Land Cover Classification and Forest Change Analysis, Using Satellite Imagery-A Case Study in Dehdez Area of Zagros Mountain in Iran

The importance of accurate and timely information describing the nature and extent of land resources and changes over time is increasing, especially in mountainous areas. We have developed a methodology to map and monitor land cover change using multitemporal Landsat Thematic Mapper (TM) and ASTER data in Zagros mountains of Iran for 1990, 1998, and 2006.Land-use/cover mapping is achieved through interpreta-tion of Landsat TM satellite images of 1990, 1998 and TERRA-ASTER image of 2006 using ENVI 4.3. Based on the Anderson land-use/cover classification system, the land-use and land-covers are classified as forest land, rangeland, water bodies, agricultural land and residential land. The unsupervised image classifi-cation method carried out prior to field visit, in order to determine strata for ground truth. Fieldwork carried out to collect data for training and validating land-use/cover interpretation from satellite image of 2006, and for qualitative description of the characteristics of each land-use/cover class. The land-use/cover maps of 1990, 1998 and 2006 were produced by using supervised image classification technique based on the Maxi-mum Likelihood Classifier (MLC) and 132 training samples. Error matrices as cross-tabulations of the mapped class vs. the reference class were used to assess classification accuracy. Overall accuracy, user’s and producer’s accuracies, and the Kappa statistic were then derived from the error matrices. A multi-date post-classification comparison change detection algorithm was used to determine changes in land cover in three intervals, 1990–1998, 1998–2006 and 1990–2006. To evaluate the change maps for the 1990 to 2006 interval, we randomly sampled the areas that classified as change and no-change and determined whether they were correctly classified. The maps showed that between 1990 and 2006 the amount of forest land de-creased from 67% to 38.5% of the total area, while rangelands, agriculture, settlement and surface water in-creased from 30.8% to 45%, 1.2% to 7.0%, 0.3% to 7.5% and 0.6% to 1.8%, respectively. The area was dominated by 35.9%, 28.9% and 29.3% dense forest, 42.2%, 46.4% and 43.2% open forest and 21.9%, 24.8% and 27.5% degraded forest in 1990, 1998 and 2006, respectively. During 16 years span period (1990-2006) about 10170.3 ha, 2963.4 ha, 351.7 ha and 3039.2 ha of forest lands were converted to range-land, agriculture, water body and settlement. The overall five-class classification accuracies averaged 78.6% for the three years. The overall accuracy of land cover change maps, generated from post-classification change detection methods and evaluated using several approaches, reached to 80.1%. The results quantify the land cover change patterns in the Zagrous highlands and demonstrate the potential of multitemporal Landsat and ASTER data to provide an accurate, economical means to map and analyze changes in land cover over time that can be used as inputs to land management and policy decisions.

Using classification assignment rules to assess land-use change impacts on forest biodiversity at local-to-national scales

Background： Ecosystem representation is one key component in assessing the biodiversity impacts of land-use changes that will irrevocably alter natural ecosystems. We show how detailed vegetation plot data can be used to assess the potential impact of inundation by a proposed hydroelectricity dam in the Mokihinui gorge, New Zealand, on representation of natural forests. Specifically we ask： 1） How well are the types of forest represented Locally, regionally, and nationally; and 2） How does the number of distinct communities （i.e. beta diversity） in the target catchment compare with other catchments nationally？ Methods： For local and regional comparisons plant species composition was recorded on 45 objectively located 400 m2 vegetation plots established in each of three gorges, with one being the proposed inundation area of the Mokihinui lower gorge. The fuzzy classification framework of noise clustering was used to assign these plots to a specific alliance and association of a pre-existing national-scale classification. NationaLly, we examined the relationship between the number of alliances and associations in a catchment and either catchment size or the number of plots per catchment by fitting Generalised Additive Models. Results： The four alliances and five associations that were observed in the Mokihinui lower gorge arepresent in the region but limited locally. One association was narrowly distributed nationally, but is the mostfrequent association in the Mokihinui lower gorge; inundation may have consequences of national importance to its long-term persistence. That the Mokihinui lower gorge area had nearly twice as many plots that could not be assigned to pre- existing alliances and associations than either the Mokihinui upper or the Karamea lower gorges and proportionally more than the national dataset emphasises the compositional distinctiveness of this gorge. These outlier plots in the Mokihinui lower gorge may be unsorted assemblages of species or reflect sampling bias or that native- dominated woody riparian vegetation is rare on the landscape. At a national scale, the Mokihinui catchment has a higher diversity of forest alliances and associations （i.e. beta-diversity） than predicted based on catchment size and sampling intensity. Conclusions： Our analytical approach demonstrates one transparent solution to a common conservation planning problem： assessing how well ecosystems that will be destroyed by a proposed land-use change are represented using a multi-scale spatial and compositional framework. We provide a useful tool for assessing potential consequences of land-use change that can help guide decision making.

