Simulation of the Dynamics of Land Cover and Land Use in the Lobo River Watershed Upstream of Nibéhibé(Center-West of Côte d’Ivoire)

From medium-resolution satellite images (Landsat TM, ETM+ and OLI), the spatial dynamics of land cover and land use are highlighted. The objective of this study is to quantify the evolution of land use in the watershed of the Lobo River upstream of Nibéhibé between 1986 and 2019 in order to analyze the impacts of human activities on the landscape. The study method was based, on the one hand, on the processing of satellite images, for the analysis of the dynamics of land use and, on the other hand, on the CA-Markov model, for the prediction of land use by 2050. It emerged from this study that the land use maps produced made it possible to highlight the spatio-temporal dynamics of land use on the basin. For the period from 1986 to 2019, there is a decrease in the area of forests in favor of built-bare ground and crops and fallows. A land use scenario for the years 2019 and 2050 was simulated with an accuracy of 87.11%. The regressive trend in forests seems to continue in the future with current land use practices.

Land Use and Land Cover Dynamics in the Eseka Alluvial Gold Mining District, Centre Region, Cameroon

Local populations in Cameroon thrive on forest resources and the flow of ecosystem services they provide are pivotal in sustaining national economy, improving people’s lives, safeguarding biodiversity, and mitigating the impacts of environmental changes. The exploitation of these resources invariably leads to deforestation and forest degradation. This study was designed to evaluate land use land cover change (LULCC) in the Eseka alluvial gold mining district with the aid of Landsat images. In the investigation of forest cover change, four Landsat satellite images for (1990, 2002, 2015 and 2022) were used. Ground-truthing also helped to identify the activities carried out by the local population and to determine agents, drivers and pressures of land use and land cover change. Four main land cover classes namely: forest, agricultural land, settlement/mining camps and water bodies were selected. Between 1990 and 2022, the proportion of forest decreased from 98% to 34% while those of agricultural land and settlement/mining camps increased from 2% to 60% and 0.54% to 6% respectively. Analysis showed ongoing deforestation with forest cover loss of ~98,263 ha in 32 years giving a cover change percentage of 63.94%. Kappa coefficient for the study period ranged from 0.92 to 0.99. Forest cover loss could be attributed to farming activities, wood extraction and alluvial gold mining activities. Economic motives notably the need to increase household income from a frequent demand for farm and wood products in neighbouring towns and the quest for gold were the main drivers of these activities. Hence, this study assesses the impact of human activities from the mining sector on the forest ecosystem in a bid to inform mitigation policies.

Characteristics and drivers of the soil multifunctionality under different land use and land cover types in the drylands of China

The drylands of China cover approximately 6.6×106 km2 and are home to approximately 5.8×10^(8)people,providing important ecosystem services for human survival and development.However,dryland ecosystems are extremely fragile and sensitive to external environmental changes.Land use and land cover(LULC)changes significantly impact soil structure and function,thus affecting the soil multifunctionality(SMF).However,the effect of LULC changes on the SMF in the drylands of China has rarely been reported.In this study,we investigated the characteristics of the SMF changes based on soil data in the 1980s from the National Tibetan Plateau Data Center.We explored the drivers of the SMF changes under different LULC types(including forest,grassland,shrubland,and desert)and used structural equation modeling to explore the main driver of the SMF changes.The results showed that the SMF under the four LULC types decreased in the following descending order:forest,grassland,shrubland,and desert.The main driver of the SMF changes under different LULC types was mean annual temperature(MAT).In addition to MAT,pH in forest,soil moisture(SM)and soil biodiversity index in grassland,SM in shrubland,and aridity index in desert are crucial factors for the SMF changes.Therefore,the SMF in the drylands of China is regulated mainly by MAT and pH,and comprehensive assessments of the SMF in drylands need to be performed regarding LULC changes.The results are beneficial for evaluating the SMF among different LULC types and predicting the SMF under global climate change.

