INTEGRATED VEGETATION CLASSIFICATION AND MAPPINGUSING REMOTE SENSING AND GIS TECHNIQUES

NOAA-AVHRR data have been more and more used by scientists because of its short temporal resolution,large scope, inexpensive cost and broad wave bands. On macro and middle scale of vegetation remote sensing, NOAAAVHRR possesses an advantage when compared with other satellites. However, because NOAA-AVHRR also problem of low resolution, data distortion and geometrical distortion, in the area of application of NOAA-AVHRR in largescale vegetation - mapping, the accuracy of vegetation classification should be improved. This paper discuss the feasibilityof integrating the geographic data in GIS(Geographical Information System) and remotely sensed data in GIS. Under theenvironment of GIS, temperature, precipitation and elevation, which serve as main factors affecting vegetation growth,were processed by a mathematical model and qualified into geographic image under a certain grid system. The geographicimage were overlaid to the NOAA-AVHRR data which had been compressed and processed. In order to evaluate the usefulness of geographic data for vegetation classification, the area under study was digitally classified by two groups of interpreter: the proposed methodology using maximum likelihood classification assisted by the geographic database and a conventional maximum likelihood classification only. Both result were compared using Kappa statistics. The indices to both theproposed and the conventional digital classification methodology were 0. 668(yew good) and 0. 563(good), respetively.The geographic database rendered an improvement over the conventional digital classification. Furthermore, in this study,some problems related to multi-sources data integration are also discussed.

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.

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.

Scale Issues of Wetland Classification and Mapping Using Remote Sensing Images: A Case of Honghe National Nature Reserve in Sanjiang Plain, Northeast China

Wetland research has become a hot spot linking multiple disciplines presently. Wetland classification and mapping is the basis for wetland research. It is difficult to generate wetland data sets using traditional methods because of the low accessibility of wetlands, hence remote sensing data have become one of the primary data sources in wetland research. This paper presents a case study conducted at the core area of Honghe National Nature Reserve in the Sanjiang Plain, Northeast China. In this study, three images generated by airship, from Thematic Mapper and from SPOT 5 were selected to produce wetland maps at three different wetland landscape levels. After assessing classification accuracies of the three maps, we compared the different wetland mapping results of 11 plant communities to the airship image, 6 plant ecotypes to the TM image and 9 landscape classifications to the SPOT 5 image. We discussed the different characteristics of the hierarchical ecosystem classifications based on the spatial scales of the different images. The results indicate that spatial scales of remote sensing data have an important link to the hierarchies of wetland plant ecosystems displayed on the wetland landscape maps. The richness of wetland landscape information derived from an image closely relates to its spatial resolution. This study can enrich the ecological classification methods and mapping techniques dealing with the spatial scales of different remote sensing images. With a better understanding of classification accuracies in mapping wetlands by using different scales of remote sensing data, we can make an appropriate approach for dealing with the scale issue of remote sensing images.

Application of Earth Remote Sensing and GIS in Mapping Land Cover Patterns in Kinangop Division, Kenya

Land cover is a fundamental variable that links many facets of the natural environment and a key driver of global environmental change.Alterations in its status can have significant ramifications at local,regional and global levels.Hence,it is imperative to map land cover at a range of spatial and temporal scales with a view to understanding the inherent patterns for effective characterization,prediction and management of the potential environmental impacts.This paper presents the results of an effort to map land cover patterns in Kinangop division,Kenya,using geospatial tools.This is a geographic locality that has experienced rapid land use transformations since Kenya's independence culminating in uncontrolled land cover changes and loss of biodiversity.The changes in land use/cover constrain the natural resource base and presuppose availability of quantitative and spatially explicit land cover data for understanding the inherent patterns and facilitating specific and multi-purpose land use planning and management.As such,the study had two objectives viz.(i) mapping the spatial patterns of land cover in Kinangop using remote sensing and GIS and;(ii) evaluating the quality of the resultant land cover map.ASTER satellite imagery acquired in January 23,2007 was procured and field data gathered between September l0 and October 16,2007.The latter were used for training the maximum likelihood classifier and validating the resultant land cover map.The land cover classification yielded 5 classes,overall accuracy of 83.5%and kappa statistic of 0.79,which conforms to the acceptable standards of land cover mapping. This qualifies its application in environmental decision-making and manifests the utility of geospatial techniques in mapping land resources.

