Vegetation Delineation Research Articles

Identifying Factors That Influence Accuracy of Riparian Vegetation Classification and River Channel Delineation Mapped Using 1 m Data

Riparian vegetation delineation includes both the process of delineating the riparian zone and classifying vegetation within that zone. We developed a holistic framework to assess riparian vegetation delineation that includes evaluating channel boundary delineation accuracy using a combination of pixel- and object-based metrics. We also identified how stream order, riparian zone width, riparian land use, and image shadow influenced the accuracy of delineation and classification. We tested the framework by evaluating vegetation vs. non-vegetation riparian zone maps produced by applying random forest classification to aerial photographs with a 1 m pixel size. We assessed accuracy of the riparian vegetation classification and channel boundary delineation for two rivers in the northeastern United States. Overall accuracy for the channel boundary delineation was generally above 80% for both sites, while object-based accuracy revealed that 50% of delineated channel was less than 5 m away from the reference channel. Stream order affected channel boundary delineation accuracy while land use and image shadows influenced riparian vegetation classification accuracy; riparian zone width had little impact on observed accuracy. The holistic approach to quantification of accuracy that considers both channel boundary delineation and vegetation classification developed in this study provides an important tool to inform riparian management.

Integration of normalised different vegetation index and Soil-Adjusted Vegetation Index for mangrove vegetation delineation

Discriminating between vegetation and non-vegetation areas is of crucial importance in the study of mangroves. This will help mangrove monitoring and management since losses and degradation of the mangroves are reported to be substantial in recent years. This study investigates the integration of Normalised Difference Vegetation Index (NDVI) and Soil-Adjusted Vegetation Index (SAVI) as a tool to discriminate vegetation covers in the mangrove forest. High resolution remotely sensed images from Satellite Pour l’Observation de la Terre (SPOT-6 and SPOT-7) satellite, with 1.5 m spatial resolution over the Matang Mangrove Forest Reserve (MMFR) was acquired. A complete ground-truthing was conducted at the preliminary stages of land cover classification. A Moran's I analysis shows a random pattern of ground-truthing; (Kuala Sepetang; p-value = 0.219; Kuala Trong, p-value = 0.163; Sungai Kerang, p-value = 0.159). Since SAVI requires a suitable L-factor to be used to distinguish the vegetation areas, four different L-factors viz. 0.1, 0.25, 0.5 and 0.75 were tested, and the multiple linear regressions, using the stepwise regression method of backward elimination, found that the L-factor 0.75 was significant to be used for MMFR. A correlation analysis conducted between the results of NDVI, SAVI and supervised classification shows a high significant relationship, especially between NDVI and SAVI (0.991) at 99.99% level. This shows that NDVI and SAVI are useful analyses that can be employed to improve the accuracy of classification in the mangroves.

Agricultural growth and land use land cover change in peri-urban India.

Varanasi district is comprised of eight administrative blocks. Owing to economic development, it has shown an increase in urban activities. Analysis of remotely sensed data for a period of two decades reveals that the built-up area increased by about 345% while vegetation decreased by 86% during 1993-2013. Contrary to other observations, land use changes, due to urban growth, increased not only the built area but also the agriculture class. Agricultural area increased by 39% in the two decades. Population density increased from 1217 to 1806 person/km2 and household density grew from 152 to 273 households/km2 during 1991-2011. Land absorption coefficient (LAC) and land consumption ratio (LCR) were calculated as demographic indices of land use land cover change (LULC). Vegetation delineation shows that sparse vegetation increased from 40.2 to 90.1 km2 while dense vegetation decreased from 28.4 to 1.7 km2 in 1993-2013. There was a distinct shift from agriculture, as a primary economic activity, towards non-agricultural pursuits. In order to frame better strategies for sustainable development and food security, this phenomenon of increasing urbanization around cities needs to be studied. This micro scale study can be helpful in formulating policy for urban areas in developing countries like India which heavily depend on agriculture to sustain their population and economy.

