Vegetation often exists as patch in arid and semi-arid region throughout the world. Vegetation patch can be effectively
monitored by remote sensing images. However, not all satellite platforms are suitable to study quasi-circular vegetation
patch. This study compares fine (GF-1) and coarse (CBERS-04) resolution platforms, specifically focusing on the quasicircular
vegetation patches in the Yellow River Delta (YRD), China. Vegetation patch features (area, shape) were
extracted from GF-1 and CBERS-04 imagery using unsupervised classifier (K-Means) and object-oriented approach
(Example-based feature extraction with SVM classifier) in order to analyze vegetation patterns. These features were then
compared using vector overlay and differencing, and the Root Mean Squared Error (RMSE) was used to determine if the
mapped vegetation patches were significantly different. Regardless of K-Means or Example-based feature extraction
with SVM classification, it was found that the area of quasi-circular vegetation patches from visual interpretation from
QuickBird image (ground truth data) was greater than that from both of GF-1 and CBERS-04, and the number of patches
detected from GF-1 data was more than that of CBERS-04 image. It was seen that without expert’s experience and
professional training on object-oriented approach, K-Means was better than example-based feature extraction with SVM
for detecting the patch. It indicated that CBERS-04 could be used to detect the patch with area of more than 300 m2, but
GF-1 data was a sufficient source for patch detection in the YRD. However, in the future, finer resolution platforms such
as Worldview are needed to gain more detailed insight on patch structures and components and formation mechanism.
Natural tropical rainforests in China’s Xishuangbanna region have undergone dramatic conversion to rubber plantations in recent decades, resulting in altering the region’s environment and ecological systems. Therefore, it is of great importance for local environmental and ecological protection agencies to research the distribution and expansion of rubber plantations. The objective of this paper is to monitor dynamic changes of rubber plantations in China’s Xishuangbanna region based on multitemporal Landsat images (acquired in 1989, 2000, and 2013) using a C5.0-based decision-tree method. A practical and semiautomatic data processing procedure for mapping rubber plantations was proposed. Especially, haze removal and deshadowing were proposed to perform atmospheric and topographic correction and reduce the effects of haze, shadow, and terrain. Our results showed that the atmospheric and topographic correction could improve the extraction accuracy of rubber plantations, especially in mountainous areas. The overall classification accuracies were 84.2%, 83.9%, and 86.5% for the Landsat images acquired in 1989, 2000, and 2013, respectively. This study also found that the Landsat-8 images could provide significant improvement in the ability to identify rubber plantations. The extracted maps showed the selected study area underwent rapid conversion of natural and seminatural forest to a rubber plantations from 1989 to 2013. The rubber plantation area increased from 2.8% in 1989 to 17.8% in 2013, while the forest/woodland area decreased from 75.6% in 1989 to 44.8% in 2013. The proposed data processing procedure is a promising approach to mapping the spatial distribution and temporal dynamics of rubber plantations on a regional scale.
The Maqu alpine wetlands have irreplaceable function in maintaining ecological balance and conserving biodiversity to
the upriver regions of the Yellow River. In last 30 years, Global warming causes significant changes in
vegetation. However, the Maqu alpine wetland is undergoing a degradation caused by warming and drying climate. Aim
of this study is to investigate the vegetation changes for a better understanding the consequence of climate variations to
the wetland degradation. Based on the Landsat TM images of 2000 and 2010, the landscape pattern changes were
analyzed by classification statistics, dynamic transfer matrix and landscape pattern indices. Based on the MOD11A2 and
MOD13A2 data from 2000 to 2010, NDVI and land surface temperature (LST) dataset were extracted. NDVI time-series
data processed with S-G filtering method was used to find temporal and spatial variation characteristics, and linear trend
was analyzed by ordinary least squares regression method. NDVI and LST were used to construct Ts-NDVI feature
space, and then TVDI was obtained to explore changes of soil moisture. Relationship between climate variations and
wetland degradation were found by ordinary least squares regression method. Results indicated that both wetland area
and landscape heterogeneity decreased. Annual NDVI presented fluctuated decreasing trend and there was strong spatial
heterogeneity in patterns of NDVI change. Annual TVDI proved to have an increasing trend which showed the drought
gradually intensified. “Warming and drought” climate appear to be critical factors contributing to wetland degradation.
Precipitation has a stronger correlation rather than temperature.
Spatial pattern dynamics of plant community is a key indicator of long-term plant change in semi-arid ecosystems. This study uses high spatial resolution SPOT5 remote sensing data to detect the quasi-circular plant community patches to analyze the spatial pattern dynamics at Gudong Oil Field, China. The detection accuracy of quasi-circular plant community patches from SPOT5 is about 84%. Compared with the 2000, the amount of quasi-circular plant community patches increases from 169 to 204 in 2005, but the areas of 169 quasi-circular plant community patches in 2000 changes a little in 2005. Spatial patterns of quasi-circular plant community patches in 2000 and 2005 are analyzed using average nearest neighbor method in ArcGIS, and both of their spatial patterns are dispersed distribution. This is due to seismic exploration at Gudong Oil Field since 1980’s. These results show that high spatial resolution remote sensing data is simple and effective for detecting plant community patch, and easy to be used to study spatial pattern dynamics of plant community.
Circular or elliptical vegetation community patches resulted from seismic exploration of Shengli Oil Field occurs widely
across the Yellow River Delta in China. In order to facilitate the monitoring of vegetation extension and quantify the
mechanism of vegetation patch succession, there is a clear need for accurate and economical of detecting the different
structures of vegetation patches. This paper compares the efficacy of SPOT 5 and ALOS data in detecting vegetation
community patches at Gudong oil field, China. As a result of shape differences between vegetation community patches
and background in the image provided by each sensor, canny edge detector and mathematical morphological methods
were employed. SPOT 5 data (2.5 m Ground Spatial Distance or GSD, detection accuracy, 91.2%) proved more effective
in vegetation community patch delineation than ALOS data (2.5 m GSD, detection accuracy, 89.3%).
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.