An Analysis of Land Use and Land Cover Changes, and Implications for Conservation in Mukumbura (Ward 2), Mt Darwin, Zimbabwe, 2002-2022

Understanding trends of land use land cover (LULC) changes is important for biodiversity monitoring and conservation planning, and identifying the areas affected by change and designing sustainable solutions to reduce the changes. The study aims to evaluate and quantify the historical changes in land use and land cover in Mukumbura (Ward 2), Mt Darwin, Zimbabwe, from 2002 to 2022. The objective of the study was to analyse the LULC changes in Ward 2 (Mukumbura), Mt Darwin, Northern Zimbabwe, for a period of 20 years using geospatial techniques. Landsat satellite images were processed using Google Earth Engine (GEE) and the supervised classification with maximum likelihood algorithm was employed to generate LULC maps between 2002 and 2022 with a five (5) year interval, investigating the following variables, forest cover, barren land, water cover and the fields. Findings revealed a substantial reduction in forest cover by 38.8%, water bodies (wetlands, ponds, and rivers) declined by 55.6%, whilst fields (crop/agricultural fields) increased by 93.3% and the barren land cover increased by 26.3% from 2002 to 2022. These findings point to substantial changes in LULC over the observed years. LULC changes have resulted in habitat fragmentation, reduced biodiversity, and the disruption of ecosystem functions. The study concludes that if these deforestation trends, cultivation, and settlement land expansion continue, the ward will have limited indigenous fruit trees. Therefore, the causes for LULC changes must be controlled, sustainable forest resources use practiced, hence the need to domesticate the indigenous fruit trees in arborloo toilets.

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.

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.

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.

Prediction of the joint impacts of sea level rise and land development on distribution patterns of mangrove communities

Mangrove distribution along shorelines shows distinct zonation patterns;thus,different communities may face various influences from sea level rise(SLR)and land use.However,long-term change predictions are usually based only on the total extent of mangroves.Few studies have revealed how SLR and land development such as agriculture,aquaculture,and urbanization jointly affect different intertidal mangrove communities.This study proposed a novel framework combining SLAMM(Sea Level Affecting Marshes Model)and the CLUE-S(Conversion of Land Use and its Effect at Small regional extent)model to assess the potential impacts on upper and lower intertidal mangrove communities.Maoweihai in Guangxi,China,was selected as the study area and the potential impacts from the squeeze effect and mangrove expansion potential were evaluated.We established three scenarios combining SLR and land use patterns to predict mangrove coverage projections by 2070.The results showed that,under a single SLR driver,the upper intertidal mangroves would be more adaptive to rapid SLR than the lower intertidal mangroves.However,under the combined influence of the two drivers,the upper intertidal mangroves would experience larger squeeze effects than the lower intertidal mangroves,with up to 80.5%of suitable habitat lost.Moreover,the expansion potential of upper intertidal mangroves would be considerably more limited than that of lower intertidal mangroves.The length of the expandable habitat patch boundary of upper intertidal mangroves only reached 1.4–1.8 km,while that of the lower intertidal mangroves reached up to99.2–111.2 km.Further,we found that aquaculture ponds and cropland are the top two land development types that could occupy suitable habitat and restrict the mangrove expansion potential.Our results highlight that timely improvement of land use policies to create available landward accommodation space for mangrove migration is essential to maintain the coverage and diversity of mangrove communities under SLR.The proposed method can be a helpful tool for adaptive mangrove conservation and management under climate change.

