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| 1 | Change detection:how has urban expansion in Buenos Aires metropolitan region affected croplands显示文摘Cropland is one of the essential elements of our ecological systems for producing agricultural products.In developing countries,urban expansion is a frequently appearing phenomena,which is a type of land cover land use(LCLU)change.This change can drastically alter the features on the land surface including croplands.It can lead to detrimental consequences which has considerable effects on the socialecological systems when croplands are lost.Argentina is an extremely agricultural intense developing country,and Buenos Aires province is a top agricultural production site and has been urbanizing during the last 30 years.Thus studying and analyzing the metropolitan area of this province will contribute to our understanding of the relationship between urban expansion and its effect on croplands.So far,no research has used measurable quantitative methodologies on the Buenos Aires metropolitan region(BAMR)to reveal the relationship between urbanization and cropland.By using 30-meter resolution Landsat images of June 1985 and July 2015,this study finds urban land has expanded from 937.16 km^(2) to 1835.47 km^(2),and 30.28% of the new urban lands comes from existing croplands. | Sike Li | 2018 | International Journal of Digital Earth2018，11，2： | 1 |
| 2 | Inter-satellite variability of grassland curing maps produced by different satellite sensors-Victoria,Australia显示文摘Grassland fires are a serious problem in Victoria,Australia due to large quantity of dry grass.Grassland curing degree(GCD)measures the dryness of the grass and is an important factor for assessing grassland fire danger.Grassland curing maps(GCMs)display the spatial distribution of GCDs,but the quality of GCMs varies depending on the spatial resolution of the observing satellite remote sensing system.The higher the spatial resolution,the finer the GCD details and more spatial variations the GCM can reveal.In this study,GCD calculation algorithm named MapVictoria based on MODIS data is tested for Landsat 8 Sentinel 2;GCMs generated from these three satellites are contrasted by their GCD differences,defined here as inter-satellite variability(ISV).ISV is used to identify areas where higher resolution satellite GCMs should be used.Results show that spatial resolution difference(ΔSR),seasonality and geographical locations affect the magnitude of the ISV.Based on these findings,this paper provides recommendations to decision makers on where and when to use which satellite for grassland observations. | Sike Li | 2021 | International Journal of Digital Earth2021，14，7： | 1 |
| 3 | Inferring species membership using DNA sequences with back-propagation neural networks显示文摘 | Zhang AB Sikes DS Muster C Li SQ | | 0，，02： | 1 |
| 4 | Analysis of Landsat 8 detection of the interannual variability of grassland curing in Greater Melbourne,Australia显示文摘As one of the main components of Grassland Fire Danger Index,grassland curing degree provides crucial information for determining grassland fire danger.Accurate estimates of grassland curing are critical for determining grassland fire risk.This research focuses on the use of Landsat 8 to estimate grassland curing.Results demonstrate that Landsat 8 observations can be used to estimate curing percentages as assessed by visual and ground sampling measurements.Grassland interannual variability for the Greater Melbourne region using Landsat 8 imagery from 2013 to 2019 is examined.Slight differences in curing times and degree are observed for sample sites surrounding Greater Melbourne due to climatic differences across the region.Precipitation is regarded as an essential variable affecting curing degree and this relationship is evident for all five sample sites.Landsat 8 curing results are compared to both visual observations and destructive sampling,the most accurate method,for accuracy assessment.At 95%confidence level,Landsat 8 estimations are no different from destructive ground sampling estimations.Overall,this study validates the use of Landsat 8 data as an effective and accurate way for grassland curing monitoring. | Sike Li | 2020 | International Journal of Digital Earth2020，13，11： | 1 |
| 5 | Inferring species membership using DNA sequences with back-propagation neural networks显示文摘 | Zhang A B Sikes D S Muster C Li S Q | | 0，，： | 1 |
| 6 | Improved modeling and analysis of the patch size–frequency distribution of forest disturbances in China based on a Landsat forest cover change product显示文摘Forest disturbances have been altering the ecological properties of ecosystems;meanwhile,disturbance events of varying sizes create different structures and functions for a forest landscape.Therefore,size and frequency are important attributes of disturbances,and their relationship should be studied.We present a hierarchical method through the modeling of the overall trend of the size–frequency distribution and the characterization of the non-constant variances of disturbance sizes at each frequency level.This method was demonstrated to accurately model the sizes as well as the corresponding frequencies;thus,the total disturbed area and number of disturbance patches were both accurately estimated.By applying the method to 13 provinces in China,consistent patterns were revealed by the modeling results and remote-sensing-based product,showing that between 2000 and 2005,forests in most provinces were dominated by moderate disturbances(10 ha1000 ha)occurred in the northeastern and northwestern provinces.This study concludes that the proposed method can improve the representation of the size–frequency distribution of forest disturbances. | Dan-Xia Song Chengquan Huang Tao He Joseph O.Sexton Ainong Li Sike Li Hao Wu John R.Townshend | 2021 | International Journal of Digital Earth2021，14，2： | 0 |
| 7 | Assessing factors impacting inter-satellite variability of grassland curing estimates for fire monitoring in Victoria, Australia using remote sensing显示文摘Grassland fires in Victoria,Australia pose significant environmental issues.Monitoring these fres relies on the Grassland Curing Degree(GCD)indicator and the corresponding Grassland Curing Map(GCM)for spatial distribution.Inter-satellite variability(IsV)assesses the variations in Grassland Curing Maps(GCMs)produced from remote sensing data with varying spatial resolutions(SR).Higher SR data improves GCM accuracy but increases processing time.IsV helps identify priority areas that need higher SR imageries.This study analyzes sample sites in Victoria and finds correlations between Isv,seasonality,temperature,precipitation,and distance to residential areas.Results reveal lower Isv during summers and autumns compared to winters and springs.Temperature shows a strong negative linear relationship with ISV,indicating that higher temperatures result in lower ISVv.Precipitation exhibits a weak positive correlation with IsV,suggesting heavier precipitation leads to increased ISV.Distance to grasslands negatively correlates with IsV,indicating that greater distances from residential lands result in lower Isv.Based on these findings,it is recommended to use higher SR satellite data for GCM creation during winters and springs when temperatures are low,precipitation is heavy,and areas are closer to residential lands.Implementation suggestions are provided for fire management based on these results. | Sike Li | 2023 | International Journal of Digital Earth2023，16，1： | 0 |
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