Land Cover Change Information Research Articles

Environmental Assessment of Land Use Land Cover Using Remote Sensing and GIS: A Case Study of Khirthar National Park

National Park area is a natural reserve with an outstanding beauty having rich biodiversity, natural eco-system and sustainable environment is a gift from nature to conserve. It is extremely important to monitor the sustainability of National Park by extracting land use land cover change information over the period of time using satellite imagery which is the main application of change detection. The aim of this research study is to assess the land use land cover change at Khirthar National Park by acquiring multispectral Land sat satellite imagery to detect decadal information from 1992 to 2019 using supervised classification Maximum Likelihood classifier. The Land sat satellite data used in this research study was almost of the summer season to assess land use land cover change using supervised classification Maximum Likelihood Classifier and results showed increasing trend in agriculture while decreasing trend in natural vegetation at Khirthar National Park which is a clear indication of increasing anthropogenic activities. The supervised classification Maximum Likelihood Algorithm used to detect land use land cover change at Khirthar National Park results overall accuracy in the year 1992, 2000, 2010 and 2019 was 99.7446%, 99.4536%, 99.6495% and 96.7530% while the kappa statistics results achieved in the year 1992, 2000, 2010 and 2019 was 0.9951, 0.9879, 0.9648 and 0.9503. These increasing human activities will not only disturbs the ecology of National Park but also will impact on the local flora and fauna of the park, therefore it is highly recommended to prevent further loss of biodiversity, natural eco-system and the sustainable management for such natural heritage.

A Web Service-Oriented Geoprocessing System for Supporting Intelligent Land Cover Change Detection

Remotely sensed imagery-based change detection is an effective approach for identifying land cover change information. A large number of change detection algorithms have been developed that satisfy different requirements. However, most change detection algorithms have been developed using desktop-based software in offline environments; thus, it is increasingly difficult for common end-users, who have limited remote sensing experience and geographic information system (GIS) skills, to perform appropriate change detection tasks. To address this challenge, this paper proposes an online geoprocessing system for supporting intelligent land cover change detection (OGS-LCCD). This system leverages web service encapsulation technology and an automatic service composition approach to dynamically generate a change detection service chain. First, a service encapsulation strategy is proposed with an execution body encapsulation and service semantics description. Then, a constraint rule-based service composition method is proposed to chain several web services into a flexible change detection workflow. Finally, the design and implementation of the OGS-LCCD are elaborated. A step-by-step walk-through example for a web-based change detection task is presented using this system. The experimental results demonstrate the effectiveness and applicability of the prototype system.

Monitoring and mapping rural urbanization and land use changes using Landsat data in the northeast subtropical region of Vietnam

Rapid land use change has taken place in many neighboring provinces of the capital of Vietnam such as Thai Nguyen province over the past 2 decades due to urbanization and industrialization. Deriving accurate and updated land cover and land-use change information is essential for the environmental monitoring, evaluation and management. In this study, a robust classification algorithm, Random Forest (RF) was employed in R programming to map and monitor temporal and spatial characteristics of urban expansion and land-use change in Thai Nguyen province, Vietnam. The results showed that there has been a substantial and uneven urban growth and a significant loss of forest and cropland between 2000 and 2016. Most of the conversion of agriculture and forest into built-up and mining uses were largely detected in rural regions and suburbs of Thai Nguyen. Further GIS analysis revealed that rapid urban and industrial expansion was spatially occurred in the southern rural portions and central area of the province. This study also demonstrates the potential of Landsat data and combination of R programming language and GIS to provide a timely, accurate and economical means to map and analyze temporal land cover and land use changes for future national and local land development planning.

The effect of land cover change to the biomass value in the forest region of West Java province

Due to the issue of climate change as a public concern, information of carbon stock availability play an important role to describe the condition of forest ecosystems in the context of sustainable forest management. This study has the objective to identify land cover change during 2 decades (1996 – 2016) in the forest region and estimate the value of forest carbon stocks in west Java Province using remote sensing imagery. The land cover change information was obtained by visually interpreting the Landsat image, while the estimation of the carbon stock value was performed using the transformation of the NDVI (Normalized Difference Vegetation Index) which extracted from Landsat image. Biomass value is calculated by existing allometric equations. The results of this study shows that the forest area in the forest region of West Java Province have decreased from year to year, and the estimation value of forest carbon stock in the forest region of West Java Province also decreased from year to year.

