東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Frontiers in Using LiDAR to Analyze ...

Singh, Kunwar Krishna Veer.

FindBook

Google Book

Amazon

博客來

Frontiers in Using LiDAR to Analyze Urban Landscape Heterogeneity.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Frontiers in Using LiDAR to Analyze Urban Landscape Heterogeneity./
作者:	Singh, Kunwar Krishna Veer.
面頁冊數:	154 p.
附註:	Source: Dissertation Abstracts International, Volume: 76-07(E), Section: B.
Contained By:	Dissertation Abstracts International76-07B(E).
標題:	Remote Sensing. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3690369
ISBN:	9781321584233

Frontiers in Using LiDAR to Analyze Urban Landscape Heterogeneity.
Singh, Kunwar Krishna Veer.

Frontiers in Using LiDAR to Analyze Urban Landscape Heterogeneity. - 154 p.

Source: Dissertation Abstracts International, Volume: 76-07(E), Section: B.

Thesis (Ph.D.)--North Carolina State University, 2014.

This item must not be sold to any third party vendors.

Light Detection and Ranging (LiDAR) technology has facilitated extraordinary advances in our ability to remotely sense precise details of both built and natural environments. The inherent complexity of urban landscapes and the massive data volumes produced by LiDAR require unique methodological considerations for big data remote sensing over large metropolitan regions. The heterogeneous landscapes of the rapidly urbanizing Charlotte Metropolitan Region of North Carolina provided an ideal testing ground for developing methods of analysis for urban ecosystems over large regional extents, including: (1) fusion of LiDAR digital surface models (DSMs) with Landsat TM imagery to balance spatial resolution, data volume, and mapping accuracy of urban land covers, (2) comparison of LiDAR-derived metrics to fine grain optical imagery -- and their integration -- for detecting forest understory plant invaders, and (3) data reduction techniques for computationally efficient estimation of aboveground woody biomass in urban forests.

ISBN: 9781321584233Subjects--Topical Terms:

1018559
Remote Sensing.

Frontiers in Using LiDAR to Analyze Urban Landscape Heterogeneity.
LDR:04560nmm a2200325 4500 001 2057129
005 20150630140246.5
008 170521s2014 ||||||||||||||||| ||eng d
020 $a 9781321584233
035 $a (MiAaPQ)AAI3690369
035 $a AAI3690369
040 $a MiAaPQ $c MiAaPQ
100 1 $a Singh, Kunwar Krishna Veer. $3 3170941
245 1 0 $a Frontiers in Using LiDAR to Analyze Urban Landscape Heterogeneity.
300 $a 154 p.
500 $a Source: Dissertation Abstracts International, Volume: 76-07(E), Section: B.
500 $a Adviser: Ross K. Meentemeyer.
502 $a Thesis (Ph.D.)--North Carolina State University, 2014.
506 $a This item must not be sold to any third party vendors.
520 $a Light Detection and Ranging (LiDAR) technology has facilitated extraordinary advances in our ability to remotely sense precise details of both built and natural environments. The inherent complexity of urban landscapes and the massive data volumes produced by LiDAR require unique methodological considerations for big data remote sensing over large metropolitan regions. The heterogeneous landscapes of the rapidly urbanizing Charlotte Metropolitan Region of North Carolina provided an ideal testing ground for developing methods of analysis for urban ecosystems over large regional extents, including: (1) fusion of LiDAR digital surface models (DSMs) with Landsat TM imagery to balance spatial resolution, data volume, and mapping accuracy of urban land covers, (2) comparison of LiDAR-derived metrics to fine grain optical imagery -- and their integration -- for detecting forest understory plant invaders, and (3) data reduction techniques for computationally efficient estimation of aboveground woody biomass in urban forests.
520 $a In Chapter 1, I examined tradeoffs between potential gains in mapping accuracy and computational costs by integrating DSMs (structural and intensity) extracted from LiDAR with TM imagery and evaluating the degree to which TM, LiDAR, and LiDAR-TM fusion data discriminated land covers. I used Maximum Likelihood and Classification Tree algorithms to classify TM data, LiDAR data, and LiDAR-TM fusions. I assessed the relative contributions of LiDAR DSMs to map classification accuracy and identified an optimal spatial resolution of LiDAR DSMs for large area assessments of urban land cover. In Chapter 2, I analyzed combinations of datasets developed from categorized LiDAR-derived variables (Overstory, Understory, Topography, and Overall Vegetation Characteristics) and IKONOS imagery ( Optical) to detect and map the understory plant invader, Ligustrum sinense, using Random Forest (RF) and logistic regression (LR) algorithms, and I assessed the relative contributions of sensors and forest landscape structures. I compared the top performing models developed using RF and LR and used the best overall model to map the distribution of L. sinense occurrence across the urbanizing forest landscapes of the region. In chapter 3, I examined the effects of LiDAR point density and landscape context on the estimation of biomass (of general Urban Forest and of three specific Forest Types) using multiple linear regression. I compared biomass estimation accuracies of the Urban Forest and Forest Type models and between the top-performing models of these two Forest categories. For the effect of landscape context, I quantified the degree to which the presence of built development influenced biomass estimation, and I analyzed the effect of canopy stratification on the estimation of biomass.
520 $a A unifying theme of my dissertation is to advance LiDAR analytics for accurate and detailed estimation of urban landscape heterogeneity over large regional extents. The results of the three studies suggest that establishing optimal resolution and point density for LiDAR data is a highly effective method of pursuing large area studies of urban landscape heterogeneity, and the fusion of LiDAR-derived variables and multispectral data is beneficial in some applications such as improving class discrimination of spectrally similar land cover types. Finally, the direct measurement of forest understory and overstory structure through LiDAR has proven valuable for the study of complex and heterogeneous ecosystems like urban forests.
590 $a School code: 0155.
650 4 $a Remote Sensing. $3 1018559
650 4 $a Agriculture, Forestry and Wildlife. $3 783690
650 4 $a Natural Resource Management. $3 676989
690 $a 0799
690 $a 0478
690 $a 0528
710 2 $a North Carolina State University. $b Forestry and Environmental Resources. $3 3170942
773 0 $t Dissertation Abstracts International $g 76-07B(E).
790 $a 0155
791 $a Ph.D.
792 $a 2014
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3690369