Are they sustainable?

DL 57/2016/CP1453/CT0105 SFRH/BD/61544/2009 UIDB/04647/2020 UIDP/04647/2020In this study, past and current land-use and land-cover (LULC) change trajectories between 1947 and 2018 were analysed in terms of sustainability using a unique set of nine detailed, high-precision LULC thematic maps for the municipality of Portimão (Algarve region), Portugal. Several Geographic Information System (GIS)-based spatial analysis techniques were used to process LULC data and assess the spatiotemporal dynamics of LULC change processes. The dynamics of LULC change were explored by analysing LULC change trajectories. In addition, spatial pattern metrics were introduced to further investigate and quantify the spatial patterns of such LULC change trajectories. The findings show that Portimão has been experiencing complex LULC changes. Nearly 52% of the study area has undergone an LULC change at least once during the 71-year period. The analysis of spatial pattern metrics on LULC change trajectories confirmed the emergence of more complex, dispersed, and fragmented shapes when patches of land were converted from non-built categories into artificial surface categories from 1947 to 2018. The combined analysis of long-term LULC sequences by means of LULC change trajectories and spatial pattern metrics provided useful, actionable, and robust empirical information that can support sustainable spatial planning and smart growth, which is much needed since the results of this study have shown that the pattern of LULC change trajectories in Portimão municipality has been heading towards unsustainability.publishersversionpublishe

The challenges of using satellite data sets to assess historical land use change and associated greenhouse gas emissions: a case study of three Indonesian provinces

Advances in satellite remote sensing and the wealth of earth observation (EO) data now available have improved efforts toward determining and quantifying historical land use and land cover (LULC) change. Satellite imagery can overcome the absence of accurate records of historical land use; however, the variability observed in the case study regions demonstrates a number of current challenges. Differences in spatial coverage, resolution and land cover classification can lead to challenges in analyzing historical data sets to estimate LULC change and associated GHG emissions. This paper demonstrates the calculation of LULC change from three existing, open-source data sets to show how this can lead to significant variation in estimates of GHG emissions related to differences in land classification methodologies, EO input data and period of investigation. This article focuses on selected regions of Indonesia, where quantifying land use change is important for GHG assessments of agricultural commodities and for evidencing progress against corporate and government deforestation commitments. Given the significance of GHG emissions arising from LULC change and the increasing need for emissions monitoring, this research highlights a need for consensus building to develop consistency in historical and future LULC change estimates. This paper concludes with a set of recommendations for improvements to ensure consistent LULC mapping

Tracking the Land Use Land Cover Changes from 2000 to 2018 in Local Area of East Java Province

Land Use Land Cover (LULC) change represent the human influences on the natural ecosystem. This study aims to analyze LULC change in the eastern part of East Java. The region covers an area ± 3320,3 km2. The change analysed by comparing two editions of maps (the National Digital Map and Landsat-8). The five subsets explore to understand the change. The development of transportation infrastructure, industrial sites, agricultural sectors, tourism, urbanisation and sub-urbanisation caused the significant LULC change. Regional development has increased the built-up area by 8,66% (287.4 km2) of the total area. Then increase of the paddy-field by 13.93% and forest-plantation area by 7.20%. Oppositely, the development decreases rural areas by -29.43% (977.2 km2) and water body by -0.88 % (29.1 km2). The LULC change has significantly converted the natural to human-induced landscapes that potentially fragile to disasters in this region

Hotspots of uncertainty in land-use and land-cover change projections: a global-scale model comparison

Model-based global projections of future land use and land cover (LULC) change are frequently used in environmental assessments to study the impact of LULC change on environmental services and to provide decision support for policy. These projections are characterized by a high uncertainty in terms of quantity and allocation of projected changes, which can severely impact the results of environmental assessments. In this study, we identify hotspots of uncertainty, based on 43 simulations from 11 global-scale LULC change models representing a wide range of assumptions of future biophysical and socio-economic conditions. We attribute components of uncertainty to input data, model structure, scenario storyline and a residual term, based on a regression analysis and analysis of variance. From this diverse set of models and scenarios we find that the uncertainty varies, depending on the region and the LULC type under consideration. Hotspots of uncertainty appear mainly at the edges of globally important biomes (e.g. boreal and tropical forests). Our results indicate that an important source of uncertainty in forest and pasture areas originates from different input data applied in the models. Cropland, in contrast, is more consistent among the starting conditions, while variation in the projections gradually increases over time due to diverse scenario assumptions and different modeling approaches. Comparisons at the grid cell level indicate that disagreement is mainly related to LULC type definitions and the individual model allocation schemes. We conclude that improving the quality and consistency of observational data utilized in the modeling process as well as improving the allocation mechanisms of LULC change models remain important challenges. Current LULC representation in environmental assessments might miss the uncertainty arising from the diversity of LULC change modeling approaches and many studies ignore the uncertainty in LULC projections in assessments of LULC change impacts on climate, water resources or biodiversity

