Monitoring Land Cover Change Research Articles

Monitoring land cover changes in African protected areas in the 21st century

Africa is home to some of the most vulnerable natural ecosystems and species on the planet. Around 7000 protected areas seek to safeguard the continent's rich biodiversity, but many of them face increasing management challenges. Human disturbances permeating into the parks directly and indirectly affect the ecological functioning and integrity of protected areas. With the envisaged expansion of the protected area network and further expected population and economic growth in the region, the competition between nature conservation and resources demands is likely to increase. The regular monitoring of land cover in and around protected areas can support the early detection of conservation conflicts. In this paper, we evaluate the use of the annual time series of MODIS Land Cover (LC) type product between 2003 and 2009 to monitor land cover changes at continental scale. We use the mean classification confidence and change frequency as indicators to assess the temporal consistency of the MODIS LC classifier for accurately monitoring land cover changes. We discuss the perspectives and issues for an automated monitoring of land cover changes in African protected areas.

ASSESSING THE IMPACTS OF CLIMATE CHANGE ON TERRESTRIAL ECOSYSTEMS IN BHAVANISAGAR RESERVOIR AND ITS SURROUNDINGS, USING MULTITEMPORAL SATELLITE DATA AND GIS TECHNIQUES

Abstract. Despite the warnings against climate change since the early century, recent human activities are still geared towards enhancing them. Because of climate change coupled by other human activities, natural ecosystems are facing difficulties in coping with the impacts of climate change. The evaluation of the impact of climate change on ecosystems implies studying a multi-component system in which the complexity of links is high enough to present doing it as a whole. Because of this, it is a reasonable approach to study separately the behavior of each element, to be afterwards linked into a general conceptual model for ecosystems behavior. Moreover, in the case of largely regulated region, the main impacts of climatic change will be fluvial-related since those related to the catchment areas will be severely damped by river regulation and management policies. This fact implies that Terrestrial fringes may become the main frontier between the ecosystems and climatic change effects. This paper describes the potential impacts of warming temperatures and changing precipitation on vegetation, wildlife, land use/ land covers, and agricultural ecosystems across the Bhavanisagar Reservoir region centered on Coimbatore district, Tamilnadu. We examine a geographic area that captures much of Reservoir, including a complex mosaic of urbanization and agriculture surrounding the Bhavanisagar Reservoir. The comparative study of land cover and soil status are provided using space images with middle resolution. Multitemporal satellite data provide the capability for mapping and monitoring land cover and land use change, but require the development of accurate and repeatable techniques that can be extended to a broad range of environments and conditions. The Landsat TM and ETM+ and SRTM-DEM images are included in analysis. The unsupervised and supervised classifications are applied for determination of different types of land cover. The research present here has focus on devising a simple and operational rule-based approach to map land cover changes, based on the classification of Landsat imagery and the conceptual analysis of the information regarding change detection. The use of ancillary GIS data such as Digital Elevation Model, existing thematic maps and the knowledge of the ecosystem dynamics, formed the basis for setting the rules for the post-processing of the classified Multitemporal images that led to a more accurate assessment and mapping of land cover changes. The above framework has proved to be a promising and practical approach in order to quantify, understand, conceptualize and better present the dynamics of land cover/use changes in Terrestrial Ecosystems.

Monitoring land cover change in urban and peri-urban areas using dense time stacks of Landsat satellite data and a data mining approach

Given the pace and scale of urban expansion in many parts of the globe, urban environments are playing an increasingly important role in daily quality-of-life issues, ecological processes, climate, material flows, and land transformations. Remote sensing has emerged as a powerful tool to monitor rates and patterns of urban expansion, but many early challenges – such as distinguishing new urban land from bare ground – remain unsolved. To deal with the high temporal and spatial variability as well as complex, multi-signature classes within settlements, this paper presents a new approach that exploits multi-seasonal information in dense time stacks of Landsat imagery using a multi-date composite change detection technique. The central premise of the approach is that lands within/near urban areas have distinct temporal trajectories both before and after change occurs, and that these lead to characteristic temporal signatures in several spectral regions. The method relies on a supervised classification that exploits training data of stable/changed areas interpreted from Google Earth images, and a ‘brute force’ approach of providing all available Landsat data as input, including scenes with data gaps due to the Scan Line Corrector (SLC) problem. Three classification algorithms (maximum likelihood, boosted decision trees, and support vector machines) were tested for their ability to monitor expansion across five time periods (1988–1995, 1996–2000, 2001–2003, 2004–2006, 2007–2009) in three study areas that differ in size, eco-climatic conditions, and rates/patterns of development. Both the decision trees and support vector machines outperformed the maximum likelihood classifier (overall accuracy of 90–93%, compared to 65%), but the decision trees were superior at handling missing data. Adding transformed features such as band metrics to the Landsat data stack increased accuracy 1–4%, while experiments with a reduced number of features (designed to mimic noisy or missing data) led to a drop in accuracy of 1–9%. The methodology also proved particularly effective for monitoring peri-urbanization outside the urban core, capturing >98% of village settlements.

