Study on Dynamic Remote Sensing Monitoring of LUCC and Soilwind Erosion Intensity in the Beijing-Tianjin Sandstorm Source Control Project Region

The Land Use/Cover Change(LUCC) and soil wind erosion intensity of the Beijing\|Tianjin sandstorm source control project region were monitored by remote sensing.The spatial and temporal patterns of LUCC and soil wind erosion in the project region were analyzed.The results showed that there was significant difference in LUCC and soil erosion intensity before and after the project was implemented.In the recent 30 years,the LUCC process mainly manifested the change from cultivated land reclamation to ecological conversion of farmland to forest and grass,with the ecological restoration and desertification effectively inhibited.The overall arable land showed an increase and then decreased.The area of arable land increased from 2000 to 2015,the area of cultivated land converted to forest and grassland was 446.10 km² and 1 129.32 km²,with the most obvious in the west;the area of land for construction expanded obviously;the trend of unutilized land decreased significantly The type of conversion is dominated by grassland conversion to grassland with an area of 493.12 km².The erosion-mitigating modulus of soil erosion in the project region with wind-blown sand control decreased overall,especially after the implementation of ecological engineering (p<0.001).The eastern and southern areas are covered with high-coverage grassland and soil wind erosion in the area with the main type is small;Soil wind erosion in the Hunshandake sandy land is larger,but the overall trend is decreasing.Different land use/cover types have a greater impact on soil wind erosion intensity.The order of soil wind erosion modulus is Sandy land> Sparse grass> Moderate grass>dryland> Shrub>Paddy>Dense grass> Other woods> Sparse woods> Forest;The conversion of low coverage to high coverage grassland types effectively inhibited soil erosion (-66.12%),and the increase of vegetation coverage effectively reduced soil erosion.The soil wind erosion increased (58.26%) in the surrounding area of sandy area,the soil wind erosion increasedduring the conversion process of low coverage grassland type,and the grassland was converted into sand,and the wind erosion in the dry land increased.     

基于Landsat TM 数据的若尔盖县LUCC 时空特征研究

若尔盖县是世界著名若尔盖湿地的主要组成部分, 是青藏高原高寒生态系统的典型代表。基于1989 年、1997 年和2004 年3 期Landsat TM 影像的土地利用ö土地覆被分类结果, 运用地理信息系统空间分析方法和数理统计学方法, 深入分析了四川省若尔盖县近15 年来各土地利用/覆被类型尤其是草地和沼泽的数量和空间变化特征。结果表明: ①研究区主要土地利用与土地覆被类型为草地、沼泽、林地和裸地, 其中草地与沼泽面积逐步减少, 而裸地面积成倍增长。②通过建立研究区LU CC 幅度、LU CC 数量和空间变化模型以及趋势与状态指数模型, 很好的表现了研究区LU CC 的时空特征。从整个区域来看, 前期综合LU CC 趋势和状态指数为0. 37, 处于准平衡状态;后期小于前期, 为0. 23, 处于平衡状态, 整个时段其指数为0. 35, 为准平衡状态, 呈现双向转换态势。③定位分析了研究区LU CC 情况, 发现区域草地和沼泽退化相当严重, 而且前后两期退化区在空间上有所转移。     

张掖市土地利用/覆盖变化模拟

土地利用/覆盖变化是全球变化的主要原因,也是与可持续发展密切相关的课题。土地利用/覆盖变化模拟是预测未来土地利用/覆盖变化的重要方法。将国际上先进的CLUE-S模型应用到位于黑河中上游的张掖市,模拟该地区的土地利用/覆盖变化。模拟时段为2001～2020年。模型中将土地利用类型分为:①耕地;②林地;③草地;④水域;⑤城镇用地;⑥未利用地。用回归分析的方法,选择了对该地区土地利用/覆盖变化有重要贡献的7种驱动因子,分别为:与城市的距离、与河流的距离、与道路的距离、人口密度、海拔、坡度、坡向。模拟结果显示:到2020年,林地、草地、水域和城镇用地面积增加,耕地,未利用地面减少。     

Accuracy and applicability of linear spectral unmixing in delineating potential erosion areas in tropical watersheds

This study was undertaken to assess the accuracy of linear spectral unmixing (LSU) in estimating fractional abundance of land cover components and to examine its applicability in delineating potential erosion areas in tropical watershed. Five image end‐members (mixed vegetation, grass, Acacia auriculiformis, bare soil and water/shadow) were selected and used in different combinations in unmixing Landsat Enhanced Thematic Mapper (ETM) into fraction images. The accuracy assessment was conducted by comparing the land cover abundance estimates derived from unmixing with the land cover abundance measured from field‐validated classified QuickBird imagery. Good agreement was obtained using a four‐end‐member combination in which shadow was eliminated. The results suggest that LSU could be implemented for soil erosion detection. In general, soil erosion increases when vegetation cover decreases; hence, we used the fraction images to derive a bare soil/vegetation cover ratio and used that as a simple indicator to map high potential erosion areas. Comparison with field assessment of actual erosion levels in the study area showed that the technique is effective in identifying areas on which erosion control efforts should be concentrated.     

