One century of land use changes in the metropolitan area of Milan (Italy)

Abstract

This paper presents the possible contribution of multi-temporal Landsat Thematic Mapper (TM) data to the assessment of land-use modifications in the most important portion of the metropolitan area of Milan where rapid transformations, starting from urban areas and then gradually extending to rural areas, took place. The study area corresponds to the so-called ‘Great Milan’ which includes a protected area, the ‘South Milan Agricultural Park’, where a widespread conflict between agricultural and-urban land use has arisen. Park realisation will contribute improving agricultural activities and creating a belt for environment protection around the city. Digital thematic maps, digitizing Istituto Geografico Militare dTtalia cartography of 1888–90 and 1945–50, were extracted. Normalized Difference Vegetation Indices (NDVI) were produced from three Landsat-5 Thematic Mapper images of 1984, January, June and August, and a Multi-temporal Colour NDVI Composition (MCNC) output was produced. Maximum Likelihood Classification for land use mapping was applied both on MCNC data jointly with band 5 of June, and on 12 April 1990 Landsat TM image. Classification accuracy was assessed and results summarized. An historical analysis of land-use changes from XIX century up to today was performed by comparison of different surface classes from historical (1888–90 and 1945–50) and satellite (1984 and 1990) thematic maps. Results confirm the useful contribution of satellite remote sensing studying land-use/land-cover modifications in areas affected by phenomena of agriculture rapid transformation and residential or industrial development.     

Forest change monitoring of a remote biosphere reserve

A forest monitoring framework using Universal Transverse Mercator (UTM) grid cells to report forest change estimates derived from time-series satellite imagery was established for the Maya Biosphere Reserve (MBR) in northern Guatemala. Five dates of Landsat Thematic Mapper imagery were acquired and digitally processed to quantify forest change for four time periods: 1986 to 1990, 1990 to 1993, 1993 to 1995, and 1995 to 1997. Time-series change estimates are reported for 215 UTM grid cells approximately 100 km2 each. For the period 1990 to 1997, after the designation of the MBR, the percentage of grid cells with detectable annual forest clearing increased from 38% (1990-93) to 41% (1993-95) and 45% (1995-97). Prior to the establishment of the MBR (1986-90), none of the grid cells exhibited greater than 4.0% annual forest clearing. However in the next three time periods, 7.0%, 8.8% and 9.3% of the grid cells had clearing rates exceeding 4.0% per year. The accuracy of detecting forest clearing was 86.5% over all time periods (Kappa 0.82). Estimates of forest change and user's and producer's accuracy are reported for each time period between 1990 and 1997. The time-series forest change and spatial arrangement of grid locations indicate hot spots where rates and trends of agricultural expansion can be monitored. The baseline survey and the establishment of the UTM grid network to localize forest change estimates provides a framework for future satellite estimates of forest and land cover conversion to be monitored through time. The UTM grid is proposed as the first level in a multi-level ecological monitoring system for the Maya Biosphere Reserve where there are few permanent landmarks in the remote forest region.     

Detection of fire impact and vegetation recovery over tropical peat swamp forest by satellite data and ground-based NDVI instrument

Multitemporal Principal Component Analysis (MPCA) was used for processing Landsat Thematic Mapper (TM)/Enhanced Thematic Mapper plus (ETM+) satellite images. MPCA was able to merge spectral data corresponding to TM-1996 (pre-fire in 1997), ETM-2000 (post-fire 1997 and pre-fire 2002) and ETM-2003 (post-fire in 2002), which was crucial for detecting the fire impact and vegetation recovery. Results indicate that the burnt areas of 1997 and 2002 were 89,086 ha (16.5%) and 31,859 ha (5.9%), respectively, within the study area of 540,000 ha. Satellite Pour 1’Observation de la Terre (SPOT)-VEGETATION 10-day Maximum Value Composite (MVC) data were also used and compared with Normalized Difference Vegetation Index (NDVI) from ground-based NDVI. Our research demonstrates the strong relationship between Landsat- TM/ETM+, SPOT-VEGETATION data and ground-based NDVI in identifying land-cover changes and vegetation recovery over the tropical peat swamp forest area in Central Kalimantan, Indonesia that is affected by forest fires that occurred in 1997 and 2002.     