Using Remote Sensing of Land Cover Change in Coastal Watersheds to Predict Downstream Water Quality

Huang, J. and Klemas, V., 2012. Using remote sensing of land cover change in coastal watersheds to predict downstream water quality. Journal of Coastal Research, 28(4), 930–944. West Palm Beach (Florida), ISSN 0749-0208. Land cover and land use data are important for watershed assessment and runoff modeling. Satellite and airborne remote sensors can map land cover/use effectively. Whenever a strong linkage exists between land cover/use and runoff water quality, remotely sensed land cover trends can help predict long-term changes in water and habitat quality of downstream estuaries and bays. This paper reviews practical remote sensing techniques for land cover change monitoring and presents a case study that relates land cover/use, landscape patterns, and temporal scales to the water quality of runoff from a coastal watershed in SE China. The results of the case study show that the percentage of built-up land was a good predictor for downstream water quality and that the linkage among NH z 4 -N, CODMn, and landscape variables during wet precipitation years was stronger than during dry precipitation years.

Multi-temporal remote sensing of land cover change and urban sprawl in the coastal city of Yantai, China

The importance of accurately mapping and monitoring land cover changes over time is increasing, especially in rapidly growing coastal cities. In this study, three pairs of Landsat images of Yantai, a representative coastal city in China, from 1989, 1999, and 2009 were selected to monitor land cover changes and urban sprawl dynamics. To improve the classification accuracy, three classification methods together with the minimum noise fraction (MNF) and pixel purity index (PPI) calculations were performed on the images. The classification results showed that the overall five-class classification accuracies averaged 91.38% for the 20-year period, which produced an accuracy of 83.78% for change maps. The analysis of change maps indicated that from 1989 to 2009, the percentage of urban area increased from 31.41% to 50.28% of the total area, and the newly urbanized area was mainly located in residential areas and the reclaimed harbor region. Analysis of the relationships between urban area and its driving forces obtained from statistical data found that the urban sprawl of Yantai before 2000 was relatively extensive, which is consistent with the conclusion drawn by using remote sensing techniques. The research results could be used as inputs for sustainable urban management and establishing Digital Earth database.

Opening the archive: How free data has enabled the science and monitoring promise of Landsat

Landsat occupies a unique position in the constellation of civilian earth observation satellites, with a long and rich scientific and applications heritage. With nearly 40years of continuous observation – since launch of the first satellite in 1972 – the Landsat program has benefited from insightful technical specification, robust engineering, and the necessary infrastructure for data archive and dissemination. Chiefly, the spatial and spectral resolutions have proven of broad utility and have remained largely stable over the life of the program. The foresighted acquisition and maintenance of a global image archive has proven to be of unmatched value, providing a window into the past and fueling the monitoring and modeling of global land cover and ecological change. In this paper we discuss the evolution of the Landsat program as a global monitoring mission, highlighting in particular the recent change to an open (free) data policy. The new data policy is revolutionizing the use of Landsat data, spurring the creation of robust standard products and new science and applications approaches. Open data access also promotes increased international collaboration to meet the Earth observing needs of the 21st century.

Assessment of landscape change and occurrence at watershed level in city of Nairobi

To ecologically manage urban spaces, it is necessary to understand changes in spatial configuration of land uses within definitive ecosystem level processes. Change in landscape cover types and pattern within Nairobi city and its environs were investigated using landsat satellite data spanning three decades. Image data were georeferenced, classified and analyzed along watershed zones delineated from a digital elevation model (DEM) of Nairobi city and peripheral region on a geographical information system (GIS) platform. Land cover categories of riverine vegetation and forest land showed the most marked decrease in areal coverage by about 67 and 60%, respectively, while barren surfaces and urban areas increased by more than 100 and 98%, respectively, between 1976 and year 2000. At the watershed scale, land cover diversity was high in the upper and middle basins than on the lower basin of the watershed zones. Expansion of urban areas was confined mainly in the middle elevation watershed zones. Forest area declined notably in the central watersheds while savannah areas increased towards the southern watershed zones. Monitoring land cover change at the watershed scale is more indicative of impact level and where efforts for managing and conserving the urban landscape should be prioritized.

