Addressing the need for improved land cover map products for policy support

The continued increase of anthropogenic pressure on the Earth’s ecosystems is degrading the natural environment and then decreasing the services it provides to humans. The type, quantity, and quality of many of those services are directly connected to land cover, yet competing demands for land continue to drive rapid land cover change, affecting ecosystem services. Accurate and updated land cover information is thus more important than ever, however, despite its importance, the needs of many users remain only partially attended. A key underlying reason for this is that user needs vary widely, since most current products – and there are many available – are produced for a specific type of end user, for example the climate modelling community. With this in mind we focus on the need for flexible, automated processing approaches that support on-demand, customized land cover products at various scales. Although land cover processing systems are gradually evolving in this direction there is much more to do and several important challenges must be addressed, including high quality reference data for training and validation and even better access to satellite data. Here, we 1) present a generic system architecture that we suggest land cover production systems evolve towards, 2) discuss the challenges involved, and 3) propose a step forward. Flexible systems that can generate on-demand products that match users’ specific needs would fundamentally change the relationship between users and land cover products – requiring more government support to make these systems a reality

Occurrence-habitat mismatching and niche truncation when modelling distributions affected by anthropogenic range contractions

Aims Human-induced pressures such as deforestation cause anthropogenic range contractions (ARCs). Such contractions present dynamic distributions that may engender data misrepresentations within species distribution models. The temporal bias of occurrence data-where occurrences represent distributions before (past bias) or after (recent bias) ARCs-underpins these data misrepresentations. Occurrence-habitat mismatching results when occurrences sampled before contractions are modelled with contemporary anthropogenic variables; niche truncation results when occurrences sampled after contractions are modelled without anthropogenic variables. Our understanding of their independent and interactive effects on model performance remains incomplete but is vital for developing good modelling protocols. Through a virtual ecologist approach, we demonstrate how these data misrepresentations manifest and investigate their effects on model performance. Location Virtual Southeast Asia. Methods Using 100 virtual species, we simulated ARCs with 100-year land-use data and generated temporally biased (past and recent) occurrence datasets. We modelled datasets with and without a contemporary land-use variable (conventional modelling protocols) and with a temporally dynamic land-use variable. We evaluated each model's ability to predict historical and contemporary distributions. Results Greater ARC resulted in greater occurrence-habitat mismatching for datasets with past bias and greater niche truncation for datasets with recent bias. Occurrence-habitat mismatching prevented models with the contemporary land-use variable from predicting anthropogenic-related absences, causing overpredictions of contemporary distributions. Although niche truncation caused underpredictions of historical distributions (environmentally suitable habitats), incorporating the contemporary land-use variable resolved these underpredictions, even when mismatching occurred. Models with the temporally dynamic land-use variable consistently outperformed models without. Main conclusions We showed how these data misrepresentations can degrade model performance, undermining their use for empirical research and conservation science. Given the ubiquity of ARCs, these data misrepresentations are likely inherent to most datasets. Therefore, we present a three-step strategy for handling data misrepresentations: maximize the temporal range of anthropogenic predictors, exclude mismatched occurrences and test for residual data misrepresentations.Peer reviewe

Addressing the need for improved land cover map products for policy support

CITATION: Szantoi, Z. et al. 2020. Addressing the need for improved land cover map products for policy support. Environmental Science & Policy, 12:28-35, doi:10.1016/j.envsci.2020.04.005.The original publication is available at https://www.sciencedirect.comThe continued increase of anthropogenic pressure on the Earth’s ecosystems is degrading the natural environment and then decreasing the services it provides to humans. The type, quantity, and quality of many of those services are directly connected to land cover, yet competing demands for land continue to drive rapid land cover change, affecting ecosystem services. Accurate and updated land cover information is thus more important than ever, however, despite its importance, the needs of many users remain only partially attended. A key underlying reason for this is that user needs vary widely, since most current products – and there are many available – are produced for a specific type of end user, for example the climate modelling community. With this in mind we focus on the need for flexible, automated processing approaches that support on-demand, customized land cover products at various scales. Although land cover processing systems are gradually evolving in this direction there is much more to do and several important challenges must be addressed, including high quality reference data for training and validation and even better access to satellite data. Here, we 1) present a generic system architecture that we suggest land cover production systems evolve towards, 2) discuss the challenges involved, and 3) propose a step forward. Flexible systems that can generate on-demand products that match users’ specific needs would fundamentally change the relationship between users and land cover products – requiring more government support to make these systems a reality.Publisher's versio

