Novel Vegetation Mapping Through Remote Sensing Images Using Deep Meta Fusion Model

Preserving biodiversity and maintaining ecological balance is essential in current environmental conditions.It is challenging to determine vegetation using traditional map classification approaches.The primary issue in detecting vegetation pattern is that it appears with complex spatial structures and similar spectral properties.It is more demandable to determine the multiple spectral ana-lyses for improving the accuracy of vegetation mapping through remotely sensed images.The proposed framework is developed with the idea of ensembling three effective strategies to produce a robust architecture for vegetation mapping.The architecture comprises three approaches,feature-based approach,region-based approach,and texture-based approach for classifying the vegetation area.The novel Deep Meta fusion model(DMFM)is created with a unique fusion frame-work of residual stacking of convolution layers with Unique covariate features(UCF),Intensity features(IF),and Colour features(CF).The overhead issues in GPU utilization during Convolution neural network(CNN)models are reduced here with a lightweight architecture.The system considers detailing feature areas to improve classification accuracy and reduce processing time.The proposed DMFM model achieved 99%accuracy,with a maximum processing time of 130 s.The training,testing,and validation losses are degraded to a significant level that shows the performance quality with the DMFM model.The system acts as a standard analysis platform for dynamic datasets since all three different fea-tures,such as Unique covariate features(UCF),Intensity features(IF),and Colour features(CF),are considered very well.

Predictive Vegetation Mapping Approach Based on Spectral Data, DEM and Generalized Additive Models

This study aims to provide a predictive vegetation mapping approach based on the spectral data, DEM and Generalized Additive Models (GAMs). GAMs were used as a prediction tool to describe the relationship between vegetation and environmental variables, as well as spectral variables. Based on the fitted GAMs model, probability map of species occurrence was generated and then vegetation type of each grid was defined according to the probability of species occurrence. Deviance analysis was employed to test the goodness of curve fitting and drop contribution calculation was used to evaluate the contribution of each predictor in the fitted GAMs models. Area under curve (AUC) of Receiver Operating Characteristic (ROC) curve was employed to assess the results maps of probability. The results showed that: 1) AUC values of the fitted GAMs models are very high which proves that integrating spectral data and environmental variables based on the GAMs is a feasible way to map the vegetation. 2) Prediction accuracy varies with plant community, and community with dense cover is better predicted than sparse plant community. 3) Both spectral variables and environmental variables play an important role in mapping the vegetation. However, the contribution of the same predictor in the GAMs models for different plant communities is different. 4) Insufficient resolution of spectral data, environmental data and confounding effects of land use and other variables which are not closely related to the environmental conditions are the major causes of imprecision.

VEGETATION MAPPING USING REMOTE SENSING AND GIS

Classification accuracy of satellite imagery in complex terrain environments can be improvd by using ancillary daa and imasery spaial features extracted from the images. The classification mny be accomplished by using spaial analysis methods of geographic information System (GIS) that provide a tool for integrating all Kinds of ancillare data, or using ancillare data as an augmented subset of bands in processing imagery. The purpose of the study is to test the role of GIS spatial and spectra analysis medel in aiding the classification of satellite data and to compare the ability Of two satellite systems, SPOT and Landsat Thematic Mapper (TM) in vegetation mapping in mountainous region.

Vegetation Mapping for Regional Ecological Research and Management: A Case of the Loess Plateau in China

Vegetation maps are fundamental for regional-scale ecological research. However, information is often not sufficiently up to date for such research. The Loess Plateau is a key area for vegetation restoration projects and a suitable area for regional ecological research. To carry out regional vegetation mapping based on the principles of hierarchical classification, object-oriented methods, visual interpretation, and accuracy assessment, this study integrated land cover, high-resolution remote sensing images, background environmental data, bioclimate zoning data, and field survey data from the Loess Plateau. To further clarify the implications of vegetation mapping, we compared the deviation of the 2015 vegetation map of the Loess Plateau(VMLP) and the widely used vegetation map of China(VMC)(1 : 1 000 000) for the expressed vegetation information and the evaluation of ecosystem services. The results indicated that 1) the vegetation of the Loess Plateau could be divided into 9 vegetation type groups and 18 vegetation types with classification accuracies of 87.76% and 83.97%, respectively;2) the distribution of vegetation had obvious zonal regularity;3) a deviation of 29.56 × 10^4 km^2 occurred when the vegetation coverage area was quantified with the VMC;4) the vegetation classification accuracy affected the ecosystem service assessment, the total water yield of the Loess Plateau calculated by the VMC and other required parameters was overestimated by 2.2 × 10^6 mm in 2015. Because vegetation mapping is a basic and important activity, that requires greater attention, this study provides supporting data for subsequent multivariate vegetation mapping and vegetation management for conservation and restoration.