Evaluation of a deep-learning model for multispectral remote sensing of land use and crop classification

High-resolution deep-learning-based remote-sensing imagery analysis has been widely used in land-use and crop-classification mapping. However, the influence of composite feature bands, including complex feature indices arising from different sensors on the backbone, patch size, and predictions in transferable deep models require further testing. The experiments were conducted in six sites in Henan province from2019 to 2021. This study sought to enable the transfer of classification models across regions and years for Sentinel-2 A(10-m resolution) and Gaofen PMS(2-m resolution) imagery. With feature selection and up-sampling of small samples, the performance of UNet++ architecture on five backbones and four patch sizes was examined. Joint loss, mean Intersection over Union(m Io U), and epoch time were analyzed, and the optimal backbone and patch size for both sensors were Timm-Reg Net Y-320 and 256 × 256, respectively. The overall accuracy and Fscores of the Sentinel-2 A predictions ranged from 96.86% to 97.72%and 71.29% to 80.75%, respectively, compared to 75.34%–97.72% and 54.89%–73.25% for the Gaofen predictions. The accuracies of each site indicated that patch size exerted a greater influence on model performance than the backbone. The feature-selection-based predictions with UNet++ architecture and upsampling of minor classes demonstrated the capabilities of deep-learning generalization for classifying complex ground objects, offering improved performance compared to the UNet, Deeplab V3+, Random Forest, and Object-Oriented Classification models. In addition to the overall accuracy, confusion matrices,precision, recall, and F1 scores should be evaluated for minor land-cover types. This study contributes to large-scale, dynamic, and near-real-time land-use and crop mapping by integrating deep learning and multi-source remote-sensing imagery.

A Preliminary Study on the Problems and Improvement of the Latest Land Use Classification System in China

The establishment of a unified land use classification system is the basis for realizing the unified management of land and sea,urban and rural areas,and aboveground and underground space.In November 2020,the Ministry of Natural Resources of the People's Republic of China issued the Classification Guide for Land and Space Survey,Planning and Use Control of Land and Sea(for Trial Implementation),which aims to establish a national unified land and sea use classification system,lay an important foundation for scientific planning and unified management of natural resources,rational use and protection of natural resources,and speed up the construction of a new pattern of land and space development and protection.However,there are still some obvious shortcomings in the Classification Guide.This paper analyzes some problems existing in this classification standard from three aspects of logicality,rigorousness and comprehensiveness,and puts forward some suggestions for further improvement.This has important practical significance to better guiding the practice of land use and land resources management,and then to achieving the goal of unified management of natural resources.

Area Classification of Surrounding Parking Facility Based on Land Use Functionality

The different land use surrounding parking facility has significant impact on parking behavior. This paper studies the functional classification of land use surrounding parking facility, which is fundamentally important for indepth research on parking behavior. 37 parking facilities located between the second and sixth ring roadway in Beijing were selected for this study. Based on the surveys conducted at these parking facilities, various parking behavior were analyzed, based on which the scope of the different parking was determined. The information on location, land use characteristics, public transport, the surrounding parking situations are collected for each investigated parking facility. Applying the SPSS clustering method, the threshold was developed for the classification. Totally, five categories are proposed for the land use functionality surrounding parking facility as the results of this study.

Monitoring Land Use Dynamics in Chanthaburi Province of Thailand Using Digital Remotely Sensed Images

A comprehensive method of image classification was developed for monitoring land use dynamics in Chanthaburi Province of Tailand. RS (Remote Sensing), GIS (Geographical Information System), GPS (Global Positioning System) and ancillary data were combined by the method which adopts the main idea of classifying images by steps from decision tree method and the hybridized supervised and unsupervised classification. An integration of automatic image interpretation, ancillary materials and expert knowledge was realized. Two subscenes of Landsat 5 Thematic Mapper (TM) images of bands 3, 4 and 5 obtained on December 15, 1992, and January 17, 1999, were used for image processing and spatial data analysis in the study. The overall accuracy of the results of classification reached 90%, which was verified by field check.Results showed that shrimp farm land, urban and traffic land, barren land, bush and agricultural developing area increased in area, mangrove, paddy field, swamp and marsh land, orchard and plantation, and tropical grass land decreased, and the forest land kept almost stable. Ecological analysis on the land use changes showed that more attentions should be paid on the effect of land development on ecological environment in the future land planning and management.

