N content. Data were collected from two rainfed field sites cropped to wheat in Southern Italy (Foggia) and in Southeastern Australia (Horsham). From spectral readings, 25 vegetation indices were calculated. The Perpendicular Vegetation Index showed the best prediction of plant N concentration (%) (r² = 0.81; standard error (SE) = 0.41%; p < 0.001). The Canopy Chlorophyll Content Index showed the best predictive capability for canopy N content (g∙N∙m⁻²) (r² = 0.73; SE = 0.603; p < 0.001). Canopy N content was best related to indices developed at the canopy scale and containing a red-edge wavelength. Canopy-scale indices were related to canopy N%, but such relationships needed to be normalized with biomass. Geographical location influenced mainly simple ratio or normalized indices, while indices that contained red-edge wavelengths were more robust and able to estimate canopy parameters more accurately. Full article

There have been many studies and much attention paid to spatial sharpening for thermal imagery. Among them, TsHARP, based on the good correlation between vegetation index and land surface temperature (LST), is regarded as a standard technique because of its operational simplicity and effectiveness. However, as LST is affected by other factors (e.g., soil moisture) in the areas with low vegetation cover, these areas cannot be well sharpened by TsHARP. Thin plate spline (TPS) is another popular downscaling technique for surface data. It has been shown to be accurate and robust for different datasets; however, it has not yet been attempted in thermal sharpening. This paper proposes to combine the TsHARP and TPS methods to enhance the advantages of each. The spatially explicit errors of these two methods were firstly estimated in theory, and then the results of TPS and TsHARP were combined with the estimation of their errors. The experiments performed across various landscapes and data showed that the proposed combined method performs more robustly and accurately than TsHARP. Full article

The accuracy of different coarse-resolution land cover products is an important consideration for product users at the regional or global scale, and different evaluation methods inevitably result in discrepancies in accuracy for the same land cover product. The remote sensing community has responded to this increased interest by improving methodologies for more accurately evaluating the correctness of land cover information. In this study, a pixel-based hierarchical classification strategy followed by an object-based classification method was applied to compact airborne spectrographic imager (CASI) hyperspectral data in order to produce highly accurate, high spatial resolution classification reference data. Some aspects of the fuzzy/conventional evaluation of MODIS land cover (MODISLC) (500 m) and GlobCover (300 m) data based on sub-pixel class fractions derived from high spatial resolution reference data at different thematic resolutions are also discussed. Relationships between homogeneity and fuzzy accuracy for two land cover products were obtained at different thematic resolutions. Additionally, the influences on the relationship resulting from the thematic resolution were also studied, and these are reported in this paper. Attempts were made to establish fuzzy/conventional evaluation rules for fuzzy classes, and the different performances of the fuzzy and conventional evaluations for hard/fuzzy labels were compared. The adjusted GlobCover accuracy after theoretical removal of the effect caused by spatial resolution was calculated based on the relationship between homogeneity and accuracy; the result was a higher accuracy than for MODISLC at the same thematic resolution. In addition, the different performance characteristics of the relationships between homogeneity and adjusted GlobCover accuracy/MODISLC accuracy at different thematic resolutions were compared and analyzed over the area where the CASI transects were obtained. Full article

FengYun-3 (FY-3) Visible Infrared Radiometer (VIRR), along with its predecessor, Multispectral Visible Infrared Scanning Radiometer (MVISR), onboard FY-1C&D have had continuous global observation more than 14 years. This data record is valuable for weather prediction, climate monitoring, and environment research. Data quality is vital for satellite data assimilations in Numerical Weather Prediction (NWP) and quantitative remote sensing applications. In this paper, the accuracies of radiometric calibration for VIRR onboard FY-3A and FY-3B, in thermal infrared (TIR) channels, are evaluated using the Low Earth Orbit (LEO)-LEO simultaneous nadir overpass intercalibration method. Hyperspectral and high-quality observations from Infrared Atmosphere Sounding Instrument (IASI) onboard METOP-A are used as reference. The biases of VIRR measurements with respect to IASI over one-and-a-half years indicate that the TIR calibration accuracy of FY-3B VIRR is better than that of FY-3A VIRR. The brightness temperature (BT) measured by FY-3A/VIRR is cooler than that measured by IASI with monthly mean biases ranging from −2 K to −1 K for channel 4 and −1 K to 0.2 K for channel 5. Measurements from FY-3B/VIRR are more consistent with that from IASI, and the annual mean biases are 0.84 ± 0.16 K and −0.66 ± 0.18 K for channels 4 and 5, respectively. The BT biases of FY-3A/VIRR show scene temperature-dependence and seasonal variation, which are not found from FY-3B/VIRR BT biases. The temperature-dependent biases are shown to be attributed to the nonlinearity of detectors. New nonlinear correction coefficients of FY-3A/VIRR TIR channels are reevaluated using various collocation samples. Verification results indicate that the use of the new nonlinear correction can greatly correct the scene temperature-dependent and systematic biases. Full article