Quantifying small‐scale deforestation and forest degradation in African woodlands using radar imagery

AbstractCarbon emissions from tropical land‐use change are a major uncertainty in the global carbon cycle. In African woodlands, small‐scale farming and the need for fuel are thought to be reducing vegetation carbon stocks, but quantification of these processes is hindered by the limitations of optical remote sensing and a lack of ground data. Here, we present a method for mapping vegetation carbon stocks and their changes over a 3‐year period in a > 1000 km2 region in central Mozambique at 0.06 ha resolution. L‐band synthetic aperture radar imagery and an inventory of 96 plots are combined using regression and bootstrapping to generate biomass maps with known uncertainties. The resultant maps have sufficient accuracy to be capable of detecting changes in forest carbon stocks of as little as 12 MgC ha−1 over 3 years with 95% confidence. This allows characterization of biomass loss from deforestation and forest degradation at a new level of detail. Total aboveground biomass in the study area was reduced by 6.9 ± 4.6% over 3 years: from 2.13 ± 0.12 TgC in 2007 to 1.98 ± 0.11 TgC in 2010, a loss of 0.15 ± 0.10 TgC. Degradation probably contributed 67% (96.9 ± 91.0 GgC) of the net loss of biomass, but is associated with high uncertainty. The detailed mapping of carbon stock changes quantifies the nature of small‐scale farming. New clearances were on average small (median 0.2 ha) and were often additions to already cleared land. Deforestation events reduced biomass from 33.5 to 11.9 MgC ha−1 on average. Contrary to expectations, we did not find evidence that clearances were targeted towards areas of high biomass. Our method is scalable and suitable for monitoring land cover change and vegetation carbon stocks in woodland ecosystems, and can support policy approaches towards reducing emissions from deforestation and degradation (REDD).

Monitoring land cover change in the Lake Superior basin

Consistent, repeatable and broadly applicable land use, land cover data is needed across the Lake Superior basin to facilitate ecosystem condition assessment and trend analysis. Such a data set collected regularly through time could inform and focus field monitoring efforts, and help prioritize restoration and mitigation efforts. Unfortunately, few data sets exist that are bi-nationally consistent in time, classification method, or resolution. To this end, we integrated land cover data across both the Canadian (Ontario Provincial Land Cover data) and US portions (National Land Cover Data) of the Lake Superior basin for two time steps (approximately 1992 and 2001) roughly one decade apart. After harmonizing landcover classes across the two datasets we compared the explicit amount and relative amount (total hectares and proportion of each area as percents) for each of the common land cover classes that occurred across the two time steps for the entire Lake Superior basin, for the U.S. portion of the Lake Superior basin only, and for the Canadian portion of the Lake Superior basin only. We also compared land cover change for the entire basin within a 1 km and a 10 km buffer of the Great Lakes shoreline. We then summarized and compared these land cover types for each time period across a common set of watersheds derived from elevation data (Hollenhorst et al., 2007) for the entire Lake Superior basin. This allowed us to identify and quantify the types of change occurring generally across the entire basin, more specifically across both the U.S. and Canadian portions of the basin, and more explicitly for near coastal areas and watersheds across the entire basin. Noteworthy changes were detected across the basin, particularly an increase in mixed forest types and a corresponding decrease in coniferous forest types.

On the generation of broad-scale hyperspectral ground reflectance mosaics from aerial and ground-based observations

The monitoring of land cover changes from remotely sensed aerial or satellite observations, used in conjunction with ground-based observations, forms the basis of many operational monitoring systems used in regional natural resource management (NRM) programmes. Consistent and comparable observations are fundamental to monitoring, which for aerial or satellite acquired imagery requires the data to be geometrically aligned and then radiometrically normalised for atmospheric, viewing geometry and other effects. Where comparison of observations from multiple-sensors may be required, correction to the common reference of ground reflectance is often preferred over a sensor-specific scale. Here we consider the problem of correcting multiple flight lines of hyperspectral airborne data, acquired within days of each other, to ground reflectance. A method which incorporates regularisation constraints on the spatial and spectral smoothness of the coefficients of bidirectional reflectance distribution function (BRDF) kernels is used in conjunction with an existing atmospheric correction method. We describe the methodology applied and demonstrate the application of the methodology to near simultaneous ground and aerial acquired hyperspectral data collected near Mullewa, Western Australia, in February 2010. A 15% improvement in agreement between the ground-based reflectance observations and the mosaicked hyperspectral reflectance estimates was achieved after application of the model. Next, we simulated a Landsat satellite image by convolving the hyperspectral data to the equivalent bands of the Landsat 5 sensor and comparing the resulting corrected and non-corrected Landsat-simulants to an actual Landsat scene acquired at a similar date. We recorded an approximately 15% improvement in agreement between the simulated and real Landsat scenes.