The interplay of climate and land use change affects the distribution of EU bumblebees

Bumblebees in Europe have been in steady decline since the 1900s. This decline is expected to continue with climate change as the main driver. However, at the local scale, land use and land cover (LULC) change strongly affects the occurrence of bumblebees. At present, LULC change is rarely included in models of future distributions of species. This study's objective is to compare the roles of dynamic LULC change and climate change on the projected distribution patterns of 48 European bumblebee species for three change scenarios until 2100 at the scales of Europe, and Belgium, Netherlands and Luxembourg (BENELUX). We compared three types of models: (1) only climate covariates, (2) climate and static LULC covariates and (3) climate and dynamic LULC covariates. The climate and LULC change scenarios used in the models include, extreme growth applied strategy (GRAS), business as might be usual and sustainable European development goals. We analysed model performance, range gain/loss and the shift in range limits for all bumblebees. Overall, model performance improved with the introduction of LULC covariates. Dynamic models projected less range loss and gain than climate-only projections, and greater range loss and gain than static models. Overall, there is considerable variation in species responses and effects were most pronounced at the BENELUX scale. The majority of species were predicted to lose considerable range, particularly under the extreme growth scenario (GRAS; overall mean: 64% +/- 34). Model simulations project a number of local extinctions and considerable range loss at the BENELUX scale (overall mean: 56% +/- 39). Therefore, we recommend species-specific modelling to understand how LULC and climate interact in future modelling. The efficacy of dynamic LULC change should improve with higher thematic and spatial resolution. Nevertheless, current broad scale representations of change in major land use classes impact modelled future distribution patterns.Peer reviewe

Adapting the Dyna-CLUE model for simulating land use and land cover change in the Western Cape Province

Models which integrate and evaluate diverse factors of Land Use and Land Cover (LULC) change can be used to guide planners in making more informed decisions and achieving a balance between urban growth and preservation of the natural environment. The implementation of these models at a provincial scale is however very limited in South Africa. LULC change models are valuable if their structures are based on a deep knowledge of the system under investigation and if they produce credible results. This study therefore investigates the suitability of LULC change models in simulating LULC changes at a provincial scale in a South African context. The Dyna-CLUE model was implemented using the following as inputs: spatial policies and restrictions; land-use type conversions; land use requirements (demands) and location characteristics. The model produced probability maps and simulation maps for the years between 1990 and 2014. Validation of the simulated maps was conducted using both visual and statistical analysis and the results indicated that the simulated maps were in good agreement with the validation map. This study contributes to the implementation of LULC change models at a provincial scale in a South African context. Knowledge derived from this study can be used by planners as a guide to effectively gauge the impacts that planning policies and other driving factors might have on future LULC patterns in the Western Cape Province

The Effect of Land Cover/Land Use Changes on the Regional Climate of the USA High Plains

We present the detection of the signatures of land use/land cover (LULC) changes on the regional climate of the US High Plains. We used the normalized difference vegetation index (NDVI) as a proxy of LULC changes and atmospheric CO2 concentrations as a proxy of greenhouse gases. An enhanced signal processing procedure was developed to detect the signatures of LULC changes by integrating autoregression and moving average (ARMA) modeling and optimal fingerprinting technique. The results, which are representative of the average spatial signatures of climate response to LULC change forcing on the regional climate of the High Plains during the 26 years of the study period (1981–2006), show a significant cooling effect on the regional temperatures during the summer season. The cooling effect was attributed to probable evaporative cooling originating from the increasing extensive irrigation in the region. The external forcing of atmospheric CO2 was included in the study to suppress the radiative warming effect of greenhouse gases, thus, enhancing the LULC change signal. The results show that the greenhouse gas radiative warming effect in the region is significant, but weak, compared to the LULC change signal. The study demonstrates the regional climatic impact of anthropogenic induced atmospheric-biosphere interaction attributed to LULC change, which is an additional and important climate forcing in addition to greenhouse gas radiative forcing in High Plains region