We present and validate a method of reconstructing high-resolution sea surface wind fields from multi-sensor satellite data over the Grand Banks of Newfoundland off Atlantic Canada. Six-hourly ocean wind fields from blended products (including multi-satellite measurements) with 0.25° spatial resolution and 226 RADARSAT-2 synthetic aperture radar (SAR) wind fields with 1-km spatial resolution have been used to reconstruct new six-hourly wind fields with a resolution of 10 km for the period from August 2008 to December 2010, except July 2009 to November 2009. The reconstruction process is based on the heapsort bucket method with topdown search and the modified Gauss–Markov theorem. The result shows that the mean difference between the reconstructed wind speed and buoy-estimated wind speed is smaller than 0.6 m/s, and the standard deviation is smaller than 2.5 m/s. The mean difference in wind direction between reconstructed and buoy estimates is 3.7°; the standard deviation is 40.2°. There is fair agreement between the reconstructed wind vectors and buoy-estimated ones. Full article

In this study, a classification and performance evaluation framework for the recognition of urban patterns in medium (Landsat ETM, TM and MSS) and very high resolution (WorldView-2, Quickbird, Ikonos) multi-spectral satellite images is presented. The study aims at exploring the potential of machine learning algorithms in the context of an object-based image analysis and to thoroughly test the algorithm’s performance under varying conditions to optimize their usage for urban pattern recognition tasks. Four classification algorithms, Normal Bayes, K Nearest Neighbors, Random Trees and Support Vector Machines, which represent different concepts in machine learning (probabilistic, nearest neighbor, tree-based, function-based), have been selected and implemented on a free and open-source basis. Particular focus is given to assess the generalization ability of machine learning algorithms and the transferability of trained learning machines between different image types and image scenes. Moreover, the influence of the number and choice of training data, the influence of the size and composition of the feature vector and the effect of image segmentation on the classification accuracy is evaluated. Full article

Estimating crop nitrogen (N) status with sensors can be useful to adjust fertilizer levels to crop requirements, reducing farmers’ costs and N losses to the environment. In this study, we evaluated the potential of hyperspectral indices obtained from field data and airborne imagery for developing N fertilizer recommendations in maize (Zea mays L.). Measurements were taken in a randomized field experiment with six N fertilizer rates ranging from zero to 200 kg∙N∙ha⁻¹ and four replications on two different dates (before the second fertilizer application and at flowering) in 2012. Readings at ground level were taken with SPAD^®, Dualex^® and Multiplex^® sensors, and airborne data were acquired by flying a hyperspectral and a thermal sensor 300 m over the experimental site. The hyperspectral imagery was used to calculate greenness, chlorophyll and photochemical indices for each plot. The Pearson coefficient was used to quantify the correlation between sensor readings and agronomic measurements. A statistical procedure based on the N-sufficient index was used to determine the accuracy of each index at distinguishing between N-deficient and N-sufficient plots. Indices based on airborne measurements were found to be as reliable as measurements taken with ground-level equipment at assessing crop N status and predicting yield at flowering. At stem elongation, the reflectance ratio, R750/R710, and fluorescence retrieval (SIF760) were the only indices that yielded significant results when compared to crop yield. Field-level SPAD readings, the airborne R750/R710 index and SIF760 had the lowest error rates when distinguishing N-sufficient from N-deficient treatments, but error reduction is still recommended before commercial field application. Full article

The spatial and temporal variability of phytoplankton blooms was investigated in two tropical coastal regions of northern Australia using the MEdium Resolution Imaging Spectrometer (MERIS) full mission (2002–2012) reduced resolution dataset. Satellite-derived proxies for phytoplankton (Chlorophyll-a (Chl), Fluorescence Line Height (FLH), Maximum Chlorophyll Index (MCI)) and suspended sediment (Total Suspended Matter (TSM)) were jointly analyzed for two clusters of the Great Barrier Reef Wet tropics (GBRW; 15°–19.5°S; Queensland) and the Van Diemen Gulf (VDG; 9°–13°S; Northern Territory). The analysis of time-series and Hovmöller diagrams of the four MERIS products provided a unique perspective on the processes linking phytoplankton blooms and river runoff, or resuspension, across spatio-temporal scales. Both regions are characterized by a complex oceanography and seasonal inflows of sediment, freshwater and nutrients during the tropical wet season months (November to April). The GBRW is characterized by a great variability in water clarity (Secchi depth 0–25 m). A long history of agricultural land use has led to a large increase in the seasonal discharge of sediments and nutrients, triggering seasonal phytoplankton blooms (>0.4 mg∙m⁻³) between January and April. In contrast, the VDG is a poorly flushed, turbid (Secchi depth <5 m) environment with strong tidal-energy (4–8 m) and very limited land use. Phytoplankton blooms here were found to have higher Chl concentrations (>1.0 mg∙m⁻³) than in the GBRW, occurring up to twice a year between January and April. Over the 10-year MERIS mission, a weak decline in Chl and TSM was observed for the VDG (Sen slope: −2.85%/decade, τ = −0.32 and −3.57%/decade, τ = −0.24; p 0.05), while no significant trend in those two satellite products was observed in the GBRW. Cyanobacteria surface algal blooms occur in both regions between August and October. The MCI and FLH products were found to adequately complement Chl, while TSM provided relevant insight for the assessment of sediment resuspension and river runoff. Full article

