![]() Since 2010, China has stepped into the transition of industrialization and urbanization development, with the urbanization rate growing rapidly from 49.7% in 2010 to 51.3% in 2011, officially breaking the 50% mark. The diverse drivers of LDI in different functional zones remind us to implement differentiated spatial control according to functional positioning and to adopt refined policy tools of zoning and classification to ensure that land resources within each type of functional zone can be used sustainably. The LDI in the MAPZ is mainly affected by the facilitating role of PCL, EID, FAI, and the prominent role of FD, while that in the KEFZ is mainly inhibited by SLP, EID, and driven by the PGDP. In the UZ, LDI is influenced by natural-human multi factors, e.g., terrain conditions (SLP), proportion of cultivated land area (PCL), proportion of ecologically important area (EID), population urbanization rate (PUR), GDP per capita (PGDP), fixed asset investment size per land (FAI) and fiscal expenditure as a percentage of GDP (FD). Results demonstrate that LDI decreases in a gradient of the urbanized zone (UZ) > main agricultural production zone (MAPZ) > key ecological function zone (KEFZ). The multi-scale distribution pattern of LDI was analyzed by combining macro and micro scales, and a new spatial measurement method integrating global and local regression models was developed to quantitatively deconstruct the natural-human drivers of LDI in different functional zones. To observe the spatio-temporal evolution and drivers of LDI in the first five years since the implementation of the MFZP, we chose the Yangtze River Delta, the most urbanized region in China, as a case area. Against the background of rapid urbanization and industrialization, in order to curb the over-occupation of agricultural and ecological space by urbanized space, China has proposed the Major Functional Zoning Planning (MFZP) as a new spatial governance model, which attempts to implement differentiated LDI control according to the functional positioning of different regions. If you are using Geodetics’ Point&Pixel product, which is near-infrared, complements the RGB from camera images.Land development intensity (LDI) is an important indicator of how much human exploitation of land resources. In some applications, an intensity image can be made from the LiDAR point clouds and utilized in a variety of applications supporting additional image-processing see article in LiDAR Magazine.Ĭomplementary to 3D geo-referenced LiDAR positions, LiDAR intensity, in many applications, is a reliable source for data analysis and decision-making. Identifying wet areas in forested areas (due to the tendency of the sensor signal to be absorbed by water).Feature registration with Geo-Photomap imagery data.Feature detection and point cloud classification.Intensity of the LiDAR point clouds can be used for numerous applications, including: An advantage is that unlike passive vision sensors (cameras), it is indifferent to shadows. ![]() It must be used as a relative measurement. ![]() For these reasons, LiDAR intensity does not always lead to consistent results. This means that features under the nadir of the LiDAR sensor usually have higher intensity than the same features along the edges (tilted further), as the returned energy decreases. The intensity of the laser beam return can also be affected by the angle of arrival (scan angle), range, surface composition, roughness, and moisture content. A low number indicates low reflectivity while a high number indicates high reflectivity. This number varies with the composition of the surface object reflecting the laser beam. In the Geo-MMS LiDAR systems, it is a bi-product, provided as an integer number between 1-256. LiDAR intensity is recorded as the return strength of a laser beam. ![]()
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