This page has only limited features, please log in for full access.

Unclaimed
Nitu Wu
Graduate School of Chinese Academy of Agricultural Sciences, Beijing 100081, China

Basic Info

Basic Info is private.

Honors and Awards

The user has no records in this section


Career Timeline

The user has no records in this section.


Short Biography

The user biography is not available.
Following
Followers
Co Authors
The list of users this user is following is empty.
Following: 0 users

Feed

Journal article
Published: 19 February 2019 in Sustainability
Reads 0
Downloads 0

Grassland health assessment is the basis for formulating grassland protection policy. However, there are few assessment methods that consider the angle of natural succession for northern China’s regional native grassland with excessive human activities. The main purpose of this study is to build an assessment system for these areas from the perspective of natural succession. Besides, the minimal cumulative resistance (MCR) model was used to extract potential ecological information from the study area as a supplementary reference for the assessment results. The result for Bayinxile pasture, a typical semiarid steppe with excessive human activities located in northern China, showed that: (1) The ecological function of eastern hilly area was better than that of other regions and the western area was lowest as a whole. (2) The river was the most important ecological network in the whole grassland in that it was of vital significance in the prevention of retrogressive succession and in the linking of ecological communities. (3) The density of ecological network was closely related to the intensity of human activities, and farmland and roads had great negative influence on the connection of the grassland ecological network. We further proposed an ecological control zone and made suggestions for Bayinxile ecological management to prevent grassland degradation based on the above results. This study should provide a new perspective for grassland health assessment and sustainable development of regional grassland.

ACS Style

Nitu Wu; Aijun Liu; Yongfang Wang; Lanhua Li; Lumengqiqige Chao; Guixiang Liu. An Assessment Framework for Grassland Ecosystem Health with Consideration of Natural Succession: A Case Study in Bayinxile, China. Sustainability 2019, 11, 1096 .

AMA Style

Nitu Wu, Aijun Liu, Yongfang Wang, Lanhua Li, Lumengqiqige Chao, Guixiang Liu. An Assessment Framework for Grassland Ecosystem Health with Consideration of Natural Succession: A Case Study in Bayinxile, China. Sustainability. 2019; 11 (4):1096.

Chicago/Turabian Style

Nitu Wu; Aijun Liu; Yongfang Wang; Lanhua Li; Lumengqiqige Chao; Guixiang Liu. 2019. "An Assessment Framework for Grassland Ecosystem Health with Consideration of Natural Succession: A Case Study in Bayinxile, China." Sustainability 11, no. 4: 1096.

Journal article
Published: 19 April 2016 in Multimedia Tools and Applications
Reads 0
Downloads 0

Forage identification is primarily realized by human experts at a low efficiency, which does not meet the requirements of a digital grassland management. In this study, we propose an automatic identification system for gramineous grass seed, an important category of forage in grassland, based on seed images using Gabor filters and local preserving projections (LPP). The system includes four modules: image acquisition, image preprocessing, feature extraction, and feature matching. Seed images are first captured by a common digital camera, and then preprocessed by a morphological operation to obtain the ROI. In the feature extraction module, the integration of Gabor filters and LPP can provide robust features for varying brightness and image contrast while preserving the manifold structure of the images for efficient dimensionality reduction. The nearest neighbor classifier and linear discriminant analysis (LDA) classifier are used for classification. The novelty of the system lies in two aspects; one is that gramineous grass seeds in the study is automatically identified as valuable resources in grassland, instead of the certain species of weed to be distinguished from crops in the previous weed classification. The other is that Gabor filter and LPP are applied to extract the textural manifold features for the identification of gramineous grass, rather than the geometric features of appearance of gray-scale images, for more robust performance. The experimental results demonstrate the effectiveness of the seed identification system.

ACS Style

Xin Pan; Yao Cen; Yubao Ma; Weihong Yan; Xiaojing Gao; Xia Liu; Guixiang Liu. Identification of gramineous grass seeds using Gabor and locality preserving projections. Multimedia Tools and Applications 2016, 75, 16551 -16576.

AMA Style

Xin Pan, Yao Cen, Yubao Ma, Weihong Yan, Xiaojing Gao, Xia Liu, Guixiang Liu. Identification of gramineous grass seeds using Gabor and locality preserving projections. Multimedia Tools and Applications. 2016; 75 (23):16551-16576.

Chicago/Turabian Style

Xin Pan; Yao Cen; Yubao Ma; Weihong Yan; Xiaojing Gao; Xia Liu; Guixiang Liu. 2016. "Identification of gramineous grass seeds using Gabor and locality preserving projections." Multimedia Tools and Applications 75, no. 23: 16551-16576.