Uncertainty Analysis of Multisource Land Cover Products in China

Satellite-based land cover products play a crucial role in sustainability. There are several types of land cover products, such as qualitative products with discrete classes, semiquantitative products with several classes at a predetermined ratio, and quantitative products with land cover fractions. The proportions of land cover types in the grids with coarse resolution should be considered when used at the regional scale (e.g., modeling and remote sensing inversion). However, uncertainty, which varies with spatial distribution and resolution, needs to be studied further. This study used MCD12, ESA CCI, and MEaSURES VCF land cover data as indicators of qualitative, semiquantitative, and quantitative products, respectively, to explore the uncertainty of multisource land cover data. The methods of maximum area aggregation, deviation analysis, and least squares regression were used to investigate spatiotemporal changes in forests and nontree vegetation at diverse pixel resolutions across China. The results showed that the average difference in forest coverage for the three products was 8%, and the average deviation was 11.2%. For forest cover, the VCF and ESA CCI exhibited high consistency. For nontree vegetation, the ESA CCI and MODIS exhibited the lowest differences. The overall uncertainty in the temporal and spatial changes of the three products was relatively small, but there were significant differences in local areas (e.g., southeastern hills). Notably, as the spatial resolution decreased, the three products’ uncertainty decreased, and the resolution of 0.1° was the inflection point of consistency.

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A Comparative Analysis of Spatial Data and Land Use/Land Cover Classification in Urbanized Areas and Areas Subjected to Anthropogenic Pressure for the Example of Poland

Sustainability ◽

10.3390/su13063070 ◽

2021 ◽

Vol 13 (6) ◽

pp. 3070

Author(s):

Patrycja Szarek-Iwaniuk

Keyword(s):

Land Use ◽

Land Cover ◽

Land Cover Classification ◽

Classification Systems ◽

Anthropogenic Pressure ◽

Land Use Land Cover ◽

Land Use Classification ◽

Land Cover Data ◽

Spatial Changes ◽

Land Use Types

Urbanization processes are some of the key drivers of spatial changes which shape and influence land use and land cover. The aim of sustainable land use policies is to preserve and manage existing resources for present and future generations. Increasing access to information about land use and land cover has led to the emergence of new sources of data and various classification systems for evaluating land use and spatial changes. A single globally recognized land use classification system has not been developed to date, and various sources of land-use/land-cover data exist around the world. As a result, data from different systems may be difficult to interpret and evaluate in comparative analyses. The aims of this study were to compare land-use/land-cover data and selected land use classification systems, and to determine the influence of selected classification systems and spatial datasets on analyses of land-use structure in the examined area. The results of the study provide information about the existing land-use/land-cover databases, revealing that spatial databases and land use and land cover classification systems contain many equivalent land-use types, but also differ in various respects, such as the level of detail, data validity, availability, number of land-use types, and the applied nomenclature.

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Thematic Accuracy Assessment of Regional Scale Land-Cover Data

Remote Sensing and GIS Accuracy Assessment ◽

10.1201/9780203497586.ch7 ◽

2004 ◽

pp. 91-103 ◽

Cited By ~ 1

Author(s):

Siamak Khorram ◽

Joseph Knight ◽

Halil Cakir

Keyword(s):

Land Cover ◽

Accuracy Assessment ◽

Regional Scale ◽

Land Cover Data ◽

Thematic Accuracy

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Motif: an open-source R tool for pattern-based spatial analysis

10.32942/osf.io/kj7fu ◽

2020 ◽

Author(s):

Jakub Nowosad

Keyword(s):

Spatial Analysis ◽

Land Cover ◽

Open Source ◽

Spatial Patterns ◽

Forest Cover ◽

R Package ◽

Growth Monitoring ◽

Forest Cover Change ◽

Land Cover Data ◽

Wide Range

*Context* Pattern-based spatial analysis provides methods to describe and quantitatively compare spatial patterns for categorical raster datasets. It allows for spatial search, change detection, and clustering of areas with similar patterns. *Objectives* We developed an R package **motif** as a set of open-source tools for pattern-based spatial analysis. *Methods* This package provides most of the functionality of existing software (except spatial segmentation), but also extends the existing ideas through support for multi-layer raster datasets. It accepts larger-than-RAM datasets and works across all of the major operating systems. *Results* In this study, we describe the software design of the tool, its capabilities, and present four case studies. They include calculation of spatial signatures based on land cover data for regular and irregular areas, search for regions with similar patterns of geomorphons, detection of changes in land cover patterns, and clustering of areas with similar spatial patterns of land cover and landforms. *Conclusions* The methods implemented in **motif** should be useful in a wide range of applications, including land management, sustainable development, environmental protection, forest cover change and urban growth monitoring, and agriculture expansion studies. The **motif** package homepage is https://nowosad.github.io/motif.

