Land Cover Change Dynamics Research Articles

Spatial dynamics of land cover change in a Euro-Mediterranean catchment (1950–2008)

The Euro-Mediterranean area has experienced widespread land cover change since 1950, but few studies of land cover change explicitly explore spatial constraints on land cover change patterns. The main objective of this study was to analyze the spatial dynamics of land cover change from 1950 to 2008 in a French Mediterranean catchment. Aerial photographs (1950, 1982, and 2008) were screen digitized, and surfaces were classified into five categories: forest, vineyard, grassland, urban, and suburban. Land cover changes were concentrated mainly in the alluvial plain. Although forest remained the dominant land cover in the catchment (>85.0%), it underwent significant swapping with vineyard and grassland. Vineyard decreased (34% of initial loss) while grassland increased (43% of initial). Urban and suburban areas remained minor in the catchment (0.2% in 1950 and 3.0% in 2008), but showed a dramatic relative increase (about 20×). Changes occurred mainly at low altitudes and slopes. Vineyard located near streams was converted mainly to grassland. Built areas were dependent on roads and former built areas for expansion but expanded little near streams due to flooding risks. The rate of change was greater during the latter part of the study (1982–2008) than in the earlier phase (1950–1982).

Land cover dynamics monitoring with Landsat data in Kunming, China: a cost-effective sampling and modelling scheme using Google Earth imagery and random forests

Changes in forest composition impact ecological services, and are considered important factors driving global climate change. A hybrid sampling method along with a modelling approach to map current and past land cover in Kunming, China is reported. MODIS land cover (2001–2011) data-sets were used to detect pixels with no apparent change. Around 3000 ‘no change points’ were systematically selected and sampled using Google Earth’s high-resolution imagery. Thirty-five per cent of these points were verified and used for training and validation. We used Random forests to classify multi-temporal Landsat imagery. Results show that forest cover has had a net decrease of 14385 ha (1.3% of forest area), which was primary converted to shrublands (11%), urban and barren land (2.7%) and agriculture (2.5%). Our validation indicates an overall accuracy (Kappa) of 82%. Our methodology can be used to consistently map the dynamics of land cover change in similar areas with minimum costs.

Land-cover change dynamics and insights into ecosystem services in European stream riparian zones

Riparian zones, transitional environments between terrestrial and freshwater ecosystems, have been historically threatened in Europe by land reclamation and exploitation of their natural resources. These fragile environments deliver a large number of ecological and societal services, while simultaneously playing a key role in the maintenance of biodiversity in fragmented landscapes. At large scales, one of the clearest and most informative indicators of alteration of state and characteristics of ecological systems is land-cover change. A newly available European riparian zone distribution dataset and continental land-cover change information allowed us to obtain an unprecedented continental overview of riparian land conversion and associated drivers, as well as a broad indication of their loss of capacity to provide ecosystem services. The analysis shows that only 1.8% of riparian zones experienced land-cover changes in the period 2000–2006. The majority of riparian changes involved forest loss and forest regrowth due to forestry activities and, to a lesser extent, to fires. Approximately 9% of land-cover changes involved conversion to artificial and agricultural surface, with trajectories largely affecting their ecological integrity. Using land-cover proxy-based indicators we show that the loss of riparian zones’ capacity to support ecological integrity and ecosystem services was overall significantly higher than the proportion of converted surface. The methodological approach can be used to support the assessment of environmental policy targets, and for regional planning and management of riparian zones.