Land Cover Changes and Drivers in the Water Source Area of the Middle Route of the South-to-North Water Diversion Project in China from 2000 to 2015

The Middle Route of the South-to-North Water Diversion Project(MR-SNWDP)in China,with construction beginning in 2003,diverts water from Danjiangkou Reservoir to North China for residential,agriculture and industrial use.The water source area of the MR-SNWDP is the region that is most sensitive to and most affected by the construction of this water diversion project.In this study,we used Landsat Thematic Mapper(TM)and HJ-1 A/B images from 2000 to 2015 by an object-based approach with a hierarchical classification method for mapping land cover in the water source area.The changes in land cover were illuminated by transfer matrixes,single dynamic degree,slope zones and fractional vegetation cover(FVC).The results indicated that the area of cropland decreased by 31%and was replaced mainly by shrub over the past 15 years,whereas forest and settlements showed continuous increases of 29.2% and 77.7%,respectively.The changes in cropland were obvious in all slope zones and decreased most remarkably(–43.8%)in the slope zone above 25°.Compared to the FVC of forest and shrub,significant improvement was exhibited in the FVC of grassland,with a growth rate of 16.6%.We concluded that local policies,including economic development,water conservation and immigration resulting from the construction of the MR-SNWDP,were the main drivers of land cover changes;notably,they stimulated the substantial and rapid expansion of settlements,doubled the wetlands and drove the transformation from cropland to settlements in immigration areas.

Effects of environmental factors on classification of loessderived soils and clay minerals variations, northern Iran

Land-use type under different topographic conditions and human activities affects soil development. We investigated the effects of land-use,topography and human activity on soil classification changes in the Toshan watershed in northern Iran.Seven representative pedons derived from loess parent materials were studied on different land-uses and topographic positions. The studied pedons in forest(FO) on backslopes and footslope were classified as Calcic Haploxeralfs and Typic Haploxeralfs, respectively. The soils in abandoned lands(AB) and orchards(OR), where formerly under natural forests, located on the shoulder and backslopes positions were classified as Calcic Haploxeralfs and Vertic Haploxeralfs, respectively.Well-developed argillic horizons as indicators for higher degrees of soil evolution were observed in more-stable areas under the natural forest or less disturbed areas. Clay lessivage through these soil profiles have led to formation of Typic or Calcic Haploxeralfs, while under croplands(CP) were classified as Typic Calcixerepts. Conversion of sloping deforested areas to CP along with inappropriate management have accelerated soil erosion, resulting in unstable conditions in which decalcification and formation of developed soils cannot occur. Paddy cultivation in flat areas has caused to reduced conditions and formation of Typic Haplaquepts.Because of unfavorable conditions for chemical weathering(e.g. lower water retention compared to more-stable areas) no vermiculite was detected in the CP. The results showed that evolution and classification of the studied soils were strongly affected by land-use type, topography and management.

Land cover changes in the rural-urban interaction of Xi’an region using Landsat TM/ETM data

Landsat ETM/TM data and an artificial neural network （ANN） were applied to analyse the expansion of the city of Xi＇an and land use/cover change of its surrounding area between 2000 and 2003. Supervised classification and normalized difference barren index （NDBI） were used respectively to retrieve its urban boundary. Results showed that the urban area increased by an annual rate of 12.3%, with area expansion from 253.37 km^2 in 2000 to 358.60 km^2 in 2003. Large areas of farmland in the north and southwest were converted into urban construction land. The land use/cover changes of Xi＇an were mainly caused by fast development of urban economy, population immigration from countryside, great development of infrastructure such as transportation, and huge demands for urban market. In addition, affected by the government policy of “returning farmland to woodland”, some farmland was converted into economic woodland, such as Chinese goosebeerv garden, vineyard etc.