Integration of Remote Sensing Data and GIS Tools for Accurate Mapping of Flooded Area of Kurigram, Bangladesh

Flood is the most devastating disaster in the present world which causes damage to environmental, social, economical and human lives at about 43% of all natural disasters. There are many flood hazard occurs in Bangladesh during the 19th century and 20th century in the different regions. These flood hazards have more catastrophic damages of huge area within human lives and other necessary properties of Bangladesh. The first step of flood management is to evaluate the area which is under threat of flood disaster. In this study here showed the importance of Remote Sensing (RS) data and Geographic Information System (GIS) tools to manage the flood related problems. Remote Sensing (RS) data and Geographic Information System (GIS) provide a lot of information to flood disaster management. ArcView GIS software tools are used for digitizing the base map and to create a flood risk zone of Kurigram, Bangladesh where images of remote sensing can be helped to determine the flood inundation areas. The integrated application of RS and GIS techniques for monitoring and flood mapping provides information for the decision makers. The study also grows attentions the need of cost-efficient methodology by creating a flood vulnerable map of Bangladesh.

An Appraisal of Land Use/Land Cover Change Scenario of Tummalapalle, Cuddapah Region, India—A Remote Sensing and GIS Perspective

The study was aimed at appraising the changing land use/land cover scenario of Tummalapalle region in Cuddapah district of Andhra Pradesh using Remote sensing data and GIS technology. The region is considered as it has rich uranium reserves and is experiencing a remarkable expansion in recent times. The land use/land cover change analysis was carried out using IRS P6 LISS-III and LANDSAT-8 OLI multitemporal data pertaining to the years 2006 and 2016. The image classification resulted in five major land use/land cover classes namely built-up, agricultural, forest, wasteland and water bodies. The study noticed that the areas under built-up and agricultural classes are found increased from 0.94 km2 (0.84%) to 2.75 km2 (2.44%) and 61.68 km2 (54.84%) to 63.91 km2 (56.82%), respectively during 2006-2016. Area under forest, wasteland and water bodies are found decreased considerably from 3.09 km2 (2.75%) to 0.86 km2 (0.76%), 43.71 km2 (38.56%) to 42.60 km2 (37.88%) and 3.05 km2 (2.71%) to 2.35 km2 (2.09%), respectively. The study recommends development of industrial based economy by optimally utilizing the existing land resource (scrub and wasteland classes) and simultaneously extending the agricultural practices to other possible areas to make them more productive.

OBH-RSI:Object-Based Hierarchical Classification Using Remote Sensing Indices for Coastal Wetland

With the deterioration of the environment,it is imperative to protect coastal wetlands.Using multi-source remote sensing data and object-based hierarchical classification to classify coastal wetlands is an effective method.The object-based hierarchical classification using remote sensing indices(OBH-RSI)for coastal wetland is proposed to achieve fine classification of coastal wetland.First,the original categories are divided into four groups according to the category characteristics.Second,the training and test maps of each group are extracted according to the remote sensing indices.Third,four groups are passed through the classifier in order.Finally,the results of the four groups are combined to get the final classification result map.The experimental results demonstrate that the overall accuracy,average accuracy and kappa coefficient of the proposed strategy are over 94%using the Yellow River Delta dataset.

Geological Context Mapping of Batouri Gold District (East Cameroon) from Remote Sensing Imagering, GIS Processing and Field Works

The Batouri area is located in the Adamawa-Yade domain in East Cameroon region, and has a high geological potential as a host for gold deposits. It is covered by thick forest where outcrops are sometime scarce. The purpose of this study is to generate and combine different geological information which makes up the specificity of the Batouri gold District, in order to contribute to the better knowledge of its geological setting. From satellite imageries, GIS tools and field data;lithological units, lineament and density maps have been dressed at regional-scale of 1/400.000. The mapping has enabled the discovery of spatial and topologic relationships between shear zones, lineaments, gold occurrences and often mineralized granitic intrusions. According to the field data, lithological and lineament maps, the lithology of the Batouri gold District is characterized by alkali granitoids (tonalite, granodiorite, syenomonzo-granite, alkaline granite) hosted by orthogneisses and migmatites as gold mineralization hosts;while the lineaments show a major shear zones trending NE-SW defined by presence of mylonites. The shear zones crosscut all lithologies, mostly granodiorite where majority of gold occurrences is observed;locally, nearest these shear zones, rocks are transformed to the mylonites and gold is concentrated along. From density map, it is shown that the high gold mineralization zone corresponds to highest lineaments density. All those data suggest that gold mineralization in the Batouri district is controlled by tectonic and lithology. It is conclusive that Batouri gold deposit is epigenetic gold set emplaced in orogenic setting, during the post-collisional stage of the Central African Fold Belt (CAFB) and the Congo Craton (CC).