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Suburban frog ponds: mapping local land cover yields different estimates of landscape composition than the National Land Cover Database

10.7287/peerj.preprints.570 ◽

2014 ◽

Author(s):

Max Lambert

Keyword(s):

Land Cover ◽

Large Scale ◽

Forest Cover ◽

Spatial Scales ◽

Landscape Composition ◽

Ecological Processes ◽

Simple Method ◽

Fine Grain ◽

Land Cover Data ◽

Ecological Patterns

Suburban neighborhoods are rapidly spreading globally. As such, there is an increasing need to study the environmental and ecological effects of suburbanization. At large spatial extents, from county-level to global, remote sensing-derived land cover data, such as the National Land Cover Dataset (NLCD), have yielded insight into patterns of urbanization and concomitant large-scale ecological patterns in response. However, the components of suburban land cover (houses, yards, etc.) are dispersed throughout the landscape at a finer scale than the relatively coarse grain size (30m pixels) of NLCD may be able to detect. Our understanding of ecological processes in heterogeneous landscapes is reliant upon the accuracy and resolution of our measurements as well as the scale at which we measure the landscape. Analyses of ecological processes along suburban gradients are restricted by the currently available data. As ecologists are becoming increasingly interested in describing phenomena at spatial extents as small as individual households, we need higher-resolution landscape measurements. Here, I describe a simple method of translating the components of suburban landscapes into finer-grain, local land cover (LLC) data in GIS. Using both LLC and NLCD, I compare the suburban matrix surrounding ponds occupied by two different frog species. I illustrate large discrepancies in Forest, Yard, and Developed land cover estimates between LLC and NLCD, leading to markedly different interpretations of suburban landscape composition. NLCD, relative to LLC, estimates lower proportions of forest cover and higher proportions of anthropogenic land covers in general. These two land cover datasets provide surprisingly different descriptions of the suburban landscapes, potentially affecting our understanding of how organisms respond to an increasingly suburban world. LLC provides a free and detailed fine-grain depiction of the components of suburban neighborhoods and will allow ecologists to better explore heterogeneous suburban landscapes at multiple spatial scales.

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Land-cover data improve bioclimatic models for anurans and turtles at a regional scale

Journal of Biogeography ◽

10.1111/j.1365-2699.2009.02117.x ◽

2009 ◽

Vol 36 (9) ◽

pp. 1656-1672 ◽

Cited By ~ 25

Author(s):

R. Tingley ◽

T. B. Herman

Keyword(s):

Land Cover ◽

Regional Scale ◽

Land Cover Data

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Analysis of Forest Deforestation and its Driving Factors in Myanmar from 1988 to 2017

Sustainability ◽

10.3390/su11113047 ◽

2019 ◽

Vol 11 (11) ◽

pp. 3047 ◽

Cited By ~ 7

Author(s):

Rongfeng Yang ◽

Yi Luo ◽

Kun Yang ◽

Liang Hong ◽

Xiaolu Zhou

Keyword(s):

Land Cover ◽

Large Scale ◽

Forest Cover ◽

Agricultural Land ◽

Temporal Evolution ◽

Driving Factors ◽

Deforestation Rate ◽

Land Cover Data ◽

Spatio Temporal ◽

Landsat Satellite

Myanmar, abundant in natural resources, is one of the countries with high forest cover in Southeast Asia. Along with its rapid socio-economic development, however, the construction of large-scale infrastructure, expansion of agricultural land, and an increasing demand for timber products have posed serious threats to the forests and significantly affected regional sustainable development. However, the geographical environment in Myanmar is complex, resulting in the lack of long-term sequence of land cover data products. Based on 30 years’ Landsat satellite remote sensing imagery data and the land cover data extracted by a mixed classification method, this paper examined the spatial and temporal evolution characteristics of forest cover in Myanmar and investigated driving factors of the spatio-temporal evolution. Results show that the forest cover has decreased by 110,621 km2 in the past 30 years with the annual deforestation rate of 0.87%. Cropland expansion is the main reason for the deforestation throughout the study period. The study can provide basic information of the forest cover data to the Myanmar government for ecological environment protection. At the same time, it can provide important support to the “Belt and Road” initiative to invest in the region’s economy.