Remote sensing classification of western Sierra Leone using landsat TM and ETM+

The study examines the changes of land cover/use resources for the period under investigation.An unsupervised vegetation classification is being performed that provides five distinctive classes and thus assesses these changes in five broad land cover classes-high/moist forests,forest regrowth,mixed savanna,bare land/ grass and water.The remote sensing images used in this work are both images of TM and ETM+in different time periods(1986 to 2001)to determine land cover/use changes.A fairly accuracy report is recorded after performing the unsupervised classification,which shows vegetation has been depleted for over the years.Changes created are mostly human and to a lesser extent environment.Human activities are mainly encroachment thus altering the landscape through activities such as population growth,agriculture,settlements,etc.and environment due to some perceive climatic changes.This vegetation classification highlights the importance to acquire and publish information about the country's partial vegetation cover and vegetation change including vegetation maps and other basic vegetation influencing factors,leading to an understanding of its evolution for a period.

Research on the Applicability of Garbage Classification to Carbon Trading Market

Climate warming is one of the important environmental issues with global concern. The Bloomberg News has recorded temperature changes in the recent 135 years. As the hottest year, in 2014 the global surface temperature was as high as 1.39 degrees Fahrenheit, 0.68 degrees Celsius higher than the average in long-term. The severity of this issue has been proved with the refresh of the highest record^([1]) and the increasing temperature as well as people's personal experience. There is a demand of in-depth discussion about comprehensive and efficient reduction of carbon and greenhouse gas emission and the development of low carbon economy, with garbage classification as the most efficient breach but also most easily to be neglected by people. This article attempts to find a feasible method of carbon emission reduction from the perspective of garbage classification and resource recycling and make quantitative estimation of its value combined with local practice and data in Chengdu.

THRFuzzy:Tangential holoentropy-enabled rough fuzzy classifier to classification of evolving data streams

The rapid developments in the fields of telecommunication, sensor data, financial applications, analyzing of data streams, and so on, increase the rate of data arrival, among which the data mining technique is considered a vital process. The data analysis process consists of different tasks, among which the data stream classification approaches face more challenges than the other commonly used techniques. Even though the classification is a continuous process, it requires a design that can adapt the classification model so as to adjust the concept change or the boundary change between the classes. Hence, we design a novel fuzzy classifier known as THRFuzzy to classify new incoming data streams. Rough set theory along with tangential holoentropy function helps in the designing the dynamic classification model. The classification approach uses kernel fuzzy c-means(FCM) clustering for the generation of the rules and tangential holoentropy function to update the membership function. The performance of the proposed THRFuzzy method is verified using three datasets, namely skin segmentation, localization, and breast cancer datasets, and the evaluated metrics, accuracy and time, comparing its performance with HRFuzzy and adaptive k-NN classifiers. The experimental results conclude that THRFuzzy classifier shows better classification results providing a maximum accuracy consuming a minimal time than the existing classifiers.

Seasonal Vegetation Changes in the Malinda Wetland Using Bi-Temporal, Multi-Sensor, Very High Resolution Remote Sensing Data Sets

