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Special Issue "Monitoring of Land Changes"

A special issue of Remote Sensing (ISSN 2072-4292).

Deadline for manuscript submissions: closed (31 May 2016)

Special Issue Editors

Guest Editor
Prof. Dr. Petri Pellikka

Department of Geosciences and Geography, University of Helsinki, PO Box 64, 00014 Helsinki, Finland
Website | E-Mail
Interests: land use land cover change detection in Africa; remote sensing of glacier changes; northern rangelands studies; vegetation phenology monitoring; forest damage studies
Guest Editor
Prof. Lars Eklundh

Department of Physical Geography and Ecosystem Science, Lund University, Sölvegatan 12, 223 62 Lund, Sweden
Website | E-Mail
Interests: remote sensing methodology for carbon balance estimation; monitoring of African drylands; spectral measurements for calibration/validation; satellite time series analysis for seasonality information and smoothing of remotely sensed data; forest disturbances research using remotely sensed data

Special Issue Information

Dear Colleagues,

Humankind has changed the land cover throughout its existence by different land uses, but also by contributing to climate variations around the world. Remote sensing is the most cost-efficient method to monitor land cover and land uses changes, as well as impacts of climate change, which may be identified as glacier changes, changes in vegetation phenology or advance of new plant species to higher latitudes or elevations, for example. In addition to “traditional” satellite imagery to cover large areas we can also use advanced hyperspectral remote sensing data or laser scanning data for land change studies.

Fairly long time-series of Earth Observation data already exist for the whole area of the Earth. These time-series data make up an invaluable source of information for better understanding and management of our environment. It is a challenge and a critical need to understand the methods for extracting useful information from the data, as well as to interpret the time-series signals correctly. We need to be able to interpret both slow variations due to gradual ecosystem transformations, and faster variations due to disturbances or other rapid events. Methods based on remote sensing theory, process modelling, and statistical data analysis will help developing this understanding.

This Special Issue aims to review and synthesize the latest progress in land change monitoring using various remote sensing data types for various purposes. Prospective authors are invited to contribute to this Special Issue of Remote Sensing by submitting an original manuscript. Contributions may focus on, but are not limited to:

  • Theoretical aspects of remote sensing of land change (land use/land cover)
  • Methodological aspects in data processing
  • Phenological studies of vegetation and agricultural areas
  • Use of satellite imagery time series
  • Long-term and short-term variations
  • Land change monitoring in agriculture, forestry, grassland management
  • Linking land change to climate change
  • Urban studies
  • Cryospheric land cover monitoring (water, snow, sea ice, glaciers)

Prof. Petri Pellikka
Prof. Lars Eklundh
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (19 papers)