Comparisons of three recent moderate resolution African land cover datasets: CGLS-LC100, ESA-S2-LC20, and FROM-GLC-Africa30

Accurate and up-to-date land use land cover (LULC) mapping has long been a challenge in Africa. Recently, three LULC maps with moderate spatial resolution (20 m to 100 m) have been developed using multiple Earth observation datasets for 2015–2016 for the whole continent, which provide unprecedented spatial detail of the land surface for Africa. This study aimed to compare these three recent African LULC maps (i.e. the Copernicus Global Land Service Land Cover (CGLS-LC100, 100 m), European Space Agency Sentinel-2A Land Cover (ESA-S2-LC20, 20 m) and Finer Resolution Observation and Monitoring of Global Land Cover for Africa version 2 (FROM-GLC-Africa30, 30 m)) using a validation sample set and statistics from the FAO. The results indicated that the accuracy of the three datasets was unevenly distributed in spatial extent and area estimation. All the three datasets achieve an accuracy of above 60% and the fraction layer of CGLS-LC100 showed the best consistency with FAO statistics in the area. However, great disagreement in spatial details was found among three products, with 43.12% of the total area in Africa was in low agreement. The LULC mapping regions with the highest uncertainty were southeast Africa, the Sahel region and the Eastern Africa Plateau. Uncertainty was most closely related to elevation and precipitation changes along latitude/longitude.     

Evaluation of dynamic linkages between evapotranspiration and land-use/land-cover changes with Landsat TM and ETM+ data

Complexity embedded in coastal management leads to numerous questions as to how inherent spatial and temporal linkages among evapotranspiration (ET), depth to groundwater and land-use/land-cover change (LUCC) could affect the dynamics among these seemingly unrelated events. This article aims to address such unique dynamics in the nexus of physical geography and ecohydrology. To understand such dynamic linkages, a case study was carried out in a fast growing coastal region – the southern Laizhou Bay in Shandong Province, China – by identifying the coastal LUCC at the decadal scale in association with the variations of ET with the aid of Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper (ETM+) data. In such a coastal landscape evolutionary assessment, findings show that the major patterns of land use and land cover (LULC) in the study area are farmland, saline-alkali land, developed land, salt land and beach land. Over a 20-year time frame, declining groundwater trends were observed, while ET increased gradually with changing LULC. By using the surface energy balance algorithm for land (SEBAL) with Landsat TM/ETM+ images and additional environmental data, the concomitant response of ET variations due to LUCC becomes lucid among three significantly correlated pairs including fractional vegetation cover (FVC), land surface temperature (LST) and soil heat flux. The dynamic linkages between ET and LULC were finally confirmed with such a pair-wise analysis.     

土地利用最佳模拟尺度选择及空间格局模拟

土地利用变化是一个受到多重因素相互影响的动态过程。目前,已经成为全球环境变化和可持续发展的重要内容,而区域土地利用空间格局模拟已成为LUCC研究的关键内容。以2000年以及2010年的TM遥感影像解译数据以及数字高程模型、水系、铁路、公路、降雨量和气温等数据为基础,运用二元逻辑斯蒂回归模型对黄土台塬区的土地利用最佳模拟尺度进行了选择,并在此基础上对研究区的各种土地利用进行了空间格局模拟。研究结果显示：（1）在土地利用格局模拟的十个空间尺度上,土地利用变化空间格局与其驱动力因子之间存在着一定的尺度相关性特征;（2）黄土台塬区耕地、林地、草地的ROC值在十个空间尺度上均呈现出先增加后减少的趋势,转折点在400 m尺度附近,说明黄土台塬区的土地利用在尺度效应和尺度转换的效应下,400 m×400 m是此区域土地利用格局优化的最佳模拟尺度;（3）在400 m最佳模拟尺度上所模拟出的草地和林地的分布格局都与人均GDP和地形综合指数两个变量显著相关,而对耕地的分布影响最为明显的变量则是地形综合指数。     

Spatial agreement between two land‐cover data sets stratified by agricultural eco‐regions