Small wetlands in East Africa have grown in prominence driven by the unreliable and diminished rains and the increasing population pressure. Due to their size (less than 500 Ha), these wetlands have not been studied extensively using satellite remote sensing approaches. High spatial resolution remote sensing approaches overcome this limitation allowing detailed inventorying and research on such small wetlands. For understanding the seasonal variations in land cover within the Malinda Wetland in Tanzania (350 Ha), two periods were considered, May 2012 coinciding with the wet period (rainy season) and August 2012 coinciding with a fairly rain depressed period (substantially dry but generally cooler season). The wetland was studied using very high spatial resolution orthophotos derived from Unmanned Aerial Vehicle (UAV) photography fused with TerraSAR-X Spotlight mode dual polarized radar data. Using these fused datasets, five main classes were identified that were used to firstly delineate seasonal changes in land use activities and secondly used in determining phenology changes. Combining fuzzy maximum likelihood classification, knowledge classifier and Change Vector Analysis (CVA), land cover classification was undertaken for both seasons. From the results, manifold anthropogenic activities are taking place between the seasons as evidenced by the high conversion rates (63.01 Ha). The phenological change was also highest within the human influence class due to the growing process of cropped land (26.60 Ha). Much of the changes in both cover and phenology are occurring in the mid upper portion of the wetland, attributed to the presence of springs in this portion of the wetland along the banks of River Mkomazi. There is thus seasonality in the observed anthropogenic influence between the wetland and its periphery.

Classification of Urban Parks and their Regional Characteristics in Dhaka City, Bangladesh

Unplanned urbanization has caused serious ecological imbalances in Dhaka city. This study investigates the efficiency and utilization of urban green space especially parks of Dhaka city. The case study site covers urban parks of Dhaka metropolitan area of Bangladesh. Two aspects （physical and social） were applied for this study. For physical aspect, methods were used RS （Remote Sensing） and GIS （Geographic Information System） of three periods of images （Dhaka city）. And for social aspect, methods were used questionnaire survey, observation, photographs, sketch and previous information about parks. After calculating all data result was showed by maps using GIS. For the changing pattern of green space of periods 1972, 1989 and 2010, result showed that the variation of the green space changes rate was high and gradually green space deceased and build up area increased in Dhaka city. Again according to physical size, parks of Dhaka city were classified into four types： Small, Medium, Large and Extra Large parks. Small parks of Dhaka city were situated beside the residential area and were used as daily purpose. Medium parks were located beside commercial area and its use was for daily and weekly. Large parks were placed at city center and only one extra large park was found outer range of city area. In large parks, people mainly visited monthly or occasionally. Moreover, the spatial-temporal changes of images showed the present status of green space of the city. The consequence of the study reveals that the utilization of park was controlled by various factors in Dhaka city.

A Comparative Study of Image Classification Algorithms for Landscape Assessment of the Niger Delta Region

A critical problem associated with the southern part of Nigeria is the rapid alteration of the landscape as a result of logging, agricultural practices, human migration and expansion, oil exploration, exploitation and production activities. These processes have had both positive and negative effects on the economic and socio-political development of the country in general. The negative impacts have led not only to the degradation of the ecosystem but also posing hazards to human health and polluting surface and ground water resources. This has created the need for the development of a rapid, cost effective and efficient land use/land cover (LULC) classification technique to monitor the biophysical dynamics in the region. Due to the complex land cover patterns existing in the study area and the occasionally indistinguishable relationship between land cover and spectral signals, this paper introduces a combined use of unsupervised and supervised image classification for detecting land use/land cover (LULC) classes. With the continuous conflict over the impact of oil activities in the area, this work provides a procedure for detecting LULC change, which is an important factor to consider in the design of an environmental decision-making framework. Results from the use of this technique on Landsat TM and ETM+ of 1987 and 2002 are discussed. The results reveal the pros and cons of the two methods and the effects of their overall accuracy on post-classification change detection.

ART Based Reliable Method for Prediction of Agricultural Land Changes Using Remote Sensing

This paper focuses on prediction of change in agricultural lands by using ART2 algorithm. The existing method used ENVI and ARCGIS software to predict the changes in land, which showed less accuracy due to human errors. To overcome this user friendly GUI based ART2 algorithm has been developed in java to obtain more accuracy in prediction of changes in land. The input is satellite temporal images of the years 1990 and 2014. By using the ART2 algorithm, the input images of the years 1990 and 2014 are classified, where the features are identified to form cluster. The clustered image is given as input and pixel to pixel comparison method in ART2 is implemented in java, for detecting the changes in agricultural lands. The comparison results of ENVI and ARCGIS and GUI based ART2 with in situ data show that the prediction of changes in agricultural land is more accurate in the case of GUI based ART2 implementation.