Dynamics of Coastal Land Use Patterns of Inner Lingdingyang Bay in the Zhujiang River Estuary

To satisfy the growing of land demand from economic development,a large scale of land reclamation from sea has been carried out in Inner Lingdingyang Bay in the Zhujiang(Pearl)River estuary in recent years.As a result,the tidal flat and the water channels became narrow,the frequency of floods increased,and the environment was un-dermined.Guangzhou Marine Geological Survey(GMGS)conducted an integrated project for marine geo-environ-ment and geo-hazards survey in 2003.With the integration of multi-temporal remote sensing images of 1977,1978,1988,1996,and 2003,GIS spatial analyzing approach and GPS technique,as well as field data and other background data of the region,this research investigated the comprehensive characteristics and the drivers of coastal land use dy-namics and shoreline changes in Inner Lingdingyang Bay.The results reveal that the reclaimed coastal land was mainly for agriculture and aquaculture in early years,but now they are used for construction sites of harbors and in-dustries,especially high-tech industry.

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.

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.

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.

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.

Evaluating Land Use/Land Cover Change and Its Socioeconomic Implications in Agarfa District of Bale Zone, Southeastern Ethiopia

A systematic analysis of land use/cover change is so decisive to exactly understand the extent of change and take essential measures to curb down the rate of changes and protect the land cover resources sustainably. This land use/land cover change study was conducted in Agarfa district of Bale zone, Oromia Regional State, Southeastern Ethiopia. The objectives of this study were to evaluate the trends, drivers and its socio-economic and environmental implication in study area. A descriptive research method was employed to achieve the intended objectives of the study. In the three years (1976, 1995, and 2014) Landsat Satellite images and socio-economic survey were the main data sources for this study. ERDAS Imagine and Arch-GIS tools were used to classify and generate land use/land cover maps of the study area. Survey questionnaires, key informant interviews, and field observation were employed to obtain information on drivers and its socio-economic and environmental implication in the district. The results show that the land use/land cover of the study area had changed dramatically during the period of 38 years. A rapid loss of forest land and shrub land cover in the landscape took place between 1976 and 2014. Conversely, agriculture and grazing lands were increased by 30% and 42% respectively at the expense of the lost land use/land cover types. Forest land is the most converted cover type during the entire study period. In the 38 years, forest lands diminished by over 65% of the original forest cover that was existed at the base year (1976). Local climate change, declining agricultural productivity and livestock quantity and quality and scarcity of fuel wood and constructional materials were some of the socio-economic and livelihood impacts of land use and land cover change of the study area. Thus, this finding affords information to land users and policy makers on extent of the change and social forces leading to this changes and its subsequent implication on local socio-economic and environmental conditions of the study area.

Evaluation of Land Use &Land Cover Change Using Multi-Temporal Landsat Imagery: A Case Study Sulaimaniyah Governorate, Iraq

Land use & land cover change detection in rapid growth urbanized area have been studied by many researchers and there are many works on this topic. Commonly, settlement sprawl in area depends on many factors such as eco-nomic prosperity and population growth. Iraq is one of the countries which witnessed rapid development in the settlement area. Remote sensing and geographic information system (GIS) are analytical software technologies to evaluate this familiar worldwide phenomenon. This study illustrates settlement development in Sulaimaniyah Governorate from 2001 to 2017 using Landsat satellite imageries of different periods. All images had been classified using remote sensing software in order to proceed powerful mapping of land use classification. Maximum likelihood method is used in the accurately extracted solution information from geospatial imagery. Landsat images from the study area were categorized into four different classes. These are: forest, vegetation, soil, and settlement. Change detection analysis results illustrate that in the face of an explosive demographic shift in the settlement area where the record + 8.99 percent which is equivalent to 51.80 Km2 over a 16-year period and settlement area increasing from 3.87 percent in 2001 to 12.86 percent in 2017. Accuracy assessment model was used to evaluate (LULC) classified images. Accuracy results show an overall accuracy of 78.83% to 90.09% from 2001 to 2017 respectively while convincing results of Kappa coefficient given between substantial and almost perfect agreements. This study will help decision-makers in urban plan for future city development.