A new regional vegetation mapping method based on terrain-climate-remote sensing and its application on the Qinghai-Xizang Plateau

Understanding the impact of climate change on vegetation and its evolution trend requires long-term accurate data on regional vegetation types and their geographical distribution.Currently,land use and land cover types are mainly obtained based on remote sensing information.Little research has been conducted on remote sensing interpretation of vegetation types and their geographical distributions in terms of the comprehensive utilization of remote sensing,climate,and terrain.A new region vegetation mapping method based on terrain-climate-remote sensing was developed in this study,supported by the Google Earth Engine(GEE)and the random forest algorithm,which is a new generation of earth science data and analysis application platform,together with optimal vegetation mapping features obtained from the average impure reduction method and out-of-bag error value,using different information from remote sensing,climate,and terrain.This vegetation of Qinghai-Xizang Plateau with 10 m spatial resolution in 2020 was mapped,in terms of this new vegetation mapping method,Sentinel-2A/B remotely sensed images,climate,and terrain.The accuracy verification of vegetation mapping on the Qinghai-Xizang Plateau showed an overall accuracy of 89.5%and a Kappa coefficient of 0.87.The results suggest that the regional vegetation mapping method based on terrain-climate-remote sensing proposed in this study can provide technical support for obtaining long-term accurate data on vegetation types and their geographical distributions on the Qinghai-Xizang Plateau and the globe.

A labor-free index-guided semantic segmentation approach for urban vegetation mapping from high-resolution true color imagery

Accurate and timely information on urban vegetation(UV)can be used as an important indicator to estimate the health of cities.Due to the low cost of RGB cameras,true color imagery(TCI)has been widely used for high spatial resolution UV mapping.However,the current index-based and classifier-based UV mapping approaches face problems of the poor ability to accurately distinguish UV and the high reliance on massive annotated samples,respectively.To address this issue,an index-guided semantic segmentation(IGSS)framework is proposed in this paper.Firstly,a novel cross-scale vegetation index(CSVI)is calculated by the combination of TCI and Sentinel-2 images,and the index value can be used to provide an initial UV map.Secondly,reliable UV and non-UV samples are automatically generated for training the semantic segmentation model,and then the refined UV map can be produced.The experimental results show that the proposed CSVI outperformed the existingfive RGB vegetation indices in highlighting UV cover and suppressing complex backgrounds,and the proposed IGSS workflow achieved satisfactory results with an OA of 87.72%∼88.16%and an F1 score of 87.73%∼88.37%,which is comparable with the fully-supervised method.

Remote sensing imagery in vegetation mapping: a review

Aims Mapping vegetation through remotely sensed images involves various considerations,processes and techniques.Increasing availability of remotely sensed images due to the rapid advancement of remote sensing technology expands the horizon of our choices of imagery sources.Various sources of imagery are known for their differences in spectral,spatial,radioactive and temporal characteristics and thus are suitable for different purposes of vegetation mapping.Generally,it needs to develop a vegetation classification at first for classifying and mapping vegetation cover from remote sensed images either at a community level or species level.Then,correlations of the vegetation types(communities or species)within this classification system with discernible spectral characteristics of remote sensed imagery have to be identified.These spectral classes of the imagery are finally translated into the vegetation types in the image interpretation process,which is also called image processing.This paper presents an overview of how to use remote sensing imagery to classify and map vegetation cover.Methods Specifically,this paper focuses on the comparisons of popular remote sensing sensors,commonly adopted image processing methods and prevailing classification accuracy assessments.Important findings The basic concepts,available imagery sources and classification techniques of remote sensing imagery related to vegetation mapping were introduced,analyzed and compared.The advantages and limitations of using remote sensing imagery for vegetation cover mapping were provided to iterate the importance of thorough understanding of the related concepts and careful design of the technical procedures,which can be utilized to study vegetation cover from remote sensed images.