Detection of Changes on Temperature and Precipitation Features in Istanbul(Turkey)

In this study, the changes in the data of Istanbul’s precipitation and temperature and the features of these changes were analyzed by different methods. In the analyses the daily precipitation and temperature data sets of Florya and Goztepe Meteorological Stations which have similar locational features were used. These sets were recorded between 1960 and 2013 (for 54 years). In order to emphasize the differentiations in the last 15 years the analyses were conducted comparatively both for the 15-year and for the 54-year periods and then the results were evaluated. The changes in the monthly, annual and seasonal quantity, type and frequency of the precipitation in the form of rain and the features of the temperature’s monthly, annual and seasonal changes, the De Martonne aridity index and the Thornthwaite climate classification were carried out. The results showed that during the years from 1999 to 2013 the climate type of Istanbul changed from semi-humid climate to arid and less-humid climate. Most notably the precipitation during the warm periods has decreased, but the frequency of the intense rain has increased and the majority of these episodes of intense rain coincided with the warm periods. Other determinations were the rise in the annual average temperature and the extension of the warm periods in a year. This differentiation of the temperature features can lead to the aggravation of the evaporation and it can be effective for a longer period during the year. Being aware of this differentiation in the features of precipitation and temperature and taking these data into consideration in all sorts of planning and managing strategies have a special importance for the 14 million or more people living in Istanbul.

Land use and cover change and influencing factor analysis in the Shiyang River Basin,China

Land use and cover change(LUCC)is the most direct manifestation of the interaction between anthropological activities and the natural environment on Earth's surface,with significant impacts on the environment and social economy.Rapid economic development and climate change have resulted in significant changes in land use and cover.The Shiyang River Basin,located in the eastern part of the Hexi Corridor in China,has undergone significant climate change and LUCC over the past few decades.In this study,we used the random forest classification to obtain the land use and cover datasets of the Shiyang River Basin in 1991,1995,2000,2005,2010,2015,and 2020 based on Landsat images.We validated the land use and cover data in 2015 from the random forest classification results(this study),the high-resolution dataset of annual global land cover from 2000 to 2015(AGLC-2000-2015),the global 30 m land cover classification with a fine classification system(GLC_FCS30),and the first Landsat-derived annual China Land Cover Dataset(CLCD)against ground-truth classification results to evaluate the accuracy of the classification results in this study.Furthermore,we explored and compared the spatiotemporal patterns of LUCC in the upper,middle,and lower reaches of the Shiyang River Basin over the past 30 years,and employed the random forest importance ranking method to analyze the influencing factors of LUCC based on natural(evapotranspiration,precipitation,temperature,and surface soil moisture)and anthropogenic(nighttime light,gross domestic product(GDP),and population)factors.The results indicated that the random forest classification results for land use and cover in the Shiyang River Basin in 2015 outperformed the AGLC-2000-2015,GLC_FCS30,and CLCD datasets in both overall and partial validations.Moreover,the classification results in this study exhibited a high level of agreement with the ground truth features.From 1991 to 2020,the area of bare land exhibited a decreasing trend,with changes primarily occurring in the middle and lower reaches of the basin.The area of grassland initially decreased and then increased,with changes occurring mainly in the upper and middle reaches of the basin.In contrast,the area of cropland initially increased and then decreased,with changes occurring in the middle and lower reaches.The LUCC was influenced by both natural and anthropogenic factors.Climatic factors and population contributed significantly to LUCC,and the importance values of evapotranspiration,precipitation,temperature,and population were 22.12%,32.41%,21.89%,and 19.65%,respectively.Moreover,policy interventions also played an important role.Land use and cover in the Shiyang River Basin exhibited fluctuating changes over the past 30 years,with the ecological environment improving in the last 10 years.This suggests that governance efforts in the study area have had some effects,and the government can continue to move in this direction in the future.The findings can provide crucial insights for related research and regional sustainable development in the Shiyang River Basin and other similar arid and semi-arid areas.

