A novel class of two-dimensional chaotic maps with infinitely many coexisting attractors

We study a novel class of two-dimensional maps with infinitely many coexisting attractors.Firstly,the mathematical model of these maps is formulated by introducing a sinusoidal function.The existence and the stability of the fixed points in the model are studied indicating that they are infinitely many and all unstable.In particular,a computer searching program is employed to explore the chaotic attractors in these maps,and a simple map is exemplified to show their complex dynamics.Interestingly,this map contains infinitely many coexisting attractors which has been rarely reported in the literature.Further studies on these coexisting attractors are carried out by investigating their time histories,phase trajectories,basins of attraction,Lyapunov exponents spectrum,and Lyapunov(Kaplan–Yorke)dimension.Bifurcation analysis reveals that the map has periodic and chaotic solutions,and more importantly,exhibits extreme multi-stability.

Image encryption technique based on new two-dimensional fractional-order discrete chaotic map and Menezes–Vanstone elliptic curve cryptosystem

We propose a new fractional two-dimensional triangle function combination discrete chaotic map(2D-TFCDM)with the discrete fractional difference.Moreover,the chaos behaviors of the proposed map are observed and the bifurcation diagrams,the largest Lyapunov exponent plot,and the phase portraits are derived,respectively.Finally,with the secret keys generated by Menezes-Vanstone elliptic curve cryptosystem,we apply the discrete fractional map into color image encryption.After that,the image encryption algorithm is analyzed in four aspects and the result indicates that the proposed algorithm is more superior than the other algorithms.

Extremely hidden multi-stability in a class of two-dimensional maps with a cosine memristor

We present a class of two-dimensional memristive maps with a cosine memristor. The memristive maps do not have any fixed points, so they belong to the category of nonlinear maps with hidden attractors. The rich dynamical behaviors of these maps are studied and investigated using different numerical tools, including phase portrait, basins of attraction,bifurcation diagram, and Lyapunov exponents. The two-parameter bifurcation analysis of the memristive map is carried out to reveal the bifurcation mechanism of its dynamical behaviors. Based on our extensive simulation studies, the proposed memristive maps can produce hidden periodic, chaotic, and hyper-chaotic attractors, exhibiting extremely hidden multistability, namely the coexistence of infinite hidden attractors, which was rarely observed in memristive maps. Potentially,this work can be used for some real applications in secure communication, such as data and image encryptions.

A class of two-dimensional rational maps with self-excited and hidden attractors

This paper studies a new class of two-dimensional rational maps exhibiting self-excited and hidden attractors. The mathematical model of these maps is firstly formulated by introducing a rational term. The analysis of existence and stability of the fixed points in these maps suggests that there are four types of fixed points, i.e., no fixed point, one single fixed point, two fixed points and a line of fixed points. To investigate the complex dynamics of these rational maps with different types of fixed points, numerical analysis tools, such as time histories, phase portraits, basins of attraction, Lyapunov exponent spectrum, Lyapunov(Kaplan–Yorke) dimension and bifurcation diagrams, are employed. Our extensive numerical simulations identify both self-excited and hidden attractors, which were rarely reported in the literature. Therefore, the multi-stability of these maps, especially the hidden one, is further explored in the present work.

Mapping landslide susceptibility at the Three Gorges Reservoir, China, using gradient boosting decision tree,random forest and information value models

This work was to generate landslide susceptibility maps for the Three Gorges Reservoir(TGR) area, China by using different machine learning models. Three advanced machine learning methods, namely, gradient boosting decision tree(GBDT), random forest(RF) and information value(InV) models, were used, and the performances were assessed and compared. In total, 202 landslides were mapped by using a series of field surveys, aerial photographs, and reviews of historical and bibliographical data. Nine causative factors were then considered in landslide susceptibility map generation by using the GBDT, RF and InV models. All of the maps of the causative factors were resampled to a resolution of 28.5 m. Of the 486289 pixels in the area,28526 pixels were landslide pixels, and 457763 pixels were non-landslide pixels. Finally, landslide susceptibility maps were generated by using the three machine learning models, and their performances were assessed through receiver operating characteristic(ROC) curves, the sensitivity, specificity,overall accuracy(OA), and kappa coefficient(KAPPA). The results showed that the GBDT, RF and In V models in overall produced reasonable accurate landslide susceptibility maps. Among these three methods, the GBDT method outperforms the other two machine learning methods, which can provide strong technical support for producing landslide susceptibility maps in TGR.

