Massimo Menenti

Breast Height Diameter Estimation From High-Density Airborne LiDAR Data

High-density airborne light detection and ranging (LiDAR) data with point densities over 50 points/ m2 provide new opportunities, because previously inaccessible quantities of an individual tree can be derived directly from the data. We introduce a skeleton measurement methodology to extract the diameter at breast height (DBH) from airborne point clouds of trees. The estimates for the DBH are derived by analyzing the point distances to a suitable tree skeleton. The method is validated in three scenarios: 1) on a synthetic point cloud, simulating the point cloud acquisition over a forest; 2) on examples of free-standing and partly occluded trees; and 3) on automatically extracted trees from a sampled forest. The proposed diameter estimation performed well in all three scenarios, although influences of the tree extraction method and the field validation could not be fully excluded.

Observation and simulation of lake‐air heat and water transfer processes in a high‐altitude shallow lake on the Tibetan Plateau

Lakes are an important part of the landscape on the Tibetan Plateau. Most of the Plateau lakes area has been expanding in recent years, but lake-atmosphere energy and water interaction is poorly understood because of a lack of observational data and adequate modeling systems. Based on the eddy covariance observation over a high-altitude shallow and small lake (the small Nam Co Lake) during an ice-free period from 10 April to 30 August 2012, this study analyzes the lake-air heat and water vapor turbulent transfer processes and evaluates two popular lake-air exchange models: a bulk aerodynamic transfer model (B model) and a multilayer model (M model). Our main results are as follows: (1) observations show that the bulk transfer coefficient (CE) and roughness length (zoq) for water are higher than those for heat (CH and z0h), especially under low wind speed; (2) both models underestimate turbulent fluxes due to inaccurate values of the Charnock coefficient (α) and the roughness Reynolds number (Rr) which are both important parameters for calculating the roughness length for momentum (z0m) over water; (3) α within a reasonable range of 0.013–0.035 for rough flow and Rr for smooth flow (Rru2009=u20090.11) are 0.031 and 0.54, respectively, by our observation. The wave pattern of shorter wavelength gives a larger z0m in the small and shallow lake; and (4) the B model and the M model gave consistent results, and both models are more suitable for simulation of turbulent flux exchange after z0m optimization.

Modeling of Anthropogenic Heat Flux Using HJ-1B Chinese Small Satellite Image: A Study of Heterogeneous Urbanized Areas in Hong Kong

Anthropogenic heat is the heat flux generated by human activities and is a major contributor to the formation of an urban heat island. In a city such as Hong Kong, obtaining pure pixels from medium- or coarse-resolution remote sensing images is challenging. Considering the completely different thermal properties of vegetation and impervious surfaces, this letter developed a novel algorithm to estimate anthropogenic heat fluxes by decomposing image pixels into fractions of impervious surfaces and vegetation, and by estimating the total heat flux for the mixed pixel. The Chinese small satellite HJ-1B images with a spatial resolution of 30 and 300 m for visible and thermal wavebands, respectively, and the temporal resolution of four days were used for the heat flux modeling. Results show that anthropogenic heat fluxes in Hong Kong are correlated to the building density and the building height, with r2 = 0.92 and 0.58 on October 11, 2012 and r2 = 0.94 and 0.62 on January 13, 2013, respectively. The average anthropogenic heat fluxes in urban areas are 289.16 and 283.17 W/m2 on October 11, 2012 and on January 13, 2013, respectively, and the commercial areas emit the largest anthropogenic heat fluxes around 500-600 W/m2 compared with other land-use types. The derived anthropogenic heat fluxes can help in planning and environmental authorities to pinpoint “hot-spot” areas, and they can be used for compliance monitoring.

Automatic estimation of excavation volume from laser mobile mapping data for mountain road widening