Land cover mapping at Alkali Flat and Lake Lucero, White Sands, New Mexico, USA using multi-temporal and multi-spectral remote sensing data

The goal of this research is to map land cover patterns and to detect changes that occurred at Alkali Flat and Lake Lucero, White Sands using multispectral Landsat 7 Enhanced Thematic Mapper Plus (ETM+), Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), Advanced Land Imager (ALI), and hyperspectral Hyperion and Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) data. The other objectives of this study were: (1) to evaluate the information dimensionality limits of Landsat 7 ETM+, ASTER, ALI, Hyperion, and AVIRIS data with respect to signal-to-noise and spectral resolution, (2) to determine the spatial distribution and fractional abundances of land cover endmembers, and (3) to check ground correspondence with satellite data. A better understanding of the spatial and spectral resolution of these sensors, optimum spectral bands and their information contents, appropriate image processing methods, spectral signatures of land cover classes, and atmospheric effects are needed to our ability to detect and map minerals from space. Image spectra were validated using samples collected from various localities across Alkali Flat and Lake Lucero. These samples were measured in the laboratory using VNIR–SWIR (0.4–2.5μm) spectra and X-ray Diffraction (XRD) method. Dry gypsum deposits, wet gypsum deposits, standing water, green vegetation, and clastic alluvial sediments dominated by mixtures of ferric iron (ferricrete) and calcite were identified in the study area using Minimum Noise Fraction (MNF), Pixel Purity Index (PPI), and n-D Visualization. The results of MNF confirm that AVIRIS and Hyperion data have higher information dimensionality thresholds exceeding the number of available bands of Landsat 7 ETM+, ASTER, and ALI data. ASTER and ALI data can be a reasonable alternative to AVIRIS and Hyperion data for the purpose of monitoring land cover, hydrology and sedimentation in the basin. The spectral unmixing analysis and dimensionality eigen analysis between the various datasets helped to uncover the most optimum spatial–spectral–temporal and radiometric-resolution sensor characteristics for remote sensing based on monitoring of seasonal land cover, surface water, groundwater, and alluvial sediment input changes within the basin. The results demonstrated good agreement between ground truth data and XRD analysis of samples, and the results of Matched Filtering (MF) mapping method.

MONITORING LAND COVER CHANGES OF THE NORTHWESTERN AREA AT EL FAYOUM DEPRESSION AND ITS SOIL SUITABILITY FOR AGRICULTURAL PURPOSES

This study is a trail for monitoring land cover changes by using LandsatImagery technique as well as determining soil suitability for irrigated agricultureat the north-western outskirt of El-Fayoum depression during a period of 1984-2005. The studied area lies between latitudes of 29o 19 and 29o 26 N andlongitudes of 30o 24 and 30o 34E. To achieve this target, Landsat Imagerytechnique was carried out using two Landsat Thematic Mapper and EnhanceThematic Mapper scenes acquired on 1984 and 2005 covering the north-westernpart of El-Fayoum depression have been processed. A supervised Spectral AnglerMapper approach was also applied to classify the images into four land use/coverclasses, i.e., urban, agriculture, water and desert areas. It was observed that somedesert areas had been transformed into agriculture and settlement during theinvestigated period of twenty one year (1984-2005).This study clarified that the reclaimed area for agriculture purposes duringthe studied period reached 1726.91 ha, which constitute about 8.15 % of the totalarea under study and nation settlement (arbanization) increased with about 310.06ha, which couple nearly 1.46% of the total area. Whereas, the water area waspromoted with 31.48 ha, which equal about 0.22 %. All the land cover units wereincreased account on the expanse of desert unit, which decreased with 2097.20 ha(9.78% of the total studied area). These increases are more attributed to thegeophysical and anthropogenic processes such as salinity, alkalinity, waterlogging, wind erosion, reclamation of new agricultural land and urbanization.The satellite image interpretation map of the year 2005 was identified intotwo main landscapes and thirteen landform units, based on the visualinterpretation of Landsat data ETM7 (Enhanced Thematic Mapper 7) andapplying the Landscape feature approaches. The validity of physiographic map bounders were field checked to represent the different soil mapping units withmodel soil profiles. Thirty-two mini pits were located and studied for setting up the physiographic boundaries and characteristic of soil map legend. Also, thevariations of soil characteristics between the main identified physiographic unitswere represented by eighteen soil profiles, which chosen to be fullmorphologically described according to USDA (2003).Soil taxa were surveyed according to the USDA (1975 and 2010), and thestudied soils could be categorized into three orders and six sub-groups, asfollows: Aridisols (Typic Calcigypsids, Lithic Calcigypsids, and TypicHaplocalcids), Entisols (Typic Torriorthents and Typic Torrifluvents) andVertisols (Typic Haplotorrierts). According to the parametric system undertakenby Sys and Verheye (1978), soil suitability classes of the studied area could becategorized into four classes, i.e., highly (S1), moderately (S2), marginally (S3)and not suitable (N1 & N2).