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Inconsistency distribution patterns of different remote sensing land-cover data from the perspective of ecological zoning

Open Geosciences ◽

10.1515/geo-2020-0014 ◽

2020 ◽

Vol 12 (1) ◽

pp. 324-341

Author(s):

Lichun Sui ◽

Junmei Kang ◽

Xiaomei Yang ◽

Zhihua Wang ◽

Jun Wang

Keyword(s):

Remote Sensing ◽

Land Cover ◽

Regional Distribution ◽

Distribution Patterns ◽

Classification Systems ◽

Dominant Type ◽

Ecological Zones ◽

Land Cover Data ◽

High Consistency ◽

Land Types

AbstractAnalyzing consistency of different land-cover data is significant to reasonably select land-cover data for regional development and resource survey. Existing consistency analysis of different datasets mainly focused on the phenomena of spatial consistency regional distribution or accuracy comparison to provide guidelines for choosing the land-cover data. However, few studies focused on the hidden inconsistency distribution rules of different datasets, which can provide guidelines not only for users to properly choose them but also for producers to improve their mapping strategies. Here, we zoned the Sindh province of Pakistan by the Terrestrial Ecoregions of the World as a case to analyze the inconsistency patterns of the following three datasets: GlobeLand30, FROM-GLC, and regional land cover (RLC). We found that the inconsistency of the three datasets was relatively low in areas having a dominant type and also showing homogeneity characteristics in remote sensing images. For example, cropland of the three datasets in the ecological zoning of Northwestern thorn scrub forests showed high consistency. In contrast, the inconsistency was high in areas with strong heterogeneity. For example, in the southeast of the Thar desert ecological zone where cropland, grassland, shrubland, and bareland were interleaved and the surface cover complexity was relatively high, the inconsistency of the three datasets was relatively high. We also found that definitions of some types in different classification systems are different, which also increased the inconsistency. For example, the definitions of grassland and bareland in GlobeLand30 and RLC were different, which seriously affects the consistency of these datasets. Hence, producers can use the existing land-cover products as reference in ecological zones with dominant types and strong homogeneity. It is necessary to pay more attention on ecological zoning with complex land types and strong heterogeneity. An effective way is standardizing the definitions of complex land types, such as forest, shrubland, and grassland in these areas.

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Motif: an open-source R tool for pattern-based spatial analysis

Landscape Ecology ◽

10.1007/s10980-020-01135-0 ◽

2020 ◽

Author(s):

Jakub Nowosad

Keyword(s):

Spatial Analysis ◽

Land Cover ◽

Open Source ◽

Spatial Patterns ◽

Forest Cover ◽

R Package ◽

Growth Monitoring ◽

Forest Cover Change ◽

Land Cover Data ◽

Wide Range

Abstract Context Pattern-based spatial analysis provides methods to describe and quantitatively compare spatial patterns for categorical raster datasets. It allows for spatial search, change detection, and clustering of areas with similar patterns. Objectives We developed an R package motif as a set of open-source tools for pattern-based spatial analysis. Methods This package provides most of the functionality of existing software (except spatial segmentation), but also extends the existing ideas through support for multi-layer raster datasets. It accepts larger-than-RAM datasets and works across all of the major operating systems. Results In this study, we describe the software design of the tool, its capabilities, and present four case studies. They include calculation of spatial signatures based on land cover data for regular and irregular areas, search for regions with similar patterns of geomorphons, detection of changes in land cover patterns, and clustering of areas with similar spatial patterns of land cover and landforms. Conclusions The methods implemented in motif should be useful in a wide range of applications, including land management, sustainable development, environmental protection, forest cover change and urban growth monitoring, and agriculture expansion studies. The motif package homepage is https://nowosad.github.io/motif.