Vegetation Mapping of the Mond Protected Area of Bushehr Province(South-west Iran)

Arid regions of the world occupy up to 35% of the earth＇s surface, the basis of various definitions of climatic conditions, vegetation types or potential for food production. Due to their high ecological value, monitoring of arid regions is necessary and modern vegetation studies can help in the conservation and management of these areas. The use of remote sensing for mapping of desert vegetation is difficult due to mixing of the spectral reflectance of bright desert soils with the weak spectral response of sparse vegetation. We studied the vegetation types in the semiarid to arid region of Mond Protected Area, south-west Iran, based on unsupervised classification of the Spot XS bands and then produced updated maps. Sixteen map units covering 12 vegetation types were recognized in the area based on both field works and satellite mapping. Halocnemum strobilaceum and Suaeda fruticosa vegetation types were the dominant types and Ephedra foliata, Salicornia europaea-Suaeda heterophylla vegetation types were the smallest. Vegetation coverage decreased sharply with the increase in salinity towards the coastal areas of the Persian Gulf. The highest vegetation coverage belonged to the riparian vegetation along the Mond River, which represents the northern boundary of the protected area. The location of vegetation types was studied on the separate soil and habitat diversity maps of the study area, which helped in final refinements of the vegetation map produced.

Mapping the vegetation distribution of the permafrost zone on the Qinghai-Tibet Plateau

In this paper, an updated vegetation map of the permafrost zone in the Qinghai-Tibet Plateau （QTP） was delineated. The vegetation map model was extracted from vegetation sampling with remote sensing （RS） datasets by decision tree method. The spatial resolution of the map is 1 km×1 kin, and in it the alpine swamp meadow is firstly distinguished in the high-altitude areas. The results showed that the total vegetated area in the permafrost zone of the QTP is 1,201,751 km2. In the vegetated region, 50,260 km2 is the areas of alpine swamp meadow, 583,909 km2 for alpine meadow, 332,754 km2 for alpine steppe, and 234,828 km2 for alpine desert. This updated vegetation map in permafrost zone of QTP could provide more details about the distribution of alpine vegetation types for studying the vegetation mechanisms in the land surface processes of highaltitude areas.

Application of aerial remote sensing in the study on vegetation in Guangzhou,China

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Future of Abies pindrow in Swat district,northern Pakistan

Swat district is a biodiversity hub of Pakistan. The plant species, especially trees, in the Swat District are exposed to extinction threat from global climate change. Maximum entropy （MaxEnt） modelling of species distribution, using HADCM3 A2a global climate change scenario, pre-dicted a considerable change in the future distribution ofAbies pindrow （Royle ex D.Don） Royle. AUC （area under the curve）values of 0.972 and 0.983 were significant for the present and future distribution models of the species, respectively. It is clear that bioclimatic variables such as the mean temperature of the warmest quarter （bio_10） and the annual temperature range （bio_7） contribute significantly to the model and thus affect the predicted distribution and density of the species. The future model predicts that by the year 2080 population density will have decreased significantly. The highest density of the species is recorded in the eastern and western borders of the Valley in the areas of Sulatanr and Mankial. The changes in density and distribution of the species can have considerable impact, not only on the tree species itself, but on the associated subflora as well.