Land-Cover Change Analysis and Simulation in Conakry (Guinea), Using Hybrid Cellular-Automata and Markov Model

In this study, land-cover change in the capital Conakry of Guinea was simulated using the integrated Cellular Automata and Markov model (CA-Markov) in the Geographic Information System (GIS) and Remote Sensing (RS). Historical land-cover change information was derived from 1986, 2000 and 2016 Landsat data. Using the land-cover change maps of 1986 and 2000, the land-cover change map for 2016 was simulated based on the Markov model in IDRISSI software (Clark University, Worcester, MA, USA). The simulated result was compared with the 2016 land-cover map for validation using the Relative Operating Characteristic (ROC). The ROC result showed a very strong agreement between the two maps. From this result, the land-cover change map for 2025 was simulated using CA-Markov model. The result has indicated that the proportion of the urban area was 49% in 2016, and it is expected to increase to 52% by 2025, while vegetation will decrease from 35% in 2016 to 32% in 2025. This study suggests that the rapid land-cover change has been led by both rapid population growth and extreme poverty in rural areas, which will result in migration into Conakry. The results of this study will provide bases for assessing the sustainability and the management of the urban area and for taking actions to mitigate the degradation of the urban environment.

Rapid Detection of Land Cover Changes Using Crowdsourced Geographic Information: A Case Study of Beijing, China

Land cover change (LCC) detection is a significant component of sustainability research including ecological economics and climate change. Due to the rapid variability of natural environment, effective LCC detection is required to capture sufficient change-related information. Although such information has been available through remotely sensed images, the complicated image processing and classification make it time consuming and labour intensive. In contrast, the freely available crowdsourced geographic information (CGI) contains easily interpreted textual information, and thus has the potential to be applied for capturing effective change-related information. Therefore, this paper presents and evaluates a method using CGI for rapid LCC detection. As a case study, Beijing is chosen as the study area, and CGI is applied to monitor LCC information. As one kind of CGI which is generated from commercial Internet maps, points of interest (POIs) with detailed textual information are utilised to detect land cover in 2016. Those POIs are first classified into land cover nomenclature based on their textual information. Then, a kernel density approach is proposed to effectively generate land cover regions in 2016. Finally, with GlobeLand30 in 2010 as baseline map, LCC is detected using the post-classification method in the period of 2010–2016 in Beijing. The result shows that an accuracy of 89.20% is achieved with land cover regions generated by POIs, indicating that POIs are reliable for rapid LCC detection. Additionally, an LCC detection comparison is proposed between remotely sensed images and CGI, revealing the advantages of POIs in terms of LCC efficiency. However, due to the uneven distribution, remotely sensed images are still required in areas with few POIs.

Landsat-based long–term LUCC mapping in Xinlicheng Reservoir Basin using object-based classification

Rapid urbanization dramatically changes the local environment around Xinlicheng Reservoir Basin. Landsat images are suitable for the land use change caused by human impact. In order to obtain consistent land cover products, a hybrid classification method combining object-based classification and pre-classification alteration detection method was developed and applied to long-term multi-temporal Landsat images to obtain land cover change information. Object-based classification method was combined with Random forest (RF) classifier to classify the Landsat image in 2008. Then the changed areas in 2000, 2004, 2012, and 2016 were identified by comparing with the images in 2008 via the re–weighted multivariate alteration detection transformation method. The images in 2000, 2004, 2012 and 2016 were classified by RF classifier. Land cover maps for 2000, 2004, 2012, and 2016 were produced by combining the unchanged area in 2008 with the new classes of the changed areas in 2000, 2004, 2012 and 2016. According to the accuracy assessment, the overall accuracy of the land covers of the four periods are all greater than 93%. The accuracy assessment indicates that this hybrid method can produce consistent land cover datasets for a long time period.