Landslide susceptibility mapping using hybrid random forest with GeoDetector and RFE for factor optimization

The present study aims to develop two hybrid models to optimize the factors and enhance the predictive ability of the landslide susceptibility models.For this,a landslide inventory map was created with 406 historical landslides and 2030 non-landslide points,which was randomly divided into two datasets for model training(70%)and model testing(30%).22 factors were initially selected to establish a landslide factor database.We applied the GeoDetector and recursive feature elimination method(RFE)to address factor optimization to reduce information redundancy and collinearity in the data.Thereafter,the frequency ratio method,multicollinearity test,and interactive detector were used to analyze and evaluate the optimized factors.Subsequently,the random forest(RF)model was used to create a landslide susceptibility map with original and optimized factors.The resultant hybrid models GeoDetector-RF and RFE-RF were evaluated and compared by the area under the receiver operating characteristic curve(AUC)and accuracy.The accuracy of the two hybrid models(0.868 for GeoDetector-RF and 0.869 for RFE-RF)were higher than that of the RF model(0.860),indicating that the hybrid models with factor optimization have high reliability and predictability.Both RFE-RF GeoDetector-RF had higher AUC values,respectively 0.863 and 0.860,than RF(0.853).These results confirm the ability of factor optimization methods to improve the performance of landslide susceptibility models.

The global rainforest mapping project JERS-1: a paradigm of international collaboration for monitoring land cover change

The Global Rainforest Mapping (GRFM) project was initiated in 1995 and, through a dedicated data acquisition policy by the National Space Development Agency of Japan (NASDA), data acquisitions could be completed within a 1.5-year period, resulting in a spatially and temporally homogeneous coverage to contain the entire Amazon Basin from the Atlantic to the Pacific; Central America up to the Yucatan Peninsular in Mexico; equatorial Africa from Madagascar and Kenya in the east to Sierra Leone in the west; and Southeast Asia, including Papua New Guinea. To some extent, GRFM project is an international endeavor led by NASDA, with the goal of producing spatially and temporally contiguous Synthetic Aperture Radar (SAR) data sets over the tropical belt on the Earth by use of the JERS-1 L-band SAR, through the generation of semi-continental, 100m resolution, image mosaics. The GRFM project relies on extensive collaboration with the National Aeronautics and Space Administration (NASA), the Joint Research Center of the European Commission (JRC) and the Japanese Ministry of International Trade and Industry (MITI) for data acquisition, processing, validation and product generation. A science program is underway in parallel with product generation. This involves the agencies mentioned above, as well as a large number of international organizations, universities and individuals to perform field activities and data analysis at different levels.

An improved algorithm for mapping burnt areas in the Mediterranean forest landscape of Morocco

The identification of burnt forests and their monitoring provide essential information for the suitable management and conservation of these ecosystems. This research focuses on the use of remote sensing with MODIS sensor data in a Mediterranean environment, precisely in the Rif region known for its high occurrence of forest fires and the largest burnt areas in Morocco. It mapped the burnt areas during the summer of 2016 using spectral indices from MODIS images, namely the Normalized Burn Ratio (NBR) and the Burnt Area Index for MODIS (BAIM). Two field surveys were used to calibrate spectral indices and validate the maps. First, a monotemporal analysis using a single pre-fire image determined the appropriate threshold of the spectral indices (BAIM and NBR) for burn detecting. Secondly, a multitemporal method was applied based on dBAIM and dNBR images which represented pre-fire and postfire differences of the BAIM and NBR images, respectively. The results show that separate use of monotemporal postfire and multitemporal methods produced an overestimation of the burnt areas. Finally, we propose a new algorithm combining both methods for burnt area mapping that we name Burnt Area Algorithm. MCD45A1 and MCD64A1 MODIS burnt area products were compared to the proposed algorithm. Validation of the estimated burnt areas using reference data of the Moroccan High Commission for Water, Forests and Fight against Desertification showed satisfactory results using the proposed algorithm, with a determination coefficient of 0.68 and a root mean square error of 44.0 ha.

