scholarly journals Climatic Warming and Humidification in the Arid Region of Northwest China: Multi-Scale Characteristics and Impacts on Ecological Vegetation

2021 ◽  
Vol 35 (1) ◽  
pp. 113-127
Author(s):  
Qiang Zhang ◽  
Jinhu Yang ◽  
Wei Wang ◽  
Pengli Ma ◽  
Guoyang Lu ◽  
...  
Keyword(s):  
Arid Region ◽  
Northwest China ◽  
Climatic Warming ◽  
Multi Scale ◽  
Scale Characteristics

scholarly journals Vegetation Change and Its Response to Climate Extremes in the Arid Region of Northwest China

2021 ◽  
Vol 13 (7) ◽  
pp. 1230
Author(s):  
Simeng Wang ◽  
Qihang Liu ◽  
Chang Huang
Keyword(s):  
Arid Region ◽  
Vegetation Change ◽  
Vegetation Index ◽  
Pearson Correlation ◽  
Time Lag ◽  
Climate Extremes ◽  
Northwest China ◽  
Precipitation Extremes ◽  
Temperature Extremes ◽  
Increasing Trend

Changes in climate extremes have a profound impact on vegetation growth. In this study, we employed the Moderate Resolution Imaging Spectroradiometer (MODIS) and a recently published climate extremes dataset (HadEX3) to study the temporal and spatial evolution of vegetation cover, and its responses to climate extremes in the arid region of northwest China (ARNC). Mann-Kendall test, Anomaly analysis, Pearson correlation analysis, Time lag cross-correlation method, and Least absolute shrinkage and selection operator logistic regression (Lasso) were conducted to quantitatively analyze the response characteristics between Normalized Difference Vegetation Index (NDVI) and climate extremes from 2000 to 2018. The results showed that: (1) The vegetation in the ARNC had a fluctuating upward trend, with vegetation significantly increasing in Xinjiang Tianshan, Altai Mountain, and Tarim Basin, and decreasing in the central inland desert. (2) Temperature extremes showed an increasing trend, with extremely high-temperature events increasing and extremely low-temperature events decreasing. Precipitation extremes events also exhibited a slightly increasing trend. (3) NDVI was overall positively correlated with the climate extremes indices (CEIs), although both positive and negative correlations spatially coexisted. (4) The responses of NDVI and climate extremes showed time lag effects and spatial differences in the growing period. (5) Precipitation extremes were closely related to NDVI than temperature extremes according to Lasso modeling results. This study provides a reference for understanding vegetation variations and their response to climate extremes in arid regions.

scholarly journals A new multi-scale backbone network for object detection based on asymmetric convolutions

2021 ◽  
Vol 104 (2) ◽  
pp. 003685042110113
Author(s):  
Xianghua Ma ◽  
Zhenkun Yang
Keyword(s):  
Object Detection ◽  
Image Features ◽  
Detection Accuracy ◽  
Mobile Platforms ◽  
Multi Scale ◽  
Backbone Network ◽  
Aspect Ratios ◽  
Pascal Voc ◽  
Scale Characteristics ◽  
Detection Speed

Real-time object detection on mobile platforms is a crucial but challenging computer vision task. However, it is widely recognized that although the lightweight object detectors have a high detection speed, the detection accuracy is relatively low. In order to improve detecting accuracy, it is beneficial to extract complete multi-scale image features in visual cognitive tasks. Asymmetric convolutions have a useful quality, that is, they have different aspect ratios, which can be used to exact image features of objects, especially objects with multi-scale characteristics. In this paper, we exploit three different asymmetric convolutions in parallel and propose a new multi-scale asymmetric convolution unit, namely MAC block to enhance multi-scale representation ability of CNNs. In addition, MAC block can adaptively merge the features with different scales by allocating learnable weighted parameters to three different asymmetric convolution branches. The proposed MAC blocks can be inserted into the state-of-the-art backbone such as ResNet-50 to form a new multi-scale backbone network of object detectors. To evaluate the performance of MAC block, we conduct experiments on CIFAR-100, PASCAL VOC 2007, PASCAL VOC 2012 and MS COCO 2014 datasets. Experimental results show that the detection precision can be greatly improved while a fast detection speed is guaranteed as well.

Drought monitoring in arid and semi-arid region based on multi-satellite datasets in northwest, China

2021 ◽  
Author(s):  
Wei Wei ◽  
Haoyan Zhang ◽  
Junju Zhou ◽  
Liang Zhou ◽  
Binbin Xie ◽  
...  
Keyword(s):  
Arid Region ◽  
Northwest China ◽  
Drought Monitoring ◽  
Semi Arid Region ◽  
Semi Arid

Multi-scale characteristics of remote sensing lineaments

2019 ◽  
Vol 13 (2) ◽  
pp. 287-297
Author(s):  
Junlong Xu ◽  
Xingping Wen ◽  
Haonan Zhang ◽  
Dayou Luo ◽  
Lianglong Xu ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Multi Scale ◽  
Scale Characteristics

scholarly journals Carbon Dioxide Fluxes and Their Environmental Controls in a Riparian Forest within the Hyper-Arid Region of Northwest China

Forests ◽  
2017 ◽  
Vol 8 (10) ◽  
pp. 379 ◽  
Author(s):  
Xiaohong Ma ◽  
Qi Feng ◽  
Tengfei Yu ◽  
Yonghong Su ◽  
Ravinesh Deo
Keyword(s):  
Carbon Dioxide ◽  
Arid Region ◽  
Riparian Forest ◽  
Northwest China ◽  
Carbon Dioxide Fluxes ◽  
Environmental Controls

Temperature and precipitation changes in different environments in the arid region of northwest China

2012 ◽  
Vol 112 (3-4) ◽  
pp. 589-596 ◽  
Author(s):  
Baofu Li ◽  
Yaning Chen ◽  
Xun Shi ◽  
Zhongsheng Chen ◽  
Weihong Li
Keyword(s):  
Arid Region ◽  
Northwest China ◽  
Temperature And Precipitation ◽  
Precipitation Changes

Theory, Data, and Methods

2019 ◽  
pp. 156-202
Author(s):  
Eda Ustaoglu ◽  
Arif Çagdaş Aydinoglu
Keyword(s):  
Machine Learning ◽  
Land Use ◽  
Information System ◽  
Land Use Change ◽  
Model Development ◽  
Agent Based ◽  
Change Models ◽  
Multi Scale ◽  
Land Use Dynamics ◽  
Scale Characteristics

Land-use change models are tools to support analyses, assessments, and policy decisions concerning the causes and consequences of land-use dynamics, by providing a framework for the analysis of land-use change processes and making projections for the future land-use/cover patterns. There is a variety of modelling approaches that were developed from different disciplinary backgrounds. Following the reviews in the literature, this chapter focuses on various modelling tools and practices that range from pattern-based methods such as machine learning and GIS (Geographic Information System)-based approaches, to process-based methods such as structural economic or agent-based models. For each of these methods, an overview is given for the advances that have been progressed by geographers, natural and economy scientists in developing these models of spatial land-use change. It is noted that further progress is needed in terms of model development, and integration of models operating at various scales that better address the multi-scale characteristics of the land-use system.

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