Annual 30-m land use/land cover maps of China for 1980–2015 from the integration of AVHRR, MODIS and Landsat data using the BFAST algorithm

Annual land use land cover(LULC)change information at medium spatial resolution(i.e.at 30 m)is required in numerous subjects,such as biophysical modelling,land management and global change studies.Annual LULC information,however,is usually not available at continental or national scale due to reasons such as insufficient remote sensing data coverage or lack of computational capabilities.Here we integrate high temporal resolution and coarse spatial resolution satellite images(i.e.,Moderate Resolution Imaging Spectroradiometer(MODIS)and Global Inventory Modelling and Mapping Studies(GIMMS)normalized difference vegetation index(NDVI))with high spatial resolution datasets(China’s Land-Use/cover Datasets(CLUDs)derived from 30-meter Landsat TM/ETM+/OLI)to generate reliable annual nominal 30 m LULC maps for the whole of China between 1980 and 2015.We also test the performance of a statistical based change detection algorithm(Breaks for Additive Seasonal and Trend),originally designed for tracking forest change,in classifying all-type LULC change.As a result,a nominal 30 m annual land use/land cover datasets(CLUD-A)from 1980 to 2015 was developed for the whole China.The mapping results were assessed with a change sample dataset,a regional annual validation sample set and a three-year China sample set.Of the detected change years,75.61%matched the exact time of conversion within±1 year.Annual mapping results provided a detail process of urbanization,deforestation,afforestation,water and cropland dynamics over the past 36 years.The consistent characterization of land change dynamics for China can be further used in scientific research and to support land management for policy-makers.

Satellite remote sensing reveals overwhelming recovery of forest from disturbances in Asia

Forest ecosystems play key roles in mitigating human-induced climate change through enhanced carbon uptake;however,frequently occurring climate extremes and human activities have considerably threatened the stability of forests.At the same time,detailed accounts of disturbances and forest responses are not yet well quantified in Asia.This study employed the Breaks For Additive Seasonal and Trend method-an abrupt-change detection method-to analyze the Enhanced Vegetation Index time series in East Asia,South Asia,and Southeast Asia.This approach allowed us to detect forest disturbance and quantify the resilience after disturbance.Results showed that 20%of forests experienced disturbance with an increasing trend from 2000 to 2022,and Southeast Asian countries were more severely affected by disturbances.Specifically,95%of forests had robust resilience and could recover from disturbance within a few decades.The resilience of forests suffering from greater magnitude of disturbance tended to be stronger than forests with lower disturbance magnitude.In summary,this study investigated the resilience of forests across the low and middle latitudes of Asia over the past two decades.The authors found that most forests exhibited good resilience after disturbance and about two-thirds had recovered to a better state in 2022.The findings of this study underscore the complex relationship between disturbance and resilience,contributing to comprehension of forest resilience through satellite remote sensing.

Regional drying and wetting trends over Central Asia based on Koppen climate classification in 1961—2015

Central Asia(CA)is one of the most drought-prone regions in the world with complex climate regimes,it is extremely vulnerable to water scarcity.Many studies on drought in CA,as a whole,have been carried out,whereas there is a lack of studies on the drying and wetting trends of different climatic zones within CA.In this study,CA was divided into three different climatic zones based on the Koppen climate classification method,precipitation climatology,and aridity index.These were the temperate continental(Df),dry arid desert(BW),and Mediterranean continental(Ds)climatic zones.The regional drying and wetting trends during the years 1961—2015 were investigated using the monthly gridded Standardized Precipitation Evapotranspiration Index(SPEI).The empirical orthogonal function(EOF)was applied to analyze spatial and temporal variation patterns.EOF mode 1(EOF1)analysis found increasingly wet conditions throughout CA over the duration of the study,and EOF mode 2(EOF2)analysis found a contrast between northern and southern CA:as Df became drier and BW and Ds became wetter.EOF mode 3(EOF3)analysis found a western and eastern inverse phase distribution.The SPEI displayed a decreasing trend from 1961 to 1974 for CA as a whole,before increasing from 1975 to 2015,with a particularly significant increase over the first seven years of that period.On a regional scale,the BW and Ds zones experienced a wetting trend due to increased precipitation during 1961—2015,but the Df zone experienced a drying trend due to reduced evapotranspiration and an increasing temperature,particularly from 1961 to 1978.Thus,the spatio-temporal patterns in dryness and wetness across CA can be categorized according to climatic regions.

基于稠密Landsat数据的邛崃山大熊猫栖息地植被变化研究

深入理解大熊猫栖息地植被变化过程及其驱动力,是开展大熊猫栖息地保护和管理的重要基础。该研究利用1986–2018年所有可用的Landsat TM/ETM/OLI影像构建长时间序列归一化植被指数(NDVI),采用BFAST(Breaks For Additive Seasonal and Trend)方法实现大熊猫栖息地植被变化历史检测,从植被累积突变、累积渐变和总变化3个指标揭示植被变化空间分布特征;运用地理探测模型定量评价不同因子(年降水量、年平均气温、高程、坡度、坡向、与河流距离、土壤类型、土地覆盖类型、与道路的距离、与工程扰动区距离)对3种植被变化空间分布的影响。结果表明:1)研究区内植被突变面积比例为9.13%,主要分布于栖息地东部边界附近,2011和2013年植被突变面积较大;2)植被累积突变表现为退化面积占植被累积突变面积的40.17%,植被累积渐变和总变化表明研究区植被呈现改善趋势,改善面积比例分别占研究区的94.58%和97.02%;3)3种植被变化的空间分布主要受年降水量、年平均气温、高程、土壤类型4种因子的影响,植被累积突变、累积渐变和总变化空间分布的最强解释因子分别为年降水量、高程和土壤类型,驱动因子之间的交互作用为相互增强、非线性增强关系。

Integration of Landsat time-series vegetation indices improves consistency of change detection

Vegetation indices(VIs)were used to detect when and where vegetation changes occurred.However,different VIs have different or even diametrically opposite results,which obstructed the in-depth understanding of vegetation change.Therefore,this study examined the spatial and temporal consistency offive VIs(EVI;NBR;NDMI;NDVI;and NIRv)in detecting abrupt and gradual vegetation changes,and provided an ensemble algorithm which integrated the change detection results of thefive indices to reduce the uncertainty of change detection using a single index.The spatial consistency of thefive indices in abrupt change detection accounted for 50.6%of the study area,but the temporal consistency was low(21.6%).Wetness indices(NBR,NDMI)were more sensitive to negative abrupt changes,greenness indices(EVI,NDVI,NIRv)were more sensitive to positive abrupt changes;and both types of indices were similar in detecting gradual and total changes.The overall accuracy of the ensemble method was 81.60%and higher than that of any single index in abrupt change detection.This study provides a comprehensive evaluation of the spatial and temporal inconsistencies of change detection in model-fitting errors and various types of vegetation changes.The proposed ensemble method can support robust change-detection.