Speckle noise (salt and pepper) is inherent to synthetic aperture radar (SAR), which causes a usual noise-like granular aspect and complicates the image classification. In SAR image analysis, the spatial information might be a particular benefit for denoising and mapping classes characterized by a statistical distribution of the pixel intensities from a complex and heterogeneous spectral response. This paper proposes the Probability Density Components Analysis (PDCA), a new alternative that combines filtering and frequency histogram to improve the classification procedure for the single-channel synthetic aperture radar (SAR) images. This method was tested on L-band SAR data from the Advanced Land Observation System (ALOS) Phased-Array Synthetic-Aperture Radar (PALSAR) sensor. The study area is localized in the Brazilian Amazon rainforest, northern Rondônia State (municipality of Candeias do Jamari), containing forest and land use patterns. The proposed algorithm uses a moving window over the image, estimating the probability density curve in different image components. Therefore, a single input image generates an output with multi-components. Initially the multi-components should be treated by noise-reduction methods, such as maximum noise fraction (MNF) or noise-adjusted principal components (NAPCs). Both methods enable reducing noise as well as the ordering of multi-component data in terms of the image quality. In this paper, the NAPC applied to multi-components provided large reductions in the noise levels, and the color composites considering the first NAPC enhance the classification of different surface features. In the spectral classification, the Spectral Correlation Mapper and Minimum Distance were used. The results obtained presented as similar to the visual interpretation of optical images from TM-Landsat and Google Maps. Full article

Image orientation requires ground control as a source of information for both indirect estimation and quality assessment to guarantee the accuracy of the photogrammetric processes. However, the orientation still depends on interactive measurements to locate the control entities over the images. This paper presents an automatic technique used to generate 3D control points from vertical panoramic terrestrial images. The technique uses a special target attached to a GPS receiver and panoramic images acquired in nadir view from different heights. The reference target is used as ground control to determine the exterior orientation parameters (EOPs) of the vertical images. These acquired multi-scale images overlap in the central region and can be used to compute ground coordinates using photogrammetric intersection. Experiments were conducted in a terrestrial calibration field to assess the geometry provided by the reference target and the quality of the reconstructed object coordinates. The analysis was based on the checkpoints, and the resulting discrepancies in the object space were less than 2 cm in the studied cases. As a result, small models and ortho-images can be produced as well as georeferenced image chips that can be used as high-quality control information. Full article

The spatio-temporal variability of trends in vegetation greenness in dryland areas is a well-documented phenomenon in remote sensing studies at global to regional scales. The underlying causes differ, however, and are often not well understood. Here, we analyzed the trends in vegetation greenness for a semi-arid area in northeastern Brazil (NEB) and examined the relationships between those dynamics and climate anomalies, namely the El Nino Southern Oscillation (ENSO) for the period 1982 to 2010, based on annual Normalized Difference Vegetation Index (NDVI) values from the latest version of the Global Inventory Modeling and Mapping Studies (GIMMS) NDVI dataset (NDVI3g) dataset. Against the ample assumption of ecological and socio-economic research, the results of our inter-annual trend analysis of NDVI and precipitation indicate large areas of significant greening in the observation period. The spatial extent and strength of greening is a function of the prevalent land-cover type or biome in the study area. The regression analysis of ENSO indicators and NDVI anomalies reveals a close relation of ENSO warm events and periods of reduced vegetation greenness, with a temporal lag of 12 months. The spatial patterns of this relation vary in space and time. Thus, not every ENSO warm event is reflected in negative NDVI anomalies. Xeric shrublands (Caatinga) are more sensitive to ENSO teleconnections than other biomes in the study area. Full article

Web tools for statistical investigation with an interactive and friendly interface enable users without programming knowledge to conduct their analyses. We develop an Interactive Correlation Environment (ICE) in an open access platform to perform spectral collinearity analysis for biogeochemical activity retrieval. We evaluate its performance on different browsers and applied it to retrieve chlorophyll-a (chl-a) concentration in a tropical reservoir. The use of ICE to retrieve water chl-a concentration got a Root Mean Square Error (RMSE) lower than 7% for seasonal datasets, enhancing ICE's ability to adapt it within season. An RMSE of 17% was found for the mixed dataset with a large range of chl-a concentrations. We conclude that the use of ICE is recommended, due to its quick response, easily manipulation, high accuracy, and empirical adaptation to seasonal variability. Its use is enhanced by the development of hyperspectral sensors, which allow the identification of several biogeochemical components, such as chl-a, phycocyanin (PC), soil salinity, soil types, leaf nitrogen, and leaf chl-a concentration. Full article

This paper presents methods for 3D modeling of railway environments from airborne laser scanning (ALS) and mobile laser scanning (MLS). Conventionally, aerial data such as ALS and aerial images were utilized for 3D model reconstruction. However, 3D model reconstruction only from aerial-view datasets can not meet the requirement of advanced visualization (e.g., walk-through visualization). In this paper, objects in a railway environment such as the ground, railroads, buildings, high voltage powerlines, pylons and so on were reconstructed and visualized in real-life experiments in Kokemaki, Finland. Because of the complex terrain and scenes in railway environments, 3D modeling is challenging, especially for high resolution walk-through visualizations. However, MLS has flexible platforms and provides the possibility of acquiring data in a complex environment in high detail by combining with ALS data to produce complete 3D scene modeling. A procedure from point cloud classification to 3D reconstruction and 3D visualization is introduced, and new solutions are proposed for object extraction, 3D reconstruction, model simplification and final model 3D visualization. Image processing technology is used for the classification, 3D randomized Hough transformations (RHT) are used for the planar detection, and a quadtree approach is used for the ground model simplification. The results are visually analyzed by a comparison with an orthophoto at a 20 cm ground resolution. Full article