Integrating Logistic Regression and Geostatistics for User-Oriented and Uncertainty-Informed Accuracy Characterization in Remotely-Sensed Land Cover Change Information

Accuracy is increasingly recognized as an important dimension in geospatial information and analyses. A strategy well suited for map users who usually have limited information about map lineages is proposed for location-specific characterization of accuracy in land cover change maps. Logistic regression is used to predict the probabilities of correct change categorization based on local patterns of map classes in the focal three by three pixel neighborhood centered at individual pixels being analyzed, while kriging is performed to make corrections to regression predictions based on regression residuals at sample locations. To promote uncertainty-informed accuracy characterization and to facilitate adaptive sampling of validation data, standard errors in both regression predictions and kriging interpolation are quantified to derive error margins in the aforementioned accuracy predictions. It was found that the integration of logistic regression and kriging leads to more accurate predictions of local accuracies through proper handling of spatially-correlated binary data representing pixel-specific (in)correct classifications than kriging or logistic regression alone. Secondly, it was confirmed that pixel-specific class labels, focal dominances and focal class occurrences are significant covariates for regression predictions at individual pixels. Lastly, error measures computed of accuracy predictions can be used for adaptively and progressively locating samples to enhance sampling efficiency and to improve predictions. The proposed methods may be applied for characterizing the local accuracy of categorical maps concerned in spatial applications, either input or output.

A River Basin over the Course of Time: Multi-Temporal Analyses of Land Surface Dynamics in the Yellow River Basin (China) Based on Medium Resolution Remote Sensing Data

The Yellow River Basin is one of China’s most densely-populated, fastest growing and most dynamic regions, with abundant natural resources and intense agricultural production. Major land policies have recently resulted in remarkable landscape modifications throughout the basin. The availability of precise regional land cover change information is crucial to better understand the prevailing dynamics and underlying factors influencing the current processes in such a complex system and can additionally serve as a valuable component for modeling and decision making. Such comprehensive and detailed information is lacking for the Yellow River Basin so far. In this study, we derived land cover characteristics and dynamics from the complete last decade based on optical high-temporal MODIS Normalized Differenced Vegetation Index (NDVI) time series for the whole Yellow River Basin. After filtering and smoothing for noise reduction with the use of the adaptive Savitzky–Golay filter, the processed time series was used to derive a large variety of phenological and annual metrics. The final classifications for the basin (2003 and 2013) were based on a random forest classifier, trained by reference samples from very high-resolution imagery. The accuracy assessment for all 18 thematic classes, which was based on a 30% reference data split, yielded an overall accuracy of 87% and 84% for 2003 and 2013, respectively. Major land cover and land use changes during the last decade have occurred on the Loess Plateau, where land and conservation reforms triggered large-scale recovery of grassland and shrubland habitat that had been previously covered by agriculture or sparse vegetation. Agricultural encroachment and urban area expansion are other processes influencing the dynamics in the basin. The necessity for regionally-adapted land cover maps becomes obvious when our land cover products are compared to existing global products, where thematic accuracy remains low, particularly in a heterogeneous landscape, such as the Yellow River Basin. The basin-wide novel land cover and land use products of the Yellow River Basin hold a large potential for climate, hydrology and biodiversity modelers, as well as river basin and regional governmental authorities and will be shared upon request.

Completion of the 2011 National Land Cover Database for the Conterminous United States – Representing a Decade of Land Cover Change Information

Completion of the 2011 National Land Cover Database for the Conterminous United States – Representing a Decade of Land Cover Change Information

Performance Analysis of Different Threshold Determination Techniques for Change Vector Analysis

Abstract Change vector analysis as change detection technique is an effective tool for extracting and identifying land-cover change information from satellite imagery data. Threshold determination is one of most critical task in change vector analysis (CVA) which distinguishes “change” pixels and “no-change” pixels. Many threshold determination techniques for CVA such as empirical strategies, manual trial-and-error procedures or double-window flexible pace search (DFPS) semi-automatic, have been developed since last two decades. But the selection of appropriate threshold determination technique for specific study area is a very important and difficult process because overall accuracy of CVA depends upon threshold value. In this paper, different threshold determination techniques such as empirical strategies, manual trial-and-error procedures and DFPS, have been implemented to evaluate a method for CVA which could more effectively distinguishes the “change” pixels and “no-change” pixels on snow cover area. Experimental results confirm that a semiautomatic DFPS has greater potential than trail-and-error and empirical procedure to determine the specific threshold value for CVA technique that minimizes the overall change detection error probability and maximizes the overall accuracy.