Spatiotemporal landscape pattern changes and their effects on land surface temperature in greenbelt with semi-arid climate:A case study of the Erbil City,Iraq

Urban expansion of cities has caused changes in land use and land cover(LULC)in addition to transformations in the spatial characteristics of landscape structure.These alterations have generated heat islands and rise of land surface temperature(LST),which consequently have caused a variety of environmental issues and threated the sustainable development of urban areas.Greenbelts are employed as an urban planning containment policy to regulate urban expansion,safeguard natural open spaces,and serve adaptation and mitigation functions.And they are regarded as a powerful measure for enhancing urban environmental sustainability.Despite the fact that,the relation between landscape structure change and variation of LST has been examined thoroughly in many studies,but there is a limitation concerning this relation in semi-arid climate and in greenbelts as well,with the lacking of comprehensive research combing both aspects.Accordingly,this study investigated the spatiotemporal changes of landscape pattern of LULC and their relationship with variation of LST within an inner greenbelt in the semi-arid Erbil City of northern Iraq.The study utilized remote sensing data to retrieve LST,classified LULC,and calculated landscape metrics for analyzing spatial changes during the study period.The results indicated that both composition and configuration of LULC had an impact on the variation of LST in the study area.The Pearson's correlation showed the significant effect of Vegetation 1 type(VH),cultivated land(CU),and bare soil(BS)on LST,as increase of LST was related to the decrease of VH and the increases of CU and BS,while,neither Vegetation 2 type(VL)nor built-up(BU)had any effects.Additionally,the spatial distribution of LULC also exhibited significant effects on LST,as LST was strongly correlated with landscape indices for VH,CU,and BS.However,for BU,only aggregation index metric affected LST,while none of VL metrics had a relation.The study provides insights for landscape planners and policymakers to not only develop more green spaces in greenbelt but also optimize the spatial landscape patterns to reduce the influence of LST on the urban environment,and further promote sustainable development and enhance well-being in the cities with semi-arid climate.

Review of coastal land transformation:Factors,impacts,adaptation strategies,and future scopes

Coastal land transformation has been identified as a topic of research in many countries around the world.Several studies have been conducted to determine the causes and impacts of land transformation.However,much less is understood about coupling change detection,factors,impacts,and adaptation strategies for coastal land transformation at a global scale.This review aims to present a systematic review of global coastal land transformation and its leading research areas.From 1,741 documents of Scopus and Web of Science,60 studies have been selected using the PRISMA-2020 guideline.Results revealed that existing literature included four leading focus areas regarding coastal land transformation:change detection,driving factors,impacts,and adaptation measures.These focus areas were further analyzed,and it was found that more than 80%of studies used Landsat imagery to detect land transformation.Population growth and urbanization were among the major driving factors identified.This review further identified that about 37%of studies included impact analysis.These studies identified impacts on ecosystems,land surface temperature,migration,water quality,and occupational effects as significant impacts.However,only four studies included adaptation strategies.This review explored the scope of comprehensive research in coastal land transformation,addressing change detection,factor and impact analysis,and mitigation-adaptation strategies.The research also proposes a conceptual framework for comprehensive coastal land transformation analysis.The framework can provide potential decision-making guidance for future studies in coastal land transformation.

Deep neural network ensembles for remote sensing land cover and land use classification

With the advancement of satellite technology,a considerable amount of very high-resolution imagery has become available to be used for the Land Cover and Land Use(LCLU)classification task aiming to categorize remotely sensed images based on their semantic content.Recently,Deep Neural Networks(DNNs)have been widely used for different applications in the field of remote sensing and they have profound impacts;however,improvement of the generalizability and robustness of the DNNs needs to be progressed further to achieve higher accuracy for a variety of sensing geometries and categories.We address this problem by deploying three different Deep Neural Network Ensemble(DNNE)methods and creating a comparative analysis for the LCLU classification task.DNNE enables improvement of the performance of DNNs by ensuring the diversity of the models that are combined.Thus,enhances the generalizability of the models and produces more robust and generalizable outcomes for LCLU classification tasks.The experimental results on NWPU-RESISC45 and AID datasets demonstrate that utilizing the aggregated information from multiple DNNs leads to an increase in classification performance,achieves state-of-the-art,and promotes researchers to make use of DNNE.