An easy,accurate and efficient procedure to create forest fire risk maps using the SEXTANTE plugin Modeler

To prevent, detect, and protect against forest fires, forest personnel need to define rules for determining forest fire risk. In Portugal, all municipalities must annually produce forest fire risk （FFR） maps. To produce more reliable FFR maps more easily, we developed an open source model using the Modeler plugin of SEXTANTE in the program QGIS version 2.0 Dufour. The model provides all the maps involved in the FFR model （susceptibility map, hazard map, vulnerability map, economic value map, and potential loss map） and was produced according to Portuguese Forest Authority＇s （AFN, Autoridade Florestal Nacional） rules for determining the FFR. This model was tested for the Portuguese municipality Santa Maria da Feira, where 40 % of the total municipality area falls in the category ＂very high＂ or ＂high＂ fire risk. The ＂very high＂ fire risk area is mainly classified as broad-leaved forest and has the steepest slopes （〉15 %）. The distance of burned areas to roads was also analyzed; the proportion of burned areas increased with increasing distance to the main roads. In addition, 92.6 % of the ＂high＂ and ＂very high＂ risk zones were located in areas with lower elevation. These results confirmed that forest fire is strongly influenced not only by environmental factors but also by anthropogenic factors. The procedure implemented here was compared with our open source application already available in QGIS and also to the same procedure implemented in GIS pro- prietary software. Although the results were obviously the same, the model developed here presents several advan- tages over the other two approaches. Besides being faster, it is easy to change the model parameters according to user needs （i.e., to the rules of different countries）, and can be modified and adapted to other variables and other areas to create risk maps for different natural phenomena （e.g., floods, earthquakes, landslides）. The model is easy to use and to create risk and hazard maps rapidly in a free, open source environment that does not require any programming knowledge.

Assessing Landsat-8 and Sentinel-2 spectral-temporal features for mapping tree species of northern plantation forests in Heilongjiang Province,China

Background:Accurate mapping of tree species is highly desired in the management and research of plantation forests,whose ecosystem services are currently under threats.Time-series multispectral satellite images,e.g.,from Landsat-8(L8)and Sentinel-2(S2),have been proven useful in mapping general forest types,yet we do not know quantitatively how their spectral features(e.g.,red-edge)and temporal frequency of data acquisitions(e.g.,16-day vs.5-day)contribute to plantation forest mapping to the species level.Moreover,it is unclear to what extent the fusion of L8 and S2 will result in improvements in tree species mapping of northern plantation forests in China.Methods:We designed three sets of classification experiments(i.e.,single-date,multi-date,and spectral-temporal)to evaluate the performances of L8 and S2 data for mapping keystone timber tree species in northern China.We first used seven pairs of L8 and S2 images to evaluate the performances of L8 and S2 key spectral features for separating these tree species across key growing stages.Then we extracted the spectral-temporal features from all available images of different temporal frequency of data acquisition(i.e.,L8 time series,S2 time series,and fusion of L8 and S2)to assess the contribution of image temporal frequency on the accuracy of tree species mapping in the study area.Results:1)S2 outperformed L8 images in all classification experiments,with or without the red edge bands(0.4%–3.4%and 0.2%–4.4%higher for overall accuracy and macro-F1,respectively);2)NDTI(the ratio of SWIR1 minus SWIR2 to SWIR1 plus SWIR2)and Tasseled Cap coefficients were most important features in all the classifications,and for time-series experiments,the spectral-temporal features of red band-related vegetation indices were most useful;3)increasing the temporal frequency of data acquisition can improve overall accuracy of tree species mapping for up to 3.2%(from 90.1%using single-date imagery to 93.3%using S2 time-series),yet similar overall accuracies were achieved using S2 time-series(93.3%)and the fusion of S2 and L8(93.2%).Conclusions:This study quantifies the contributions of L8 and S2 spectral and temporal features in mapping keystone tree species of northern plantation forests in China and suggests that for mapping tree species in China's northern plantation forests,the effects of increasing the temporal frequency of data acquisition could saturate quickly after using only two images from key phenological stages.

IMIBSE与ISOMAP在旋转机械故障诊断中的应用

针对基本熵的区域划分标准不理想,导致无法有效测量振动信号的复杂度,使故障诊断的准确率不佳这一问题,提出了一种基于改进多尺度改进基本熵(IMIBSE)、等距特征映射(ISOMAP)和随机森林(RF)的旋转机械故障诊断方法。首先,采用基于方差的区域划分准则对基本熵进行了改进,结合改进的粗粒化处理,提出了IMIBSE,并将其用于提取故障特征;随后,利用ISOMAP对原始故障特征进行了特征降维,选择了对分类贡献最大的一组特征作为故障敏感特征;最后,基于RF建立了多故障分类器,将故障敏感特征输入至RF模型进行了训练和测试,实现了旋转机械的故障识别,利用齿轮箱和离心泵两种故障数据集将IMIBSE方法与复合多尺度基本熵、多尺度改进基本熵、多尺度基本熵进行了比较和分析。研究结果表明:IMIBSE不仅具有最佳的可视化效果,而且取得的识别准确率最高,二者均达到了100%,而二者的平均分类准确率分别为100%和99.8%;相较于其他故障诊断方法,IMIBSE方法的准确率更高,而且适用于小样本的故障识别问题。

