scholarly journals Multi-Level Spatial Analysis for Change Detection of Urban Vegetation at Individual Tree Scale

2014 ◽  
Vol 6 (9) ◽  
pp. 9086-9103 ◽  
Author(s):  
Jianhua Zhou ◽  
Bailang Yu ◽  
Jun Qin
Keyword(s):  
Spatial Analysis ◽  
Change Detection ◽  
Urban Vegetation ◽  
Individual Tree ◽  
Multi Level

scholarly journals Automatic Extraction of Grasses and Individual Trees in Urban Areas Based on Airborne Hyperspectral and LiDAR Data

2020 ◽  
Vol 12 (17) ◽  
pp. 2725
Author(s):  
Qixia Man ◽  
Pinliang Dong ◽  
Xinming Yang ◽  
Quanyuan Wu ◽  
Rongqing Han
Keyword(s):  
Urban Areas ◽  
Three Dimensional ◽  
Hyperspectral Data ◽  
Classification Method ◽  
Urban Vegetation ◽  
Individual Tree ◽  
Cloud Data ◽  
Random Forest Classification ◽  
The Individual ◽  
Individual Trees

Urban vegetation extraction is very important for urban biodiversity assessment and protection. However, due to the diversity of vegetation types and vertical structure, it is still challenging to extract vertical information of urban vegetation accurately with single remotely sensed data. Airborne light detection and ranging (LiDAR) can provide elevation information with high-precision, whereas hyperspectral data can provide abundant spectral information on ground objects. The complementary advantages of LiDAR and hyperspectral data could extract urban vegetation much more accurately. Therefore, a three-dimensional (3D) vegetation extraction workflow is proposed to extract urban grasses and trees at individual tree level in urban areas using airborne LiDAR and hyperspectral data. The specific steps are as follows: (1) airborne hyperspectral and LiDAR data were processed to extract spectral and elevation parameters, (2) random forest classification method and object-based classification method were used to extract the two-dimensional distribution map of urban vegetation, (3) individual tree segmentation was conducted on a canopy height model (CHM) and point cloud data separately to obtain three-dimensional characteristics of urban trees, and (4) the spatial distribution of urban vegetation and the individual tree delineation were assessed by validation samples and manual delineation results. The results showed that (1) both the random forest classification method and object-based classification method could extract urban vegetation accurately, with accuracies above 99%; (2) the watershed segmentation method based on the CHM could extract individual trees correctly, except for the small trees and the large tree groups; and (3) the individual tree segmentation based on point cloud data could delineate individual trees in three-dimensional space, which is much better than CHM segmentation as it can preserve the understory trees. All the results suggest that two- and three-dimensional urban vegetation extraction could play a significant role in spatial layout optimization and scientific management of urban vegetation.

scholarly journals Post-disaster assessment of landslides in southern Taiwan after 2009 Typhoon Morakot using remote sensing and spatial analysis

2010 ◽  
Vol 10 (10) ◽  
pp. 2179-2190 ◽  
Author(s):  
F. Tsai ◽  
J.-H. Hwang ◽  
L.-C. Chen ◽  
T.-H. Lin
Keyword(s):  
Spatial Analysis ◽  
Change Detection ◽  
Satellite Images ◽  
Regional Scale ◽  
Typhoon Morakot ◽  
Change Detection Analysis ◽  
Detection Analysis ◽  
Disaster Assessment ◽  
Southern Taiwan ◽  
Post Disaster

Abstract. On 8 August 2009, the extreme rainfall of Typhoon Morakot triggered enormous landslides in mountainous regions of southern Taiwan, causing catastrophic infrastructure and property damages and human casualties. A comprehensive evaluation of the landslides is essential for the post-disaster reconstruction and should be helpful for future hazard mitigation. This paper presents a systematic approach to utilize multi-temporal satellite images and other geo-spatial data for the post-disaster assessment of landslides on a regional scale. Rigorous orthorectification and radiometric correction procedures were applied to the satellite images. Landslides were identified with NDVI filtering, change detection analysis and interactive post-analysis editing to produce an accurate landslide map. Spatial analysis was performed to obtain statistical characteristics of the identified landslides and their relationship with topographical factors. A total of 9333 landslides (22 590 ha) was detected from change detection analysis of satellite images. Most of the detected landslides are smaller than 10 ha. Less than 5% of them are larger than 10 ha but together they constitute more than 45% of the total landslide area. Spatial analysis of the detected landslides indicates that most of them have average elevations between 500 m to 2000 m and with average slope gradients between 20° and 40°. In addition, a particularly devastating landslide whose debris flow destroyed a riverside village was examined in depth for detailed investigation. The volume of this slide is estimated to be more than 2.6 million m3 with an average depth of 40 m.

scholarly journals A multi-level spatial analysis of clinical malaria and subclinical Plasmodium infections in Pailin Province, Cambodia

Heliyon ◽  
2017 ◽  
Vol 3 (11) ◽  
pp. e00447 ◽  
Author(s):  
Daniel M. Parker ◽  
Rupam Tripura ◽  
Thomas J. Peto ◽  
Richard J. Maude ◽  
Chea Nguon ◽  
...  
Keyword(s):  
Spatial Analysis ◽  
Clinical Malaria ◽  
Multi Level

Spatial analysis methods for forest genetic trials

2002 ◽  
Vol 32 (12) ◽  
pp. 2201-2214 ◽  
Author(s):  
Gregory W Dutkowski ◽  
João Costa e Silva ◽  
Arthur R Gilmour ◽  
Gustavo A Lopez
Keyword(s):  
Spatial Analysis ◽  
Spatial Patterns ◽  
Autoregressive Model ◽  
Error Term ◽  
Genetic Model ◽  
Additive Genetic Variance ◽  
Combined Model ◽  
Individual Tree ◽  
Additive Genetic Model ◽  
Forest Genetic

Spatial analysis, using separable autoregressive processes of residuals, is increasingly used in agricultural variety yield trial analysis. Interpretation of the sample variogram has become a tool for the detection of global trend and "extraneous" variation aligned with trial rows and columns. We applied this methodology to five selected forest genetic trials using an individual tree additive genetic model. We compared the base design model with post-blocking, a first-order autoregressive model of residuals (AR1), that model with an independent error term (AR1η), a combined base and autoregressive model, an autoregressive model only within replicates and an autoregressive model applied at the plot level. Post-blocking gave substantial improvements in log-likelihood over the base model, but the AR1η model was even better. The independent error term was necessary with the individual tree additive genetic model to avoid substantial positive bias in estimates of additive genetic variance in the AR1 model and blurred patterns of variation. With the combined model, the design effects were eliminated, or their significance was greatly reduced. Applying the AR1η model to individual trees was better than applying it at the plot level or applying it on a replicate-by-replicate basis. The relative improvements achieved in genetic response to selection did not exceed 6%. Examination of the spatial distribution of the residuals and the variogram of the residuals allowed the identification of the spatial patterns present. While additional significant terms could be fitted to model some of the spatial patterns and stationary variograms were attained in some instances, this resulted in only marginal increases in genetic gain. Use of a combined model is recommended to enable improved analysis of experimental data.

Sign in / Sign up

Export Citation Format

Share Document