Characterizing Shorea robusta communities in the part of Indian Terai landscape

Shorea robusta Gaertn. f.（Sal）is one of the important tim-ber-yielding plants in India, which dominates the vegetation of Terai landscape of Uttar Pradesh state in India forming various communities based on its associations. The present study deals with delineation, map-ping and characterization of various communities of Sal （Shorea robusta） forests in Terai landscape of Uttar Pradesh, India ranging across over 16 districts. Field survey and visual interpretation based forest vegetation type classification and mapping was carried out as part of the project entitled ‘Biodiversity characterization at landscape level using remote sensing and GIS’. Indian Remote Sensing-P6 （Resourcesat-1） Linear Imaging Self Scanner-III satellite data was used during the study. The total area covered by different Sal forests was found to be approximately 2256.77 km2. Sal communities were identified and characterized based on their spectral properties, physiognomy and phytosociological charac-teristics. Following nine Sal communities were identified, delineated and mapped with reasonable accuracyviz.,Chandar,Damar, dry plains, moist plains, western alluvium, western alluvium plains, mixed moist deciduous, mixed dry deciduous andSiwalik. It is evident from the area estimates that mixed moist deciduous Sal is the most dominant commu-nity in the region covering around （1613.90 km2）, other major communi-ties were found as western alluvium plains Sal （362.44 km2）, mixed dry deciduous Sal （362.44 km2） and dry plains Sal （107.71 km2）. The Terai landscape of Uttar Pradesh faces tremendous anthropogenic pressure leading to deterioration of the forests. Community level information could be used monitoring the status as well as for micro level conserva-tion and planning of the Sal forests in Terai Landscape of Uttar Pradesh.

An updated Vegetation Map of China(1:1000000)

Vegetation maps are important sources of information for biodiversity conservation,ecological studies,vegetation management and restoration,and national strategic decision making.The current Vegetation Map of China(1:1000000)was generated by a team of more than 250 scientists in an effort that lasted over 20 years starting in the 1980s.However,the vegetation distribution of China has experienced drastic changes during the rapid development of China in the last three decades,and it urgently needs to be updated to better represent the distribution of current vegetation types.Here,we describe the process of updating the Vegetation Map of China(1:1000000)generated in the 1980s using a‘‘crowdsourcing-change detection-classification-expert knowledge"vegetation mapping strategy.A total of 203,024 field samples were collected,and 50 taxonomists were involved in the updating process.The resulting updated map has 12 vegetation type groups,55 vegetation types/subtypes,and 866 vegetation formation/sub-formation types.The overall accuracy and kappa coefficient of the updated map are 64.8%and 0.52 at the vegetation type group level,61%and 0.55 at the vegetation type/subtype level and 40%and 0.38 at the vegetation formation/sub-formation level.When compared to the original map,the updated map showed that 3.3 million km^2 of vegetated areas of China have changed their vegetation type group during the past three decades due to anthropogenic activities and climatic change.We expect this updated map to benefit the understanding and management of China’s terrestrial ecosystems.

Compilation of 1:50,000 vegetation type map with remote sensing images based on mountain altitudinal belts of Taibai Mountain in the North-South transitional zone of China

The compilation of 1:250,000 vegetation type map in the North-South transitional zone and 1:50,000 vegetation type maps in typical mountainous areas is one of the main tasks of Integrated Scientific Investigation of the North-South Transitional Zone of China.In the past,vegetation type maps were compiled by a large number of ground field surveys.Although the field survey method is accurate,it is not only time-consuming,but also only covers a small area due to the limitations of physical environment conditions.Remote sensing data can make up for the limitation of field survey because of its full coverage.However,there are still some difficulties and bottlenecks in the extraction of remote sensing information of vegetation types,especially in the automatic extraction.As an example of the compilation of 1:50,000 vegetation type map,this paper explores and studies the remote sensing extraction and mapping methods of vegetation type with medium and large scales based on mountain altitudinal belts of Taibai Mountain,using multi-temporal high resolution remote sensing data,ground survey data,previous vegetation type map and forest survey data.The results show that:1)mountain altitudinal belts can effectively support remote sensing classification and mapping of 1:50,000 vegetation type map in mountain areas.Terrain constraint factors with mountain altitudinal belt information can be generated by mountain altitudinal belts and 1:10,000 Digital Surface Model(DSM)data of Taibai Mountain.Combining the terrain constraint factors with multi-temporal and high-resolution remote sensing data,ground survey data and previous small-scale vegetation type map data,the vegetation types at all levels can be extracted effectively.2)The basic remote sensing interpretation and mapping process for typical mountains is interpretation of vegetation type-groups→interpretation of vegetation formation groups,formations and subformations→interpretation and classification of vegetation types&subtypes,which is a combination method of top-down method and bottom-up method,not the top-down or the bottom-up classification according to the level of mapping units.The results of this study provide a demonstration and scientific basis for the compilation of large and medium scale vegetation type maps.