Assessment of the land use/land cover (LU/LC) and mangrove changes along the Mandovi–Zuari estuarine complex of Goa, India

Land-use/ Land cover change dynamics along the estuarine complex of Mandovi - Zuari in the coastal state of Goa, India is studied by the application of multi-temporal remote sensing data. This tropical estuarine region has faced significant changes in the last few decades due to diverse pressures from various sectors, mainly of anthropogenic origin. Spatial and temporal changes in the LU/LC classes have been evaluated using satellite datasets from Landsat MSS, TM and IRS-LISS III for the years 1973, 1989, 2001 and 2011. Considering a spectrally heterogeneous landscape of the region, the LU/LC changes are identified by using Principal Component Analysis (PCA) as a pre-classification step, followed by the implementation of a combination of supervised and unsupervised classification called Hybrid Classification. The application of PCA has proved to be suitable to successfully identify discrete land surface classes for this study area. An overall accuracy of 95.56% (kappa = 0.9556), 92.93% (kappa = 0.92), 84.64% (Kappa=0.85) and 86.36% (Kappa=0.85) has been obtained for the year 2011, 2001, 1989 and 1973 respectively. The study reveals a gradual increase in built-up area due to urbanization corresponding to a decrease in agricultural land and a considerable loss in beach area. Changes in the mangroves are also assessed within this extent as Goa has significant pockets of mangroves which are located mainly along the estuarine complexes of Mandovi and Zuari (along with the Cumbarjua canal complex). The results highlight an increase in mangrove coverage within the study extent by 1585.8 ha from 1973 to 2011. The mangrove areal extent along Mandovi has increased from 149.45 ha to 1059.77 ha and from 142.95 ha to 676.12 ha along Zuari (including Cumbarjua canal). Increasing awareness and the steps taken by the forest department to provide suitable environment for the growth of mangroves are the main reasons for this increment.

Historic land cover change in the agricultural Midwest using an object-based approach for classification of high-resolution imagery

A semiautomated classification methodology was implemented using historic, high-resolution aerial photography in a dominant agricultural landscape. An object-based segmentation approach was applied to study land cover change from 1930 through 1990 in Johnson County, Iowa, in the Midwestern United States. A critical analysis of the approach is discussed, emphasizing the ability of the methodology to generate landscape metrics that can accurately characterize the quality of the landscape, particularly the high-resolution landscape features that are so important in a highly modified landscape. Landscape analysis includes a discussion of both the changes in the areal composition of land cover types and also the structural changes that are captured using both patch- and landscape-level metrics. Results were compared with countywide statistics from the United States Department of Agriculture as well as similar landscape studies, and provide evidence of agricultural intensification. Results also indicate some counterintuitive processes occurring from what is expected of a landscape undergoing this type of transformation, suggesting that altering the scale of study may provide different insight into land cover change dynamics.

Representation of natural and anthropogenic land cover change in MPI‐ESM

The purpose of this paper is to give a rather comprehensive description of the models for natural and anthropogenically driven changes in biogeography as implemented in the land component JSBACH of the Max Planck Institute Earth system model (MPI‐ESM). The model for natural land cover change (DYNVEG) features two types of competition: between the classes of grasses and woody types (trees, shrubs) controlled by disturbances (fire, windthrow) and within those vegetation classes between different plant functional types based on relative net primary productivity advantages. As part of this model, the distribution of land unhospitable to vegetation (hot and cold deserts) is determined dynamically from plant productivity under the prevailing climate conditions. The model for anthropogenic land cover change implements the land use transition approach by Hurtt et al. (2006). Our implementation is based on the assumption that historically pastures have been preferentially established on former grasslands (“pasture rule”). We demonstrate that due to the pasture rule, deforestation reduces global forest area between 1850 and 2005 by 15% less than without. Because of the pasture rule the land cover distribution depends on the full history of land use transitions. This has implications for the dynamics of natural land cover change because assumptions must be made on how agriculturalists react to a changing natural vegetation in their environment. A separate model representing this process has been developed so that natural and anthropogenic land cover change can be simulated consistently. Certain aspects of our model implementation are illustrated by selected results from the recent CMIP5 simulations.