Hydrological response to land use and land cover changes in a sub-watershed of West Liaohe River Basin,China

In recent years, the streamflow of the Laohahe Basin in China showed a dramatic decrease during the rainy season as a result of climate change and/or human activities. The objective of this work was to document significant streamflow changes caused by land use and land cover （LULC） changes and to quantify the impacts of the observed changes in Laohahe Basin. in the study area, the observed streamflow has been influenced by LULC changes, dams, and irrigation from rivers, industry, livestock and human consumption. Most importantly, the growth of population and gross domestic product （GDP） accompanied by the growth in industrial and agricultural activities, which led to LULC changes with increased residential land and cropland and decreased grassland since 2000s. Statistical methods and Variable Infiltration Capacity （VIC） hydrological model were used to estimate the effects of climate change and LULC changes on streamflow and evaportranspiration lET）. First, the streamflow data of the study area were divided into three sub-periods according to the Pettitt test. The hydrological process was then simulated by VIC model from 1964 to 2009. Furthermore, we compared the simulated results based on land use scenarios in 1989, 1999 and 2007, respectively for exploring the effect of LULC changes on the spatio-temporal distribution of streamflow and ET in the Laohahe Basin. The results suggest that, accompanied with climate change, the LULC changes and human water consumption appeared to be the most likely factors contributing to the sig- nificant reduction in streamflow in the Laohahe Basin by 64% from1999 to 2009.

Land use and land cover change within the Koshi River Basin of the central Himalayas since 1990

Land change is a cause and consequence of global environmental change.Land use and land cover have changed considerably due to increasing human activities and climate change,which has become the core issue of major international research projects.This study interprets land use and land cover status and the changes within the Koshi River Basin(KRB)using Landsat remote sensing(RS)image data,and employs logistic regression model to analyze the influence of natural and socioeconomic driving forces on major land cover changes.The results showed that the areas of built-up land,bare land and forest in KRB increased from 1990 to 2015,including the largest increases in forest and the highest growth rate in construction land.Areas of glacier,grassland,sparse vegetation,shrub land,cropland,and wetland all decreased over the study period.From the perspective of driving analysis,the role of human activities in land use and land cover change is significant than climate factors.Cropland expansion is the reclamation of cropland by farmers,mainly from early deforestation.However,labor force separation,geological disasters and drought are the main factors of cropland shrinkage.The increase of forest area in India and Nepal was attributed to the government’s forest protection policies,such as Nepal’s community forestry has achieved remarkable results.The expansion and contraction of grassland were both dominated by climatic factors.The probability of grassland expansion increases with temperature and precipitation,while the probability of grassland contraction decreases with temperature and precipitation.

Evaluating the impacts of land use and land cover changes on surface air temperature using the WRF-mosaic approach

Satellite-derived land surface data in 1980 and 2010 were used to represent land use and land cover（LULC） changes caused by the rapid economic development and human activities that have occurred over the past few decades in East Asia and China. The effects of LULC changes on the radiation budget and 2-m surface air temperature（SAT） were explored for the period using the Weather Research and Forecasting（WRF） model. The mosaic approach, which considers the N-most abundant land use types within a model grid cell（here, N = 3） and precisely describes the subgridscale LULC changes, was adopted in the integrations. The impacts of LULC changes based on two 36-year integrations showed that SAT generally decreased, with the sole exception being over eastern China, resulting in decreased SAT in China（-0.062 °C） and East Asian land areas（EAL,-0.061 °C）. The LULC changes induced changes in albedo, which influenced the radiation budget. The radiative forcings at the top of the atmosphere were-0.56 W m-2 across the whole of China, and-0.50 W m-2 over EAL. Meanwhile, the altered roughness length mainly influenced near-surface wind speeds, large-scale and upward moisture fluxes, latent heat fluxes, and cloud fractions at different altitudes. Though the impacts caused by the LULC changes were generally smaller at regional scales, the values at local scales were much stronger.

Land use and land cover change processes in China's eastern Loess Plateau

Using Landsat remote sensing images, we analyzed changes in each land use type and transitions among different land use types during land use and land cover change （LUCC） in Ningwu County, located in the eastern Loess Plateau of China, from 1990 to 2010. We found that grassland, woodland, and farmland were the main land use types in the study area, and the area of each type changed slightly from 1990 to 2010, whereas the area of water, construction land, and unused land increased greatly. For the whole area, the net change and total change were insignificant due to weak human activity intensity in most of the study area, and the LUCC was dominated by quasi-balanced two-way transitions from 1990 to 2010. The insignificant overall amount of LUCC appears to have resulted from offsetting of rapid increases in population, economic growth, and the im- plementation of a program to return farmland to woodland and grassland in 2000. This program converted more farmland into woodland and grassland from 2000 to 2010 than from 1990 to 2000, but reclamation of woodland and grassland for use as farmland continued from 2000 to 2010, and is a cause for concern to the local government.