Conversion of the mining complex of the Mangistau region

The most important conclusions of the research in this area should include the onethat the environmental assessment on the basis of maximum permissible concentration(MPC) of certain harmful elements in different range and areas of environment is notacceptable for a number of elements, especially heavy metals. In assessing the impactof harmful substances on the ecosystem determining factor cannot be the initial concentration in any environment, while the transfer, storage, and transformation into critical links of ecosystem, whereby already observed concentration and other secondaryproducts with other toxic properties. Thus, the environmental standards of permissibleimpact on the living component of the biosphere should be determined by the natureand patterns of distribution, accumulation, destruction and transformation of pollutantsin ecosystems, their transition from one medium to another in the local, regional andglobal scales. These rules can not be the same for all types of ecosystems. The aim ofthis study is to assess the role of gas oil-producing complex of Mangistau region in theformation of anthropogenic disturbance of the relief on the basis of a published cartographic material.The ecological condition of the territory is usually built on the resultsof monitoring of all components of the natural environment, reflecting the impact ofthe plurality of sources of pollution, the need for independent evaluation of the contribution of individual sources. On the basis of the method developed by the authorsof the inverse problem solution integrated environmental assessment with the help ofGIS technologies and models of the objective function, it provides an example of itsdecision to assess the contribution of oil and gas field in Mangistau anthropogenicallydisturbed vegetation and soil as a possible objective of the principle of «polluter pays».The used objective function is understood in this paper is not a classic mathematicalsense for individual environmental assessments – a criterion for comparing alternatives using different optimization methods, as well as a function that implements thepurpose of the evaluation – evaluation of the contribution of oil and gas productionin the anthropogenic transformation of vegetation and soil cover. For raw data on thevector scorecard anthropogenic influence on soils and vegetation delineation depositlocation of land, each land area defined in Arc-GIS. As a result, the calculation of theobjective functions for the entire territory of the region or area of the zones of oil andgas complex obtained total score area, and then dividing by the area of oil and gascomplex with an average rating of anthropogenic disturbance of soil and vegetation inMangystau region in general and the oil and gas complex (disturbance of vegetation–16,73%, dis turbance of soil – 19,31%). Thus, by comparing the results of calculationsof anthropogenic disturbance of soil and vegetation in Mangystau region, evaluatedthe ecological state.Key words: impact on the environment, oil and gas production, GIS, cartographicmaterials, target function.

A novel phylogenetic regionalization of phytogeographical zones of southern Africa reveals their hidden evolutionary affinities

AbstractAimAlthough existing bioregional classification schemes often consider the compositional affinities within regional biotas, they do not typically incorporate phylogenetic information explicitly. Because phylogeny captures information on the evolutionary history of taxa, it provides a powerful tool for delineating biogeographical boundaries and for establishing relationships among them. Here, we present the first vegetation delineation of the woody flora of southern Africa based upon evolutionary relationships.LocationSouthern Africa.MethodsWe used a published time‐calibrated phylogenetic tree for 1400 woody plant species along with their geographical distributions and a metric of phylogenetic beta diversity to generate a phylogenetic delineation of the woody vegetation of southern Africa. We then explored environmental correlates of phylogenetic turnover between them, and the evolutionary distinctiveness of the taxa within them.ResultsWe identified 15 phylogenetically distinct biogeographical units, here referred to as phyloregions. The largest phyloregion broadly overlaps with Savanna vegetation, while the phyloregion overlapping with the south‐western portion of the Fynbos biome is the most evolutionarily distinct. Potential evapotranspiration and mean annual temperature differ significantly among phyloregions and correlate with patterns of phylogenetic beta diversity between them. Our phylogeny‐based delimitation of southern Africa's woody vegetation broadly matches currently recognized phytogeographical classifications, but also highlights parts of the Namib Karoo and Greater Limpopo Transfrontier Park as distinct, but previously under‐recognized biogeographical units.Main conclusionsOur analysis provides new insights into the structure and phylogenetic relationships among the woody flora of southern Africa. We show that evolutionary affinities differentiate phyloregions closely resembling existing vegetation classifications, yet also identify ‘cryptic’ phyloregions that are as evolutionarily distinct as some of the recognized African vegetation types.

Applicability of VI in arid vegetation delineation using shadow-affected SPOT imagery.