Next generation of global land cover characterization, mapping, and monitoring

Land cover change is increasingly affecting the biophysics, biogeochemistry, and biogeography of the Earth's surface and the atmosphere, with far-reaching consequences to human well-being. However, our scientific understanding of the distribution and dynamics of land cover and land cover change (LCLCC) is limited. Previous global land cover assessments performed using coarse spatial resolution (300m–1km) satellite data did not provide enough thematic detail or change information for global change studies and for resource management. High resolution (∼30m) land cover characterization and monitoring is needed that permits detection of land change at the scale of most human activity and offers the increased flexibility of environmental model parameterization needed for global change studies. However, there are a number of challenges to overcome before producing such data sets including unavailability of consistent global coverage of satellite data, sheer volume of data, unavailability of timely and accurate training and validation data, difficulties in preparing image mosaics, and high performance computing requirements. Integration of remote sensing and information technology is needed for process automation and high-performance computing needs. Recent developments in these areas have created an opportunity for operational high resolution land cover mapping, and monitoring of the world. Here, we report and discuss these advancements and opportunities in producing the next generations of global land cover characterization, mapping, and monitoring at 30-m spatial resolution primarily in the context of United States, Group on Earth Observations Global 30m land cover initiative (UGLC).

Remote-sensing-based analysis of landscape change in the desiccated seabed of the Aral Sea—a potential tool for assessing the hazard degree of dust and salt storms

With the recession of the Aral Sea in Central Asia, once the world's fourth largest lake, a huge new saline desert emerged which is nowadays called the Aralkum. Saline soils in the Aralkum are a major source for dust and salt storms in the region. The aim of this study was to analyze the spatio-temporal land cover change dynamics in the Aralkum and discuss potential implications for the recent and future dust and salt storm activity in the region. MODIS satellite time series were classified from 2000-2008 and change of land cover was quantified. The Aral Sea desiccation accelerated between 2004 and 2008. The area of sandy surfaces and salt soils, which bear the greatest dust and salt storm generation potential increased by more than 36%. In parts of the Aralkum desalinization of soils was found to take place within 4-8years. The implication of the ongoing regression of the Aral Sea is that the expansion of saline surfaces will continue. Knowing the spatio-temporal dynamics of both the location and the surface characteristics of the source areas for dust and salt storms allows drawing conclusions about the potential hazard degree of the dust load. The remote-sensing-based land cover assessment presented in this study could be coupled with existing knowledge on the location of source areas for an early estimation of trends in shifting dust composition. Opportunities, limits, and requirements of satellite-based land cover classification and change detection in the Aralkum are discussed.

Impact of Land Use and Land Cover Changes on Organic Carbon Stocks in Mediterranean Soils (1956–2007)

AbstractDuring the last few decades, land use changes have largely affected the global warming process through emissions of CO2. However, C sequestration in terrestrial ecosystems could contribute to the decrease of atmospheric CO2 rates. Although Mediterranean areas show a high potential for C sequestration, only a few studies have been carried out in these systems. In this study, we propose a methodology to assess the impact of land use and land cover change dynamics on soil organic C stocks at different depths. Soil C sequestration rates are provided for different land cover changes and soil types in Andalusia (southern Spain). Our research is based on the analysis of detailed soil databases containing data from 1357 soil profiles, the Soil Map of Andalusia and the Land Use and Land Cover Map of Andalusia. Land use and land cover changes between 1956 and 2007 implied soil organic C losses in all soil groups, resulting in a total loss of 16·8 Tg (approximately 0·33 Tg y−1). Afforestation increased soil organic C mostly in the topsoil, and forest contributed to sequestration of 8·62 Mg ha−1 of soil organic C (25·4 per cent). Deforestation processes implied important C losses, particularly in Cambisols, Luvisols and Vertisols. The information generated in this study will be a useful basis for designing management strategies for stabilizing the increasing atmospheric CO2 concentrations by preservation of C stocks and C sequestration. Copyright © 2012 John Wiley & Sons, Ltd.