We update the Global Inventory Modeling and Mapping Studies (GIMMS) analysis of Sahelian vegetation dynamics and trends using the normalized difference vegetation index (NDVI; version 3g) 1981 to 2012 data set. We compare the annual NDIV3g and July to October growing season averages with the three rainfall data sets: the Africa Rainfall Climatology from 1983 to 2012, the Variability Analyses of Surface Climate Observations Version-1.1 from 1951 to 2000, and the Nicholson ground-station precipitation rainfall data from 1981 to 1994. We use the Nicholson ground-station annual precipitation data to determine the reliability of the two continental precipitation data sets for specific locations and specific times, extrapolate these confirmed relationships over the Sahelian Zone from 1983 to 2012 with the Africa Rainfall Climatology, and then place these zonal findings within the 1951 to 2000 record of the Variability Analyses of Surface Climate Observations Version-1.1 precipitation data set. We confirm the extreme nature of the 1984–1985 Sahelian drought, a signature event that marked the minima during the 1980s desiccation period followed within ten years by near-maxima rainfall event in 1994 and positive departures is NDVI, marking beginning of predominantly wetter conditions that have persisted to 2012. We also show the NDVI3g data capture “effective” rainfall, the rainfall that is utilized by plants to grow, as compared to rainfall that evaporates or is runoff. Using our effective rainfall concept, we estimate average effective rainfall for the entire Sahelian Zone for the 1984 extreme drought was 223 mm/yr as compared to 406 mm/yr in during the 1994 wet period. We conclude that NDVI3g data can used as a proxy for analyzing and interpreting decadal-scale land surface variability and trends over semi arid-lands. Full article

MODerate resolution Imaging Spectroradiometer (MODIS) data are largely used in multitemporal analysis of various Earth-related phenomena, such as vegetation phenology, land use/land cover change, deforestation monitoring, and time series analysis. In general, the MODIS products used to undertake multitemporal analysis are composite mosaics of the best pixels over a certain period of time. However, it is common to find bad pixels in the composition that affect the time series analysis. We present a filtering methodology that considers the pixel position (location in space) and time (position in the temporal data series) to define a new value for the bad pixel. This methodology, called Window Regression (WR), estimates the value of the point of interest, based on the regression analysis of the data selected by a spatial-temporal window. The spatial window is represented by eight pixels neighboring the pixel under evaluation, and the temporal window selects a set of dates close to the date of interest (either earlier or later). Intensities of noises were simulated over time and space, using the MOD13Q1 product. The method presented and other techniques (4253H twice, Mean Value Iteration (MVI) and Savitzky–Golay) were evaluated using the Mean Absolute Percentage Error (MAPE) and Akaike Information Criteria (AIC). The tests revealed the consistently superior performance of the Window Regression approach to estimate new Normalized Difference Vegetation Index (NDVI) values irrespective of the intensity of the noise simulated. Full article

Climate change is expected to alter vegetation and carbon cycle processes, with implications for ecosystems. Notably, understanding the sensitivity of vegetation to the anomalies of precipitation and temperature over different land cover classes and the corresponding temporal response is essential for improved climate prediction. In this paper, we analyze vegetation response to hydroclimatic forcings using the Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) derived from SeaWiFS (Sea-viewing Wide Field-of-view Sensor) (1998–2002) and (Medium Resolution Imaging Spectrometer) (2003–2011) satellite sensors at ∼1-km resolution. Based on land cover and pixel-wise analysis, we quantify the extent of the dependence of the FAPAR and, ultimately, the phenology on the anomalies of precipitation and temperature over Europe. Statistical tests are performed to establish where this correlation may be regarded as statistically significant. Furthermore, we assess a statistical link between the climate variables and a set of phenological metrics defined from FAPAR measurement. Variation in the phenological response to the unusual values of precipitation and temperature can be interpreted as the result of the balanced opposite effects of water and temperature on vegetation processes. Results suggest very different responses for different land cover classes and seasons. Correlation analysis also indicates that European phenology may be quite sensitive to perturbations in precipitation and temperature regimes, such as those induced by climate change. Full article

Soil water content (SWC) is a crucial variable in the thermal infrared research and is the major control for land surface hydrological processes at the watershed scale. Estimating the surface SWC from remotely sensed data using the triangle method proposed by Price has been demonstrated in previous studies. In this study, a new soil moisture index (Temperature Rising Rate Vegetation Dryness Index—TRRVDI) is proposed based on a triangle constructed using the mid-morning land surface temperature (LST) rising rate and the vegetation index to estimate the regional SWC. The temperature at the dry edge of the triangle is determined by the surface energy balance principle. The temperature at the wet edge is assumed to be equal to the air temperature. The mid-morning land surface temperature rising rate is calculated using Meteosat Second Generation—Spinning Enhanced Visible and Infrared Imager (MSG-SEVIRI) LST products over 4 cloud-free days (day of year: 206, 211, 212, 242) in 2007. The developed TRRVDI is validated by in situ measurements from 19 meteorological stations in Spain. The results indicate that the coefficient of determination (R²) between the TRRVDI derived using the theoretical limiting edges and the in situ SWC measurements is greater than that derived using the observed limiting edges. The R² values are 0.46 and 0.32; respectively (p < 0.05). Additionally, the TRRVDI is much better than the soil moisture index that was developed using one-time LST and fractional vegetation cover (FVC) with the theoretically determined limiting edges. Full article