Land Use and Land Cover Dynamics in the Urban Watershed of Kimemi River (Butembo/D.R.C)

Changes in land use and land cover (LULC) influence hydrological processes in a watershed. This study analyses the dynamics of LULC in the Kimemi watershed from 1987 to 2021. GIS and remote sensing tools as well as landscape pattern analysis were used to achieve this purpose. The results reveal that the LULC change is globally marked by an increase in the bare land and building at the expense of the low vegetation (grassland). Between 1987 and 2011, the bare land and buildings (Tg = 61.33%) and the woodland (Tg = 34.2%) classes increased, whereas the grassland class decreased (Tg = -39.5%). On the other hand, between 2011 and 2015, the bare land and building class still increased (Tg = 29.9%) while that of grassland and woodland decreased with Tg = -37.3% and Tg = -4.9%, respectively. Finally, the dynamics observed from 2015 to 2021 is marked by small changes between classes with Tg values of 2.1%, 1.9% and -8.9%, respectively, for the bare land and building, grassland and woodland classes, respectively. The main spatial transformation processes observed are creation and dissection for the bare land and building class, and the grassland class respectively. In particular, the woodland class underwent the creation process between 1987 and 2011 before undergoing attrition (2011-2015-2021). Reduced vegetated areas give rise to new planning decisions to mitigate the hydrological risks that could result from this situation.

Change Detection of Land Use and Land Cover over a Period of 20 Years in Papua New Guinea

People have an inherent tenacity to throng coastal regions in pursuit of better living conditions. As such the brisk dynamism of land use/land cover activities in a coastal region becomes obvious. The former keeps changing rapidly due to burgeoning population. A digital change detection analysis is performed with the help of Geographic Information System (GIS) on the Remote Sensing data spanning over last 20 years, complemented by in-situ data and ground truth information. This current research briefly endeavours to find out the nature of change happening in the major three coastal cities of Papua New Guinea (PNG), namely Alotau, capital of Milnebay province;Lae, capital of Morobe province and Port Moresby, capital of Papua New Guinea. Changes in land use and land cover that took place over 20 years have been recorded using Landsat 5 thematic mapper (TM) data of 1992 and Landsat 8 operational land imager (OLI) data. Land use and land cover maps of 1992, and 2013/14, and change detection matrix of 1992-2013/14 are derived. Results show an immensely sprawling urban landscape, evincing about five times growth during 1992 to 2014. At the same time “natural forests” dwindled by 444.96 hectares in Alotau, 6977.25 hectares in Lae and “mangrove” and “grass/shrub land” decreased by 127.78 and 4859.39 hectares respectively around Port Moresby. The above changes owe to ever increasing population pressure, land tenure shift, agriculture and industrial development.

Land Use/Land Cover Changes of Ago-Owu Forest Reserve, Osun State, Nigeria Using Remote Sensing Techniques

Remote sensing (RS) and GIS are important methods for land use assessment and land cover transition. In this study, land use/land cover changes in the Ago-Owu Forest Reserve, Osun State, Nigeria have been assessed. Landsat 5 TM, Landsat 7 ETM+ and Landsat 8 OLI were acquired for 1986, 2002 and 2017 respectively. The three scenes corresponded to path 190 and row 055 of WRS-2 (Worldwide Reference System). The processing of the imagery was preceded by the clipping of the study area from the satellite image. The boundary of the reserve was carefully digitized and used to clip the imagery to produce an image map of the forest reserve. Using the supervised image classification procedure, training sites were used to produce land use/land cover maps. The same classification scheme was used for the 1986, 2002 and 2017 images to facilitate the detection of change. The differences in the area covered by the different polygons between the three sets of images were measured in km2. The results show that during 1986 and 2017, there is a dramatic increase of build-up areas with a change of 55.65 km2 and sparse vegetation (farmland and grassland) with a change of 53.97 km2, while a dramatic decrease of dense vegetation (forest areas) with a change of 109.61 km2. The consequence of these results is that over the years, the population of people living in the forest reserve has increased and many of them are engaged in farming, leading to an increase in farmland. In addition, logging activities continued unabated in the forest reserve, as demonstrated by a sharp increase in the deforested area within the reserve. The maps produced in this study will serve as a planning tool for the Osun State Forestry Department to plan reforestation activities for the forest reserve.