GDVI(3), GDVI(2), NDVI, MSAVI and SAVI were evaluated for their dynamic ranges, the class accuracy of the Vegetation Index (VI) classifications, the effects of shadow delineation on the other land use classes and their applicability in vegetation delineation in Al-Qara Mountains, Oman. Supervised classifications of a SPOT scene by Support Vector Machines (SVM) algorithm were employed. GDVI(3) showed the widest dynamic range in all land use types, while GDVI(2) also exhibited evidently wider dynamic ranges for arid to semi-arid Al-Qara than NDVI, MSAVI and SAVI. GDVI(3) reported the highest accuracies in delineating natural vegetation (dense - 74.80%, medium-dense- 43.19%), except for low-dense vegetation (40.51%). It also performs the best in delineating bare soil and dry grass with over 80% and 60% accuracies. The attenuated reflectance created by the shadows results in VI signals in the range of dry grass to bare soil, enabling us to neglect the shadow effect on natural vegetation delineation due to below 9.50% omissions from the shadows class. GDVI(3) also limits shadow delineation better than the other indices, which will enable us to analyze spectral information recovery by the VI with the help of ground truth information under the shadows. For applications such as land degradation assessments, GDVI(3) has better prospects over the other indices explored. Saturation at high-vigor vegetation is an issue in GDVI(3), GDVI(2) and NDVI. Our study also points to a dependency of a VI's capability to weaken shadows on the number of training data pixels to be utilized in a supervised classification.

HYPERSPECTRAL HYPERION IMAGERY ANALYSIS AND ITS APPLICATION USING SPECTRAL ANALYSIS

Abstract. Rapid advancement in remote sensing open new avenues to explore the hyperspectral Hyperion imagery pre-processing techniques, analysis and application for land use mapping. The hyperspectral data consists of 242 bands out of which 196 calibrated/useful bands are available for hyperspectral applications. Atmospheric correction applied to the hyperspectral calibrated bands make the data more useful for its further processing/ application. Principal component (PC) analysis applied to the hyperspectral calibrated bands reduced the dimensionality of the data and it is found that 99% of the data is held in first 10 PCs. Feature extraction is one of the important application by using vegetation delineation and normalized difference vegetation index. The machine learning classifiers uses the technique to identify the pixels having significant difference in the spectral signature which is very useful for classification of an image. Supervised machine learning classifier technique has been used for classification of hyperspectral image which resulted in overall efficiency of 86.6703 and Kappa co-efficient of 0.7998.

IDENTIFICATION OF INDIVIDUAL TREES AND GROUPS OF TREES IN THE LANDSCAPE USING AIRBORNE LASER SCANNING DATA

In the work, a fully automatic approach for vegetation delineation using ALS data is presented. Nowadays, in Slovakia, aerial images and satellite scenes are used for this purpose. For vegetation identification, the measurement of local transparency and roughness directly in 3D point cloud was used. The aim was the identification of groups of trees with area bigger than 0.1 ha and individual trees. On the experimental area, 33 polygons representing groups of trees and 120 individual trees were identified. For groups of trees the accuracy of identification was 100%. For comparison, an area with reference polygons, which were manually vectorised by the operator on the orthophotos with spatial resolution 30 cm, was used. The average difference in the area was –0.26%, with standard deviation ±8.17%. The distance of borders of reference polygons and polygons derived from ALS data was also compared, average distance for border parts that fall inside the reference polygons was 2.24 m with standard deviation of ±2.8 m. The average distance for borders parts that fall outside of the reference polygons was 1.84 m with standard deviation ±2.04 m. The accuracy of individual trees identification was 98%.

Object-based urban vegetation mapping with high-resolution aerial photography as a single data source

In this article, we demonstrate an object-oriented method for detailed urban vegetation delineation by using 1 m resolution, four-band digital aerial photography as the only input data. A hierarchical classification scheme was developed to discriminate vegetation types at both coarse and fine scales. The processes of vegetation extraction include the examination of spectral and spatial relationships, object geometry, and the hierarchical relationship of image objects. The advantages of four different segmentation methods were combined to identify feature similarities, both among image objects and with their neighbours. Image growth took place if those neighbours satisfied a series of criteria given a set of features of class-defined objects. Object-based classification results demonstrated higher accuracy than those using pixel-based classification methods. The object-oriented method achieved overall classification accuracies of 87.5%, 90.5%, and 90.5% at three different levels of class hierarchy, and very high producer's accuracies were demonstrated in the classes of tree, crop, and different types of grass. The object-oriented classification method described here proved effective for separating vegetation types defined by life form, area, or shape without using additional remote-sensing data sources with different resolutions or any ancillary data such as digital elevation models.