Application of Remote Sensing and GIS Technology in Mapping Partition Saline Intrusion to Paddy Land:A Case Study at Phu Vang District,Thua Thien Hue Province

Adapting and restricting salinity intrusion in Vietnam is being concerned by more researchers as well as the local authorities.This study aimed to use remote sensing and geographic information system(GIS)technology for mapping paddy areas and salinity intrusion in spring crop 2015 at Phu Vang district,thereby helping precondition for assessing and monitoring changes in salinity intrusion to serve for salinization management in study area.Based on acquisition imagery,land use map and normalized difference vegetation index(NDVI)were extracted to interpolate the salinity of area by combining the laboratory analysis of collected soil samples from the field.The result showed that there were 1,067.107 ha of salinity land area accounting for 10.04%of the rice land in Phu Vang district,where the moderate salinity level was 180.67 ha and low salinity level was 866.431 ha.The salinity rice land was mainly distributed in Vinh Ha commune,Phu An commune and Phu Dien commune.The salinity in this area ranged from 0.4 mS/cm to 1.41 mS/cm and the moderate salinity was approximately 0.9 mS/cm.Besides,this research also showed that the salinity(electrical conductivity)and the development of vegetation(NDVI)were closely related with each other up to 61.4%.

Diachronic Study of the Vegetation Covers Spatiotemporal Change Using GIS and Remote Sensing in the Ferkla Oasis: Case Study, Bour El Khourbat, Tinjdad, Morocco

The Oasis of Ferkla is part of the Oases of Tafilalt in southern Morocco. These are classified by UNESCO as the Oases of Southern Morocco Biosphere Reserve. The Ferkla Oasis is increasingly experiencing a situation of increased regression and degradation, aggravated by the effects of climate change. These foreshadow a considerable acceleration of desertification and drought with the effect of the loss of production systems whose social, ecological and economic role remains major for the whole country. In order to contribute to a better understanding of the dynamics of the vegetation in this territory and the impact of climate change in the Oasis of Ferkla, we used spatial remote sensing to trace the evolution of changes in the vegetation cover in an agricultural extension called Bour El Khourbat. Calculation of the Normalized Difference Vegetation Index for seven multidate satellite images allowed us to follow the vegetation in this oasis zone from the year 1984 to 2019. Indeed, from these multi-temporal images, this study clearly shows the evolution of the vegetation with a remarkable agricultural extension towards the South-East of the zone. This extension is due not only to the installation of a diversion dam upstream but also to the development of the localized irrigation system “Drop by Drop” which is a technique that saves water resources in addition to the presence in the area. Bour El Khourbat specifies a geological structure, in the primary, relatively favorable to having water linked to cracks.

Application of Remote Sensing Detection and GIS in Analysis of Vegetation Pattem Dynamics in the Yellow River Delta

Regional vegetation pattem dynamics has a great im- pact on ecosystem and climate change.Remote sensing data and geographical information system (GIS) analysis were widely used in the detection of vegetation pattern dynamics.In this study,the Yellow River Delta was selected as the study area.By using 1986, 1993,1996,1999 and 2005 remote sensing data as basic informa- tion resource,with the support of GIS,a wetland vegetation spa- tial information dataset was built up.Through selecting the land- scape metrics such as class area (CA),class percent of landscape (PL),number of patch (NP),largest patch index (LPI) and mean patch size (MPS) etc.,the dynamics of vegetation pattern was analyzed.The result showed that the change of vegetation pattern is significant from 1986 to 2005.From 1986-1999,the area of the vegetation,the percent of vegetation,LPI and MPS decreased,the NP increased,the vegetation pattern tends to be fragmental.The decrease in vegetation area may well be explained by the fact of the nature environment evolution (Climate change and decrease in Yellow River runoff) and the increase in the population in the Yellow River Delta.However,from 1999-2005,the area of the vegetation,the percent of vegetation,LPI and MPS increased, while the NP decreased.This trend of restoration may be due to the implementation of water resources regulation for the Yellow River Delta since 1999.