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Research

Open AccessArticle Mapping Urban Impervious Surface by Fusing Optical and SAR Data at the Decision Level
Remote Sens. 2016, 8(11), 945; https://doi.org/10.3390/rs8110945
Received: 31 July 2016 / Revised: 30 October 2016 / Accepted: 7 November 2016 / Published: 12 November 2016
Cited by 11 | PDF Full-text (23653 KB) | HTML Full-text | XML Full-text
Abstract
The proliferation of impervious surfaces results in a series of environmental issues, such as the decrease of vegetated areas and the aggravation of the urban heat island effects. The mapping of impervious surface and its spatial distributions is of significance for the ecological [...] Read more.
The proliferation of impervious surfaces results in a series of environmental issues, such as the decrease of vegetated areas and the aggravation of the urban heat island effects. The mapping of impervious surface and its spatial distributions is of significance for the ecological study of urban environment. Currently, the integration of optical and synthetic aperture radar (SAR) data has shown advantages in accurately characterizing impervious surface. However, the fusion mainly occurs at the pixel and feature levels which are subject to influences of data noises and feature selections, respectively. In this paper, an innovative and effective method was developed to extract urban impervious surface by synergistically utilizing optical and SAR images at the decision level. The objective of this paper was to obtain an accurate urban impervious surface map based on the random forest classifier and the evidence theory and to provide a detailed uncertainty analysis accompanying the fused impervious surface maps. In this study, both the GaoFen (GF-1) and Sentinel-1A imagery were first used as independent data sources for mapping urban impervious surfaces. Then additional spectral features and texture features were extracted and integrated with the original GF-1 and Sentinel-1A images in generating impervious surfaces. Finally, based on the Dempster-Shafer (D-S) theory, impervious surfaces were produced by fusing the previously estimated impervious surfaces from different datasets at the decision level. Results showed that impervious surfaces estimated from the combined use of original images and features yielded a higher accuracy than those from the original optical or SAR data. Further validations suggested that optical data was better than SAR data in separating impervious surfaces from non-impervious surfaces. The fused impervious surfaces at the decision level had a higher overall accuracy than those produced independently by optical or SAR data. It was also highlighted that the fusion of GF-1 and Sentinel-1A images reduced the amount of confusions among the low reflectance of impervious surface and water, as well as for low reflectance of bare land. An overall accuracy of 95.33% was achieved for extracting urban impervious surfaces by fused datasets. The spatial distributions of uncertainties provided by the evidence theory displayed a confidence level of at least 75% for the impervious surfaces derived from the fused datasets. Full article
(This article belongs to the Special Issue Monitoring of Land Changes)
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Open AccessArticle Forest Fragmentation in the Lower Amazon Floodplain: Implications for Biodiversity and Ecosystem Service Provision to Riverine Populations
Remote Sens. 2016, 8(11), 886; https://doi.org/10.3390/rs8110886
Received: 1 July 2016 / Revised: 13 September 2016 / Accepted: 17 October 2016 / Published: 27 October 2016
Cited by 5 | PDF Full-text (5154 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
This article analyzes the process of forest fragmentation of a floodplain landscape of the Lower Amazon over a 30-year period and its implications for the biodiversity and the provision of ecosystem services to the riverine population. To this end, we created a multi-temporal [...] Read more.
This article analyzes the process of forest fragmentation of a floodplain landscape of the Lower Amazon over a 30-year period and its implications for the biodiversity and the provision of ecosystem services to the riverine population. To this end, we created a multi-temporal forest cover map based on Landsat images, and then analyzed the fragmentation dynamics through landscape metrics. From the analyses of the landscape and bibliographic information, we made inferences regarding the potential impacts of fragmentation on the biodiversity of trees, birds, mammals and insects. Subsequently, we used data on the local populations’ environmental perception to assess whether the inferred impacts on biodiversity are perceived by these populations and whether the ecosystem services related to the biodiversity of the addressed groups are compromised. The results show a 70% reduction of the forest habitat as well as important changes in the landscape structure that constitute a high degree of forest fragmentation. The perceived landscape alterations indicate that there is great potential for compromise of the biodiversity of trees, birds, mammals and insects. The field interviews corroborate the inferred impacts on biodiversity and indicate that the ecosystem services of the local communities have been compromised. More than 95% of the communities report a decreased variety and/or abundance of animal and plant species, 46% report a decrease in agricultural productivity, and 19% confirm a higher incidence of pests during the last 30 years. The present study provides evidence of an accelerated process of degradation of the floodplain forests of the Lower Amazon and indicate substantial compromise of the ecosystem services provision to the riverine population in recent decades, including reductions of food resources (animals and plants), fire wood, raw material and medicine, as well as lower agricultural productivity due to probable lack of pollination, impoverishment of the soil and an increase of pests. Full article
(This article belongs to the Special Issue Monitoring of Land Changes)
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Open AccessArticle Indicators for Assessing Habitat Values and Pressures for Protected Areas—An Integrated Habitat and Land Cover Change Approach for the Udzungwa Mountains National Park in Tanzania
Remote Sens. 2016, 8(10), 862; https://doi.org/10.3390/rs8100862
Received: 27 May 2016 / Revised: 28 September 2016 / Accepted: 11 October 2016 / Published: 19 October 2016
Cited by 3 | PDF Full-text (4104 KB) | HTML Full-text | XML Full-text
Abstract
Assessing the status and monitoring the trends of land cover dynamics in and around protected areas is of utmost importance for park managers and decision makers. Moreover, to support the Convention on Biological Diversity (CBD)’s Strategic Action Plan including the Aichi Biodiversity Targets, [...] Read more.
