Canopy height model

Using Montarice in central Adriatic Italy as a case study, this paper focuses on the extraction of the spectral (i.e., plant colour) and geometrical (i.e., plant height) components of a crop canopy from archived aerial photographs, treating both parameters as proxies for archaeological prospection. After the creation of orthophotographs and a canopy height model using image-based modelling, new archaeological information is extracted from this vegetation model by applying relief-enhancing visualisation techniques. Through interpretation of the resulting data, a combination of the co-registered spectral and geometrical vegetation dimensions clearly add new depth to interpretative mapping, which is typically based solely on colour differences in orthophotographs.

Aerial imagery have long been used for forest Inventories due to the high correlation between tree height and forest biophysical properties to determine the vertical canopy structure which is an important variable in forest inventories. The development in photogrammetric softwares and large availability of aerial imagery has carved the path in 3D mapping and has accelerated significantly the use of photogrammetry in forest inventory. There is tremendous capacity of 3D mapping which has been recognized in research, development and management of forest ecosystem. The aim of this article is to provide insights of 3D mapping (photogrammetry including Lidar) in forest-related disciplines. We utilizing the satellite stereo pair and LiDAR point cloud as a case study for producing the anaglyph map and Canopy Height Model (CHM) respectively. The study also revealed the area verses canopy height graph. Present study has some strength because it was demonstrated the use of advance software module of ARC/GIS and Erdas Imagine for 3D mapping using Photogrammetry and LiDAR data sets which is highly useful in forest management, planning and decision making. .

The processing of aerial imagery acquired over Montarice hill (central Adriatic Italy) during the Potenza Valley Survey will form the key focus of this paper. Since this site has repeatedly revealed itself in terms of interesting vegetation and soil marks, the most interesting footage, acquired during two observer-directed sorties more than a decade ago, will be examined. First, the potential of state-of-the-art image-based modelling (IBM) techniques is explored to create high-resolution orthophotographs from these analogue frame images. Since dense image matching-as a part of IBM-allows to model the vegetation surface at the moment of the photographic survey, a geometrical three-dimensional representation of the plant canopy is possible. This contribution shows how the latter can be the focus of its own information extraction process, using techniques mainly developed in the field of airborne laser scanning. However, the true interpretative power lies in the combination of the co-registered spectral and geometrical dimensions of the vegetation. Using Montarice as a case study, it will become clear that crop height data allows for powerful visualisations that can aid and even alter interpretative mapping that is solely based on colour differences in orthophotographs.

Eddy covariance (EC) measurements have greatly advanced our knowledge of carbon exchange in terrestrial ecosystems. However, appropriate techniques are required to upscale these spatially discrete findings globally. Satellite remote sensing provides unique opportunities in this respect, but remote sensing of the photosynthetic light-use efficiency (ε), one of the key components of Gross Primary Production, is challenging. Some progress has been made in recent years using the photochemical reflectance index, a narrow waveband index centered at 531 and 570 nm. The high sensitivity of this index to various extraneous effects such as canopy structure, and the view observer geometry has so far prevented its use at landscape and global scales. One critical aspect of upscaling PRI is the development of generic algorithms to account for structural differences in vegetation. Building on previous work, this study compares the differences in the PRI: ɛ relationship between a coastal Douglas-fir forest located on Vancouver Island, British Columbia, and a mature Aspen stand located in central Saskatchewan, Canada. Using continuous, tower-based observations acquired from an automated multi-angular spectro-radiometer (AMSPEC II) installed at each site, we demonstrate that PRI can be used to measure ɛ throughout the vegetation season at the DF-49 stand (r2 = 0.91, p < 0.00) as well as the deciduous site (r2 = 0.88, p < 0.00). It is further shown that this PRI signal can be also observed from space at both sites using daily observations from the Moderate Resolution Imaging Spectro-radiometer (MODIS) and a multi-angular implementation of atmospheric correction (MAIAC) (r2 = 0.54 DF-49; r2 = 0.63 SOA; p < 0.00). By implementing a simple hillshade model derived from airborne light detection and ranging (LiDAR) to approximate canopy shadow fractions (αs), it is further demonstrated that the differences observed in the relationship between PRI and ε at DF-49 and SOA can be attributed largely to differences in αs. The findings of this study suggest that algorithms used to separate physiological from extraneous effects in PRI reflectance may be more broadly applicable and portable across these two climatically and structurally different biome types, when the differences in canopy structure are known.