Two of the most widely used land‐cover data sets for the United States are the National Land‐Cover Data (NLCD) at 30‐m resolution and the Global Land‐Cover Characteristics (GLCC) at 1‐km nominal resolution. Both data sets were produced around 1992 and expected to provide similar land‐cover information. This study investigated the spatial distribution of NLCD within major GLCC classes at 1‐km unit over a total of 11 agricultural‐related eco‐regions across the continental United States. Our results exhibited that data agreement or relationship between the GLCC and NLCD was higher for the eco‐regions located in the corn belt plains with homogeneous or less complicated land‐cover distributions. The GLCC cropland primarily corresponded to NLCD row crops, pasture/hay and small grains, and was occasionally related to NLCD forest, grassland and shrubland in the remaining eco‐regions due to high land‐cover diversity. The unique GLCC classes of woody savanna and savanna were mainly related to the NLCD orchard and grassland, respectively, in the eco‐region located in the Central Valley of California. The GLCC urban/built‐up among vegetated areas strongly agreed to the NLCD urban for the eco‐regions in the corn belt plains. A set of sub‐class land‐cover information provided through this study is valuable to understand the degrees of spatial similarity for the major global vegetated classes. The sub‐class information from this study provides reference for substituting less‐detailed global data sets for detailed NLCD to support national environment studies.     

Landsat TM spectral information to enhance the land cover of Scotland 1988 dataset

The Scottish Office's Land Cover of Scotland 1988 Survey (LCS88), was announced in May 1987 and was intended to provide the first-ever detailed census of land cover in Scotland. It came about as a result of increasing concern about the nature and rate of land use change in rural Scotland and the need to obtain objective baseline information on which to build and evaluate future countryside policy. One of the recommendations of a Scottish Office feasibility study carried out prior to the LCS88 survey, was that satellite remotely-sensed data should be considered for measuring landscape change in the future. This paper relates specifically to this recommendation and presents the results of an evaluation study to investigate the use of limited acquisition satellite imagery from Landsat Thematic Mapper, to derive a land cover classification and spectral segmentation information to enhance the existing LCS88 dataset. Although a successful land cover, primary as well as some individual cover features, was obtained from the satellite data, the overall accuracy comparison with the LCS88 cover features was limited. However, the opportunistic mapping of important agricultural crops and primary cover types, such as oilseed rape and forestry cover features, or the interpretation of some of the considerable confusion between semi-natural grassland and improved grassland cover features, provided for an enhanced LCS88 dataset. This was also true for the illustration of the considerable potential of a satellite classification and spectral data, for identifying the component parts of LCS88 Mosaic cover features and estimating vegetation quality.     

Analysis of land use/cover changes and urban expansion of Nairobi city using remote sensing and GIS

We used three Landsat images together with socio‐economic data in a post‐classification analysis to map the spatial dynamics of land use/cover changes and identify the urbanization process in Nairobi city. Land use/cover statistics, extracted from Landsat Multi‐spectral Scanner (MSS), Thematic Mapper (TM) and Enhanced Thematic Mapper plus (ETM+) images for 1976, 1988 and 2000 respectively, revealed that the built‐up area has expanded by about 47?km². The road network has influenced the spatial patterns and structure of urban development, so that the expansion of the built‐up areas has assumed an accretive as well as linear growth along the major roads. The urban expansion has been accompanied by loss of forests and urban sprawl. Integration of demographic and socio‐economic data with land use/cover change revealed that economic growth and proximity to transportation routes have been the major factors promoting urban expansion. Topography, geology and soils were also analysed as possible factors influencing expansion. The integration of remote sensing and Geographical Information System (GIS) was found to be effective in monitoring land use/cover changes and providing valuable information necessary for planning and research. A better understanding of the spatial and temporal dynamics of the city's growth, provided by this study, forms a basis for better planning and effective spatial organization of urban activities for future development of Nairobi city.     

Remote sensing of land use and vegetation for mesoscale hydrological studies

In this paper, methods for mapping land use changes and vegetation parameters using remote sensing data are presentedin the context of hydrological studies. In the first part, a land use and land cover classification system (RUB-LUCS: Ruhr University Bochum - Land Use and Land Cover Classification System) is developed for providing distributed information for hydrological modelling and for detection of distributed land use changes. Applying this system to Landsat data, land use time series is created for hydrological modelling of effects of man-made changes in the Sauer River Basin. In the second part, equations are established for estimating leaf area indices using vegetation indices calculated from remote sensing data and a two stream approximation model for estimating leaf area indices is applied to the Sauer River Basin. Combining the two approaches, a method has been found for calculating leaf area indices for mesoscale river basins using remote sensing data.     