Assessment of Land Degradation Factors

Land degradation is a phenomenon that threatens food security and ecosystem balance observed on a global scale. At the beginning of the 20th century on a global scale, its importance was not yet understood due to low climate change, population growth, and industrialization pressure, but today, with the increasing effect of these factors, it has affected more than 25% of the world’s terrestrial areas. Land use/cover change, destruction of forest areas, opening to agriculture, or conversion of forest areas to high economic plantations are the main factors of land degradation. Population growth and increasing demand for food, water, and energy are increasing pressure on natural resources, primarily agricultural and forest land. Due to its dynamic relationship with the climate change, land degradation creates more pessimistic results in arid and semi-arid areas that are more vulnerable and have a high population density. Despite the intergovernmental meetings, commissions, and decisions taken, land degradation continues on a global scale and the human-climate change dilemma creates uncertainties in achieving the targeted results

The Global 2000-2020 Land Cover and Land Use Change Dataset Derived From the Landsat Archive: First Results

Recent advances in Landsat archive data processing and characterization enhanced our capacity to map land cover and land use globally with higher precision, temporal frequency, and thematic detail. Here, we present the first results from a project aimed at annual multidecadal land monitoring providing critical information for tracking global progress towards sustainable development. The global 30-m spatial resolution dataset quantifies changes in forest extent and height, cropland, built-up lands, surface water, and perennial snow and ice extent from the year 2000 to 2020. Landsat Analysis Ready Data served as an input for land cover and use mapping. Each thematic product was independently derived using locally and regionally calibrated machine learning tools. Thematic maps validation using a statistical sample of reference data confirmed their high accuracy (user’s and producer’s accuracies above 85% for all land cover and land use themes, except for built-up lands). Our results revealed dramatic changes in global land cover and land use over the past 20 years. The bitemporal dataset is publicly available and serves as a first input for the global land monitoring system

Remote Sensing in Mangroves

The book highlights recent advancements in the mapping and monitoring of mangrove forests using earth observation satellite data. New and historical satellite data and aerial photographs have been used to map the extent, change and bio-physical parameters, such as phenology and biomass. Research was conducted in different parts of the world. Knowledge and understanding gained from this book can be used for the sustainable management of mangrove forests of the worl

Agroforestry-Based Ecosystem Services

As a dynamic interface between agriculture and forestry, agroforestry has only recently been formally recognized as a relevant part of land use with ‘trees outside forest’ in important parts of the world—but not everywhere yet. The Sustainable Development Goals have called attention to the need for the multifunctionality of landscapes that simultaneously contribute to multiple goals. In the UN decade of landscape restoration, as well as in response to the climate change urgency and biodiversity extinction crisis, an increase in global tree cover is widely seen as desirable, but its management by farmers or forest managers remains contested. Agroforestry research relates tree–soil–crop–livestock interactions at the plot level with landscape-level analysis of social-ecological systems and efforts to transcend the historical dichotomy between forest and agriculture as separate policy domains. An ‘ecosystem services’ perspective quantifies land productivity, flows of water, net greenhouse gas emissions, and biodiversity conservation, and combines an ‘actor’ perspective (farmer, landscape manager) with that of ‘downstream’ stakeholders (in the same watershed, ecologically conscious consumers elsewhere, global citizens) and higher-level regulators designing land-use policies and spatial zoning