Bridging mapping and simulation modelling in the ecosystem service assessments of boreal forests:effects of bioenergy production on carbon dynamics

Background:Increasing the use of forest harvest residues for bioenergy production reduces greenhouse emissions from the use of fossil fuels.However,it may also reduce carbon stocks and habitats for deadwood dependent species.Consequently,simple tools for assessing the trade-offs of alternative management practices on forest dynamics and their services to people are needed.The objectives of this study were to combine mapping and simulation modelling to investigate the effects of forest management on ecosystem services related to carbon cycle in the case of bioenergy production;and to evaluate the suitability of this approach for assessing ecosystem services at the landscape level.Stand level simulations of forest growth and carbon budget were combined with extensive multi-source forest inventory data across a southern boreal landscape in Finland.Stochastic changes in the stand age class distribution over the study region were simulated to mimic variation in management regimes.Results:The mapping framework produced reasonable estimates of the effects of forest management on a set of key ecosystem service indicators:the annual carbon stocks and fluxes of forest biomass and soil,timber and energy-wood production and the coarse woody litter production over a simulation period 2012–2100.Regular harvesting,affecting the stand age class distribution,was a key driver of the carbon stock changes at a landscape level.Extracting forest harvest residues in the final felling caused carbon loss from litter and soil,particularly with combined aboveground residue and stump harvesting.It also reduced the annual coarse woody litter production,demonstrating negative impacts on deadwood abundance and,consequently,forest biodiversity.Conclusions:The refined mapping framework was suitable for assessing ecosystem services at the landscape level.The procedure contributes to bridging the gap between ecosystem service mapping and detailed simulation modelling in boreal forests.It allows for visualizing ecosystem services as fine resolution maps to support sustainable land use planning.In the future,more detailed models and a wider variety of ecosystem service indicators could be added to develop the method.

Mapping Glitches of Juniper Forests in Lebanon under Natural Conditions and Anthropogenic Activities

In 1965, the first forest map of Lebanon was produced. It is the oldest spatial distribution representation of junipers. Landcover maps of 2002 and 2010 are the most detailed spatial distribution that spatially shows forests. Juniper forests are found in Lebanon as mainly as clear to low density coverage. High-density juniper forests are rarely found and only on Mount-Lebanon. Juniper forests are also mixed with oaks on the Eastern flank of Mount-Lebanon. Mapping juniper forests have demonstrated high degree of complexity, especially because of their low density and being mixed. The spatial representation of juniper forests was compared between the 1965 forest map and the landcover maps of 2002 and 2010. GIS environment was used to extract juniper forests from all maps. The degree of matching between juniper forests was investigated regarding the total area and spatial overlapping. Juniper forests were examined to their spatial locations, comparing the three maps. Spatial changes and anthropogenic effect were obtained, using Google Earth facilities. Google earth had satellite images acquired since 2014. Landcover maps of 2002 and 2010 have spatially matched forest map of 1965 by about 90% and 50% respectively. Spatial coverage of juniper forests were about 12,000, 26,000 and 28,000 ha on the 1965 forest map, landcover maps of 2003 and 2010 respectively. Anti-Lebanon juniper forests were not well represented on both landcover maps. Anthropogenic activities were mainly agriculture that affected juniper forests. Cultivations have replaced about 2% of the spatial coverage of 1965 Juniper forests. Quarries and urban existed inside juniper forests but in very limited areas. Juniper forests delineation did not completely match neither between the available maps, nor to the ground. Some juniper forests were not spatially represented on all maps or existing maps represented only portion of juniper forests. Juniper forest mapping requires more consideration and field investigation. High spatial resolution satellite images are among the solutions but delimiting juniper would require extensive fieldwork and specific remote sensing treatments. Being centuries old forests and characterized by High Mountain elevations, these important conifer forests are needed to be mapped with higher accuracy for better statistics and conservation.