Fraction images in multitemporal change detection

The concept of mixed pixels allows the interpretation of remote sensing digital image data at sub-pixel level. Fraction-image data, obtained using the notion of mixed pixels, offer a potentially powerful method to detect changes in land-cover over a given period of time. This study proposes a new approach to detect land-cover changes, using two sets of fraction-image data obtained from sets of multispectral image data acquired at two different dates, over the same area. Changes based on the selected pixel components are then used to generate the fraction-change image data, including both positive (increase) and negative (decrease) changes in each component. The proposed analysis is then performed in the fraction-change space in two different ways: (1) by implementing unsupervised classification methods and (2) by comparing the fraction-change images among themselves. The proposed methodology is tested on two sets of Landsat Thematic Mapper (TM) multispectral image data obtained at two different dates and covering a test area mapped in previous works. Results obtained by the proposed methodology are presented and discussed.     

A model-based approach to estimating forest area

A logistic regression model based on forest inventory plot data and transformations of Landsat Thematic Mapper satellite imagery was used to predict the probability of forest for 15 study areas in Indiana, USA, and 15 in Minnesota, USA. Within each study area, model-based estimates of forest area were obtained for circular areas with radii of 5 km, 10 km, and 15 km and were compared to design-based estimates based on inventory plot data. Precision estimates for the circular areas were also obtained using variance formulae developed for this application that incorporated spatial correlation among model predictions for individual pixels. The model-based estimates were generally comparable to the design-based estimates. The advantages of the model-based approach are that maps and small areas estimates may be obtained and the necessity of releasing exact plot locations for user-specific applications is alleviated.     

Historical and future land-cover changes in the Upper Ganges basin of India

The green revolution represents one of the greatest environmental changes in India over the last century. The Upper Ganges (UG) basin is experiencing rapid rates of change of land cover and irrigation practices. In this study, we investigated the historical rate of change and created future scenario projections by means of 30 m-resolution multi-temporal Landsat 5 Thematic Mapper and Landsat 7 Enhanced Thematic Mapper Plus data of the UG basin. Post-classification change analysis methods were applied to Landsat images in order to detect and quantify land-cover changes in the UG basin. Subsequently, Markov chain analysis was applied to project future scenarios of land-cover change. Fifteen different scenarios were generated based on historic land-cover change. These scenarios diverged in terms of future projections, highlighting the dynamic nature of the changes. This study has shown that between the years 1984 and 2010 the main land-cover change trends are conversion from shrubs to forest (+4.7%), urbanization (+5.8%), agricultural expansion (+1.3%), and loss of barren land (–9.5%). The land-cover change patterns in the UG basin were mapped and quantified, showing the capability of Landsat data in providing accurate land-cover maps. These results, in combination with those derived from the Markov model, provide the necessary evidence base to support regional land-use planning and develop future-proof water resource management strategies.     

Mapping near-surface soil moisture on regional scale using ERS-2 SAR data

Soil moisture is an important hydrologic variable of great consequence in both natural and agricultural ecosystems. Unfortunately, it is virtually impossible to accurately assess the spatial and temporal variability of surface soil moisture using conventional, point measurement techniques. Remote sensing has the potential to provide areal estimates of soil moisture at a variety of spatial scales. This investigation evaluates the use of European Remote Sensing Satellite (ERS-2) C-band, VV polarization, synthetic aperture radar (SAR) data for regional estimates of surface soil moisture. Radar data were acquired for three contiguous ERS-2 scenes in the Southern Great Plains (SGP) region of central Oklahoma from June 1999 to October 2000. Twelve test sites (each approximately 800?m×800?m) were sampled during the ERS-2 satellite overpasses in order to monitor changes in soil moisture and vegetation on the ground. An average radar backscattering coefficient was calculated for each test site. Landsat-5 and -7 Thematic Mapper (TM) scenes of the experimental sites close in time to the ERS-2 acquisition dates were also analysed. The TM scenes were used to monitor land cover changes and to calculate the Normalized Difference Vegetation Index (NDVI). Land cover and ground data were used to interpret the radar-derived soil moisture data. Linear relationships between soil moisture and the backscattering coefficient were established. Using these equations, soil moisture maps of the Little Washita and the El Reno test areas were produced.     