Trends of Land Use and Land Cover Change in the Savannah Ecological of the Protected Area Reserve Partielle de Dosso, Niger

Information on the dynamics of savannah is important to a country's plan to overcome the problems of uncontrolled development and environmental hazards. Taking the reserve partielle de Dosso, Niger as the case study area, this paper analyzed the long-term land use land cover change from 2002 to 2022. Satellite images were processed by using Google Earth Engine (GEE). Therefore, four major land cover classes were identified based on spectral characteristics of Land sat, namely, built-up, vegetation, cropland, bare land and water. The result revealed that barren and built-up areas increased at the expense of vegetation and water. From the four major land use land cover the large area is covered by vegetation which comprises about 192963.5 hectares followed by cropland and water consisting of 32506.43 and 1596.4 hectares respectively. The built-up area gained substantial area (most) during the study period. The reduction in some of the land cover/uses underlines the dangerous trend of the pressure poised by population growth and the changing functionality. Land cover change is influenced by a variety of societal factors operating on several spatial and temporal levels. The area estimates and spatial distributions of the LULC classes produced from the current study will assist local authorities, managers, and other stakeholders in decision-making and planning regarding forest land cover and uses.

Migration and Spatiotemporal Land Cover Change: A Case of Bosomtwe Lake Basin, Ghana

Internal migration is highly valued due to its increasingly acknowledged potential for social and economic development. However, despite its significant contribution to the development of towns and cities, it has led to the deterioration of many ecosystems globally. Lake Bosomtwe, a natural Lake in Ghana and one of the six major meteoritic lakes in the world is affected by land cover changes caused by the rising effects of migration, population expansion, and urbanization, owing to the development of tourist facilities on the lakeshore. This study investigated land cover change trajectories using a post-classification comparison approach and identified the factors influencing alteration in the Lake Bosomtwe Basin. Using Landsat imagery, an integrated approach of remote sensing, geographical information systems (GIS), and statistical analysis was successfully employed to analyze the land cover change of the basin. The findings show that over the 17 years, the basin’s forest cover decreased significantly by 16.02%, indicating that population expansion significantly affects changes in land cover. Ultimately, this study will raise the awareness of stakeholders, decision-makers, policy-makers, government, and non-governmental agencies to evaluate land use development patterns, optimize land use structures, and provide a reference for the formulation of sustainable development policies to promote the sustainable development of the ecological environment.

Land cover change in Ningbo and its surrounding area of Zhejiang Province, 1987~2000

Ningbo and its surrounding area is the forefront in the rapid economic development in the Yangtse delta, and the main production area for food supplies, cotton, edible oil and hemp; and at the same time, is the main area for wetland protection in Zhejiang Province. Our objectives were to quantify land cover change in Ningbo and its surrounding area from 1987 to 2000 and to analyze the causative factors of the change. Using 30-m resolution Landsat TM/ETM+ data and maximum likelihood classifica- tion method, we classified the study area into six land cover types: forest, agriculture, urban, freshwater, seawater and bottomland. The research results showed that significant changes in land cover occurred in the study area, and that agriculture and urban land cover change dominated most of the land cover change and were main causes for the changes of other types with human activities, such as urbanization, industrialization, etc. being the main factor while it was not very obvious whether climatic conditions have any role in the land cover changes. Agriculture, bottomland and other nature dominated land cover types are undergoing signifi- cant changes due to industrialization and urbanization, which threaten the stabilization of the environment. The study conclusion called for finding reasonable ways to solve the problems between land cover change and land use.

Land Use and Land Cover Change Detection in the Saudi Arabian Desert Cities of Makkah and Al-Taif Using Satellite Data

Land use/land cover (LULC) changes have become a central issue in current global change and sustainability research. Saudi Arabia has undergone significant change in land use and land cover since the government embarked on a course of intense national development 30 years ago, as a result of huge national oil revenues. This study evaluates LULC change in Makkah and Al-Taif, Saudi Arabia from 1986 to 2013 using Landsat images. Maximum likelihood and object-oriented classification were used to develop LULC maps. The change detection was executed using post-classification comparison and GIS. The results indicated that urban areas have increased over the period by approximately 174% in Makkah and 113% in Al-Taif. Analysis of vegetation cover over the study area showed a variable distribution from year to year due to changing average precipitation in this environment. Object-based classification provided slightly greater accuracy than maximum likelihood classification. Information provided by satellite remote sensing can play an important role in quantifying and understanding the relationship between population growth and LULC changes, which can assist future planning and potential environmental impacts of expanding urban areas.