Deep convolutional neural network for damaged vegetation segmentation from RGB images based on virtual NIR-channel estimation

Performing accurate and automated semantic segmentation of vegetation is a first algorithmic step towards more complex models that can extract accurate biological information on crop health,weed presence and phenological state,among others.Traditionally,models based on normalized difference vegetation index(NDVI),near infrared channel(NIR)or RGB have been a good indicator of vegetation presence.However,these methods are not suitable for accurately segmenting vegetation showing damage,which precludes their use for downstream phenotyping algorithms.In this paper,we propose a comprehensive method for robust vegetation segmentation in RGB images that can cope with damaged vegetation.The method consists of a first regression convolutional neural network to estimate a virtual NIR channel from an RGB image.Second,we compute two newly proposed vegetation indices from this estimated virtual NIR:the infrared-dark channel subtraction(IDCS)and infrared-dark channel ratio(IDCR)indices.Finally,both the RGB image and the estimated indices are fed into a semantic segmentation deep convolutional neural network to train a model to segment vegetation regardless of damage or condition.The model was tested on 84 plots containing thirteen vegetation species showing different degrees of damage and acquired over 28 days.The results show that the best segmentation is obtained when the input image is augmented with the proposed virtual NIR channel(F1=0:94)and with the proposed IDCR and IDCS vegetation indices(F1=0:95)derived from the estimated NIR channel,while the use of only the image or RGB indices lead to inferior performance(RGB(F1=0:90)NIR(F1=0:82)or NDVI(F1=0:89)channel).The proposed method provides an end-to-end land cover map segmentation method directly from simple RGB images and has been successfully validated in real field conditions.

Characterization and Fine Mapping of a Novel Vegetative Senescence Lethal Mutant Locus in Rice

Mutants showing spontaneous cell death in the absence of pathogen attack are called lesion mimic mutants （lmm） （Lorrain et al., 2003）. These mutants usually exhibit typical hypersensitive responses （HRs） within or around the lesion spots, which are frequently observed in plants challenged with avirulent pathogens （Lorrain et al., 2003）. A number of these mutants have been characterized in rice （Zeng et al., 2004）, Arabidopsis （Guo et al., 2013）, maize （Wang et al., 2013） and barley （Persson et al., 2008）. Most lmm show enhanced resistance to various pathogens （Huang et al., 2011）, because HR is usually accompanied with enhanced defense responses, such as reactive oxygen species （ROS） activation （Qiao et al., 2010） and increased expression of pathogenesis-related genes （Lorrain et al., 2003）. Additionally, most lmm exhibit defects in growth and development due to the disordered physiolog- ical and molecular processes caused by the lesion spots. Thus, lmm are powerful tools for the study of the molecular mech- anisms of cell death, plant development and disease resistance （Lorrain et al., 2003; Babu et al., 2011）.

Toward automatic estimation of urban green volume using airborne LiDAR data and high resolution Remote Sensing images

Urban green volume is an important indicator for analyzing urban vegetation structure, ecological evaluation, and green-economic estimation. This paper proposes an object-based method for automated estimation of urban green volume combining three-dimensional （3D） information from airborne Light Detection and Ranging （LiDAR） data and vegetation information from high resolution remotely sensed images through a case study of the Lujiazui region, Shanghai, China. High resolution airborne near-infrared photographs are used for identifying the urban vegetation distribution. Airborne LiDAR data offer the possibility to extract individual trees and to measure the attributes of trees, such as tree height and crown diameter. In this study, individual trees and grassland are identified as the independent objects of urban vegetation, and the urban green volume is computed as the sum of two broad portions： individual trees volume and grassland volume. The method consists of following steps： generating and filtering the normalized digital surface model （nDSM）, extracting the nDSM of urban vegetation based on the Normalized Difference Vegetation Index （NDVI）, locating the local maxima points, segment- ing the vegetation objects of individual tree crowns and grassland, and calculating the urban green volume of each vegetation object. The results show the quantity and distribution characteristics of urban green volume in the Lujiazui region, and provide valuable parameters for urban green planning and management. It is also concluded from this paper that the integrated application of LiDAR data and image data presents an effective way to estimate urban green volume.