Comparing the spectral signal of burned surfaces between Landsat 7 ETM+ and Landsat 8 OLI sensors

There is a long history of the use of Landsat data in burned land mapping mainly due to certain characteristics of the Landsat imagery including the spatial, spectral, and temporal data resolution, the low cost (Landsat data are now freely available), and the existence of an almost 35-year historical archive (excluding Landsat 1–3). Landsat 8 (Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS)) was launched on 11 February 2013 and it captures data in three new bands along with two additional thermal bands. However, is the spectral signal of burned surfaces in satellite remote-sensing data of Landsat series consistent and robust enough to allow the successful application of the techniques developed so far for Landsat 8? In this article, we compare the spectral signal of burned surfaces between Landsat 7 Enhanced Thematic Mapper Plus (ETM+) and Landsat 8 OLI sensors using five case studies that correspond to five large fire events in different biophysical environments in Greece, for which both Landsat 7 ETM+ and Landsat 8 OLI data were available. From the comparative analysis using histogram data plots of burned (post-fire image) and vegetated (pre-fire image) areas, spectral signature plots and separability indices of certain land-cover types, estimated using the same sampling areas over both satellite images, a general consistency was observed between the two sensors. Slight differences between the sensors were attributed to differences in the acquisition dates and were related to the type of vegetation rather than the sensors used to record the satellite images. Neither sensor provided improved discrimination over the other.     

Improving 30 m global land-cover map FROM-GLC with time series MODIS and auxiliary data sets: a segmentation-based approach

FROM-GLC (Fine Resolution Observation and Monitoring of Global Land Cover) is the first 30 m resolution global land-cover map produced using Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper Plus (ETM+) data. Due to the lack of temporal features as inputs in producing FROM-GLC, considerable confusion exists among land-cover types (e.g. agriculture lands, grasslands, shrublands, and bareland). The Moderate Resolution Imaging Spectrometer (MODIS) provides high-temporal frequency information on surface cover. Other auxiliary bioclimatic, digital elevation model (DEM), and world maps on soil-water conditions are possible sources for improving the accuracy of FROM-GLC. In this article, a segmentation-based approach was applied to Landsat imagery to down-scale coarser-resolution MODIS data (250 m) and other 1 km resolution auxiliary data to the segment scale based on TM data. Two classifiers (support vector machine (SVM) and random forest (RF)) and two different strategies for use of training samples (global and regional samples based on a spatial temporal selection criterion) were performed. Results show that RF based on the global use of training samples achieves an overall classification accuracy of 67.08% when assessed by test samples collected independently. This is better than the 64.89% achieved by FROM-GLC based on the same set of test samples. Accuracies for vegetation cover types are most substantially improved.     

Assessment of forest cover in Russia by combining a wall-to-wall coarse-resolution land-cover map with a sample of 30 m resolution forest maps

The process of gathering land-cover information has evolved significantly over the last decade (2000–2010). In addition to this, current technical infrastructure allows for more rapid and efficient processing of large multi-temporal image databases at continental scale. But whereas the data availability and processing capabilities have increased, the production of dedicated land-cover products with adequate accuracy is still a prerequisite for most users. Indeed, spatially explicit land-cover information is important and does not exist for many regions. Our study focuses on the boreal Eurasia region for which limited land-cover information is available at regional level.

The main aim of this paper is to demonstrate that a coarse-resolution land-cover map of the Russian Federation, the ‘TerraNorte’ map at 230 m × 230 m resolution for the year 2010, can be used in combination with a sample of reference forest maps at 30 m resolution to correctly assess forest cover in the Russian federation.

First, an accuracy assessment of the TerraNorte map is carried out through the use of reference forest maps derived from finer-resolution satellite imagery (Landsat Thematic Mapper (TM) sensor). A sample of 32 sites was selected for the detailed identification of forest cover from Landsat TM imagery. A methodological approach is developed to process and analyse the Landsat imagery based on unsupervised classification and cluster-based visual labelling. The resulting forest maps over the 32 sites are then used to evaluate the accuracy of the forest classes of the TerraNorte land-cover map. A regression analysis shows that the TerraNorte map produces satisfactory results for areas south of 65° N, whereas several forest classes in more northern areas have lower accuracy. This might be explained by the strong reflectance of background (i.e. non-tree) cover.