Land use and land cover change in Liaocheng Prefecture of China

Liaocheng Prefecture is located in North China Plain with a long reclamation history of more than 10000 years. In this study the author applied data to explain the relationship between land use pattern and physical, social and economic factors and further to find out driving forces which lead to land use changes in such an agricultural region. Data of three different time points on township level were taken into account to explain the land use pattern, and land use changes. And 40-year county level data were applied to analyze the driving forces. Canonical Correlation Analysis was conducted to explain the relationship between land use pattern and social and economic factors; and Linear Regression Analysis was used to find out driving forces of land use change, thus to project the future trend of land use change in Liaocheng Prefecture.

Identifying the Rates and Drivers of Spatiotemporal Patterns of Land Use and Land Cover Changes in the Hurungwe District, Zimbabwe: A GIS and Remote Sensing Approach

Identifying spatiotemporal patterns of land use and land cover changes (LULCC) and their impacts on the natural environment is essential in policy decisions for effective, sustainable natural resource management solutions. This study employed supervised image classification in Google Earth Engine (GEE) cloud-based platform to assess the land cover land use changes for the past 30 years (1989-2020), as well as predict the land cover states and the risk of future forest loss in the next ten years, using TerrSet 20 software in Hurungwe district, Zimbabwe. The study findings revealed a net forest area and shrub loss of 32% and 10%, while croplands, water bodies, and bare lands have increased by about 171%, 7%, and 119% between 1989 and 2020, respectively. Croplands are the major contributor to the net change in forests, particularly tobacco farming. The predictive model estimated that by 2030 the district would lose approximately 7% of the current forest cover area, most likely converted into croplands, shrubs, and settlements. The results reinforce the importance of bridging the gap between socioeconomic activities and institutional policies to ensure proper natural resource management. Integrating institutional policy and socioeconomic goals is indispensable to ensure sustainable development.

Assessment of vegetation cover changes and the contributing factors in the Al-Ahsa Oasis using Normalized Difference Vegetation Index(NDVI)

The abandonment of date palm grove of the former Al-Ahsa Oasis in the eastern region of Saudi Arabia has resulted in the conversion of delicate agricultural area into urban area.The current state of the oasis is influenced by both expansion and degradation factors.Therefore,it is important to study the spatiotemporal variation of vegetation cover for the sustainable management of oasis resources.This study used Landsat satellite images in 1987,2002,and 2021 to monitor the spatiotemporal variation of vegetation cover in the Al-Ahsa Oasis,applied multi-temporal Normalized Difference Vegetation Index(NDVI)data spanning from 1987 to 2021 to assess environmental and spatiotemporal variations that have occurred in the Al-Ahsa Oasis,and investigated the factors influencing these variation.This study reveals that there is a significant improvement in the ecological environment of the oasis during 1987–2021,with increase of NDVI values being higher than 0.10.In 2021,the highest NDVI value is generally above 0.70,while the lowest value remains largely unchanged.However,there is a remarkable increase in NDVI values between 0.20 and 0.30.The area of low NDVI values(0.00–0.20)has remained almost stable,but the region with high NDVI values(above 0.70)expands during 1987–2021.Furthermore,this study finds that in 1987–2002,the increase of vegetation cover is most notable in the northern region of the study area,whereas from 2002 to 2021,the increase of vegetation cover is mainly concentrated in the northern and southern regions of the study area.From 1987 to 2021,NDVI values exhibit the most pronounced variation,with a significant increase in the“green”zone(characterized by NDVI values exceeding 0.40),indicating a substantial enhancement in the ecological environment of the oasis.The NDVI classification is validated through 50 ground validation points in the study area,demonstrating a mean accuracy of 92.00%in the detection of vegetation cover.In general,both the user’s and producer’s accuracies of NDVI classification are extremely high in 1987,2002,and 2021.Finally,this study suggests that environmental authorities should strengthen their overall forestry project arrangements to combat sand encroachment and enhance the ecological environment of the Al-Ahsa Oasis.

Road Impacts on Spatial Patterns of Land Use and Landscape Fragmentation in Three Parallel Rivers Region,Yunnan Province,China