Assessing the status and monitoring the trends of land cover dynamics in and around protected areas is of utmost importance for park managers and decision makers. Moreover, to support the Convention on Biological Diversity (CBD)’s Strategic Action Plan including the Aichi Biodiversity Targets, such efforts are necessary to set a framework to reach the agreed national, regional or global targets. The integration of land use/cover change (LULCC) data with information on habitats and population density provides the means to assess potential degradation and disturbance resulting from anthropogenic activities such as agriculture and urban area expansion. This study assesses the LULCC over a 20 year (1990–2000–2010) period using freely available Landsat imagery and a dedicated method and toolbox for the Udzungwa Mountains National Park (UMNP) and its surroundings (20 km buffer) in Tanzania. Habitat data gathered from the Digital Observatory for Protected Areas (DOPA)’s eHabitat+ Web service were used to perform ecological stratification of the study area and to develop similarity maps of the potential presence of comparable habitat types outside the protected area. Finally, integration of the habitat similarity maps with the LULCC data was applied in order to evaluate potential pressures on the different habitats within the national park and on the linking corridors between UMNP and other protected areas in the context of wildlife movement and migration. The results show that the UMNP has not suffered from relevant human activities during the study period. The natural vegetation area has remained stable around 1780 km2. In the surrounding 20 km buffer area and the connecting corridors, however, the anthropogenic impact has been strong. Artificially built up areas increased by 14.24% over the last 20 years and the agriculture area increased from 11% in 1990 to 30% in the year 2010. The habitat functional types and the similarity maps confirmed the importance of the buffer zone and the connecting corridors for wildlife movements, while the similarity maps detected other potential corridors for wildlife. Full article
(This article belongs to the Special Issue Monitoring of Land Changes)
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Open AccessCommunication A General-Purpose Spatial Survey Design for Collaborative Science and Monitoring of Global Environmental Change: The Global Grid
Remote Sens. 2016, 8(10), 813; https://doi.org/10.3390/rs8100813
Received: 4 June 2016 / Revised: 18 September 2016 / Accepted: 26 September 2016 / Published: 30 September 2016
Cited by 5 | PDF Full-text (1408 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Recent guidance on environmental modeling and global land-cover validation stresses the need for a probability-based design. Additionally, spatial balance has also been recommended as it ensures more efficient sampling, which is particularly relevant for understanding land use change. In this paper I describe [...] Read more.
Recent guidance on environmental modeling and global land-cover validation stresses the need for a probability-based design. Additionally, spatial balance has also been recommended as it ensures more efficient sampling, which is particularly relevant for understanding land use change. In this paper I describe a global sample design and database called the Global Grid (GG) that has both of these statistical characteristics, as well as being flexible, multi-scale, and globally comprehensive. The GG is intended to facilitate collaborative science and monitoring of land changes among local, regional, and national groups of scientists and citizens, and it is provided in a variety of open source formats to promote collaborative and citizen science. Since the GG sample grid is provided at multiple scales and is globally comprehensive, it provides a universal, readily-available sample. It also supports uneven probability sample designs through filtering sample locations by user-defined strata. The GG is not appropriate for use at locations above ±85° because the shape and topological distortion of quadrants becomes extreme near the poles. Additionally, the file sizes of the GG datasets are very large at fine scale (resolution ~600 m × 600 m) and require a 64-bit integer representation. Full article
(This article belongs to the Special Issue Monitoring of Land Changes)
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Open AccessArticle Collect Earth: Land Use and Land Cover Assessment through Augmented Visual Interpretation
Remote Sens. 2016, 8(10), 807; https://doi.org/10.3390/rs8100807
Received: 2 June 2016 / Revised: 11 September 2016 / Accepted: 22 September 2016 / Published: 28 September 2016
Cited by 32 | PDF Full-text (4996 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Collect Earth is a free and open source software for land monitoring developed by the Food and Agriculture Organization of the United Nations (FAO). Built on Google desktop and cloud computing technologies, Collect Earth facilitates access to multiple freely available archives of satellite [...] Read more.
Collect Earth is a free and open source software for land monitoring developed by the Food and Agriculture Organization of the United Nations (FAO). Built on Google desktop and cloud computing technologies, Collect Earth facilitates access to multiple freely available archives of satellite imagery, including archives with very high spatial resolution imagery (Google Earth, Bing Maps) and those with very high temporal resolution imagery (e.g., Google Earth Engine, Google Earth Engine Code Editor). Collectively, these archives offer free access to an unparalleled amount of information on current and past land dynamics for any location in the world. Collect Earth draws upon these archives and the synergies of imagery of multiple resolutions to enable an innovative method for land monitoring that we present here: augmented visual interpretation. In this study, we provide a full overview of Collect Earth’s structure and functionality, and we present the methodology used to undertake land monitoring through augmented visual interpretation. To illustrate the application of the tool and its customization potential, an example of land monitoring in Papua New Guinea (PNG) is presented. The PNG example demonstrates that Collect Earth is a comprehensive and user-friendly tool for land monitoring and that it has the potential to be used to assess land use, land use change, natural disasters, sustainable management of scarce resources and ecosystem functioning. By enabling non-remote sensing experts to assess more than 100 sites per day, we believe that Collect Earth can be used to rapidly and sustainably build capacity for land monitoring and to substantively improve our collective understanding of the world’s land use and land cover. Full article
(This article belongs to the Special Issue Monitoring of Land Changes)
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Open AccessArticle Short-Term Change Detection in Wetlands Using Sentinel-1 Time Series
Remote Sens. 2016, 8(10), 795; https://doi.org/10.3390/rs8100795