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

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

Tracking desertification on the Mongolian steppe through NDVI and field-survey data

Changing environmental and socio-economic conditions make land degradation, a major concern in Central and East Asia. Globally satellite imagery, particularly Normalized Difference Vegetation Index (NDVI) data, has proved an effective tool for monitoring land cover change. This study examines 33 grassland water points using vegetation field studies and remote sensing techniques to track desertification on the Mongolian plateau. Findings established a significant correlation between same-year field observation (line transects) and NDVI data, enabling an historical land cover perspective to be developed from 1998 to 2006. Results show variable land cover patterns in Mongolia with a 16% decrease in plant density over the time period. Decline in cover identified by NDVI suggests degradation; however, continued annual fluctuation indicates desertification – irreversible land cover change – has not occurred. Further, in situ data documenting greater cover near water points implies livestock overgrazing is not causing degradation at water sources. In combination of the two research methods – remote sensing and field surveys – strengthen findings and provide an effective way to track desertification in dryland regions.

Monitoring land cover in Acre State, western Brazilian Amazonia, using multitemporal remote sensing data

This article presents the use of multitemporal remote sensing data for monitoring land cover changes in Acre State, western Brazilian Amazonia. For this study, the 2000 Landsat ETM+ (ETM+, Enhanced Thematic Mapper Plus), the 1990 Landsat TM (TM, Thematic Mapper) and 1980 Landsat MSS (MSS, Multispectral Scanner System) were used. The 2005 and 2007 Terra MODIS (MODIS, Moderate Resolution Imaging Spectroradiometer) images were also used to map deforestation that had occurred during the recent years and to map burned areas that occurred in the 2005 dry year. The ETM+, TM, MSS and MODIS images were converted to vegetation, soil and shade fraction images, using linear spectral mixing model, in order to reduce the data volume for digital processing. Then land cover maps were produced by digital classification of these fraction images. The results showed that deforestation increased by 7100 km2 from 1980 to 1990, by 5100 km2 from 1990 to 2000 and by 3300 km2 from 2000 to 2007. About 2100 km2 of vegetation regrowth areas were observed in the 2000 ETM+ images. The analysis of 2005 MODIS images showed that 3700 km2 of deforested areas and 2800 km2 of forested areas were burned in Acre State in 2005. This kind of information is important for regional and global environmental studies and for efforts to control such burning and deforestation activities in the future.

Monitoring land cover changes in a newly reclaimed area of Egypt using multi-temporal Landsat data

Monitoring land cover changes using multi-temporal remotely-sensed data provides an effective and accurate evaluation of human impact on the environment. Agriculture is a key element of Egyptian social fabric and economy. The Egyptian government adopted policies aimed at extending cultivated lands near both fringes of the Nile delta. The Bustan 3 area, located in the desert region of the West Delta of Egypt, was targeted for reclamation in the 1990s, and occupies 341.27 km 2. The main objective of this study was to monitor land cover changes in this area using multi-temporal Landsat images captured in 1984, 1990, 1999, 2004, and 2008. Temporal changes were determined using both a hybrid classification approach and normalized difference vegetation index (NDVI) in that time series. The hybrid classification results showed that this area involves four land cover classes: urban or built-up land, agricultural land, water, and barren land. From 1984 to 1990, the study area was barren land, but during the 1990s, reclamation processes produced remarkable changes in land cover. Assessment carried out on the produced thematic images indicates accuracies of 94.5%–100% were achieved. From 1999 to 2004, around 62% of the area experienced land cover change. The NDVI results indicate that the vegetated area increased after reclamation efforts were initiated. However, NDVI results were not fitted in 2008 as the accuracy assessment for the produced image during this period was only 77%. Generally, from 1984 to 2008, the Bustan 3 area of Egypt has experienced a transformation from 100% barren land to 79% agricultural land, as a result of successful land reclamation efforts.