Tracking Land Use/Land Cover Dynamics in Cloud Prone Areas Using Moderate Resolution Satellite Data: A Case Study in Central Africa

Tracking land surface dynamics over cloud prone areas with complex mountainous terrain is an important challenge facing the Earth Science community. One such region is the Lake Kivu region in Central Africa. We developed a processing chain to systematically monitor the spatio-temporal land use/land cover dynamics of this region over the years 1988, 2001, and 2011 using Landsat data, complemented by ancillary data. Topographic compensation was performed on Landsat reflectances to avoid the strong illumination angle impacts and image compositing was used to compensate for frequent cloud cover and thus incomplete annual data availability in the archive. A systematic supervised classification was applied to the composite Landsat imagery to obtain land cover thematic maps with overall accuracies of 90% and higher. Subsequent change analysis between these years found extensive conversions of the natural environment as a result of human related activities. The gross forest cover loss for 1988–2001 and 2001–2011 period was 216.4 and 130.5 thousand hectares, respectively, signifying significant deforestation in the period of civil war and a relatively stable and lower deforestation rate later, possibly due to conservation and reforestation efforts in the region. The other dominant land cover changes in the region were aggressive subsistence farming and urban expansion displacing natural vegetation and arable lands. Despite limited data availability, this study fills the gap of much needed detailed and updated land cover change information for this biologically important region of Central Africa. These multi-temporal datasets will be a valuable baseline for land use managers in the region interested in developing ecologically sustainable land management strategies and measuring the impacts of biodiversity conservation efforts.

Land cover changed object detection in remote sensing data with medium spatial resolution

Land cover change information is crucial to analyse the process and the change patterns of environments and ecological systems. Recent studies have incorporated object-based image analysis for its ability to generate meaningful geographical objects into studies of change detection. In this research, we developed a systematic methodology to realise multi-type land cover changed object detection with medium spatial resolution remote sensing images in Beijing, China. Optimum index factor (OIF) was applied to determine the best change indicators and the chi-square transformation was carried out to determine the change threshold of the 4 classes of changed object. The clustering change vectors in the feature space were proposed to discriminate the change types. According to the accuracy assessment, the overall accuracy of changed/unchanged object detection was approximately 93.9% with an overall kappa of 0.824, and the change type discrimination also achieved an overall accuracy of 81.67%, indicating the effectiveness of the proposed method.

Land Cover Characterization and Mapping of South America for the Year 2010 Using Landsat 30 m Satellite Data

Detailed and accurate land cover and land cover change information is needed for South America because the continent is in constant flux, experiencing some of the highest rates of land cover change and forest loss in the world. The land cover data available for the entire continent are too coarse (250 m to 1 km) for resource managers, government and non-government organizations, and Earth scientists to develop conservation strategies, formulate resource management options, and monitor land cover dynamics. We used Landsat 30 m satellite data of 2010 and prepared the land cover database of South America using state-of-the-science remote sensing techniques. We produced regionally consistent and locally relevant land cover information by processing a large volume of data covering the entire continent. Our analysis revealed that in 2010, 50% of South America was covered by forests, 2.5% was covered by water, and 0.02% was covered by snow and ice. The percent forest area of South America varies from 9.5% in Uruguay to 96.5% in French Guiana. We used very high resolution (<5 m) satellite data to validate the land cover product. The overall accuracy of the 2010 South American 30-m land cover map is 89% with a Kappa coefficient of 79%. Accuracy of barren areas needs to improve possibly using multi-temporal Landsat data. An update of land cover and change database of South America with additional land cover classes is needed. The results from this study are useful for developing resource management strategies, formulating biodiversity conservation strategies, and regular land cover monitoring and forecasting.

Semi-supervised change detection method for multi-temporal hyperspectral images

Abstract Change detection is one of the most important open topics for multi-temporal remote sensing technology to observe the earth. Recently, many methods are proposed to detect the land-cover change information by multi-temporal hyperspectral images. However, many existing traditional change detection methods failed to utilize the spectral information effectively. Hence the models are not robust enough for more widely applications with “noise” bands. In this case, a semi-supervised distance metric learning method is proposed to detect the change areas by abundant spectral information of hyperspectral image under the “noisy” condition. This paper focuses on semi-supervised change detection method, and proposes a new distance metric learning framework for change detection in “noisy” condition with three mainly contributions: (1) Distance metric learning is demonstrated to be an effective method for revealing the change information by high spectral features. (2) An evolution regular framework is utilized to handle change detection under a “noisy” condition without removing any noise bands, which is impacted by atmosphere (or water) and always removed manually in other literatures. (3) A semi-supervised Laplacian Regularized Metric Learning method is exploited to tackle the ill-posed sample problem, and large unlabeled data is exploited in our method. The proposed method is performed on two multi-temporal hyperspectral datasets. Experimental results show that the proposed method outperforms the state-of-the-art change detection methods under both “ideal” and “noisy” conditions.

