Harmonizing and Combining Existing Land Cover/Land Use Datasets for Cropland Area Monitoring at the African Continental Scale

This study compares different nationwide multi-temporal spatial data sources and analyzes the cropland area, cropland abandonment rates and transformation of cropland to other land cover/land use categories in Slovakia. Four multi-temporal land cover/land use data sources were used: The Historic Land Dynamics Assessment (HILDA), the Carpathian Historical Land Use Dataset (CHLUD), CORINE Land Cover (CLC) data and Landsat images classification. We hypothesized that because of the different spatial, temporal and thematic resolution of the datasets, there would be differences in the resulting cropland abandonment rates. We validated the datasets, compared the differences, interpreted the results and combined the information from the different datasets to form an overall picture of long-term cropland abandonment in Slovakia. The cropland area increased until the Second World War, but then decreased after transition to the communist regime and sharply declined following the 1989 transition to an open market economy. A total of 49% of cropland area has been transformed to grassland, 34% to forest and 15% to urban areas. The Historical Carpathian dataset is the more reliable long-term dataset, and it records 19.65 km2/year average cropland abandonment for 1836–1937, 154.44 km2/year for 1938–1955 and 140.21 km2/year for 1956–2012. In comparison, the Landsat, as a recent data source, records 142.02 km2/year abandonment for 1985–2000 and 89.42 km2/year for 2000–2010. These rates, however, would be higher if the dataset contained urbanisation data and more precise information on afforestation. The CORINE Land Cover reflects changes larger than 5 ha, and therefore the reported cropland abandonment rates are lower.

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Historical Changes in Land Use and Suitability for Future Agriculture Expansion in Western Bahia, Brazil

Remote Sensing ◽

10.3390/rs13061088 ◽

2021 ◽

Vol 13 (6) ◽

pp. 1088

Author(s):

Fernando Martins Pimenta ◽

Allan Turini Speroto ◽

Marcos Heil Costa ◽

Emily Ane Dionizio

Keyword(s):

Land Use ◽

Land Cover ◽

Land Suitability ◽

Agricultural Area ◽

Landsat Images ◽

Historical Changes ◽

Cropland Area ◽

Irrigated Area ◽

Agriculture Expansion ◽

Forest Code

Western Bahia is a critical region in Brazil’s recent expansion of agricultural output. Its outstanding increase in production is associated with strong growth in cropland area and irrigation. Here we present analyses of Western Bahian historical changes in land use, including irrigated area, and suitability for future agricultural expansion that respects permanent protection areas and the limits established by the Brazilian Forest Code in the Cerrado biome. For this purpose, we developed a land use and land cover classification database using a random forest classifier and Landsat images. A spatial multicriteria decision analysis to evaluate land suitability was performed by combining this database with precipitation and slope data. We demonstrate that between 1990 and 2020, the region’s total agricultural area increased by 3.17 Mha and the irrigated area increased by 193,480 ha. Throughout the region, the transition between the different classes of land use and land cover followed different pathways and was strongly influenced by land suitability and also appears to be influenced by Brazil’s new Forest Code of 2012. We conclude that even if conservation restrictions are considered, agricultural area could nearly double in the region, with expansion possible mostly in areas we classify as moderately suitable for agriculture, which are subject to climate hazards when used for rainfed crops but are otherwise fine for pastures and irrigated croplands.

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Accuracy, Bias, and Improvements in Mapping Crops and Cropland across the United States Using the USDA Cropland Data Layer

Remote Sensing ◽

10.3390/rs13050968 ◽

2021 ◽

Vol 13 (5) ◽

pp. 968 ◽

Cited By ~ 1

Author(s):

Tyler J. Lark ◽

Ian H. Schelly ◽

Holly K. Gibbs

Keyword(s):

United States ◽

Land Use ◽

Land Cover ◽

Agricultural Land ◽

The United States ◽

Error Rates ◽

Agricultural Landscapes ◽

Cropland Area ◽

Data Layer ◽

Cropland Data Layer

The U.S. Department of Agriculture’s (USDA) Cropland Data Layer (CDL) is a 30 m resolution crop-specific land cover map produced annually to assess crops and cropland area across the conterminous United States. Despite its prominent use and value for monitoring agricultural land use/land cover (LULC), there remains substantial uncertainty surrounding the CDLs’ performance, particularly in applications measuring LULC at national scales, within aggregated classes, or changes across years. To fill this gap, we used state- and land cover class-specific accuracy statistics from the USDA from 2008 to 2016 to comprehensively characterize the performance of the CDL across space and time. We estimated nationwide area-weighted accuracies for the CDL for specific crops as well as for the aggregated classes of cropland and non-cropland. We also derived and reported new metrics of superclass accuracy and within-domain error rates, which help to quantify and differentiate the efficacy of mapping aggregated land use classes (e.g., cropland) among constituent subclasses (i.e., specific crops). We show that aggregate classes embody drastically higher accuracies, such that the CDL correctly identifies cropland from the user’s perspective 97% of the time or greater for all years since nationwide coverage began in 2008. We also quantified the mapping biases of specific crops throughout time and used these data to generate independent bias-adjusted crop area estimates, which may complement other USDA survey- and census-based crop statistics. Our overall findings demonstrate that the CDLs provide highly accurate annual measures of crops and cropland areas, and when used appropriately, are an indispensable tool for monitoring changes to agricultural landscapes.

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Impacts and trade-offs of future land use and land cover change in Scotland: spatial simulation modelling of shared socioeconomic pathways (SSPs) at regional scales

10.31235/osf.io/fc6he ◽

2020 ◽

Author(s):

Richard J Hewitt ◽

Andrea Baggio Compagnucci ◽

Marie Castellazzi ◽

Rob W. Dunford ◽

Paula A. Harrison ◽

...