Received: 30 May 2016 / Revised: 31 August 2016 / Accepted: 19 September 2016 / Published: 24 September 2016
Cited by 18 | PDF Full-text (11568 KB) | HTML Full-text | XML Full-text
Abstract
Automated monitoring systems that can capture wetlands’ high spatial and temporal variability are essential for their management. SAR-based change detection approaches offer a great opportunity to enhance our understanding of complex and dynamic ecosystems. We test a recently-developed time series change detection approach [...] Read more.
Automated monitoring systems that can capture wetlands’ high spatial and temporal variability are essential for their management. SAR-based change detection approaches offer a great opportunity to enhance our understanding of complex and dynamic ecosystems. We test a recently-developed time series change detection approach (S1-omnibus) using Sentinel-1 imagery of two wetlands with different ecological characteristics; a seasonal isolated wetland in southern Spain and a coastal wetland in the south of France. We test the S1-omnibus method against a commonly-used pairwise comparison of consecutive images to demonstrate its advantages. Additionally, we compare it with a pairwise change detection method using a subset of consecutive Landsat images for the same period of time. The results show how S1-omnibus is capable of capturing in space and time changes produced by water surface dynamics, as well as by agricultural practices, whether they are sudden changes, as well as gradual. S1-omnibus is capable of detecting a wider array of short-term changes than when using consecutive pairs of Sentinel-1 images. When compared to the Landsat-based change detection method, both show an overall good agreement, although certain landscape changes are detected only by either the Landsat-based or the S1-omnibus method. The S1-omnibus method shows a great potential for an automated monitoring of short time changes and accurate delineation of areas of high variability and of slow and gradual changes. Full article
(This article belongs to the Special Issue Monitoring of Land Changes)
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Open AccessArticle Object-Based Change Detection in Urban Areas: The Effects of Segmentation Strategy, Scale, and Feature Space on Unsupervised Methods
Remote Sens. 2016, 8(9), 761; https://doi.org/10.3390/rs8090761
Received: 11 May 2016 / Revised: 31 August 2016 / Accepted: 9 September 2016 / Published: 16 September 2016
Cited by 15 | PDF Full-text (5481 KB) | HTML Full-text | XML Full-text
Abstract
Object-based change detection (OBCD) has recently been receiving increasing attention as a result of rapid improvements in the resolution of remote sensing data. However, some OBCD issues relating to the segmentation of high-resolution images remain to be explored. For example, segmentation units derived [...] Read more.
Object-based change detection (OBCD) has recently been receiving increasing attention as a result of rapid improvements in the resolution of remote sensing data. However, some OBCD issues relating to the segmentation of high-resolution images remain to be explored. For example, segmentation units derived using different segmentation strategies, segmentation scales, feature space, and change detection methods have rarely been assessed. In this study, we have tested four common unsupervised change detection methods using different segmentation strategies and a series of segmentation scale parameters on two WorldView-2 images of urban areas. We have also evaluated the effect of adding extra textural and Normalized Difference Vegetation Index (NDVI) information instead of using only spectral information. Our results indicated that change detection methods performed better at a medium scale than at a fine scale where close to the pixel size. Multivariate Alteration Detection (MAD) always outperformed the other methods tested, at the same confidence level. The overall accuracy appeared to benefit from using a two-date segmentation strategy rather than single-date segmentation. Adding textural and NDVI information appeared to reduce detection accuracy, but the magnitude of this reduction was not consistent across the different unsupervised methods and segmentation strategies. We conclude that a two-date segmentation strategy is useful for change detection in high-resolution imagery, but that the optimization of thresholds is critical for unsupervised change detection methods. Advanced methods need be explored that can take advantage of additional textural or other parameters. Full article
(This article belongs to the Special Issue Monitoring of Land Changes)
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Open AccessArticle Analyzing Landscape Trends on Agriculture, Introduced Exotic Grasslands and Riparian Ecosystems in Arid Regions of Mexico
Remote Sens. 2016, 8(8), 664; https://doi.org/10.3390/rs8080664
Received: 4 May 2016 / Revised: 13 July 2016 / Accepted: 1 August 2016 / Published: 18 August 2016
Cited by 4 | PDF Full-text (4439 KB) | HTML Full-text | XML Full-text
Abstract
Riparian Zones are considered biodiversity and ecosystem services hotspots. In arid environments, these ecosystems represent key habitats, since water availability makes them unique in terms of fauna, flora and ecological processes. Simple yet powerful remote sensing techniques were used to assess how spatial [...] Read more.
Riparian Zones are considered biodiversity and ecosystem services hotspots. In arid environments, these ecosystems represent key habitats, since water availability makes them unique in terms of fauna, flora and ecological processes. Simple yet powerful remote sensing techniques were used to assess how spatial and temporal land cover dynamics, and water depth reflect distribution of key land cover types in riparian areas. Our study area includes the San Miguel and Zanjon rivers in Northwest Mexico. We used a supervised classification and regression tree (CART) algorithm to produce thematic classifications (with accuracies higher than 78%) for 1993, 2002 and 2011 using Landsat TM scenes. Our results suggest a decline in agriculture (32.5% area decrease) and cultivated grasslands (21.1% area decrease) from 1993 to 2011 in the study area. We found constant fluctuation between adjacent land cover classes and riparian habitat. We also found that water depth restricts Riparian Vegetation distribution but not agricultural lands or induced grasslands. Using remote sensing combined with spatial analysis, we were able to reach a better understanding of how riparian habitats are being modified in arid environments and how they have changed through time. Full article
(This article belongs to the Special Issue Monitoring of Land Changes)
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Open AccessArticle Towards an Improved Environmental Understanding of Land Surface Dynamics in Ukraine Based on Multi-Source Remote Sensing Time-Series Datasets from 1982 to 2013
Remote Sens. 2016, 8(8), 617; https://doi.org/10.3390/rs8080617