Land Cover Classification Method Oriented to Geographic National Conditions Investigation

Abstract. Developing the project of geographic national conditions investigation is in order to obtain land cover change information which is caused by natural and human social and economic activities, and serve the information for government, enterprise and public. Land cover is an important method to describe the geographic national conditions information, which can truly reflect the land surface material type and its natural attribute. It has been contained in the content system preliminary scheme as an important portion. In this paper, it discusses and analyzes on the method and key technology, with according to the land cover content that geographic national conditions watches on. Then it evaluates the applicability of automatic classification method using in land cover information extraction, and comprehensively analyzes various extraction methods’ maximum effectiveness. Finally, it proposes a method that is based on high spatial resolution remote sensing imagery and can be used in engineering applications, which provides a reference method for geographic national conditions investigation.

Orchard identification using landform and landscape factors based on a spatial–temporal classification framework

Ecological restoration measures have been undertaken in loess hilly and gully regions since the 1970s to prevent soil loss and to improve the ecological environment in those regions. Orchard construction was the main ecological measure undertaken in the Luo-Yu-Gou watershed, and in this article we propose a coupled maximum a posteriori decision rule and Markov random field (MAP-MRF) framework for orchard identification based on landform and landscape factors. Support vector machine (SVM) classification was first performed to obtain initial classification results for the years 2003 and 2008. A series of factors including landform factor, landscape factor, and the spatial–temporal neighbourhood factor are used to obtain land-cover change information including the change in orchard class. Finally, field experiments were carried out in the case study region of the Luo-Yu-Gou watershed, and based on the experimental results, it was found that the quantity error and the allocation error of the classification results for 2008 were 0.0441 and 0.1037, respectively.

Land Cover Change Monitoring Using Landsat MSS/TM Satellite Image Data over West Africa between 1975 and 1990

Monitoring land cover changes from the 1970s in West Africa is important for assessing the dynamics between land cover types and understanding the anthropogenic impact during this period. Given the lack of historical land cover maps over such a large area, Landsat data is a reliable and consistent source of information on land cover dynamics from the 1970s. This study examines land cover changes occurring between 1975 and 1990 in West Africa using a systematic sample of satellite imagery. The primary data sources for the land cover classification were Landsat Multispectral Scanner (MSS) for 1975 and Landsat Thematic Mapper (TM) for the 1990 period. Dedicated selection of the appropriate image data for land cover change monitoring was performed for the year 1975. Based on this selected dataset, the land cover analysis is based on a systematic sample of 220 suitable Landsat image extracts (out of 246) of 20 km × 20 km at each one degree latitude/longitude intersection. Object-based classification, originally dedicated for Landsat TM land cover change monitoring and adapted for MSS, was used to produce land cover change information for four different land cover classes: dense tree cover, tree cover mosaic, other wooded land and other vegetation cover. Our results reveal that in 1975 about 6% of West Africa was covered by dense tree cover complemented with 12% of tree cover mosaic. Almost half of the area was covered by other wooded land and the remaining 32% was represented by other vegetation cover. Over the 1975–1990 period, the net annual change rate of dense tree cover was estimated at −0.95%, at −0.37% for the other wooded land and very low for tree cover mosaic (−0.05%). On the other side, other vegetation cover increased annually by 0.70%, most probably due to the expansion of agricultural areas. This study demonstrates the potential of Landsat MSS and TM data for large scale land cover change assessment in West Africa and highlights the importance of consistent and systematic data processing methods with targeted image acquisition procedures for long-term monitoring.

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.

Temporal logic and operation relations based knowledge representation for land cover change web services

Providing land cover spatio-temporal information and geo-computing through web service is a new challenge for supporting global change research, earth system simulation and many other societal benefit areas. This requires an integrated knowledge representation and web implementation of static land cover and change information, as well as the related operations for geo-computing. The temporal logic relations among land cover snapshots and increments were examined with a matrix-based three-step analysis. Twelve temporal logic relations were identified and five basic spatial operations were formalized with set operators, which were all used to develop algorithms for deriving implicit change information. A knowledge representation for land cover change information was then developed based on these temporal logic and operation relations. A prototype web-service system was further implemented based on OWL-DL. Both online access and conversion of land cover spatio-temporal information can be facilitated with such a web service system.