Assessing implications of land use and land cover changes in forest ecosystems of NE Turkey

Monitoring land use and land cover change (LUCC) and understanding forest cover dynamics is extremely important in sustainable development and management of forest ecosystems. This study analyzed the spatial and temporal pattern of LUCC in the Yalnızçam and Uğurlu forest planning units which are located in the northeast corner of Turkey. The investigation also evaluates the temporal changes of the spatial structure of forest conditions through the spatial analysis of forest-cover type maps from 1972 and 2005 using geographical information systems and FRAGSTATS(TM). As an overall change between 1972 and 2005, there was a net increase of 1,823ha in forested areas, and cumulative forest improvement accounted for 2.06%. In terms of spatial configuration, the landscape structure in the study area changed substantially over the 33-year study period, resulting in fragmentation of the landscape as indicated by large patch numbers and smaller mean patch sizes, owing to heavy grazing, illegal cutting, and uncontrolled stand treatments.

Landscape responses to a century of land use along the northern Patagonian forest-steppe transition

Land use history reconstructions in temperate regions of the Northern Hemisphere indicate that periods of deforestation are often followed by natural afforestation, so that the long-term outcome at the landscape level will be a balance of retractions and advances of plant communities associated with varying local land uses. During the last decades of the XIX century, large forest areas were cleared in Northwestern Patagonia to open farmland. In this article, we compared historical land use/land cover maps with land cover maps derived from Landsat images to analyze the factors that may have influenced the dynamics of land cover change of the forest-steppe ecotone during the last 100 years. Our results indicate that Patagonian forests underwent a rapid initial recovery after the extensive fires of last century, replacing mainly shrublands. More than 50% of the old burns are currently covered by forests, and modern fires affect areas characterized by fire-prone vegetation. Whereas natural afforestation is an ongoing process positively associated with moisture, the rate of forest losses has increased during the last three decades, concentrating on xeric aspects and the vicinity of roads. We conclude that the outcome of the dynamics between fire-intolerant forests and fire-prone plant communities will largely depend on human-related activities, modeled by structural features of the landscape (i.e., topography, dominant winds), and processes triggered by past land uses.

Changes in land cover and vegetation carbon stocks in Andalusia, Southern Spain (1956–2007)

Land use has significantly changed during the recent decades at global and local scale, while the importance of ecosystems as sources/sinks of C has been highlighted, emphasizing the global impact of land use changes. Land use changes can increase C loss rates which are extremely difficult to reverse, in the short term, opposite to organic carbon (OC) which accumulates in soil in the long-term. The aim of this research is to improve and test methodologies to assess land cover change (LCC) dynamics and temporal and spatial variability in C stored in vegetation at a wide scale. LCCs between 1956 and 2007 in Andalusia (Southern Spain) were selected for this pilot study, assessed by comparison of spatial data from 1956 to 2007 and were reclassified following land cover flows (LCFs) reported in major areas in Europe. Carbon vegetation densities were related to land cover, and C vegetation stocks for 1956 and 2007 were calculated by multiplying C density for each land cover class with land cover areas. The study area has supported important changes during the studied period with significant consequences for vegetation C stocks, mainly due to afforestation and intensification of agriculture, resulting in a total vegetation C stock of 156.08 Tg in 2007, with an increase of 17.24 Tg since 1956. This study demonstrates the importance of LCC for C sequestration in vegetation from Mediterranean areas, highlighting possible directions for management policies in order to mitigate climate change as well as promoting land conservation. The methodologies and information generated in this project will be a useful basis for designing land management strategies helpful for decision makers.

Land-cover change dynamics and coastal aquaculture development: a case study in the East Godavari delta, Andhra Pradesh, India using multi-temporal satellite data

The present study focuses on the identification and quantification of land-cover changes occurring in the coastal stretches of the East Godavari delta, Andhra Pradesh, India. The analysis of series of multi-temporal satellite data provides an accurate quantification and therefore a better understanding of the process of land-cover changes during 1990–2005. Land-cover changes were quantified based on normalized difference vegetation index (NDVI) image differencing and a post-classification comparison approach. The change detection results were examined in terms of the proportion of land-cover classes and change trajectories with particular emphasis on coastal aquaculture development within the study area. The study shows that the total area under aquacultural ponds increased from 2985 ha in 1990 to 7067 ha in 2005. The major changes in the study area occurred during 1990–1994, when 2873 ha of agricultural land and 762 ha of degraded mangroves were converted into aquacultural ponds. The prediction of land-cover distribution in 2010 on the basis of a Markov chain shows a continuing upward trend of the aquaculture area (8267 ha) with less impact on the mangrove area. The analysis predicts that the agricultural land area will continue to decrease from 50 122 to 46 978 ha during 2005–2010.