Geostatistical Approach for Site Suitability Mapping of Degraded Mangrove Forest in the Mahakam Delta, Indonesia

As part of operational guidance of mangrove forest rehabilitation in the Mahakam delta, Indonesia, site suitability mapping for 14 species of mangrove was modelled by combining 4 underlying factors—clay, sand, salinity and tidal inundation. Semivariogram analysis and a geographic information system (GIS) were used to apply a site-suitability model, while kriging interpolation generated surface layers, based on sample point data collection. The tidal inundation map was derived from a tide table and a digital elevation model from topographic maps. The final site-suitability maps were produced using spatial analysis technique, by overlaying all surface layers. We used a Gaussian model to adjust a semivariogram graph in order to help to understand the variation of sample data values, and create a natural surface layer of data distribution over the area of study. By examining the statistical value and the visual inspection of surface layers, we saw that the models were consistent with the expected data behavior;therefore, we assumed that interpolation has been carried out appropriately. Our site-suitability map showed that Avicennia species was the most suitable species and matched with 50% of the study area, followed by Nypa fruticans, which occupied about 42%. These results were actually consistent with the mangrove zoning pattern in the region prior to deforestation and conversion.

Mapping species assemblages of tropical forests at different hierarchical levels based on multivariate regression trees

Background: Vegetation distribution maps are of great significance for nature protection and management. In diverse tropical forests, accurate spatial mapping of vegetation types is challenging;the high species diversity and abundance of rare species challenge classification concepts, while remote sensing signals may not vary systematically with species composition, complicating the technical capability for delineating vegetation types in the landscape.Methods: We used a combination of field-based compositional data and their relations to environmental variables to predict the distribution of forest types in the Wuzhishan National Natural Reserve(WNNR), Hainan Island,China, using multivariate regression trees(MRT). The MRT was based on arboreal vegetation composition in 132plots of 20 m×20 m with a regular spacing of 1 km. Apart from the MRT, non-metric multidimensional scaling(NMDS) was used to evaluate vegetation-environment relationships.Results: The MRT model worked best when using 14 key environmental variables including topography, climate,latitude and soil, although the difference with the simpler model including only topographical variables was small. The full model classified the 132 plots into 3 vegetation types, 6 formation groups, 20 formations and 65associations at different hierarchical syntaxonomic levels. This model was the basis for forest vegetation maps for the WNNR. MRT and NMDS showed that elevation was the main driving force for the distribution of vegetation types and formation groups. Climate, latitude, and soil(especially available P), together with topographic variables, all influenced the distribution of formations and associations.Conclusions: While elevation determines forest-type distributions, lower-level syntaxonomic forest classes respond to the topographic diversity typical for mountains. Apart from providing the first detailed forest vegetation map for any part of WNNR, we show how, in spite of limitations, MRT with existing environmental data can be a useful method for mapping diverse and remote tropical forests.

Risk Assessment Mapping of Landscape Development Based on Ecological Service and Goods in Malaysia Lowland Tropical Rainforest

Amongst the impacts of converting forest to agricultural activities are soil erosion and degradation of ecology service values and goods （ESVG）. The soil erosion can be seen as on-site impacts, such as the problems of decreasing soil fertility and also its off-site impact such as the problems of sedimentation of the nearby rivers, whilst the degradation of ESVG are more holistie in nature, These impacts can be devastating in environmental, biological, and socio-economic manners. This paper reports the study undertaken on the impacts of agricultural development in 0.8 million ha of forest dominated landscape in Pasoh Forest Region （PFR）, Malaysia, within period of 8 years from 1995 to 2003. Three folds of impacts on agricultural development examined and analysed, are： （i） relationship of total soil loss and changes in land use pattern, （ii） mapping trends of ESVG for PFR in 1995 and 2003, and （iii） risk assessment of ESVG based on simulation of converting 339,630 ha of primary forest into mass-scale oil palm plantation. Results of this study indicated that although only minor changes of about 1464 ha （about 0.2% of PFR） of primary forest was converted to agricultural activities, it have significantly increased the total soil loss from 59 to 69 million ton/ha/yr. The mean rate of soil is loss for PFR is 0.8 mil ton/ha/yr and if translated into ESVG term, the soil loss costs about US$ 4.8mil/yr. However, majority of the soil loss within all land use classes are within range of very low-low risk categories （〈10 ton/ha/yr）. ESVG for PFR were costing US$ 179 millions in 1995, declined to US$114 millions in 2003 due to 0.2% reduction of forested land. The ESVG of converting 339,630 ha primary forest into mass plantation cost less than original forest within period of 20 years examined; the 20th year of conversion, the ESVG of plantation and to-remain as forest cost US$ 963 and US$ 575 millions, respectively. However, this difference is only marginal when full attributes of ESVG are considered.