Received: 1 May 2016 / Revised: 13 July 2016 / Accepted: 19 July 2016 / Published: 26 July 2016
Cited by 6 | PDF Full-text (3662 KB) | HTML Full-text | XML Full-text
Abstract
Ukraine has experienced immense environmental and institutional changes during the last three decades. We have conducted this study to analyze important land surface dynamics and to assess processes underlying the changes. This research was conducted in two consecutive steps. To analyze monotonic changes [...] Read more.
Ukraine has experienced immense environmental and institutional changes during the last three decades. We have conducted this study to analyze important land surface dynamics and to assess processes underlying the changes. This research was conducted in two consecutive steps. To analyze monotonic changes we first applied a Mann–Kendall trend analysis of the Normalized Difference Vegetation Index (NDVI3g) time series. Gradual and abrupt changes were studied by fitting a seasonal trend model and detecting the breakpoints. Secondly, essential environmental factors were used to quantify their possible relationships with land surface changes. These factors included soil moisture as well as gridded air temperature and precipitation data. This was done using partial rank correlation analysis based on annually aggregated time-series. Our results demonstrate that positive NDVI trends characterize approximately one-third of Ukraine’s land surface, located in the northern and western areas of the country. Negative trends occurred less frequently, covering less than 2% of the area and are distributed irregularly across the country. Monotonic trends were rarely found; shifting trends were identified with a greater frequency. Trend shifts were seen to occur with an increased frequency following the period of the 2000s. We determined that land surface dynamics and climate variability are functionally interdependent; however, the relative influence of the drivers varies in different locations. Among the factors analyzed, the air temperature variable explains the largest portion of NDVI variability. High air temperature/NDVI correlation coefficients (r = 0.36 − 0.77) are observed over the entire country. The soil moisture content is of significant influence in the eastern portion of Ukraine (r = 0.68); precipitation (r = 0.65) was most influential in the central regions of the country. These results increase our understanding of ecosystem responses to climatic changes and anthropogenic activities. Full article
(This article belongs to the Special Issue Monitoring of Land Changes)
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Open AccessArticle Quantifying the Impacts of Environmental Factors on Vegetation Dynamics over Climatic and Management Gradients of Central Asia
Remote Sens. 2016, 8(7), 600; https://doi.org/10.3390/rs8070600
Received: 17 May 2016 / Revised: 2 July 2016 / Accepted: 13 July 2016 / Published: 15 July 2016
Cited by 7 | PDF Full-text (3297 KB) | HTML Full-text | XML Full-text
Abstract
Currently there is a lack of quantitative information regarding the driving factors of vegetation dynamics in post-Soviet Central Asia. Insufficient knowledge also exists concerning vegetation variability across sub-humid to arid climatic gradients as well as vegetation response to different land uses, from natural [...] Read more.
Currently there is a lack of quantitative information regarding the driving factors of vegetation dynamics in post-Soviet Central Asia. Insufficient knowledge also exists concerning vegetation variability across sub-humid to arid climatic gradients as well as vegetation response to different land uses, from natural rangelands to intensively irrigated croplands. In this study, we analyzed the environmental drivers of vegetation dynamics in five Central Asian countries by coupling key vegetation parameter “overall greenness” derived from Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI time series data, with its possible factors across various management and climatic gradients. We developed nine generalized least-squares random effect (GLS-RE) models to analyze the relative impact of environmental factors on vegetation dynamics. The obtained results quantitatively indicated the extensive control of climatic factors on managed and unmanaged vegetation cover across Central Asia. The most diverse vegetation dynamics response to climatic variables was observed for “intensively managed irrigated croplands”. Almost no differences in response to these variables were detected for managed non-irrigated vegetation and unmanaged (natural) vegetation across all countries. Natural vegetation and rainfed non-irrigated crop dynamics were principally associated with temperature and precipitation parameters. Variables related to temperature had the greatest relative effect on irrigated croplands and on vegetation cover within the mountainous zone. Further research should focus on incorporating the socio-economic factors discussed here in a similar analysis. Full article
(This article belongs to the Special Issue Monitoring of Land Changes)
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Open AccessArticle Distribution of Artisanal and Small-Scale Gold Mining in the Tapajós River Basin (Brazilian Amazon) over the Past 40 Years and Relationship with Water Siltation
Remote Sens. 2016, 8(7), 579; https://doi.org/10.3390/rs8070579
Received: 27 April 2016 / Revised: 2 July 2016 / Accepted: 5 July 2016 / Published: 9 July 2016
Cited by 12 | PDF Full-text (19403 KB) | HTML Full-text | XML Full-text
Abstract
An innovative remote sensing approach that combines land-use change and water quality information is proposed in order to investigate if Artisanal and Small-scale Gold Mining (ASGM) area extension is associated with water siltation in the Tapajós River Basin (Brazil), containing the largest small-scale [...] Read more.
An innovative remote sensing approach that combines land-use change and water quality information is proposed in order to investigate if Artisanal and Small-scale Gold Mining (ASGM) area extension is associated with water siltation in the Tapajós River Basin (Brazil), containing the largest small-scale gold mining district in the world. Taking advantage of a 40-year period of the multi-satellite imagery archive, the objective of this paper is to build a normalized time-series in order to evaluate the influence of temporal mining expansion on the water siltation data (TSS, Total Suspended Solids concentration) derived from previous research. The methodological approach was set to deliver a full characterization of the ASGM expansion from its initial stages in the early 1970s to the present. First, based on IRS/LISSIII images acquired in 2012, the historical Landsat image database (1973–2001) was corrected for radiometric and atmospheric effects using dark vegetation as reference to create a normalized time-series. Next, a complete update of the mining areas distribution in 2012 derived from the TerraClass Project (an official land-use classification for the Brazilian Amazon) was