By Murat Özyavuz
This e-book is for panorama architects and different making plans professions. This booklet comprises chapters on fresh advancements in stories of panorama architecture.
Theoretical foundations, theories, tools, and purposes can be crucial elements of this reference publication. additionally, this ebook addresses numerous very assorted topics of analysis; panorama administration, biodiversity, panorama recovery, panorama layout, and concrete layout on the topic of conception, perform and the implications may be covered.
Section 1 panorama Planning
1 Biosphere Reserves
2 Land Use/Cover category strategies utilizing Optical Remotely Sensed information in panorama Planning
3 GIS in panorama Planning
4 An method of panorama making plans in Borders
5 city eco-friendly area method Planning
6 Tourism making plans in Rural parts and association Possibilities
7 Agriculture and Rurality as of Sustainable Cultural Landscape
8 citizens' Perceptions of and Attitudes towards Sustainable Tourism making plans and administration in Amasra (Turkey)
9 Woody crops in panorama making plans and panorama Design
10 Integration of Infrastructures in panorama - a chance to panorama making plans Improvement
11 Ecological panorama making plans, with a spotlight at the Coastal Zone
Section 2 panorama Design
12 panorama Perception
13 city panorama Design
15 inner most Plantation Techniques
16 Xeriscape in panorama layout
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Extra resources for Landscape Planning
Land Use/Cover Classification Techniques Using Optical Remotely Sensed Data in Landscape Planning 25 3. Remotely sensed data sources Data characteristics are the most important issue to select appropriate available one for a LUC mapping. Both airborne and spaceborne data have various spatial, radiometric, spectral and temporal resolutions. Large numbers of studies have focused on characteristics of remotely sensed data (Barnsley 1999, Lefsky and Cohen 2003). Additionally, scan width (cover size in one scene), data availability (accessibility) and lunch date (data archive potential) are the other important factors (Table 3).
Shaban and Dikshit (2001) investigated GLCM, grey-level difference histogram (GLDH), and sum and difference histogram (SADH) textures from SPOT spectral data in an Indian urban environment, and found that a combination of texture and spectral features improved the classification accuracy. The results based solely on spectral features increased about 9% to 17% with an addition of one or two texture measures. Furthermore, contrast, entropy, variance, and inverse difference 48 Landscape Planning moment provided larger accuracy and the most appropriate window size was 7X7 and 9X9.
For fuzzy sets there exists a degree of membership μs(X) that is mapped on [0, 1]. In the case of LUC map, every area simultaneously belongs to interest LUC clusters with a different degree of membership (Kandel, 1992). 1 if X S μs(X) = (1) 0 if X S There are several soft classification techniques and these are variable according to training and testing dataset, scale of the study. In this frame, linear mixture modeling (LMM), Regression tree (RT), multi linear regression (MLR) and artificial neural network (ANN) soft classification techniques were evaluated in Eastern Mediterranean area called Upper Seyhan Plane (USP).