A RBF classification method of remote sensing image based on genetic algorithm

The remote sensing image classification has stimulated considerable interest as an effective method for better retrieving information from the rapidly increasing large volume, complex and distributed satellite remote imaging data of large scale and cross-time, due to the increase of remote image quantities and image resolutions. In the paper, the genetic algorithms were employed to solve the weighting of the radial basis faction networks in order to improve the precision of remote sensing image classification. The remote sensing image classification was also introduced for the GIS spatial analysis and the spatial online analytical processing (OLAP), and the resulted effectiveness was demonstrated in the analysis of land utilization variation of Daqing city.

Studying the Impact of Pollution from Wadi Gaza on the Mediterranean Sea Using GIS and Remote Sensing Techniques

Wadi Gaza is considered as one of the most important coastal wetlands located on the Eastern Mediterranean Basin. It is witnessing rapid degradation due to anthropogenic activities including but not limited to discharge of municipal sewage, dumping of solid wastes, rampant use of pesticides and illegal poaching. They form a river of untreated wastewater, more than 5 km long, before its discharge into the Mediterranean Sea. This study aims to perform an analytical study of Wadi Gaza and study its effects on the pollution of the seawater opposite to it using GIS and remote sensing techniques. The flow accumulation, the watershed and the stream orders inside and outside the Gaza Strip are determined based on a DEM which involves a radar terrestrial scanning of Palestine carried out by NASA’s Endeavor Space Shuttle. The area of the watershed inside Gaza is estimated to be equal to 58.792 km2. The Study also shows that the total amount of contaminated water that flows into the sea can be estimated to reach 146.5 mm3/year. The total area of coastal sea contamination approximately reaches 38.8 km2 and is oriented to the north direction along the coastal shore and its influence extends to Gaza seaport, 10 km apart from the Wadi.

Integrated GIS, Remote Sensing and Survey Data for Damage Assessment of Buildings in Tsunami Event, Ishinomaki City, Japan

The 2011 Tsunami event in the eastern coastal area of Japan caused a huge amount of damages or devastations on buildings. To this date, several field surveys have been conducted which provide detailed information about inundation areas and building damage characteristics in attacking east coastal areas by this tsunami. In this study, building damage data of Ishinomaki city, with special attention to the plain coast affected area, are classified and analyzed using data surveyed by the Ministry of Lands, Infrastructure and Transportation of Japan (MLIT) for more than 52,000 structures. The classification includes information on six levels of damage, four types of building materials and damages due to tsunami inundation for each building material which are necessary information for an effective hazard mitigation. Notably, damage level percentage distribution of different building materials is plotted for different inundation depth ranges in several sets of figures. This graphic illustration not only shows a better resistant performance of Reinforced Concrete (RC) and steel buildings over wood or other buildings for all inundation depth ranges, but also can explain clearly the inundation-induced damage behavior for each building material as well as the threshold depth for each damage level. Moreover, this research contains an analysis of vulnerable areas due to the coastal topography and the geographical factors. Surveyed data provided by Geospatial information authority of Japan (GSI) that classifies Ishinomaki plain coast area into three classes are compared with the damage map produced using an Analytical Hierarchy Process (AHP) methodology in ArcGIS 10.2 environment. The influence of key geographical features on tsunami-induced building damage, notably Kitakami river and water canals flooding, is taken into account with respect to the weighting of factors. A good agreement produced building damage map with surveyed GSI data shows the power of a GIS tool based on the AHP approach for tsunami damage assessment. The results of this study are useful to understand the damage behavior of buildings with different structural materials located in coastal areas vulnerable to the tsunami disaster.

Land Use Land Cover Analysis for Godavari Basin in Maharashtra Using Geographical Information System and Remote Sensing