Hydrocarbon seeps cause chemical and mineralogical changes at the surface, which can be detected by remote sensing. This paper aims at the detection of mineral alteration induced by gas seeps in a marly limestone formation, SW Iran. For this purpose, the multispectral Advance Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and the high spatial resolution WorldView-2 (WV-2) data were utilized for mapping surficial rock alteration. In addition, the potential of Visible Near Infrared (VNIR) bands of the WV-2 and its high spatial resolution for mapping alterations was determined. Band ratioing, principal component analysis (PCA), data fusion and the boosted regression trees (BRT) were applied to enhance and classify the altered and unaltered marly limestone formation. The alteration zones were identified and mapped by remote sensing analyses. Integrating the WV-2 into the ASTER data improved the spatial accuracy of the BRT classifications. The results showed that the BRT classification of the multiple band imagery (created from ASTER and WV-2) using regions of interest (ROIs) around field data provides the best discrimination between altered and unaltered areas. It is suggested that the WV-2 dataset can provide a potential tool along higher spectral resolution data for mapping alteration minerals related to hydrocarbon seeps in arid and semi-arid areas. Full article

Rapid land use changes are substantially altering the global carbon budget, yet quantifying the impact of these changes, or assessing efforts to mitigate them, remains challenging. Methods for assessing forest carbon range from precise ground surveys to remote-sensing approaches that provide proxies for canopy height and structure. We introduce a method for extracting a proxy for canopy heights from Interferometric Synthetic Aperture Radar (InSAR) data. Our method focuses on short-spatial scale differences between forested and cleared regions, reducing the impact of errors from variations in atmospheric water vapor or satellite orbital positions. We generate time-varying, Landsat-based maps of land use and perform our analysis on the original wrapped (modulo-2π) data to avoid errors introduce by phase unwrapping and to allow assessment of the confidence of our results (within 3–4 m in many cases). We apply our approach to the Pacific Northwest, which contains some of the world’s tallest trees and has experienced extensive clearcutting. We use SAR imagery acquired at L-band by the PALSAR instrument on the Japanese Aerospace Exploration Agency’s (JAXA) Advanced Land Observation Satellite (ALOS). As SAR data archives expand, our approach can complement other remote-sensing methods and allow time-variable assessment of forest carbon budgets worldwide. Full article

Consistent, detailed and up-to-date forest resource information is required for allocation of forestry activities and national and international reporting obligations. We evaluated the forest stand attribute prediction accuracy when radargrammetry was used to derive height information from TerraSAR-X stereo imagery. Radargrammetric elevations were normalized to heights above ground using an airborne laser scanning (ALS)-derived digital terrain model (DTM). Derived height metrics were used as predictors in the most similar neighbor (MSN) estimation approach. In total, 207 field measured plots were used in MSN estimation, and the obtained results were validated using 94 stands with an average area of 4.1 ha. The relative root mean square errors for Lorey’s height, basal area, stem volume, and above-ground biomass were 6.7% (1.1 m), 12.0% (2.9 m²/ha), 16.3% (31.1 m³/ha), and 16.1% (15.6 t/ha). Although the prediction accuracies were promising, it should be noted that the predictions included bias. The respective biases were −4.6% (−0.7 m), −6.4% (−1.6 m²/ha), −9.3% (−17.8 m³/ha), and −9.5% (−9.1 t/ha). With detailed DTM, TerraSAR-X stereo radargrammetry-derived forest information appears to be suitable for providing consistent forest resource information over large areas. Full article

The diurnal cycle of land surface temperature (LST) is an important element of the climate system. Geostationary satellites can provide the diurnal cycle of LST with low spatial resolution and incomplete global coverage, which limits its applications in some studies. In this study, we propose a method to estimate the diurnal cycle of LST at high temporal and spatial resolution from clear-sky MODIS data. This method was evaluated using the MSG-SEVIRI-derived LSTs. The results indicate that this method fits the diurnal cycle of LST well, with root mean square error (RMSE) values less than 1 K for most pixels. Because MODIS provides at most four observations per day at a given location, this method was further evaluated using only four MSG-SEVIRI-derived LSTs corresponding to the MODIS overpass times (10:30, 13:30, 22:30, and 01:30 local solar time). The results show that the RMSE values using only four MSG-SEVIRI-derived LSTs are approximately two times larger than those using all LSTs. The spatial distribution of the modeled LSTs at the MODIS pixel scale is presented from 07:00 to 05:00 local solar time of the next day with an increment of 2 hours. The diurnal cycle of the modeled LSTs describes the temporal evolution of the LSTs at the MODIS pixel scale. Full article