Classification accuracy and trend assessments of land cover- land use changes from principal components of land satellite images

The paper evaluated the accuracy of classifying Land Cover-Land Use (LCLU) types and assessed the trends of their changes from Principal Components (PC) of Land satellite (Landsat) images. The accuracy of the image classification of LCLU was evaluated using the confusion matrices and assessed with cross-referencing of samples of LCLU types interpreted and classified from System Pour l’Observation de la Terre (SPOT) images and topographical map. LCLU changes were detected, quantified, and statistically analysed. The interpretation error of the composite image of Landsat Enhanced Thematic Mapper Plus (Landsat ETM⁺) (2006) was high compared with that from the PC image of Landsat ETM⁺ (2006). From 1986 – 2006 the area covered by settlements increased by 0.8% (230,380.00 km²), agricultural land decreased by 7.5% (1009.40 km²), vegetation cover decreased by 0.9% (114.00 km²) while waterbody increased by 0.2% (25.91 km²). Also, from 1986 – 2006 the average annual rates of change in the area of settlements was 6.7%. Agricultural land and bare land showed fluctuations of change rates from 6.7% and 5.0% annually in 1986 and 2006 respectively. The quantitative evidences of LCLU changes revealed the growth of settlements. The conversions of land from agriculture to urban land represent the most significant land cover changes. The rate of change was as high as 4.8% for settlements while agricultural lands were converted at 5.0% per year. The Principal Component Analysis (PCA) of the Landsat images and supervised classification method used made it possible to classify and determine the area of LCLU classes from the set of Landsat images without prior depiction and delimitation of individual LCLU type. It permitted the measurement of area of each LCLU class at a high accuracy level and kept the level of error relatively constant. The PCA analysis in this study affirms the previous research findings. Future research works should focus on the use of remotely sensed images with high temporal and spatial resolutions such as Quick Bird and SPOT 6 to develop effective and accurate LCLU change mapping and monitoring at the local scale.

The PCA technique has been used quite widely to study changes in land cover and land use in many ‘developed’ countries but much still needs to be done in developing and undeveloped countries where land cover and land use change is poorly mapped and knowledge of such changes is very important for planning development of the country.     

ECOCLIMAP-II: An ecosystem classification and land surface parameters database of Western Africa at 1 km resolution for the African Monsoon Multidisciplinary Analysis (AMMA) project

This work is devoted to a presentation of the ECOCLIMAP-II database for Western Africa, which is an upgrade for this region of the former initiative, ECOCLIMAP-I, implemented at global scale. ECOCLIMAP-II is a dual database at 1-km resolution that comprises an ecosystem classification and a coherent set of land surface parameters. This new physiographic information (e.g. leaf area index, fractional vegetation cover, albedo and land cover classification), was especially developed in the framework of the African Monsoon Multidisciplinary Analysis (AMMA) programme in order to support the modelling of land-atmosphere interactions, which stresses the importance of the present study. Criteria for coherence between prevalent land cover classifications and the analysis of time series of the satellite leaf area index (LAI) between 2000 and 2007 constitute the analysis tools for setting up ECOCLIMAP-II. The LAI and inferred fraction of vegetation cover are spatially distributed per land cover unit. The fraction of vegetation cover is handled to split the land surface albedo into vegetation and bare soil albedo components, as is required for a large number of applications. The new ECOCLIMAP-II land cover product is improved with regard to the spatial coherence compared to former version. The reliability of the physiographic details is also confirmed through verification with land cover products at higher resolution.     

Estimating above‐ground burned biomass and CO2 emissions for tropical Africa for the year 1990 with the NOAA–NASA Pathfinder AVHRR 8 km land dataset

Biomass burning combusts Earth's vegetation (in forests, savannas and agricultural lands) and occurs over huge areas of the Earth's surface. Global estimates of biomass burning are thus required in order to provide exact figures of the gas fluxes derived from this source. In this paper we use coarse resolution images for estimating above‐ground burned biomass and CO₂ emissions for tropical Africa for the year 1990. The burned land cover areas have been derived from burn scar and land cover maps using the global daily National Oceanic and Atmospheric Administration–National Aeronautics and Space Administration (NOAA–NASA) Pathfinder AVHRR 8?km land dataset. A burned area estimation of (742±222)?Mha has been considered. Monthly maximum Normalized Difference Vegetation Index (NDVI) composites and biomass density measurements have been used for modelling the temporal behaviour of above‐ground biomass for the main seasonal vegetation classes in Africa (humid savanna, derived humid savanna, dry savanna grassland and broadleaf savanna). The amount of above‐ground burned biomass and therefore CO₂ emissions can be estimated from burned land cover area, above‐ground biomass density, burn efficiency and emission factor of trace gas by land cover class. A total of 6494 (3675–9312) Tg for CO₂ emissions was computed for tropical Africa for the year 1990.     