Keyword(s):

Land Use ◽

Land Cover ◽

Land Cover Change ◽

Food Production ◽

Forest Cover ◽

Simulation Modelling ◽

Spatial Modelling ◽

Continental Scale ◽

Trade Offs ◽

High Degree

Current estimates suggest that the world is on track for ~3°C of heating relative to pre-industrial levels by 2100. This is likely to bring great disruption to earth systems, leading to increased natural hazard risks, crop failures, civil unrest and population migration. There is, however, a high degree of uncertainty about the impacts that such events may have on land use and ecosystems in individual countries. Integrated assessment modelling (IAM) of scenarios like the Shared Socioeconomic Pathways (SSPs) offers one way to address this uncertainty, allowing outcomes such as the relative land cover under food production or forestry to be compared for each scenario. However, global and continental-scale IAMs need to be complemented by landscape scale spatial modelling to inform national and regional policy making. In this paper, we demonstrate impacts and trade-offs of future land cover change in Scotland, a UK region with a high degree of political autonomy, using downscaled SSPs from Europe to the national and finally the regional level. Our methods integrate participatory knowledge co-construction approaches with land-use modelling. Firstly, a stakeholder workshop held in November 2018 led to the development of detailed narratives for 5 UK SSPs. Two contrasting UK SSPs, SSP1 (Sustainability), and SSP5 (Fossil-Fuelled Development) were then adapted to the case of Scotland and simulated to the year 2040 using a land use change model (APoLUS). Land use demands for each scenario were quantified based on historical tendencies, narrative information derived from the workshop, and future Scottish Government targets. Results highlight trade-offs between forest cover, grasslands, natural areas including marginal peatlands important for carbon sequestration, and cropland for food production and the drinks industry. We discuss these preliminary findings, highlight key areas of uncertainty and present pathways for future work.

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A new European land systems representation accounting for landscape characteristics

Landscape Ecology ◽

10.1007/s10980-021-01227-5 ◽

2021 ◽

Author(s):

Yue Dou ◽

Francesca Cosentino ◽

Ziga Malek ◽

Luigi Maiorano ◽

Wilfried Thuiller ◽

...

Keyword(s):

Land Use ◽

Land Use Change ◽

Land Cover ◽

Large Scale ◽

Bird Species ◽

Data Availability ◽

Landscape Characteristics ◽

Continental Scale ◽

Land Systems ◽

Land Cover Maps

Abstract Context While land use change is the main driver of biodiversity loss, most biodiversity assessments either ignore it or use a simple land cover representation. Land cover representations lack the representation of land use and landscape characteristics relevant to biodiversity modeling. Objectives We developed a comprehensive and high-resolution representation of European land systems on a 1-km2 grid integrating important land use and landscape characteristics. Methods Combining the recent data on land cover and land use intensities, we applied an expert-based hierarchical classification approach and identified land systems that are common in Europe and meaningful for studying biodiversity. We tested the benefits of using this map as compared to land cover information to predict the distribution of bird species having different vulnerability to landscape and land use change. Results Next to landscapes dominated by one land cover, mosaic landscapes cover 14.5% of European terrestrial surface. When using the land system map, species distribution models demonstrate substantially higher predictive ability (up to 19% higher) as compared to models based on land cover maps. Our map consistently contributes more to the spatial distribution of the tested species than the use of land cover data (3.9 to 39.1% higher). Conclusions A land systems classification including essential aspects of landscape and land management into a consistent classification can improve upon traditional land cover maps in large-scale biodiversity assessment. The classification balances data availability at continental scale with vital information needs for various ecological studies.

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How Will Rwandan Land Use/Land Cover Change under High Population Pressure and Changing Climate?

Applied Sciences ◽

10.3390/app11125376 ◽

2021 ◽

Vol 11 (12) ◽

pp. 5376

Author(s):

Chaodong Li ◽

Mingyi Yang ◽

Zhanbin Li ◽

Baiqun Wang

Keyword(s):

Land Use ◽

Land Cover ◽

Urban Land ◽

Future Climate ◽

Population Pressure ◽

Chain Model ◽

Land Use Land Cover ◽

Lulc Change ◽

Climate Conditions ◽

Cropland Area

In recent decades, population growth and economic development have greatly influenced the pattern of land use/land cover (LULC) in Rwanda. Nevertheless, LULC patterns and their underlying change mechanisms under future climate conditions are not well known. Therefore, it is particularly important to explore the direction of LULC transfer in the study area, identify the factors driving the transfer of different types of LULC and their changes, and simulate future LULC patterns under future climate conditions. Based on LULC analyses of Rwanda in 1990, 2000, 2010, and 2015, the LULC pattern of Rwanda in the next 30 years was simulated using an LULC transition matrix, random forest sampling, the Markov chain model, and the PLUS model. The results showed that LULC change in the study area primarily comprised a decrease in forest area and expansion of cropland area, accompanied by a small increase in grassland area and an annual increase in urban land area. Prior to 2000, the LULC in Rwanda was mainly converted from forest and grassland to cropland, with the ratio being 0.72:0.28. After 2010, the LULC was mainly converted from forest to grassland and cropland, with the ratio being 0.83:0.17. Changes in forests, grasslands, and cropland are driven by multiple factors, whereas changes in wetlands, water, urban land, and unused land are more likely to be driven by a single factor. The existing trend of LULC change will continue for the next 30 years, and the future LULC pattern will exhibit a trend in which cropland area will increase in the west and grassland area will decrease, whereas grassland area will increase in the east and cropland area will decrease.

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