Understanding how the dynamics of vegetation growth respond to climate change at different temporal and spatial scales is critical to projecting future ecosystem dynamics and the adaptation of ecosystems to global change. In this study, we investigated vegetated growth dynamics (annual productivity, seasonality and the minimum amount of vegetated cover) in China and their relations with climatic factors during 1982–2011, using the updated Global Inventory Modeling and Mapping Studies (GIMMS) third generation global satellite Advanced Very High Resolution Radiometer (AVHRR) Normalized Difference Vegetation Index (NDVI) dataset and climate data acquired from the National Centers for Environmental Prediction (NCEP). Major findings are as follows: (1) annual mean NDVI over China significantly increased by about 0.0006 per year from 1982 to 2011; (2) of the vegetated area in China, over 33% experienced a significant positive trend in vegetation growth, mostly located in central and southern China; about 21% experienced a significant positive trend in growth seasonality, most of which occurred in northern China (>35°N); (3) changes in vegetation growth dynamics were significantly correlated with air temperature and precipitation (p < 0.001) at a region scale; (4) at the country scale, changes in NDVI was significantly and positively correlated with annual air temperature (r = 0.52, p < 0.01) and not associated with annual precipitation (p > 0.1); (5) of the vegetated area, about 24% showed significant correlations between annual mean NDVI and air temperature (93% positive and remainder negative), and 12% showed significant correlations of annual mean NDVI with annual precipitation (65% positive and 35% negative). The spatiotemporal variations in vegetation growth dynamics were controlled primarily by temperature and secondly by precipitation. Vegetation growth was also affected by human activities; and (6) monthly NDVI was significantly correlated with the preceding month’s temperature and precipitation in western, central and northern China. The effects of a climate lag of more than two months in southern China may be caused mainly by the abundance of precipitation. These findings suggest that continuing efforts to monitor vegetation changes (in situ and satellite observations) over time and at broad scales are greatly needed, and are critical for the management of ecosystems and adapting to global climatic changes. It is likewise difficult to predict well future vegetation growth without linking these observations to mechanistic terrestrial ecosystem processes models that integrate all the satellite and in situ observations. Full article

This paper presents a new approach for roof facet segmentation based on ridge detection and hierarchical decomposition along ridges. The proposed approach exploits the fact that every roof can be composed of a set of gabled roofs and single facets which are separated by the gabled roofs. In this work, firstly, building footprints stored in OpenStreetMap are used to extract 3D points on roofs. Then, roofs are segmented into roof facets. The algorithm starts with detecting roof ridges using RANSAC since they are parallel to the horizon and situated on the top of the roof. The roof ridges are utilized to indicate the location and direction of the gabled roof. Thus, points on the two roof facets along a roof ridge can be identified based on their connectivity and coplanarity. The results of the segmentation benefit the further process of roof reconstruction because many parameters, including the position, angle and size of the gabled roof can be calculated and used as priori knowledge for the model-driven approach, and topologies among the point segments are made known for the data-driven approach. The algorithm has been validated in the test sites of two towns next to Bavaria Forest national park. The experimental results show that building roofs can be segmented with both high correctness and completeness simultaneously. Full article

Airborne LiDAR has been traditionally used for power line cruising. Nevertheless, data acquisition with airborne LiDAR is constrained by the complex environments in urban areas as well as the multiple parallel line structures on the same power line tower, which means it is not directly applicable to the extraction of urban power lines. Vehicle-borne LiDAR system has its advantages upon airborne LiDAR and this paper tries to utilize vehicle-borne LiDAR data for the extraction of urban power lines. First, power line points are extracted using a voxel-based hierarchical method in which geometric features of each voxel are calculated. Then, a bottom-up method for filtering the power lines belonging to each power line is proposed. The initial clustering and clustering recovery procedures are conducted iteratively to identify each power line. The final experiment demonstrates the high precision of this technique. Full article

How well parameterization will improve gross primary production (GPP) estimation using the MODerate-resolution Imaging Spectroradiometer (MODIS) algorithm has been rarely investigated. We adjusted the parameters in the algorithm for 21 selected eddy-covariance flux towers which represented nine typical plant functional types (PFTs). We then compared these estimates of the MOD17A2 product, by the MODIS algorithm with default parameters in the Biome Property Look-Up Table, and by a two-leaf Farquhar model. The results indicate that optimizing the maximum light use efficiency (εmax) in the algorithm would improve GPP estimation, especially for deciduous vegetation, though it could not compensate the underestimation during summer caused by the one-leaf upscaling strategy. Adding the soil water factor to the algorithm would not significantly affect performance, but it could make the adjusted εmax more robust for sites with the same PFT and among different PFTs. Even with adjusted parameters, both one-leaf and two-leaf models would not capture seasonally photosynthetic dynamics, thereby we suggest that further improvement in GPP estimaiton is required by taking into consideration seasonal variations of the key parameters and variables. Full article

Point-like targets are useful in providing surface deformation with the time series of synthetic aperture radar (SAR) images using the multi-temporal interferometric synthetic aperture radar (MTInSAR) methodology. However, the spatial density of point-like targets is low, especially in non-urban areas. In this paper, a hierarchical MTInSAR method is proposed to increase the spatial density of deformation measurements by tracking both the point-like targets and the distributed targets with the temporal steadiness of radar backscattering. To efficiently reduce error propagation, the deformation rates on point-like targets with lower amplitude dispersion index values are first estimated using a least squared estimator and a region growing method. Afterwards, the distributed targets are identified using the amplitude dispersion index and a Pearson correlation coefficient through a multi-level processing strategy. Meanwhile, the deformation rates on distributed targets are estimated during the multi-level processing. The proposed MTInSAR method has been tested for subsidence detection over a suburban area located in Tianjin, China using 40 high-resolution TerraSAR-X images acquired between 2009 and 2010, and validated using the ground-based leveling measurements. The experiment results indicate that the spatial density of deformation measurements can be increased by about 250% and that subsidence accuracy can reach to the millimeter level by using the hierarchical MTInSAR method. Full article