Monitoring land cover change of the dryland forest landscape of Central Chile (1975–2008)

Land cover and its configuration in the landscape are crucial components in the provision of biodiversity and ecosystem services. In Mediterranean regions, natural landscapes mostly covered by evergreen vegetation have been to a large extent transformed into cultural landscapes since long time ago. We investigated land cover changes in Central Chile using multi-temporal satellite imagery taken in 1975, 1985, 1999 and 2008. The major trends in this highly dynamic landscape were reduction of dryland forest and conversion of shrubland to intensive land uses such as farmland. The average net annual deforestation rate was −1.7%, and shrubland reduction occurred at an annual rate of −0.7%; agriculture, urban areas and timber plantations increased at annual rates of 1.1%, 2.7% and 3.2%, respectively, during the 1975–2008 period. Total forest and shrubland loss rates were partly offset by passive revegetation. However, most of the areas that were passively revegetated remained as shrubland and did not turn into forests due to a low capacity of forest recovery. This resulted in a progressive loss and degradation of dryland forest over the entire region. Overall, the documented land cover changes increase provisioning services such as crops, cattle, and timber that are characteristic of cultural landscapes in the area but may cause an irreversible loss of biodiversity and a depletion of other ecological services provided by forests and shrubland. The implications for conservation of this area and the need for territorial planning and adapted land-use strategies are discussed.

The potential of remote sensing for monitoring land cover changes and effects on physical geography in the area of Kayisdagi Mountain and its surroundings (Istanbul).

The potential of remote sensing for monitoring land cover changes and effects on physical geography in the area of Kayisdagi Mountain and its surroundings (Istanbul).

TROPICAL FOREST BIOMASS AND ITS RELATIONSHIP WITH P - BAND SAR DATA

The objective of this research is to show the potential of P-band polarimetric SAR images to define the space of
 attributes of primary and regenerating forest and also to estimate aerial biomass of such formations. The approach used
 was the analysis of the relation between backscatter and biomass data estimated by four specific allometric equations.
 The Bivariate Intensities HH-HV image was segmented and the mean s° of each segment was converted into biomass
 by the best fit function (the heat capacity model) and following that the biomass was mapped. As a conclusion, the best
 allometric equations for primary and secondary forest biomass estimation were defined, considering the different
 polarizations of P-band SAR data. The methodology used in this treatment of P-band data might improve the regional
 monitoring of Amazonian land cover change, a process whose speed was accelerated as a result of human action in the
 Amazon during the last two decades.

Application of multitemporal Landsat data to monitor land cover changes in the Eastern Nile Delta region, Egypt

Due to the progressive increase in development of desert land in Egypt, the demand for efficient and accurate land cover change information is increasing. In this study, we apply the methodology of post‐classification change detection to map and monitor land cover change patterns related to agricultural development and urban expansion in the desert fringes of the Eastern Nile Delta region. Using a hybrid classification approach, we employ multitemporal Landsat Thematic Mapper (TM)/Enhanced Thematic Mapper Plus (ETM+) images from 1984, 1990 and 2003 to produce three land cover/land‐use maps. Post‐classification comparison of these maps was used to obtain ‘from–to’ statistics and change detection maps. The change detection results show that agricultural development increased by 14% through the study period. The average annual rate of land reclamation during 1990–2003 (4511 ha a−1) was comparable to that during 1984–1990 (4644 ha a−1), reflecting a systematic national plan for desert reclamation that went into effect. We find that the increase in urbanization (by ca 21 300 ha) during 1990–2003 was predominantly due to encroachment into traditionally cultivated land at the fringes of urban centres. Our results accurately quantify the land cover changes and delineate their spatial patterns, demonstrating the utility of Landsat data in analysing landscape dynamics over time. Such information is critical for making efficient and sustainable policies for resource management.