Uncertainty in hydromorphological and ecological modelling of lowland river floodplains resulting from land cover classification errors

Land cover maps provide essential input data for various hydromorphological and ecological models, but the effect of land cover classification errors on these models has not been quantified systematically. This paper presents the uncertainty in hydromorphological and ecological model output for a large lowland river depending on the classification accuracy (CA) of a land cover map. Using four different models, we quantified the uncertainty for the three distributaries of the Rhine River in The Netherlands with respect to: (1) hydrodynamics (WAQUA model), (2) annual average suspended sediment deposition (SEDIFLUX model), (3) ecotoxicological hazards of contaminated sediment for a bird of prey, and (4) floodplain importance for desired habitat types and species (BIO-SAFE model). We carried out two Monte Carlo (n = 15) analyses: one at a 69% land cover CA, the other at 95% CA. Subsequently we ran all four models with the 30 realizations as input.The error in the current land cover map gave an uncertainty in design water levels of up to 19 cm. Overbank sediment deposition varied up to 100% in the area bordering the main channel, but when aggregated to the whole study area, the variation in sediment trapping efficiency was negligible. The ecotoxicological hazards, represented by the fraction of Little Owl habitat with potential cadmium exposure levels exceeding a corresponding toxicity threshold of 148 μg d⁻¹, varied between 54 and 60%, aggregated over the distributaries. The 68% confidence interval of floodplain importance for protected and endangered species varied between 10 and 15%. Increasing the classification accuracy to 95% significantly lowered the uncertainty of all models applied. Compared to landscaping measures, the effects due to the uncertainty in the land cover map are of the same order of magnitude. Given high financial costs of these landscaping measures, increasing the classification accuracy of land cover maps is a prerequisite for improving the assessment of the efficiency of landscaping measures.     

Toward remote sensing methods for land cover dynamic monitoring: Application to Morocco

An extensive remotely sensed dataset recently available to the scientific community, The Global Land 1-km AVHRR Project, has been used to examine the possibilities of multi-temporal imagery for mapping and monitoring changes in the biophysical characteristics of land cover. Our goal was to investigate the regional response of the soil-vegetation system to climate in arid zones. We addressed this problemby applying theoretical models to obtain parameters such as Land Surface Temperature (LST) and Normalized Difference Vegetation Index (NDVI) from satellite data and by analysing the spatial-temporal dynamics of these parameters. Morocco was selected as the area of study due to its high environmental diversity. This area is also clearly affected by the risk of the advance of the desert. Using The Global Land 1-km AVHRR Project dataset, two methodologies are proposed for the monitoring of land cover dynamics in different areas of interest defined using as mapping criteria the Annual Average of NDVI (AANDVI): (1) The Method of the Area of the Triangle (MAT), based on a form described by the annual evolution of LST and NDVI in each area; (2) the Method of the Slope, which analyses the slope of the line defined by the months of the maximum NDVI and the minimum LST.     

Detection of land cover classes in agro-ecosystems of northern Egypt by remote sensing

The objective of this study is to detect the current land cover classes of a representative area in the coastal agricultural land of Egypt, where changes have been observed recently due to the urban sprawl. This has been achieved after the production of different vegetation indices, and the comparison between them and their sensitivity to the environmental conditions of the area under study. This was followed by the production of profiles of different Vegetation Index (VI) images, and supervised Maximum Likelihood Classification (MLC), for the assessment of land cover classes and their cover percentage.

It was found that different greenness levels were best estimated by the Environmental Vegetation Index (EVI) and the Normalized Difference Vegetation Index (NDVI). The MLC resulted in classifying the study area into 10 classes with an accuracy above 90 per cent. Agriculture lands cover about 46 per cent only of the total area, which was estimated to cover at least 80 per cent 15 years ago. Land transformation is proceeding by a rate of about 1-47 per cent every year. This means that if such a rate continues in the same pattern, all agricultural land in the area will be lost to urbanization and other activities within less than 70 years.     

Characterizing land cover dynamics using multi-temporal imagery

An analysis of land cover changes was performed using a time-series of five SPOT HRV images for an area of the State of Rond°onia (western Brazilian Amazon) from 1986 to 1992. The total deforested area and the fraction of land abandoned to secondary vegetation were determined by means of image classification and Geographical Information System (GIS) techniques. Areas deforested by 1986 were traced throughout the period to estimate the fraction of land remaining continuously in the secondary vegetation category, possibly forming older secondary vegetation.