conducted having IRS/LISSIII as the base map with the support of auxiliary data and vector editing. Once the ASGM in 2012 was quantified (261.7 km2) and validated with photos, a reverse classification of ASGM in 2001 (171.7 km2), 1993 (166.3 km2), 1984 (47.5 km2), and 1973 (15.4 km2) with the use of Landsat archives was applied. This procedure relies on the assumption that ASGM changes in the land cover are severe and remain detectable from satellite sensors for decades. The mining expansion area over time was then combined with the (TSS) data retrieved from the same atmospherically corrected satellite imagery based on the literature. In terms of gold mining expansion and water siltation effects, four main periods of ASGM activities were identified in the study area: (i) 1958–1977, first occurrence of mining activities and low water impacts; (ii) 1978–1993, introduction of low-budget mechanization associated with very high gold prices resulting in large mining area expansion and high water siltation levels; (iii) 1994–2003, general recession of ASGM activities and exhaustion of easy-access gold deposits, resulting in decreased TSS; (iv) 2004 to present, intensification of ASGM encouraged by high gold prices, resulting in an increase of TSS. Full article
(This article belongs to the Special Issue Monitoring of Land Changes)
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Open AccessArticle Learning a Transferable Change Rule from a Recurrent Neural Network for Land Cover Change Detection
Remote Sens. 2016, 8(6), 506; https://doi.org/10.3390/rs8060506
Received: 3 March 2016 / Revised: 23 May 2016 / Accepted: 12 June 2016 / Published: 16 June 2016
Cited by 41 | PDF Full-text (2864 KB) | HTML Full-text | XML Full-text
Abstract
When exploited in remote sensing analysis, a reliable change rule with transfer ability can detect changes accurately and be applied widely. However, in practice, the complexity of land cover changes makes it difficult to use only one change rule or change feature learned [...] Read more.
When exploited in remote sensing analysis, a reliable change rule with transfer ability can detect changes accurately and be applied widely. However, in practice, the complexity of land cover changes makes it difficult to use only one change rule or change feature learned from a given multi-temporal dataset to detect any other new target images without applying other learning processes. In this study, we consider the design of an efficient change rule having transferability to detect both binary and multi-class changes. The proposed method relies on an improved Long Short-Term Memory (LSTM) model to acquire and record the change information of long-term sequence remote sensing data. In particular, a core memory cell is utilized to learn the change rule from the information concerning binary changes or multi-class changes. Three gates are utilized to control the input, output and update of the LSTM model for optimization. In addition, the learned rule can be applied to detect changes and transfer the change rule from one learned image to another new target multi-temporal image. In this study, binary experiments, transfer experiments and multi-class change experiments are exploited to demonstrate the superiority of our method. Three contributions of this work can be summarized as follows: (1) the proposed method can learn an effective change rule to provide reliable change information for multi-temporal images; (2) the learned change rule has good transferability for detecting changes in new target images without any extra learning process, and the new target images should have a multi-spectral distribution similar to that of the training images; and (3) to the authors’ best knowledge, this is the first time that deep learning in recurrent neural networks is exploited for change detection. In addition, under the framework of the proposed method, changes can be detected under both binary detection and multi-class change detection. Full article
(This article belongs to the Special Issue Monitoring of Land Changes)
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Open AccessArticle A Comparative Study of Urban Expansion in Beijing, Tianjin and Tangshan from the 1970s to 2013
Remote Sens. 2016, 8(6), 496; https://doi.org/10.3390/rs8060496
Received: 12 March 2016 / Revised: 19 May 2016 / Accepted: 3 June 2016 / Published: 14 June 2016
Cited by 24 | PDF Full-text (8661 KB) | HTML Full-text | XML Full-text
Abstract
Although the mapping of spatiotemporal patterns of urban expansion has been widely studied, relatively little attention has been paid to detailed comparative studies on spatiotemporal patterns of urban growth at the regional level over a relatively longer timeframe. This paper was based on [...] Read more.
Although the mapping of spatiotemporal patterns of urban expansion has been widely studied, relatively little attention has been paid to detailed comparative studies on spatiotemporal patterns of urban growth at the regional level over a relatively longer timeframe. This paper was based on multi-sensor remote sensing image data and employs several landscape metrics and the centroid shift model to conduct a multi-angle quantitative analysis on urban expansion in Beijing, Tianjin and Tangshan (Jing-Jin-Tang) in the period from 1970–2013. In addition, the impact analysis of urban growth on land use was adopted in this research. The results showed that Beijing, Tianjin and Tangshan all experienced rapid urbanization, with an average annual urban growth rate of 7.28%, 3.9%, and 0.97%, respectively. Beijing has especially presented a single choropleth map pattern, whereas Tianjin and Tangshan have presented a double surface network pattern in orientation analysis. Furthermore, urban expansion in Beijing was mainly concentrated in Ring 4 to Ring 6 in the northwest and southeast directions, whereas the major expansion was observed in the southeast in Tianjin, primarily affected by dramatic development of Binhai New Area and Tianjin South Railway Station. Naturally, the urban expansion in Tangshan was significantly influenced by the expansion of Beijing and was primarily southwestward. The hot-zones of urbanization were observed within the ranges of 7–25 km, 6–18 km, and 0–15 km, accounting for 93.49%, 89.44% and 72.44% of the total expansion area in Beijing, Tianjin and Tangshan, respectively. The majority of the newly developed urban land was converted from cultivated land and integrated from other built-up land over the past four decades. Of all new urban land in the Beijing, Tianjin and Tangshan, more than 50% was converted from cultivated land, and there was a general tendency for smaller cities to have higher percentages of converted land, accounting for 50.84%, 51.19%, and 51.58%, respectively. The study revealed significant details of the temporal and spatial distributions of urban expansion in Beijing, Tianjin and Tangshan and provided scientific support for the collaborative development of the Beijing, Tianjin and Hebei (Jing-Jin-Ji) regions. Full article