A forest area estimate is then derived by calibration of the TerraNorte Russian map using a sample of Landsat-derived reference maps (using a regression estimator approach). This estimate compares very well with the FAO FRA exercise for 2010 (1% difference for total forested area). We conclude that the TerraNorte map combined with finer-resolution reference maps can be used as a reliable spatial information layer for forest resources assessment over the Russian Federation at national scale.     

An analysis of thematic mapper simulator data for urban environments

Thematic mapper simulator data collected for the Los Angeles Basin in 1980 were examined to assess their utility for urban and near-urban land-cover delimitations. Spectral data for six of the thematic mapper channels were reprojected to a UTM grid and aggregated to 30-m resolution, 120 m for the thermal band. Statistics for 21 training sites representing 8 land-cover types were obtained and examined using transformed divergence calculations for intraclass variability, optimal number of channels for classification, and best channels for classification. Four channels of data are adequate for classification with the best results obtained by selection of one channel from each of the available major portions of the electromagnetic spectrum. The thermal channel data is useful for urban land-cover delineations at 30-m resolution, but its utility at 120-m resolution is not clear from this study.     

An assessment of land-use change in the Cocoa Belt of south-west Nigeria

The Nigerian government is reviving the agricultural sector to shift from its sole dependence on crude oil for foreign exchange earnings. Thus, the Cocoa Belt (agro-ecological region) of southwest Nigeria is important to the national economy. With the increasing demand for land to grow export crops and to meet other needs such as settlement expansion, land use is changing. Land-use data and mapping are essential inputs for the process of formulating, implementing, and monitoring policy with the aim of reducing the impact of land-cover/land-use (LCLU) change. Land-use types, their spatial extent and dynamics over a 25 year period are examined from multispectral images of the Landsat Thematic Mapper and Enhanced Thematic Mapper Plus. This study examines the main drivers of LCLU change and the environmental impact. Results show that forest conversion to agricultural lands is the main trend, and cultivation is the main cause of forest loss in the study area. The need to produce food for the teeming population, coupled with the government's policy to expand export crop production is resulting in the loss of native forest, including areas designated as forest reserves. Results underscore the need for deliberate land-use planning and management in this belt. This study reveals the situation of unplanned and rapid changes to land use in the context of a developing country where explicit policies to cater for such activities are absent.     

Mapping and monitoring riparian vegetation distribution,structure and composition with regression tree models and post-classification change metrics

Riparian systems have become increasingly susceptible to both natural and human disturbances as cumulative pressures from changing land use and climate alter the hydrological regimes. This article introduces a landscape dynamics monitoring protocol that incorporates riparian structural classes into the land-cover classification scheme and examines riparian change within the context of surrounding land-cover change. We tested whether Landsat Thematic Mapper (TM) imagery could be used to document a riparian tree die-off through the classification of multi-date Landsat images using classification and regression tree (CART) models trained with physiognomic vegetation data. We developed a post-classification change map and used patch metrics to examine the magnitude and trajectories of riparian class change relative to mapped disturbance parameters. Results show that catchments where riparian change occurred can be identified from land-cover change maps; however, the main change resulting from the die-off disturbance was compositional rather than structural, making accurate post-classification change detection difficult.     

Spatial–temporal patterns of urban anthropogenic heat discharge in Fuzhou,China, observed from sensible heat flux using Landsat TM/ETM+ data