Analysis of forest cover change at Khadimnagar National Park, Sylhet,Bangladesh, using Landsat TM and GIS data

We mapped the forest cover of Khadimnagar National Park （KNP） of Sylhet Forest Division and estimated forest change over a period of 22 years （1988-2010） using Landsat TM images and other GIS data. Supervised classification and Normalized Difference Vegetation Index （NDVI） image classification approaches were applied to the images to produce three cover classes, viz. dense forest, medium dense forest, and bare land. The change map was produced by differencing classified imageries of 1988 and 2010 as before image and after image, respectively, in ERDAS IMAGINE. Error matrix and kappa statistics were used to assess the accuracy of the produced maps. Overall map accuracies resulting from supervised classification of 1988 and 2010 imageries were 84.6% （Kappa 0.75） and 87.5% （Kappa 0.80）, respec- tively. Forest cover statistics resulting from supervised classification showed that dense forest and bare land declined from 526 ha （67%） to 417 ha （59%） and 105 ha （13%） to 8 ha （1%）, respectively, whereas medium dense forest increased from 155 ha （20%） to 317 ha （40%）. Forest cover change statistics derived from NDVI classification showed that dense forest declined from 525 ha （67%） to 421 ha （54%） while medium dense forest increased from 253 ha （32%） to 356 ha （45%）. Both supervised and NDVI classification approaches showed similar trends of forest change, i.e. decrease of dense forest and increase of medium dense forest, which indicates dense forest has been converted to medium dense forest. Area of bare land was unchanged. Illicit felling, encroachment, and settlement near forests caused the dense forest decline while short and long rotation plantations raised in various years caused the increase in area of medium dense forest. Protective measures should be undertaken to check further degradation of forest at KNP.

Using multispectral landsat and sentinel-2 satellite data to investigate vegetation change at Mount St. Helens since the great volcanic eruption in 1980

Long-term analyses of vegetation succession after catastrophic events are of high interest for an improved understanding of succession dynamics. However, in many studies such analyses were restricted to plot-based measurements. Contrarily, spatially continuous observations of succession dynamics over extended areas and timeperiods are sparse. Here, we applied a change vector analysis(CVA) to investigate vegetation succession dynamics at Mount St. Helens after the great volcanic eruption in 1980 using Landsat. We additionally applied a supervised random forest classification using Sentinel-2 data to map the currently prevailing vegetation types. Change vector analysis was performed with the normalized difference vegetation index(NDVI) and the urban index(UI) for three subsequent decades after the eruption as well as for the whole observation time between 1984 and 2016. The influence of topography on the current vegetation distribution was examined by comparing altitude, slope angles and aspect values of vegetation classes derived by the random forest classification. WilcoxRank-Sum test was applied to test for significant differences between topographic properties of the vegetation classes inside and outside of the areas affected by the eruption. For the full time period, a total area of 516 km2 was identified as re-vegetated, whereas the area and magnitude of re-growing vegetation decreased during the three decades and migrated closer to the volcanic crater. Vegetation losses were mainly observed in regions unaffected by the eruption and related mostly to timber harvesting. The vegetation type classification reached a high overall accuracy of approximately 90%. 36 years after the eruption, coniferous and deciduous trees have established at formerly devastated areas dominating with a proportion of 66%, whereas shrubs are more abundant in riparian zones. Sparse vegetation dominates at regions very close to the crater. Elevation was found to have a great influence on the reestablishment and distribution of the vegetation classes within the devastated areas showing in almost all cases significant differences in altitude distribution. Slope was less important for the different classes-only representing significantly higher values for meadows, whereas aspect seems to have no notable influence on the reestablishment of vegetation at Mount St. Helens. We conclude that major vegetation succession dynamics after catastrophic events can be assessed and characterized over large areas from freely available remote sensing data and hence contribute to an improved understanding of succession dynamics.