Modeling Spatial Processes of Urban Growth in African Cities: A Case Study of Nairobi City

Africa's urban population growth has been especially rapid, averaging about 5% per year over the past two decades. As a result, many urban areas have experienced dramatic growth that is seriously outstripping the capacity of most cities to provide adequate services for their residents. Although population growth and urbanization rates in Africa have slowed recently due to a number of factors including HIV/AIDS, urban growth is still expected to double by 2030, leading to dramatic sprawl with serious environmental and social consequences. Using Nairobi as an example of a rapidly urbanizing African city, we studied the dynamics of land use and land cover change using satellite data and addressed the need for models and urban management tools that can guide sustainable urban planning policies. Cellular Automata, which integrate biophysical factors with dynamic spatial modeling, are used in this study. The model was calibrated and tested using time series of urbanized areas derived from land use/cover maps, produced from remotely sensed imagery, with future urban growth projected to 2030. Model assessment results showed high levels of accuracy, indicating that simulation findings were realistic, thereby confirming the effectiveness of the model. Results further showed that the model is a useful and effective tool to foresee the spatial consequences of planning policies in the context of many African cities. The forecast for Nairobi showed unsustainable sprawl.

Patterns and dynamics of land-cover changes since the 1960s over three experimental areas in Mali

This paper addresses a critical need to provide a better quantitative understanding of how the Sudano-Sahelian environments actually been changing under the combined impacts of climate variability and the increasing pressure of human activity. Using Corona, Landsat and SPOT satellite images of three areas (90–250km2) along the climatic gradient of a large catchment in Mali, significant land-cover changes since the 1960s were identified through visual interpretation of images following a common classification scheme. The pattern and trajectory of changes differed markedly between the three areas studied. Overall, the 40-year trends indicate: (i) in the Sahelian area, a steady increase in croplands and erosional surfaces with sparse vegetation and a corresponding drastic reduction in woody covers; (ii) in the Sudano-Sahelian area, a large increase in croplands and a moderate reduction in woody covers; (iii) in the Sudanian area, agricultural extension, deforestation, but also reforestation and land rehabilitation, due to alternating periods of exploitation and recolonization by natural vegetation. These patterns and dynamics can be partially explained by the differences in demographic pressure between the three areas. They also highlight differences in response to anthropogenic and climate forcings depending on the areas’ respective climatic and environmental endowments. This study is a first step towards an in-depth analysis of the various forces and processes driving these changes and the formulation of prospective environmental scenarios for the catchment in line with hydrological studies.

Predicting forest fire in the Brazilian Amazon using MODIS imagery and artificial neural networks

The presented work describes a methodology that employs artificial neural networks (ANN) and multi-temporal imagery from the MODIS/Terra-Aqua sensors to detect areas of high risk of forest fire in the Brazilian Amazon. The hypothesis of this work is that due to characteristic land use and land cover change dynamics in the Amazon forest, forest areas likely to be burned can be separated from other land targets. A study case was carried out in three municipalities located in northern Mato Grosso State, Brazilian Amazon. Feedforward ANNs, with different architectures, were trained with a backpropagation algorithm, taking as inputs the NDVI values calculated from MODIS imagery acquired during five different periods preceding the 2005 fire season. Selected samples were extracted from areas where forest fires were detected in 2005 and from other non-burned forest and agricultural areas. These samples were used to train, validate and test the ANN. The results achieved a mean squared error of 0.07. In addition, the model was simulated for an entire municipality and its results were compared with hotspots detected by the MODIS sensor during the year. A histogram analysis showed that the spatial distribution of the areas with fire risk were consistent with the fire events observed from June to December 2005. The ANN model allowed a fast and relatively precise method to predict forest fire events in the studied area. Hence, it offers an excellent alternative for supporting forest fire prevention policies, and in assisting the assessment of burned areas, reducing the uncertainty involved in currently used methods.