FLOOD RISK ASSESSMENT UNDER HISTORICAL AND PREDICTED LAND USE CHANGE USING CONTINUOUS HYDROLOGIC MODELING

Current procedures for flood risk estimation assume flood distributions are stationary over time, meaning annual maximum flood (AMF) series are not affected by climatic variation, land use/land cover (LULC) change, or management practices. Thus, changes in LULC and climate are generally not accounted for in policy and design related to flood risk/control, and historical flood events are deemed representative of future flood risk. These assumptions need to be re-evaluated, however, as climate change and anthropogenic activities have been observed to have large impacts on flood risk in many areas. In particular, understanding the effects of LULC change is essential to the study and understanding of global environmental change and the consequent hydrologic responses. The research presented herein provides possible causation for observed nonstationarity in AMF series with respect to changes in LULC, as well as a means to assess the degree to which future LULC change will impact flood risk. Four watersheds in the Midwest, Northeastern, and Central United States were studied to determine flood risk associated with historical and future projected LULC change. Historical single framed aerial images dating back to the mid-1950s were used along with Geographic Information Systems (GIS) and remote sensing models (SPRING and ERDAS) to create historical land use maps. The Forecasting Scenarios of Future Land Use Change (FORE-SCE) model was applied to generate future LULC maps annually from 2006 to 2100 for the conterminous U.S. based on the four IPCC-SRES future emission scenario conditions. These land use maps were input into previously calibrated Soil and Water Assessment Tool (SWAT) models for two case study watersheds. In order to isolate effects of LULC change, the only variable parameter was the Runoff Curve Number associated with the land use layer. All simulations were run with daily climate data from 1978-1999, consistent with the \u27base\u27 model which employed the 1992 NLCD to represent \u27current\u27 conditions. Output daily maximum flows were converted to instantaneous AMF series and were subsequently modeled using a Log-Pearson Type 3 (LP3) distribution to evaluate flood risk. Analysis of the progression of LULC change over the historic period and associated SWAT outputs revealed that AMF magnitudes tend to increase over time in response to increasing degrees of urbanization. This is consistent with positive trends in the AMF series identified in previous studies, although there are difficulties identifying correlations between LULC change and identified change points due to large time gaps in the generated historical LULC maps, mainly caused by unavailability of sufficient quality historic aerial imagery. Similarly, increases in the mean and median AMF magnitude were observed in response to future LULC change projections, with the tails of the distributions remaining reasonably constant. FORE-SCE scenario A2 was found to have the most dramatic impact on AMF series, consistent with more extreme projections of population growth, demands for growing energy sources, agricultural land, and urban expansion, while AMF outputs based on scenario B2 showed little changes for the future as the focus is on environmental conservation and regional solutions to environmental issues

Prediction and Simulation of Land Use and Land Cover Changes Using Open Source QGIS. A Case Study of Purwokerto, Central Java, Indonesia

Population size multiplies along with the increasing need for residential space. As often occurs in developing cities like Purwokerto, population growth is associated with land use/land cover (LULC) change to accommodate housing demand both in the present and future. Therefore, this study was intended to map LULC changes in three different years: 2008, 2013, and 2018, and predict the change in 2023. For LULC data extraction, a pixel-based digital classification with a maximum likelihood algorithm was applied to Landsat images. In addition, the LULC change prediction was modeled with Modules for Land Use Change Simulations (MOLUSCE) from the QGIS plugins. It used two algorithms: artificial neural network (ANN) with a multilayer perceptron (MLP) and cellular automata (CA). The LULC classifications for 2008, 2013, and 2018 were 88%, 86%, and 88% accurate, while the prediction was 75.26% accurate, with a kappa of 0.634. Predictions and simulations indicate fluctuations in LULC change in the City of Purwokerto periodically, especially for built-up land, showing growth that continues to increase significantly