The urban heat island (UHI) effect is the phenomenon of increased surface temperatures in urban environments compared to their surroundings. It is linked to decreased vegetation cover, high proportions of artificial impervious surfaces, and high proportions of anthropogenic heat discharge. We evaluated the surface heat balance to clarify the contribution of anthropogenic heat discharges into the urban thermal environment. We used a heat balance model and satellite images (Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper Plus (ETM+) images acquired in 1989 and 2001), together with meteorological station data to assess the urban thermal environment in the city of Fuzhou, China. The objective of this study was to estimate the anthropogenic heat discharge in the form of sensible heat flux in complex urban environments. In order to increase the accuracy of the anthropogenic heat flux analysis, the sub-pixel fractional vegetation cover (FVC) was calculated by linear spectral unmixing. The results were then used to estimate latent heat flux in urban areas and to separate anthropogenic heat discharge from heat radiation due to insolation. Spatial and temporal distributions of anthropogenic heat flux were analysed as a function of land-cover type, percentage of impervious surface area, and FVC. The accuracy of heat fluxes was assessed using the ratios of sensible heat flux (H), latent heat flux (L), and ground heat flux (G) to net radiation (R _n), which were compared to the results from other studies. It is apparent that the contribution of anthropogenic heat is smaller in suburban areas and larger in high-density urban areas. However, seasonal disparities of anthropogenic heat discharge are small, and the variance of anthropogenic heat discharge is influenced by urban expansion, land-cover change, and increasing energy consumption. The results suggest that anthropogenic heat release probably plays a significant role in the UHI effect, and must be considered in urban climate change adaptation strategies. Remote sensing can play a role in mapping the spatial and temporal patterns of UHIs and can differentiate the anthropogenic heat from the solar radiative fluxes. The findings presented here have important implications for urban development planning.     

Classification of Landsat images based on spectral and topographic variables for land-cover change detection in Zagros forests

Detection of land-cover changes through time can be complicated because of sensor-specific differences in spatial and spectral resolutions; classified land-cover changes can be due to either real changes on the ground or a switch in sensors used to collect data. This study focused on two objectives: (1) selecting the best predictor variables for the classification of semi-arid Zagros forests given the characteristics of the study area and available data sets and (2) evaluating the application of the random forest (RF) algorithm as a unified technique for the classification of data sets acquired from different sensors. Three images of the same study area were acquired from the Landsat-5 Thematic Mapper (TM) sensor in 2009, the Landsat-7 Enhanced Thematic Mapper (ETM+) sensor with Scan Line Corrector (SLC) in 1999 and the Landsat-2 Multispectral Scanner (MSS) sensor in 1975. Following image preprocessing, the RF algorithm was applied for variable selection and classification. A test of equivalence was used to compare the overall accuracy of the classified maps from the three sensors. Slope, normalized difference vegetation index (NDVI) and elevation were determined to be the most important predictor variables for all three images. High overall classification accuracies were achieved for all three images (97.90% for MSS, 95.43% for TM and 95.29% for ETM). The ETM- and TM-derived maps had equivalent overall accuracy and even significantly higher overall accuracy was obtained for the MSS-derived map. The post-classification comparison showed an increase in agriculture and a decrease in forest cover. The selected predictor variables were consistent with ecological reality and showed more details on the changes of the land-cover classes across biophysical variables of the study area through time.     

Monitoring land-use change-associated land development using multitemporal Landsat data and geoinformatics in Kom Ombo area,South Egypt

Due to the progressive increase in population, sustainable development of desert land in Egypt has become a strategic priority in order to meet the increasing demands of a growing population for food and housing. Such obligations require efficient compilation of accurate land-cover information in addition to detailed analysis of archival land-use changes over an extended time span. In this study, we applied a methodology for mapping land cover and monitoring change in patterns related to agricultural development and urban expansion in the desert of the Kom Ombo area. We utilized the available records of multitemporal Landsat Thematic Mapper and Enhanced Thematic Mapper Plus images to produce three land-use/land-cover maps for 1988, 1999 and 2008.

Post-classification change detection analysis shows that agricultural development increased by 39.2% through the study period with an average annual rate of land development of 8.7 km² year^?1. We report a total increase in urbanization over the selected time span of approximately 28.0 km² with most of this urban growth concentrated to the east of the Nile and occurring through encroachment on the former old cultivated lands. The archival record of the length of irrigation canals showed that their estimated length was 341.5, 461.8 and 580.1 km in the years 1988, 1999 and 2008, respectively, with a 70% increase in canal length from 1988 to 2008. Our results not only accurately quantified the land-cover changes but also delineated their spatial patterns, showing the efficiency of Landsat data in evaluating landscape dynamics over a particular time span. Such information is critical in making effective policies for efficient and sustainable natural resource management.     