Describing and quantifying the spatial heterogeneity of land cover in urban systems is crucial for developing an ecological understanding of cities. This paper presents a new approach to quantifying the fine-scale heterogeneity in urban landscapes that capitalizes on the strengths of two commonly used approaches—visual interpretation and object-based image analysis. This new approach integrates the ability of humans to detect pattern with an object-based image analysis that accurately and efficiently quantifies the components that give rise to that pattern. Patches that contain a mix of built and natural land cover features were first delineated through visual interpretation. These patches served as pre-defined boundaries for finer-scale segmentation and classification of within-patch land cover features which were classified using object-based image analysis. Patches were then classified based on the within-patch proportion cover of features. We applied this approach to the Gwynns Falls watershed in Baltimore, Maryland, USA. The object-based classification approach proved to be effective for classifying within-patch land cover features. The overall accuracy of the classification maps of 1999 and 2004 were 92.3% and 93.7%, respectively. This exercise demonstrates that by integrating visual interpretation with object-based classification, the fine-scale spatial heterogeneity in urban landscapes and land cover change can be described and quantified in a more efficient and ecologically meaningful way than either purely automated or visual methods alone. This new approach provides a tool that allows us to quantify the structure of the urban landscape including both built and non-built components that will better accommodate ecological research linking system structure to ecological processes. Full article

Accurate mapping of land cover on a regional scale is useful for climate and environmental modeling. In this study, we present a novel land cover classification product based on spectral and phenological information for the Xinjiang Uygur Autonomous Region (XUAR) in China. The product is derived at a 500 m spatial resolution using an innovative approach employing moderate resolution imaging spectroradiometer (MODIS) surface reflectance and the enhanced vegetation index (EVI) time series. The classification results capture regional scale land cover patterns and small-scale phenomena. By applying a regionally specified classification scheme, an extensive collection of training data, and regionally tuned data processing, the quality and consistency of the phenological maps are significantly improved. With the ability to provide an updated land cover product considering the heterogenic environmental and climatic conditions, the novel land cover map is valuable for research related to environmental change in this region. Full article

Frame hyperspectral sensors, in contrast to push-broom or line-scanning ones, produce hyperspectral datasets with, in general, better geometry but with unregistered spectral bands. Being acquired at different instances and due to platform motion and movements (UAVs, aircrafts, etc.), every spectral band is displaced and acquired with a different geometry. The automatic and accurate registration of hyperspectral datasets from frame sensors remains a challenge. Powerful local feature descriptors when computed over the spectrum fail to extract enough correspondences and successfully complete the registration procedure. To this end, we propose a generic and automated framework which decomposes the problem and enables the efficient computation of a sufficient amount of accurate correspondences over the given spectrum, without using any ancillary data (e.g., from GPS/IMU). First, the spectral bands are divided in spectral groups according to their wavelength. The spectral borders of each group are not strict and their formulation allows certain overlaps. The spectral variance and proximity determine the applicability of every spectral band to act as a reference during the registration procedure. The proposed decomposition allows the descriptor and the robust estimation process to deliver numerous inliers. The search space of possible solutions has been effectively narrowed by sorting and selecting the optimal spectral bands which under an unsupervised manner can quickly recover hypercube’s geometry. The developed approach has been qualitatively and quantitatively evaluated with six different datasets obtained by frame sensors onboard aerial platforms and UAVs. Experimental results appear promising. Full article

Anthropogenic interventions in natural and semi-natural ecosystems often lead to substantial changes in their functioning and may ultimately threaten ecosystem service provision. It is, therefore, necessary to monitor these changes in order to understand their impacts and to support management decisions that help ensuring sustainability. Remote sensing has proven to be a valuable tool for these purposes, and especially hyperspectral sensors are expected to provide valuable data for quantitative characterization of land change processes. In this study, simulated EnMAP data were used for mapping shrub cover fractions along a gradient of shrub encroachment, in a study region in southern Portugal. We compared three machine learning regression techniques: Support Vector Regression (SVR); Random Forest Regression (RF); and Partial Least Squares Regression (PLSR). Additionally, we compared the influence of training sample size on the prediction performance. All techniques showed reasonably good results when trained with large samples, while SVR always outperformed the other algorithms. The best model was applied to produce a fractional shrub cover map for the whole study area. The predicted patterns revealed a gradient of shrub cover between regions affected by special agricultural management schemes for nature protection and areas without land use incentives. Our results highlight the value of EnMAP data in combination with machine learning regression techniques for monitoring gradual land change processes. Full article