Roads play an indispensable role as part of the infrastructure of society. In recent years, society has witnessed the rapid development of laser mobile mapping systems (LMMS) which, at high measurement rates, acquire dense and accurate point cloud data. This paper presents a way to automatically estimate the required excavation volume when widening a road from point cloud data acquired by an LMMS. Firstly, the input point cloud is down-sampled to a uniform grid and outliers are removed. For each of the resulting grid points, both on and off the road, the local surface normal and 2D slope are estimated. Normals and slopes are consecutively used to separate road from off-road points which enables the estimation of the road centerline and road boundaries. In the final step, the left and right side of the road points are sliced in 1-m slices up to a distance of 4 m, perpendicular to the roadside. Determining and summing each sliced volume enables the estimation of the required excavation for a widening of the road on the left or on the right side. The procedure, including a quality analysis, is demonstrated on a stretch of a mountain road that is approximately 132 m long as sampled by a Lynx LMMS. The results in this particular case show that the required excavation volume on the left side is 8% more than that on the right side. In addition, the error in the results is assessed in two ways. First, by adding up estimated local errors, and second, by comparing results from two different datasets sampling the same piece of road both acquired by the Lynx LMMS. Results of both approaches indicate that the error in the estimated volume is below 4%. The proposed method is relatively easy to implement and runs smoothly on a desktop PC. The whole workflow of the LMMS data acquisition and subsequent volume computation can be completed in one or two days and provides road engineers with much more detail than traditional single-point surveying methods such as Total Station or GPS profiling. A drawback is that an LMMS system can only sample what is within the view of the system from the road.

Multi-angular Thermal Infrared Observations of Terrestrial Vegetation

This chapter reviews the experimental evidence on the anisotropy of emittance by the soilvegetation system and describes the interpretation of this signal in terms of the thermal heterogeneity and geometry of the canopy space. Observations of the dependence of exitance on view angle by means of ground-based goniometers, airborne and space-borne imaging radiometers are reviewed first to conclude that under most conditions a two-components, i.e., soil and foliage, model of observed Top Of Canopy (TOC) brightness temperature is adequate to interpret observations. Particularly, airborne observations by means of the Airborne Multi-angle TIR/VNIR Imaging System (AMTIS) and space-borne observations by means of the Along Track Scanning Radiometers (ATSR-s) are described and examples presented. Modeling approaches to describe radiative transfer in the soil– vegetation–atmosphere system, with emphasis on the thermal infrared region, are reviewed. Given the dependence of observed TOC brightness temperature on leaflevel radiation and heat balance, energy and water transfer in the soil–vegetation– atmosphere system must be included to construct a realistic model of exitance by soil–vegetation systems. A detailed modeling approach of radiation, heat and water transfer is first described then applied to generate realistic, multi-angular image data of terrestrial landscapes. Finally, a generic algorithm to retrieve soil and foliage component temperatures from Top Of Atmosphere (TOA) radiometric data is described. Column water vapor and aerosols optical depth are estimated first, to obtain TOC radiometric data from the TOA multi-angular and multi-spectral observations. M. Menenti TRIO/LSIIT, University Louis Pasteur (ULP), Strasbourg, France Istituto per i Sistemi Agricoli e Forestali del Mediterraneo (ISAFOM), Naples, Italy m.menenti@isafom.cnr.it L. Jia Alterra, Wageningen University and Research Centre, The Netherlands Z.-L. Li TRIO/LSIIT, University Louis Pasteur (ULP), Strasbourg, France Institute of Geographic Sciences and Natural Resources Research, Beijing, China S. Liang (ed.), Advances in Land Remote Sensing, 51–93. 51 c

Monitoring of Irrigation Schemes by Remote Sensing: Phenology versus Retrieval of Biophysical Variables

The appraisal of crop water requirements (CWR) is crucial for the management of water resources, especially in arid and semi-arid regions where irrigation represents the largest consumer of water, such as the Doukkala area, western Morocco. Simple and (semi) empirical approaches have been applied to estimate CWR: the first one is called Kc-NDVI method, based on the correlation between the Normalized Difference Vegetation Index (NDVI) and the crop coefficient (Kc); the second one is the analytical approach based on the direct application of the Penman-Monteith equation with reflectance-based estimates of canopy biophysical variables, such as surface albedo (r), leaf area index (LAI) and crop height (hc). A time series of high spatial resolution RapidEye (REIS), SPOT4 (HRVIR1) and Landsat 8 (OLI) images acquired during the 2012/2013 agricultural season has been used to assess the spatial and temporal variability of crop evapotranspiration ETc and biophysical variables. The validation using the dual crop coefficient approach (Kcb) showed that the satellite-based estimates of daily ETc were in good agreement with ground-based ETc, i.e., R2 = 0.75 and RMSE = 0.79 versus R2 = 0.73 and RMSE = 0.89 for the Kc-NDVI, respective of the analytical approach. The assessment of irrigation performance in terms of adequacy between water requirements and allocations showed that CWR were much larger than allocated surface water for the entire area, with this difference being small at the beginning of the growing season. Even smaller differences were observed between surface water allocations and Irrigation Water Requirements (IWR) throughout the irrigation season. Finally, surface water allocations were rather close to Net Irrigation Water Requirements (NIWR).