Delineation of Phenoregions in Geographically Diverse Regions Using k-means++ Clustering: A Case Study in the Upper Colorado River Basin

Phenological monitoring and modeling over a geographically diverse area has long been a problem due to the spatially variable nature of phenological forcing. Phenoregions, which are phenologically and climatically self-similar clusters, have many potential benefits as a geographic unit for monitoring and modeling of vegetation dynamics. This research develops an improved method to delineate regions of similar phenological forcing in geographically diverse regions, using the Upper Colorado River Basin (UCRB) as a case study. Principal component analysis plus k-means++ clustering are adopted to delineate phenoregions in the UCRB, using variables related to elevation, temperature, precipitation, soil, and vegetation history. Raster data at 1 km spatial resolution are used to extract these variables. A series of hierarchical, non-nestable phenoregion maps is generated. The optimal phenoregion map is selected based on spatial homogeneity and spatial concordance with other phenoregion maps generated using different numbers of clusters. This series of phenoregion maps can be considered as a framework for phenological modeling and monitoring, as well as for useful potential vegetation delineation, natural resource conservation, and policymaking.

An automated image analysis approach for classification and mapping of woody vegetation from digital aerial photograph

This paper presents a recent study on woody vegetation delineation and mapping using digital aerial photograph and geographic information system (GIS) in Hunter Region, Australia. The aim of the study was to develop automated and repeatable digital image processing methods for woody vegetation classification and mapping using aerial photograph or high-resolution satellite images and GIS. Parallelepiped classification or density slice method was used to classify woody and non-woody vegetation, and ancillary GIS data were used as quality controls in the classification processing. Specific scripts were developed for automated image processing in a GIS environment. The classification accuracy was assessed against traditional aerial photograph interpretation using adequate random points. The automated process reached an overall classification accuracy of 94% and 97% after post-classification correction. The automated approach can be applied to any other type of high-resolution imagery such as SPOT 5, ALOS, IKONOS and QuickBird images.

Apports et perspectives de la technologie LiDAR aéroporté pour la pratique forestière | Contributions and perspectives of airborne LiDAR technology for forestry

LiDAR is an optical remote sensing system that determines the distance to an object or to a surface by measuring the time delay between the transmission of a pulse and the detection of the reflected signal. A wide variety of applications arise from this technology which facilitate a better knowledge of land use. In forestry, airborne LiDAR systems provide, in addition to an accurate model of the relief, a 3D representation of the forest structure at various scales. This contribution presents the foundations of LiDAR technology and illustrates several forestry applications: underneath vegetation topography, canopy height and texture, vegetation delineation and stand delimitation. This contribution also outlines the potential of airborne laser scanning for forestry.

Integrated use of remote sensing and geographic information systems in riparian vegetation delineation and mapping

This paper presents a pilot study on riparian vegetation delineation and mapping using remote sensing and geographic information systems (GIS) in the Hunter Region, Australia. The aim of the study was to develop appropriate and repeatable assessment and mapping techniques to quantify the extent of riparian vegetation within the region. Ortho‐rectified digital aerial photographs, SPOT‐4 multispectral (XS) and Landsat‐7 ETM+ images were tested to delineate the riparian vegetation and to develop a quantifiable and repeatable method of mapping the extent of that vegetation. Image processing techniques such as parallelepiped classification, tasselled cap transformation, and vegetation index clustering were used in an attempt to delineate riparian vegetation from remotely sensed images. Specific GIS analysis techniques were used for riparian zone buffering and segmentation, vegetation cover estimation, and mapping. Specific GIS scripts were developed for those processes so that they were automatic, fast, and repeatable. Various vegetation indices (VI) were evaluated and compared for their ability to discriminate riparian vegetation and its extent. The riparian vegetation was assessed and mapped at designed segmental interval (e.g. 1 km) with polygon and lineal representations. The classification accuracy was assessed against field observation and air photo interpretation (API). The overall accuracy of the photo‐based classification was about 81%, SPOT‐4 63%, and Landsat‐7 ETM+ 53%. Statistic analysis shows that there is little agreement between photo‐based classification and that from satellite imagery.