Genetic dissection of growth trajectories in forest trees:From FunMap to FunGraph

Growth is the developmental process involving important genetic components.Functional mapping(FunMap)has been used as an approach to map quantitative trait loci(QTLs)governing growth trajectories by incorporating growth equations.FunMap is based on reductionism thinking,with a power to identify a small set of significant QTLs from the whole pool of genome-wide markers.Yet,increasing evidence shows that a complex trait is controlled by all genes the organism may possibly carry.Here,we describe and demonstrate a different mapping approach that encapsulates all markers into genetic interaction networks.This approach,symbolized as FunGraph,combines functional mapping,evolutionary game theory,and prey-predator theory into mathematical graphs,allowing the observed genetic effect of a locus to be decomposed into its independent component(resulting from this locus’intrinsic capacity)and dependent component(due to extrinsic regulation by other loci).Using FunGraph,we can visualize and trace the roadmap of how each locus interact with every other locus to impact growth.In a population-based association study of Euphrates poplar,we use FunGraph to identify the previously neglected genetic interaction effects that contribute to the genetic architecture of juvenile stem growth.FunGraph could open up a novel gateway to comprehend the global genetic control mechanisms of complex traits.

Uncertainties of landslide susceptibility prediction:influences of different study area scales and mapping unit scales

This study aims to investigate the effects of different mapping unit scales and study area scales on the uncertainty rules of landslide susceptibility prediction(LSP).To illustrate various study area scales,Ganzhou City in China,its eastern region(Ganzhou East),and Ruijin County in Ganzhou East were chosen.Different mapping unit scales are represented by grid units with spatial resolution of 30 and 60 m,as well as slope units that were extracted by multi-scale segmentation method.The 3855 landslide locations and 21 typical environmental factors in Ganzhou City are first determined to create spatial datasets with input-outputs.Then,landslide susceptibility maps(LSMs)of Ganzhou City,Ganzhou East and Ruijin County are pro-duced using a support vector machine(SVM)and random forest(RF),respectively.The LSMs of the above three regions are then extracted by mask from the LSM of Ganzhou City,along with the LSMs of Ruijin County from Ganzhou East.Additionally,LSMs of Ruijin at various mapping unit scales are generated in accordance.Accuracy and landslide suscepti-bility indexes(LSIs)distribution are used to express LSP uncertainties.The LSP uncertainties under grid units significantly decrease as study area scales decrease from Ganzhou City,Ganzhou East to Ruijin County,whereas those under slope units are less affected by study area scales.Of course,attentions should also be paid to the broader representativeness of large study areas.The LSP accuracy of slope units increases by about 6%–10%compared with those under grid units with 30 m and 60 m resolution in the same study area's scale.The significance of environmental factors exhibits an averaging trend as study area scale increases from small to large.The importance of environmental factors varies greatly with the 60 m grid unit,but it tends to be consistent to some extent in the 30 m grid unit and the slope unit.

The effects of data aggregation on long-term projections of forest stands development

Forest management planning often relies on Airborne Laser Scanning(ALS)-based Forest Management Inventories(FMIs)for sustainable and efficient decision-making.Employing the area-based(ABA)approach,these inventories estimate forest characteristics for grid cell areas(pixels),which are then usually summarized at the stand level.Using the ALS-based high-resolution Norwegian Forest Resource Maps(16 m×16 m pixel resolution)alongside with stand-level growth and yield models,this study explores the impact of three levels of pixel aggregation(standlevel,stand-level with species strata,and pixel-level)on projected stand development.The results indicate significant differences in the projected outputs based on the aggregation level.Notably,the most substantial difference in estimated volume occurred between stand-level and pixel-level aggregation,ranging from-301 to+253 m^(3)·ha^(-1)for single stands.The differences were,on average,higher for broadleaves than for spruce and pine dominated stands,and for mixed stands and stands with higher variability than for pure and homogenous stands.In conclusion,this research underscores the critical role of input data resolution in forest planning and management,emphasizing the need for improved data collection practices to ensure sustainable forest management.

基于MAPINFO的森林资源空间信息管理系统开发与应用

为了实现森林资源空间信息管理系统的应用 .该文结合五道河林场的实际资料 ,设计和实现了一个基于MAPINFO的森林资源空间信息管理子系统 .并对林业专题图制作 。