The structure and function of network is a central issue in landscape ecology.Road networks with hierarchical structure are crucial for understanding landscape dynamics.In this study,we compared the distribution of national road,provincial road,county road and rural road in the Three Parallel Rivers Region(TPRR)in Yunnan Province of China,and estimated the effect of roads(and other factors)on the spatial patterns of land use and land cover with logistic regression.In addition,we analyzed the land use and land cover change(LUCC)and landscape fragmentation in 1989–2005 along a buffer zone of the primary traffic corridor,national road G214.The results showed that,county and rural roads had much higher percentage of length extending into more natural habitats at higher elevation and steeper slope,compared with the higher level roads in this region.While the distributions of natural land cover types were dominated by environmental factors,human land use types i.e.,building land and farmland types were significantly related with roads,linking more closely with lower level roads.The LUCC dynamics(1989–2005)of the G214 buffer zone showed a general trend of land transformation from conifer forests and valley arid shrubs to building land and farmland,and from ice and snow to alpine shrubs and forests.With the length of G214 unchanged during the time,the overall landscape pattern changed little in the buffer zone,but habitat fragmentation and area decrease had occurred for the natural vegetation types,in contrast to patch mergence and expansion of human land use types,and landscape fragmentation was intensified above 2500 m a.s.l.but declined below the elevation.The results indicated the dynamics of landscape composition and patch type level distribution in spite of the stability of the overall landscape pattern,and implied the potential role of roads,especially the low level roads on landscape changes.

Use of Intensity Analysis to Measure Land Use Changes from 1932 to 2005 in Zhenlai County, Northeast China

Analyzing spatiotemporal dynamics of land use and land cover over time is widely recognized as important to better understand and provide solutions for social, economic, and environmental problems, especially in ecologically fragile region. In this paper, a case study was taken in Zhenlai County, which is a part of farming-pastoral ecotone of Northeast China. This study seeks to use multi-temporal satellite images and other data from various sources to analyze spatiotemporal changes from 1932 to 2005, and applied a quantitative methodology named intensity analysis in the time scale of decades at three levels: time interval, category, and transition. The findings of the case study are as follows: 1) the interval level of intensity analysis revealed that the annual rate of overall change was relatively fast in 1932–1954 and 1954–1976 time intervals. 2) The category level showed that arable land experienced less intensively gains and losses if the overall change was to have been distributed uniformly across the landscape while the gains and losses of forest land, grassland, water, settlement, wetland and other unused land were not consistent and stationary across the four time intervals. 3) The transition level illustrated that arable land expanded at the expense of grassland before 2000 while it gained intensively from wetland from 2000 to 2005. Settlement targets arable land and avoids grassland, water, wetland and other unused land. Besides, the loss of grassland was intensively targeted by arable land, forest land and wetland in the study period while the loss of wetland was targeted by water except for the time interval of 1976–2000. 4) During the early reclamation period, land use change of the study area was mainly affected by the policy, institutional and political factors, followed by the natural disasters.

Assessing the Impact of Using Different Land Cover Classification in Regional Modeling Studies for the Manaus Area,Brazil

Land cover classification is one of the main components of the modern weather research and forecasting models, which can influence the meteorological variable, and in turn the concentration of air pollutants. In this study the impact of using two traditional land use classifications, the United States Geological Survey (USGS) and the Moderate-resolution Imaging Spectroradiometer (MODIS), were evaluated. The Weather Research and Forecasting model (WRF, version 3.2.1) was run for the period 18 - 22 August, 2014 (dry season) at a grid spacing of 3 km centered on the city of Manaus. The comparison between simulated and ground-based observed data revealed significant differences in the meteorological fields, for instance, the temperature. Compared to USGS, MODIS classification showed better skill in representing observed temperature for urban areas of Manaus, while the two files showed similar results for nearby areas. The analysis of the files suggests that the better quality of the simulations favorable to the MODIS file is straightly related to its better representation of urban class of land use, which is observed to be not adequately represented by USGS.

An integrated classification method for thematic mapper imagery of plain and highland terrains

The classification of thematic mapper imagery in areas with strong topographic variations has proven problematic in the past using a single classifier, due to the changing sun illumination geometry. This often results in the phenomena of identical object with dissimilar spectrum and different objects with similar spectrum. In this paper, an integrated classification method that combines a decision tree with slope data, tasseled cap transformation indices and maximum likelihood classifier is introduced, to find an optimal classification method for thematic mapper imagery of plain and highland terrains. A Landsat 7 ETM+ image acquired over Hangzhou Bay, in eastern China was used to test the method. The results indicate that the performance of the inte- grated classifier is acceptably good in comparison with that of the existing most widely used maximum likelihood classifier. The integrated classifier depends on hypsography (variation in topography) and the characteristics of ground truth objects (plant and soil). It can greatly reduce the influence of the homogeneous spectrum caused by topographic variation. This integrated classifier might potentially be one of the most accurate classifiers and valuable tool for land cover and land use mapping of plain and highland terrains.