(This article belongs to the Special Issue Monitoring of Land Changes)
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Open AccessArticle Detecting Different Types of Directional Land Cover Changes Using MODIS NDVI Time Series Dataset
Remote Sens. 2016, 8(6), 495; https://doi.org/10.3390/rs8060495
Received: 26 January 2016 / Revised: 13 April 2016 / Accepted: 23 May 2016 / Published: 14 June 2016
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Abstract
This study proposed a multi-target hierarchical detection (MTHD) method to simultaneously and automatically detect multiple directional land cover changes. MTHD used a hierarchical strategy to detect both abrupt and trend land cover changes successively. First, Grubbs’ test eliminated short-lived changes by considering them [...] Read more.
This study proposed a multi-target hierarchical detection (MTHD) method to simultaneously and automatically detect multiple directional land cover changes. MTHD used a hierarchical strategy to detect both abrupt and trend land cover changes successively. First, Grubbs’ test eliminated short-lived changes by considering them outliers. Then, the Brown-Forsythe test and the combination of Tomé’s method and the Chow test were applied to determine abrupt changes. Finally, Sen’s slope estimation coordinated with the Mann-Kendall test detection method was used to detect trend changes. Results demonstrated that both abrupt and trend land cover changes could be detected accurately and automatically. The overall accuracy of abrupt land cover changes was 87.0% and the kappa index was 0.74. Detected trends of land cover change indicated high consistency between NDVI (Normalized Difference Vegetation Index), change trends from LTS (Landsat Thematic Mapper and Enhanced Thematic Mapper Plus time series dataset), and MODIS (Moderate Resolution Imaging Spectroradiometer) time series datasets with the percentage of samples indicating consistency of 100%. For cropland, trends of millet yield per unit and average NDVI of cropland indicated high consistency with a linear regression determination coefficient of 0.94 (p < 0.01). Compared with other multi-target change detection methods, the changes detected by the MTHD could be related closely with specific ecosystem changes, reducing the risk of false changes in the area with frequent and strong interannual fluctuations. Full article
(This article belongs to the Special Issue Monitoring of Land Changes)
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Open AccessArticle Identifying Categorical Land Use Transition and Land Degradation in Northwestern Drylands of Ethiopia
Remote Sens. 2016, 8(5), 408; https://doi.org/10.3390/rs8050408
Received: 11 January 2016 / Revised: 5 April 2016 / Accepted: 4 May 2016 / Published: 12 May 2016
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Abstract
Land use transition in dryland ecosystems is one of the major driving forces to landscape change that directly impacts the welfare of humans. In this study, the support vector machine (SVM) classification algorithm and cross tabulation matrix analysis are used to identify systematic [...] Read more.
Land use transition in dryland ecosystems is one of the major driving forces to landscape change that directly impacts the welfare of humans. In this study, the support vector machine (SVM) classification algorithm and cross tabulation matrix analysis are used to identify systematic and random processes of change. The magnitude and prevailing signals of land use transitions are assessed taking into account net change and swap change. Moreover, spatiotemporal patterns and the relationship of precipitation and the Normalized Difference Vegetation Index (NDVI) are explored to evaluate landscape degradation. The assessment showed that 44% of net change and about 54% of total change occurred during the study period, with the latter being due to swap change. The conversion of over 39% of woodland to cropland accounts for the existence of the highest loss of valuable ecosystem of the region. The spatial relationship of NDVI and precipitation also showed R2 of below 0.5 over 55% of the landscape with no significant changes in the precipitation trend, thus representing an indicative symptom of land degradation. This in-depth analysis of random and systematic landscape change is crucial for designing policy intervention to halt woodland degradation in this fragile environment. Full article
(This article belongs to the Special Issue Monitoring of Land Changes)
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Open AccessArticle The Variations of Land Surface Phenology in Northeast China and Its Responses to Climate Change from 1982 to 2013
Remote Sens. 2016, 8(5), 400; https://doi.org/10.3390/rs8050400
Received: 4 February 2016 / Revised: 31 March 2016 / Accepted: 4 May 2016 / Published: 12 May 2016
Cited by 11 | PDF Full-text (9550 KB) | HTML Full-text | XML Full-text
Abstract
Northeast China is located at high northern latitudes and is a typical region of relatively high sensitivity to global climate change. Studies of the land surface phenology in Northeast China and its response to climate change are important for understanding global climate change. [...] Read more.
Northeast China is located at high northern latitudes and is a typical region of relatively high sensitivity to global climate change. Studies of the land surface phenology in Northeast China and its response to climate change are important for understanding global climate change. In this study, the land surface phenology parameters were calculated using the third generation dataset from the Global Inventory Modeling and Mapping Studies (GIMMS 3g) that was collected from 1982 to 2013 were estimated to analyze the variations of the land surface phenology in Northeast China at different scales and to discuss the internal relationships between phenology and climate change. We examined the phonological changes of all ecoregions. The average start of the growing season (SOS) did not exhibit a significant trend throughout the study area; however, the end of the growing season (EOS) was significantly delayed by 4.1 days or 0.13 days/year (p < 0.05) over the past 32 years. The SOS for the Hulunbuir Plain, Greater Khingan Mountains and Lesser Khingan Mountains was earlier, and the SOS for the Sanjing, Songnen and Liaohe Plains was later. In addition, the EOS of the Greater Khingan Mountains, Lesser Khingan Mountains and Changbai Mountains was later than the EOS of the Liaohe Plain. The spring temperature had the greatest impact on the SOS. Precipitation had an insignificant impact on forest SOS and a relatively large impact on grassland SOS. The EOS was affected by both temperature and precipitation. Furthermore, although temperature had a lag effect on the EOS, no significant lag effect was observed for the SOS. Full article
(This article belongs to the Special Issue Monitoring of Land Changes)