Improved Surface Reflectance from Remote Sensing Data with Sub-Pixel Topographic Information

Several methods currently exist to efficiently correct topographic effects on the radiance measured by satellites. Most of those methods use topographic information and satellite data at the same spatial resolution. In this study, the 30 m spatial resolution data of the Digital Elevation Model (DEM) from ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) are used to account for those topographic effects when retrieving land surface reflectance from satellite data at lower spatial resolution (e.g., 1 km). The methodology integrates the effects of sub-pixel topography on the estimation of the total irradiance received at the surface considering direct, diffuse and terrain irradiance. The corrected total irradiance is then used to compute the topographically corrected surface reflectance. The proposed method has been developed to be applied on various kilometric pixel size satellite data. In this study, it was tested and validated with synthetic Landsat data aggregated at 1 km. The results obtained after a sub-pixel topographic correction are compared with the ones obtained after a pixel level topographic correction and show that in rough terrain, the sub-pixel topography correction method provides better results even if it tends to slightly overestimate the retrieved land surface reflectance in some cases.

Evaluating MERIS-Based Aquatic Vegetation Mapping in Lake Victoria

Delineation of aquatic plants and estimation of its surface extent are crucial to the efficient control of its proliferation, and this information can be derived accurately with fine resolution remote sensing products. However, small swath and low observation frequency associated with them may be prohibitive for application to large water bodies with rapid proliferation and dynamic floating aquatic plants. The information can be derived from products with large swath and high observation frequency, but with coarse resolution; and the quality of so derived information must be eventually assessed using finer resolution data. In this study, we evaluate two methods: Normalized Difference Vegetation Index (NDVI) slicing and maximum likelihood in terms of delineation; and two methods: Gutman and Ignatov’s NDVI-based fractional cover retrieval and linear spectral unmixing in terms of area estimation of aquatic plants from 300 m Medium Resolution Imaging Spectrometer (MERIS) data, using as reference results obtained with 30 m Landsat-7 ETM+. Our results show for delineation, that maximum likelihood with an average classification accuracy of 80% is better than NDVI slicing at 75%, both methods showing larger errors over sparse vegetation. In area estimation, we found that Gutman and Ignatov’s method and spectral unmixing produce almost the same root mean square (RMS) error of about 0.10, but the former shows larger errors of about 0.15 over sparse vegetation while the latter remains invariant. Where an endmember spectral library is available, we recommend the spectral unmixing approach to estimate extent of vegetation with coarse resolution data, as its performance is relatively invariant to the fragmentation of aquatic vegetation cover.

Multiscale curvatures for identifying channel locations from DEMs

Abstract Curvature based methods are suitable for channel identification in digital elevation models. One obstacle in using these methods is the fact that channels generally occur at multiple scales in the landscape, from small creeks to large rivers. In this paper, we show how likely channel pixels can be identified simultaneously at a range of scales using multiscale curvature operators applied to digital elevation models. Our proposed Hyperscale Channel Extraction (HCE) method localizes channels at the smallest scale while simultaneously tracking the shape of the channel at a full interval of scales (the hyperscale). We test the method using two different types of curvature, and apply and validate it to a catchment representing terrain with a high slope sampled by airborne laser altimetry. The test results demonstrate that by explicitly employing the extra dimension of scale to localize channels, (a) we are able to robustly identify channel pixels, as possible channel locations are tracked through a full interval of scales, (b) no more a priori determination of the relevant scale is necessary, and (c) only one parameter remains to be set: a threshold on the curvature value that has a clear physical interpretation.

Estimation of Aerodynamic Roughness Length over Oasis in the Heihe River Basin by Utilizing Remote Sensing and Ground Data

Most land surface models require information on aerodynamic roughness length and its temporal and spatial variability. This research presents a practical approach for determining the aerodynamic roughness length at fine temporal and spatial resolution over the landscape by combining remote sensing and ground measurements. The basic framework of Raupach, with the bulk surface parameters redefined by Jasinski et al., has been applied to optical remote sensing data collected by the HJ-1A/1B satellites. In addition, a method for estimating vegetation height was introduced to derive the aerodynamic roughness length, which is preferred by users over the height-normalized form. Finally, mapping different vegetation classes was validated taking advantage of the data-dense field experiments conducted in the Heihe Watershed Allied Telemetry Experimental Research (HiWATER) project. Overall, the roughness model performed well against the measurements collected at most HiWATER flux tower sites. However, deviations still occurred at some sites, which have been further analyzed.