Dynamic changes of vegetation coverage in China-Myanmar economic corridor over the past 20 years

[1]  I. Farah,et al.  Phenology as accuracy metrics for vegetation index forecasting over Tunisian forest and cereal cover types , 2021 .

[2]  Wei Liu,et al.  An Open Data and Citizen Science Approach to Building Resilience to Natural Hazards in a Data-Scarce Remote Mountainous Part of Nepal , 2020, Sustainability.

[3]  Marco Heurich,et al.  Linking the Remote Sensing of Geodiversity and Traits Relevant to Biodiversity - Part II: Geomorphology, Terrain and Surfaces , 2020, Remote. Sens..

[4]  Q. Fu,et al.  Analysis and prediction of vegetation dynamic changes in China: Past, present and future , 2020 .

[5]  N. Tsendbazar,et al.  Monitoring vegetation change and their potential drivers in Yangtze River Basin of China from 1982 to 2015 , 2020, Environmental Monitoring and Assessment.

[6]  T. Fischer,et al.  Land use and land cover changes along the China-Myanmar Oil and Gas pipelines – Monitoring infrastructure development in remote conflict-prone regions , 2020, PloS one.

[7]  Ying Jiang,et al.  Discussion on Remote Sensing Big Data to Promote the Development of Smart City , 2020 .

[8]  Qinhuo Liu,et al.  Estimating fractional vegetation cover from leaf area index and clumping index based on the gap probability theory , 2020, Int. J. Appl. Earth Obs. Geoinformation.

[9]  R. Corlett,et al.  Climate change promotes transitions to tall evergreen vegetation in tropical Asia , 2020, Global change biology.

[10]  P. Ciais,et al.  Characteristics, drivers and feedbacks of global greening , 2019, Nature Reviews Earth & Environment.

[11]  Kevin M. Woods Green Territoriality: Conservation as State Territorialization in a Resource Frontier , 2019, Human Ecology.

[12]  Jin Cao,et al.  Urbanization effects on vegetation cover in major African cities during 2001-2017 , 2019, Int. J. Appl. Earth Obs. Geoinformation.

[13]  I. Macadam,et al.  High-resolution climate projections for South Asia to inform climate impacts and adaptation studies in the Ganges-Brahmaputra-Meghna and Mahanadi deltas. , 2019, The Science of the total environment.

[14]  V. Brovkin,et al.  China and India lead in greening of the world through land-use management , 2019, Nature Sustainability.

[15]  B. Fu,et al.  Increasing global vegetation browning hidden in overall vegetation greening: Insights from time-varying trends , 2018, Remote Sensing of Environment.

[16]  Michael X Cohen,et al.  A better way to define and describe Morlet wavelets for time-frequency analysis , 2018, NeuroImage.

[17]  B. Timbal,et al.  Observed and modelled temperature and precipitation extremes over Southeast Asia from 1972 to 2010 , 2018 .

[18]  Ji Chen,et al.  Characteristics of climate change and its relationship with land use/cover change in Yunnan Province, China , 2018 .

[19]  Changhui Peng,et al.  Large-scale detection of vegetation dynamics and their potential drivers using MODIS images and BFAST: A case study in Quebec, Canada , 2018 .

[20]  Vicente Pérez-Muñuzuri,et al.  Extreme Wave Height Events in NW Spain: A Combined Multi-Sensor and Model Approach , 2017, Remote. Sens..

[21]  A. Ziegler,et al.  Untangling the proximate causes and underlying drivers of deforestation and forest degradation in Myanmar , 2017, Conservation biology : the journal of the Society for Conservation Biology.

[22]  Guangjian Yan,et al.  Estimating fractional vegetation cover and the vegetation index of bare soil and highly dense vegetation with a physically based method , 2017, Int. J. Appl. Earth Obs. Geoinformation.

[23]  Ned Horning,et al.  Losing a jewel—Rapid declines in Myanmar’s intact forests from 2002-2014 , 2017, PloS one.

[24]  Claire Marais-Sicre,et al.  Estimation of corn yield using multi-temporal optical and radar satellite data and artificial neural networks , 2017, Int. J. Appl. Earth Obs. Geoinformation.

[25]  Mohammad Yusri Hassan,et al.  Building electrical energy consumption forecasting analysis using conventional and artificial intelligence methods: A review , 2017 .

[26]  Shufang Tian,et al.  Human Activity Influences on Vegetation Cover Changes in Beijing, China, from 2000 to 2015 , 2017, Remote. Sens..

[27]  Wenji Zhao,et al.  Spatio-temporal variation of vegetation coverage and its response to climate change in North China plain in the last 33 years , 2016, Int. J. Appl. Earth Obs. Geoinformation.

[28]  Yaozhong Pan,et al.  Vegetation dynamics in Qinling-Daba Mountains in relation to climate factors between 2000 and 2014 , 2016, Journal of Geographical Sciences.

[29]  Thomas Hilker,et al.  On the measurability of change in Amazon vegetation from MODIS , 2015 .

[30]  P. Luo,et al.  Ecosystem Evapotranspiration as a Response to Climate and Vegetation Coverage Changes in Northwest Yunnan, China , 2015, PloS one.

[31]  Krishna Prasad Vadrevu,et al.  Fire Disturbance in Tropical Forests of Myanmar—Analysis Using MODIS Satellite Datasets , 2015, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[32]  B. Poulter,et al.  Detection and attribution of vegetation greening trend in China over the last 30 years , 2015, Global change biology.

[33]  D. Jourdain,et al.  Farmers' perception of and adaptation to climate-change impacts in the Dry Zone of Myanmar , 2015 .

[34]  Nicholas C. Coops,et al.  Changes in vegetation photosynthetic activity trends across the Asia-Pacific region over the last three decades , 2014 .

[35]  Lilik Budi Prasetyo,et al.  Characterizing the dynamics change of vegetation cover on tropical forestlands using 250 m multi-temporal MODIS EVI , 2014, Int. J. Appl. Earth Obs. Geoinformation.

[36]  Ingolf Kühn,et al.  Phase difference analysis of temperature and vegetation phenology for beech forest: a wavelet approach , 2013, Stochastic Environmental Research and Risk Assessment.

[37]  Rasmus Fensholt,et al.  Greenness in semi-arid areas across the globe 1981–2007 — an Earth Observing Satellite based analysis of trends and drivers , 2012 .

[38]  S. Bruin,et al.  Analysis of monotonic greening and browning trends from global NDVI time-series , 2011 .

[39]  Ladislav Kristoufek,et al.  On Hurst exponent estimation under heavy-tailed distributions , 2010, 1201.4786.

[40]  Shixiong Cao,et al.  Why large-scale afforestation efforts in China have failed to solve the desertification problem. , 2008, Environmental science & technology.

[41]  P. Webster,et al.  Interdecadal changes in the ENSO-monsoon system , 1999 .

[42]  N. Menzies Three hundred years of Taungya: A sustainable system of forestry in south China , 1988 .

[43]  J. R. Wallis,et al.  Noah, Joseph, and Operational Hydrology , 1968 .

[44]  M. Boori,et al.  Food vulnerability analysis in the central dry zone of Myanmar , 2017, Computer Optics.

[45]  F. Lil Dynamic monitoring of the fractional vegetation cover in Jilin province based on MODIS-NDVI data , 2014 .

[46]  C. Torrence,et al.  A Practical Guide to Wavelet Analysis. , 1998 .