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Detecting hotspots of changes in spatial pattern of forest fragmentation in the Romanian Carpathian Mountains

10.5194/egusphere-egu2020-474 ◽

2020 ◽

Author(s):

Vasilică-Dănuț Horodnic ◽

Vasile Efros ◽

Dumitru Mihăilă ◽

Luminița-Mirela Lăzărescu ◽

Petruț-Ionel Bistricean

Keyword(s):

Land Use ◽

Land Cover ◽

Forest Cover ◽

Regional Scale ◽

Landscape Fragmentation ◽

Forest Landscape ◽

Forest Area Change ◽

Forest Cover Changes ◽

Land Restitution ◽

Cover Pattern

Landscape fragmentation is the expression of patchiness and spatial heterogeneity of land cover pattern. After the breakdown of the socialism regime in 1989, Romania has undergone significant changes at the level of political, institutional and socio-economic profile, which determined researchers to consider this country an experimental territory for land use and landscape research.The aim of present study is to detect hotspots of changes of forests landscape fragmentation patterns in the Romanian Carpathian Mountains over the last 28 years. In order to meet our demand we applied a holistic approach to assess the multiple teleconnections between forest cover changes and the degree of fragmentation at regional scale for two distinct periods that make up the 1990-2018 period: (1) 1990-2006 (land restitution period or transition period to the market economy) and (2) 2006-2018 (post-accession period to the European Union).The analysis were carried out using freely available time series CORINE Land Cover data of 1990, 2006 and 2018 provided by Copernicus Land Monitoring Services. The initial spatial datasets were processed with the help of Geographic Information Systems (GIS), while GUIDOS, a free software toolbox dedicated to quantitative analysis of digital landscape images, was used to generate spatial and statistics data of the degree of forest landscape fragmentation.Our findings indicate that the first period of analysis was more dynamic regarding forest cover changes with a gross area gain of 316 304 ha (7.59%) and a gross area loss of 147 496 ha (3.54%) leading to a net forest area change of 168 808 ha (4.05%) which reflects the level of forest recovery. The change pattern of fragmentation classes showed that 332 045 ha (71.47%) of fragmentation decrease is found for the transition of dominant forest in 1990 into the less fragmented class interior in 2006, while 67 418 ha (65.10%) of all fragmentation increase is found for transition from interior in 1990 to dominant in 2006. The other side, for the period from 2006 to 2018 we found a gross area gain of 127 146 ha (2.93%) and a gross area loss of 212 933 ha (4.91%) leading to a net forest area change of -85 787 ha (-1.98%) which emphasizes the level of forest disturbance. In the same time frame, the high values of fragmentation pattern have been registered for the same classes, 56.82% for fragmentation decrease and 70.60% for fragmentation increase, respectively. The results highlight the reversible impact of land use change on land cover pattern, spatially shaped through afforestation in the first period of analysis and through deforestation in the second period. The afforestation process were determined by high rate of external migration, while deforestation process is a consequence of land restitution laws (Law no. 247/2005), which caused considerable mutations in the ownership of land.The study emphasizes the impacts of land use policies and land management practices on the pattern of forest landscape and the usefulness of Guidos Toolbox, a universal digital image object analysis, to detect hotspots of changes at regional scale.

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Optimization of land use at Mt. Yuelu scenic area: an analysis using the ecological green equivalent

Earth Sciences Research Journal ◽

10.15446/esrj.v20n1.54138 ◽

2016 ◽

Vol 20 (1) ◽

pp. 1-5

Author(s):

Xi Chen ◽

Ke-lin Wang ◽

Wei-jun Zhou ◽

Hong-bin Li ◽

Muhammad Ashraf

Keyword(s):

Mathematical Model ◽

Land Use ◽

Land Cover ◽

Ecological System ◽

Hunan Province ◽

Infrastructure Development ◽

Land Use Structure ◽

Green Area ◽

Land Cover Data ◽

Forest Coverage

This study applies the ecological green equivalent approach to evaluating the land use structure of Mt. Yuelu scenic area in Hunan Province, China. A mathematical model is established based on land use and land cover data, and then ArcGIS used to extract the spatial extent of the ecological green equivalent within each of the relevant elements. Results show that the area has a relatively reasonable land use structure even though the forest coverage rate is slight below the optimum. The overall green equivalent (1.13) was higher than the optimum forest coverage ecological green equivalent (1.00). The distribution of forest was uneven, with most of the forest within the site; the area’s land use structure could thus be improved by extending the green area outside of Mt. Yuelu. We conclude by reiterating that landscape and infrastructure development should consider ecological system conditions.

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