Rain Use Efficiency (RUE), defined as Aboveground Net Primary Production (ANPP) divided by rainfall, is increasingly used to diagnose land degradation. Yet, the outcome of RUE monitoring has been much debated since opposite results were found about land degradation in the Sahel region. The debate is fueled by methodological issues, especially when using satellite remote sensing data to estimate ANPP, and by differences in the ecological interpretation. An alternative method which solves part of these issues relies on the residuals of ANPP regressed against rainfall (“ANPP residuals”). In this paper, we use long-term field observations of herbaceous vegetation mass collected in the Gourma region in Mali together with remote sensing data (GIMMS-3g Normalized Difference Vegetation Index) to estimate ANPP, RUE, and the ANPP residuals, over the period 1984–2010. The residuals as well as RUE do not reveal any trend over time over the Gourma region, implying that vegetation is resilient over that period, when data are aggregated at the Gourma scale. We find no conflict between field-derived and satellite-derived results in terms of trends. The nature (linearity) of the ANPP/rainfall relationship is investigated and is found to have no impact on the RUE and residuals interpretation. However, at odds with a stable RUE, an increased run-off coefficient has been observed in the area over the same period, pointing towards land degradation. The divergence of these two indicators of ecosystem resilience (stable RUE) and land degradation (increasing run-off coefficient) is referred to as the “second Sahelian paradox”. When shallow soils and deep soils are examined separately, high resilience is diagnosed on the deep soil sites. However, some of the shallow soils show signs of degradation, being characterized by decreasing vegetation cover and increasing run-off coefficient. Such results show that contrasted changes may co-exist within a region where a strong overall re-greening pattern is observed, highlighting that both the scale of observations and the scale of the processes have to be considered when performing assessments of vegetation changes and land degradation. Full article

The stem diameter distribution, stem form and quality information must be measured as accurately as possible to optimize cutting. For a detailed measurement of the stands, we developed and demonstrated the use of a multisource single-tree inventory (MS-STI). The two major bottlenecks in the current airborne laser scanning (ALS)-based single-tree-level inventory, tree detection and tree species recognition, are avoided in MS-STI. In addition to airborne 3D data, such as ALS, MS-STI requires an existing tree map with tree species information as the input information. In operational forest management, tree mapping would be carried out after or during the first thinning. It should be highlighted that the tree map is a challenging prerequisite, but that the recent development in mobile 2D and 3D laser scanning indicates that the solution is within reach. In our study, the tested input tree map was produced by terrestrial laser scanning (TLS) and by using a Global Navigation Satellite System. Predictors for tree quality attributes were extracted from ALS data or digital stereo imagery (DSI) and used in the nearest-neighbor estimation approach. Stem distribution was compiled by summing the predicted single-tree measures. The accuracy of the MS-STI was validated using harvester data (timber assortments) and field measures (stem diameter, tree height). RMSEs for tree height, diameter, saw log volume and pulpwood volume varied from 4.2% to 5.3%, from 10.9% to 19.9%, from 28.7% to 43.5% and from 125.1% to 134.3%, respectively. Stand-level saw log recoveries differed from −2.2% to 1.3% from the harvester measurements, as the respective differences in pulpwood recovery were between −3.0% and 10.6%. We conclude that MS-STI improves the predictions of stem-diameter distributions and provides accurate estimates for tree quality variables if an accurate tree map is available. Full article

Satellite remote sensing is a highly useful tool for monitoring chlorophyll-a concentration (Chl-a) in water bodies. Remote sensing algorithms based on near-infrared-red (NIR-red) wavelengths have demonstrated great potential for retrieving Chl-a in inland waters. This study tested the performance of a recently developed NIR-red based algorithm, SAMO-LUT (Semi-Analytical Model Optimizing and Look-Up Tables), using an extensive dataset collected from five Asian lakes. Results demonstrated that Chl-a retrieved by the SAMO-LUT algorithm was strongly correlated with measured Chl-a (R² = 0.94), and the root-mean-square error (RMSE) and normalized root-mean-square error (NRMS) were 8.9 mg∙m⁻³ and 72.6%, respectively. However, the SAMO-LUT algorithm yielded large errors for sites where Chl-a was less than 10 mg∙m⁻³ (RMSE = 1.8 mg∙m⁻³ and NRMS = 217.9%). This was because differences in water-leaving radiances at the NIR-red wavelengths (i.e., 665 nm, 705 nm and 754 nm) used in the SAMO-LUT were too small due to low concentrations of water constituents. Using a blue-green algorithm (OC4E) instead of the SAMO-LUT for the waters with low constituent concentrations would have reduced the RMSE and NRMS to 1.0 mg∙m⁻³ and 16.0%, respectively. This indicates (1) the NIR-red algorithm does not work well when water constituent concentrations are relatively low; (2) different algorithms should be used in light of water constituent concentration; and thus (3) it is necessary to develop a classification method for selecting the appropriate algorithm. Full article

Research on global climate change requires plant functional type (PFT) products. Although several PFT mapping procedures for remote sensing imagery are being used, none of them appears to be specifically designed to map and evaluate PFTs over broad mountainous areas which are highly relevant regions to identify and analyze the response of natural ecosystems. We present a methodology for generating soft classifications of PFTs from remotely sensed time series that are based on a hierarchical strategy by integrating time varying integrated NDVI and phenological information with topography: (i) Temporal variability: a Fourier transform of a vegetation index (MODIS NDVI, 2006 to 2010). (ii) Spatial partitioning: a primary image segmentation based on a small number of thresholds applied to the Fourier amplitude. (iii) Classification by a supervised soft classification step is based on a normalized distance metric constructed from a subset of Fourier coefficients and complimentary altitude data from a digital elevation model. Applicability and effectiveness is tested for the eastern Tibetan Plateau. A classification nomenclature is determined from temporally stable pixels in the MCD12Q1 time series. Overall accuracy statistics of the resulting classification reveal a gain of about 7% from 64.4% compared to 57.7% by the MODIS PFT products. Full article