Impact of Land Use/Land Cover Change on Surface Temperature Condition of Awka Town, Nigeria

This paper is aimed at identifying the land use/cover types in Awka in relation to their temporal dynamics, the extent of land use change in the city and effects of land use change on surface temperature. Multitemporal Landsat TM, ETM+ and OLI imageries were obtained at 15 years interval for 1986, 2000 and 2015 respectively. Image classification was conducted using supervised classification method. The result showed that built-up area has been on the increase, expanding from 9.55 sqkm in 1986 to 21.3 sqkm in 2000 and 21.45 sqkm in 2015. Cultivated lands have maintained a steady decline since 2000 having lost about 3.29 sqkm of its area. Similarly, vegetation, made up of dense, savanna and riparian, has been on a decline from a total of 33.69sqkm in 1986 to 21.407 sqkm losing about 12.29 sqkm of its area and increased by a mere 4.07 sqkm in 2015. These alterations had given rise to an average increase of 2.2°C in surface radiant temperature. This study recommends that relevant government planning agencies (ACTDA, ASHDC, etc.) should factor in the concept of greening and green spaces into their development policies and strategies to ensure that fair, conducive microclimate and sustainable environment is maintained in the Awka urban area.

Towards Sustainable Land Uses within the Elbe River Biosphere Reserve in Lower Saxony, Germany by Means of TerraSAR-X Images

Floods are one of the major hazards worldwide. They are the source of huge risks in rural and urban areas, resulting in severe impacts on the civil society, industry and the economy. The Elbe River has suffered from many severe floods during recent decades. In this study, the zones flooded during 2011 were analyzed using TerraSAR-X images and a digital elevation model for the area in order to identify possible ways to mitigate flood hazards in the future, regarding sustainable land-use. Two study areas are investigated, around the Walmsburg oxbow and the Wehningen oxbow. These are located between Elbe-Kilometer (505-520) and (533-543), respectively, within the Lower Saxonian Elbe River Biosphere Reserve. Those areas are characterized by several types of land use, with agricultural land use being predominant. The study investigated the possibility of using a Decision-Tree object-based classifier for determining the major land uses and the extent of the inundation areas. The inundation areas identify for 2011 submerged some agricultural fields that must be added to existing flood risk maps, and future cultivation activities there prevented to avoid the possible economic losses. Furthermore, part of the residential area is located within the high flood zone, and must be included in risk maps to avoid the possible human and economic losses, to achieve sustainable land use for the areas studied.

Analysis of the Land Use Structure in Alpine Valley Area of Jinsha River:Taking Xueshan Township,Luquan County,Yunnan Province as an Example

Oriented by ecological,production and living functions of land,this paper analyzes the status of land use in Xueshan Township,Luquan County,Yunnan Province using the method of assignment.The land is classified into general production land,production and ecological land,general living land,living and production land,living and ecological land,general ecological land and ecological and production land.The result is compared with the classification system of the second national land survey.In theory,the classification system of ecological land,living land and production land is constructed.Taking Xueshan Township,Luquan County,a typical alpine valley area of Jinsha River as an example,based on the survey data on the changes of land use in Xueshan Township in Luquan County in 2016,this paper analyzes the distribution of ecological land,living land and production land in Xueshan Township.The results show that in Xueshan Township,ecological land(including general ecological land and ecological and production land)has the largest proportion(54.84%)in area,followed by production land(including general production land and production and ecological land)(44.41%),and living land(including general living land and living and production land)has the lowest proportion(only 0.75%).In overall,the distribution of production land and ecological land in Xueshan Township is not reasonable enough.For example,the proportion of dry land with a slope bigger than 15°and serious oil erosion in the township is currently 79.23%,among which,the dry land with a slope bigger than 25°and particularly serious soil erosion accounts for 52.88%.Therefore,it is necessary to gradually take measures to convert cultivated land into forests and further optimize the structure and spatial configuration of ecological land,living land and production land.

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.