The dynamic transformation of land use and land cover has emerged as a crucial aspect in the effective management of natural resources and the continual monitoring of environmental shifts. This study focused on the land use and land cover (LULC) changes within the catchment area of the Godavari River, assessing the repercussions of land and water resource exploitation. Utilizing LANDSAT satellite images from 2009, 2014, and 2019, this research employed supervised classification through the Quantum Geographic Information System (QGIS) software’s SCP plugin. Maximum likelihood classification algorithm was used for the assessment of supervised land use classification. Seven distinct LULC classes—forest, irrigated cropland, agricultural land (fallow), barren land, shrub land, water, and urban land—are delineated for classification purposes. The study revealed substantial changes in the Godavari basin’s land use patterns over the ten-year period from 2009 to 2019. Spatial and temporal dynamics of land use/cover changes (2009-2019) were quantified using three Satellite/Landsat images, a supervised classification algorithm and the post classification change detection technique in GIS. The total study area of the Godavari basin in Maharashtra encompasses 5138175.48 hectares. Notably, the built-up area increased from 0.14% in 2009 to 1.94% in 2019. The proportion of irrigated cropland, which was 62.32% in 2009, declined to 41.52% in 2019. Shrub land witnessed a noteworthy increase from 0.05% to 2.05% over the last decade. The key findings underscored significant declines in barren land, agricultural land, and irrigated cropland, juxtaposed with an expansion in forest land, shrub land, and urban land. The classification methodology achieved an overall accuracy of 80%, with a Kappa Statistic of 71.9% for the satellite images. The overall classification accuracy along with the Kappa value for 2009, 2014 and 2019 supervised land use land cover classification was good enough to detect the changing scenarios of Godavari River basin under study. These findings provide valuable insights for discerning land utilization across various categories, facilitating the adoption of appropriate strategies for sustainable land use in the region.

Remote Sensing and GIS as an Advance Space Technologies for Rare Vegetation Monitoring in Gobustan State National Park, Azerbaijan

This paper describes remote sensing methodologies for monitoring rare vegetation with special emphasis on the Image Statistic Analysis for set of training samples and classification. At first 5 types of Rare Vegetation communities were defined and the Initial classification scheme was designed on that base. After preliminary Statistic Analysis for training samples, a modification algorithm of the classification scheme was defined: one led us to creating a 4 class’s scheme (Final classification scheme). The different methods analysis such as signature statistics, signature separability and scatter plots are used. According to the results, the average separability (Transformed Divergence) is 1951.14, minimum is 1732.44 and maximum is 2000 which shows an acceptable level of accuracy. Contingency Matrix computed on the results of the training on Final classi- fication scheme achieves better results, in terms of overall accuracy, than the training on Initial classification scheme.

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

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Remote Sensing and GIS Application on Forest Resource Mapping and Monitoring in Bulolo District, Morobe Province

Forest resources monitoring are particularly challenging for tropical forest due to their diverse composition and structure and a wide range of stakeholder’s expectations and requirement. New monitoring approaches and control policies directions are required to meet these different challenges. For the past decades, much of the focus of formal forest monitoring and management policy in Papua New Guinea (PNG) has been on large scale conventional harvesting to meet national requirements for economic development, with little attention given to community or small area forest management and monitoring. The current management is considered to be unsustainable and, as forest resources from primary forests are exhausted. This has resulted in extensive cutover forest areas being left to degrade over time. Forest reserve has suffered seriously and if the present trend of deforestation continues;it is just a matter of time when the whole reserve would have been converted to a bare ground. This study therefore examined the integration of remote sensing (RS) and geographic information system (GIS) application on forest resource mapping and monitoring in Bulolo district, Morobe province. Landsat satellite imageries for 1992, 2002 and 2014 were used to classify and identify forest changes through change detection techniques. A GIS database of land use categories and their location within 24 years (1992-2014) were generated and analysed with the aid of GIS analytical functions. This function includes area calculation, overlay, and image differencing, supervised classifications, cross tabulations and map representation. The result shows that population growth (anthropogenic) factors among communities around the natural forest imposes a lot of pressure on the natural forest resources. This should also include consideration of the future usage capacity of the forest resources as well as development of the capacity of local forest owner communities to participate in small scale forest management and utilization.

An automated method for mapping physical soil and water conservation structures on cultivated land using GIS and remote sensing techniques

An efficient and reliable automated model that can map physical Soil and Water Conservation（SWC） structures on cultivated land was developed using very high spatial resolution imagery obtained from Google Earth and Arc GIS？ERDAS IMAGINE？and SDC Morphology Toolbox for MATLAB and statistical techniques. The model was developed using the following procedures：（1） a high-pass spatial filter algorithm was applied to detect linear features,（2） morphological processing was used to remove unwanted linear features,（3） the raster format was vectorized,（4） the vectorized linear features were split per hectare（ha） and each line was then classified according to its compass directionand（5） the sum of all vector lengths per class of direction per ha was calculated. Finallythe direction class with the greatest length was selected from each ha to predict the physical SWC structures. The model was calibrated and validated on the Ethiopian Highlands. The model correctly mapped 80% of the existing structures. The developed model was then tested at different sites with different topography. The results show that the developed model is feasible for automated mapping of physical SWC structures. Thereforethe model is useful for predicting and mapping physical SWC structures areas across diverse areas.