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Open AccessArticle Land Cover Characterization in West Sudanian Savannas Using Seasonal Features from Annual Landsat Time Series
Remote Sens. 2016, 8(5), 365; https://doi.org/10.3390/rs8050365
Received: 22 January 2016 / Revised: 5 April 2016 / Accepted: 21 April 2016 / Published: 28 April 2016
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Abstract
With the increasing temporal resolution of medium spatial resolution data, seasonal features are becoming more readily available for land cover characterization. However, in the tropical regions, images can be severely contaminated by clouds during the rainy season and fires during the dry season, [...] Read more.
With the increasing temporal resolution of medium spatial resolution data, seasonal features are becoming more readily available for land cover characterization. However, in the tropical regions, images can be severely contaminated by clouds during the rainy season and fires during the dry season, with possible effects to seasonal features. In this study, we evaluated the performance of seasonal features based on an annual Landsat time series (LTS) of 35 images for land cover characterization in West Sudanian savanna woodlands. First, the burnt areas were detected and removed. Second, the reflectance seasonality was modelled using a harmonic model, and model parameters were used as inputs for land cover classification and tree crown cover prediction using the random forest algorithm. Furthermore, to study the sensitivity of the approach to the burnt areas, we repeated the analyses without the first step. Our results showed that seasonal features improved classification accuracy significantly from 68.7% and 66.1% to 76.2%, and decreased root mean square error (RMSE) of tree crown cover predictions from 11.7% and 11.4% to 10.4%, in comparison to the dry and rainy season single date images, respectively. The burnt areas biased the seasonal parameters in near-infrared and shortwave infrared bands, and decreased the accuracy of classification and tree crown cover prediction, suggesting that burnt areas should be removed before fitting the harmonic model. We conclude that seasonal features from annual LTS improved land cover characterization performance, and the harmonic model, provided a simple method for computing annual seasonal features with burnt area removal. Full article
(This article belongs to the Special Issue Monitoring of Land Changes)
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Open AccessArticle A Framework for Large-Area Mapping of Past and Present Cropping Activity Using Seasonal Landsat Images and Time Series Metrics
Remote Sens. 2016, 8(4), 312; https://doi.org/10.3390/rs8040312
Received: 14 December 2015 / Revised: 30 March 2016 / Accepted: 31 March 2016 / Published: 8 April 2016
Cited by 14 | PDF Full-text (9197 KB) | HTML Full-text | XML Full-text
Abstract
Crop extent and frequency maps are an important input to inform the debate around land value and competitive land uses, in particular between cropping and mining in the case of Queensland, Australia. Such spatial datasets are useful for supporting decisions on natural resource [...] Read more.
Crop extent and frequency maps are an important input to inform the debate around land value and competitive land uses, in particular between cropping and mining in the case of Queensland, Australia. Such spatial datasets are useful for supporting decisions on natural resource management, planning and policy. For the major broadacre cropping regions of Queensland, Australia, the complete Landsat Time Series (LTS) archive from 1987 to 2015 was used in a multi-temporal mapping approach, where spatial, spectral and temporal information were combined in multiple crop-modelling steps, supported by training data sampled across space and time for the classes Crop and No-Crop. Temporal information within summer and winter growing seasons were summarised for each year, and combined with various vegetation indices and band ratios computed from a pixel-based mid-season spectral synthetic image. All available temporal information was spatially aggregated to the scale of image segments in the mid-season synthetic image for each growing season and used to train a number of different predictive models for a Crop and No-Crop classification. Validation revealed that the predictive accuracy varied by growing season and region and a random forest classifier performed best, with κ = 0.88 to 0.91 for the summer growing season and κ = 0.91 to 0.97 for the winter growing season, and are thus suitable for mapping current and historic cropping activity. Full article
(This article belongs to the Special Issue Monitoring of Land Changes)
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Open AccessArticle Examining Urban Impervious Surface Distribution and Its Dynamic Change in Hangzhou Metropolis
Remote Sens. 2016, 8(3), 265; https://doi.org/10.3390/rs8030265
Received: 19 December 2015 / Revised: 25 February 2016 / Accepted: 16 March 2016 / Published: 22 March 2016
Cited by 15 | PDF Full-text (8131 KB) | HTML Full-text | XML Full-text
Abstract
Analysis of urban distribution and its expansion using remote sensing data has received increasing attention in the past three decades, but little research has examined spatial patterns of urban distribution and expansion with buffer zones in different directions. This research selected Hangzhou metropolis [...] Read more.
Analysis of urban distribution and its expansion using remote sensing data has received increasing attention in the past three decades, but little research has examined spatial patterns of urban distribution and expansion with buffer zones in different directions. This research selected Hangzhou metropolis as a case study to analyze spatial patterns and dynamic changes based on time-series urban impervious surface area (ISA) datasets. ISA was developed from Landsat imagery between 1991 and 2014 using a hybrid approach consisting of linear spectral mixture analysis, decision tree classifiers, and post-processing. The spatial patterns of ISA distribution and its dynamic changes in eight directions—east, southeast, south, southwest, west, northwest, north, and northeast—at the temporal scale were analyzed with a buffer zone-based approach. This research indicated that ISA can be extracted from Landsat imagery with both producer and user accuracies of over 90%. ISA in Hangzhou metropolis increased from 146 km2 in 1991 to 868 km2 in 2014. Annual ISA growth rates were between 15.6 km2 and 48.8 km2 with the lowest growth rate in 1994–2000 and the highest growth rate in 2005–2010. Urban ISA increase before 2000 was mainly due to infilling within the urban landscape, and, after 2005, due to urban expansion in the urban-rural interfaces. Urban expansion in this study area has different characteristics in various directions that are influenced by topographic factors and urban development policies. Full article
(This article belongs to the Special Issue Monitoring of Land Changes)
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