Statistical sampling to characterize recent United States land-cover change

The U.S. Geological Survey, in conjunction with the U.S. Environmental Protection Agency, is conducting a study focused on developing methods for estimating changes in land-cover and landscape pattern for the conterminous United States from 1973 to 2000. Eleven land-cover and land-use classes are interpreted from Landsat imagery for five sampling dates. Because of the high cost and potential effect of classification error associated with developing change estimates from wall-to-wall land-cover maps, a probability sampling approach is employed. The basic sampling unit is a 20×20 km area, and land cover is obtained for each 60×60 m pixel within the sampling unit. The sampling design is stratified based on ecoregions, and land-cover change estimates are constructed for each stratum. The sampling design and analyses are documented, and estimates of change accompanied by standard errors are presented to demonstrate the methodology. Analyses of the completed strata suggest that the sampling unit should be reduced to a 10×10 km block, and poststratified estimation and regression estimation are viable options to improve precision of estimated change.     

Finer resolution observation and monitoring of global land cover: first mapping results with Landsat TM and ETM+ data

We have produced the first 30 m resolution global land-cover maps using Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper Plus (ETM+) data. We have classified over 6600 scenes of Landsat TM data after 2006, and over 2300 scenes of Landsat TM and ETM+ data before 2006, all selected from the green season. These images cover most of the world's land surface except Antarctica and Greenland. Most of these images came from the United States Geological Survey in level L1T (orthorectified). Four classifiers that were freely available were employed, including the conventional maximum likelihood classifier (MLC), J4.8 decision tree classifier, Random Forest (RF) classifier and support vector machine (SVM) classifier. A total of 91,433 training samples were collected by traversing each scene and finding the most representative and homogeneous samples. A total of 38,664 test samples were collected at preset, fixed locations based on a globally systematic unaligned sampling strategy. Two software tools, Global Analyst and Global Mapper developed by extending the functionality of Google Earth, were used in developing the training and test sample databases by referencing the Moderate Resolution Imaging Spectroradiometer enhanced vegetation index (MODIS EVI) time series for 2010 and high resolution images from Google Earth. A unique land-cover classification system was developed that can be crosswalked to the existing United Nations Food and Agriculture Organization (FAO) land-cover classification system as well as the International Geosphere-Biosphere Programme (IGBP) system. Using the four classification algorithms, we obtained the initial set of global land-cover maps. The SVM produced the highest overall classification accuracy (OCA) of 64.9% assessed with our test samples, with RF (59.8%), J4.8 (57.9%), and MLC (53.9%) ranked from the second to the fourth. We also estimated the OCAs using a subset of our test samples (8629) each of which represented a homogeneous area greater than 500 m?×?500 m. Using this subset, we found the OCA for the SVM to be 71.5%. As a consistent source for estimating the coverage of global land-cover types in the world, estimation from the test samples shows that only 6.90% of the world is planted for agricultural production. The total area of cropland is 11.51% if unplanted croplands are included. The forests, grasslands, and shrublands cover 28.35%, 13.37%, and 11.49% of the world, respectively. The impervious surface covers only 0.66% of the world. Inland waterbodies, barren lands, and snow and ice cover 3.56%, 16.51%, and 12.81% of the world, respectively.     

Detecting date palm trees health and vegetation greenness change on the eastern coast of the United Arab Emirates using SAVI

Due to shortage of fresh water resources, the vegetation of the eastern region of the United Arab Emirates (UAE) has experienced a series of declines resulting from salinization of groundwater, which is the major source of irrigation. To assess these changes, field measurements combined with Thematic Mapper (TM) and Enhanced Thematic Mapper Plus (ETM+) based Soil Adjusted Vegetation Index (SAVI) were analysed. TM and ETM+ images from two dates, 1987 and 2000 were acquired to enable the computation of the greenness anomalies for three sites in the eastern region, Fujairah, Kalba and Hatta. The results show an overall increase in agricultural area, associated with a severe decrease in vegetation greenness and health conditions, particularly in the Kalba study area. The SAVI values decreased with increased soil salinity, permitting the identification of salt‐affected areas. This remotely sensed data offered valuable information regarding vegetation health conditions, especially when using greenness indices. However, in open canopies, like date palm trees, soil line indices, such as, SAVI are more robust, since they account for the contribution of the soil background. This research suggests, that in order for the date palm trees of this region to stay productive, considerable attention needs to be placed in managing and monitoring soil salinity conditions and progress. Potential areas of further research range from studying the effects of tree spacing and understory crops as immediate and potential solutions to maintain productivity and mitigate the salinity problem.