Dynamics of Land Cover and Land Use Changes in the Upper Ca River Basin of Nghe An, Vietnam

Dynamics of Land Cover and Land Use Changes in the Upper Ca River Basin of Nghe An, Vietnam

Monitoring 25 years of land cover change dynamics in Africa: A sample based remote sensing approach

The study examines the changes in sub-Saharan's natural land cover resources for a 25 year period. We assess these changes in four broad land cover classes – forests, natural non-forest vegetation, agriculture and barren – by using high spatial resolution Earth observing satellites. Two sets of sample images, one ‘historical’ targeted at 1975 and a second ‘recent’ targeted at the year 2000, have been selected through a stratified random sampling technique over the study area, targeting a sampling rate of 1% in each of the strata. The results, presented at eco-region level and aggregated at sub-Saharan level, show a 57% increase in agriculture area at the expense of natural vegetation which has itself decreased by 21% over the period, with nearly 5 million hectares forest and non-forest natural vegetation lost per year. The impacts of these changes on the environment on one site and on the socio-economy on the other site are discussed and possible pressures on human well being are highlighted.

Land cover changes on the coastal zone of Candarli Bay, Turkey using remotely sensed data

Land cover of the Earth is changing dramatically because of human activities. Information about changes is useful for management of natural resources. Rapid land cover changes have taken place in many coastal areas of Turkey over the last two decades due to urbanization and land degradation. In this paper, land cover change dynamics were investigated by the combined use of satellite remote sensing and geographical information systems. The main objective of the study was to determine land-cover transition rates among land cover types in coastal areas of Turkey. A time series of Landsat TM and ASTER images were used to gather land cover change data of the coastal line of Candarli Bay, Izmir, Turkey. The images were classified using supervised classification and a post-classification comparison approach was used in change detection. The results show significant increase in urban areas but decrease in semi natural and agricultural areas.

Carbon content in vegetation, litter, and soil under 10 different land-use and land-cover classes in the Central Highlands of Michoacan, Mexico

In this study we estimated the carbon content in vegetation, litter, and soil, under 10 different classes of land-use and land-cover classes (LU/LC) in the Purepecha Region, located in the Central Highlands of México. Forests in this area are representative of the montane forests of Central and Southern Mexico and are subject to rapid degradation and deforestation by human pressure. Carbon data for each of the LU/LC classes and the main pools (vegetation, soil and litter) were collected at 92 sites in 276 field plots of 0.1 ha each, based on a “nested” design which allows the collection of samples and their replicates. The following LU/LC classes were identified: pine forest, oak forest, pine–oak forest, fir forest, Plantation, Agricultural fields, Grasslands, Scrublands, Avocado plantation and Degraded forests. The following results were obtained: (a) carbon content in vegetation ranged from 0.2 (grasslands) to 169.7 (fir forest) Mg C ha −1; (b) carbon content in litter ranged from 0.6 (agriculture) to 4.1 (fir forest) Mg C ha −1, and (c) carbon content in soil from the 0–30 cm depth, ranged from 72.8 (degraded forest) to 116.4 (oak forest) Mg C ha −1. Forest classes (pine, oak, fir and pine–oak forest) presented the highest total carbon stocks with values ranging between 220.7 and 266.9 Mg C ha −1; degraded forest contained 169.2 Mg C ha −1; plantation 142 Mg C ha −1 and avocado orchards reported 156.1 Mg C ha −1; scrublands 121 Mg C ha −1; grasslands 90.8 Mg C ha −1 and agriculture 82.7 Mg C ha −1. The total carbon stock in the last three classes was mainly found in the soil. The results of the present study are relevant for national inventories of carbon stocks and can be used to derive greenhouse gas emissions (GHG), once the land-cover change dynamics are known.