Quantifying the relative importance of land cover change from climate and land-use in the representative concentration pathways

Climate change is projected to cause substantial alterations in vegetation distribution, but these have been given little attention in comparison to land-use in the Representative Concentration Pathway (RCP) scenarios. Here we assess the climate-induced land cover changes (CILCC) in the RCPs, and compare them to land-use land cover change (LULCC). To do this, we use an ensemble of simulations with and without LULCC in earth system model HadGEM2-ES for RCP2.6, RCP4.5 and RCP8.5. We find that climate change causes an expansion poleward of vegetation that affects more land area than LULCC in all of the RCPs considered here. The terrestrial carbon changes from CILCC are also larger than for LULCC. When considering only forest, the LULCC is larger, but the CILCC is highly variable with the overall radiative forcing of the scenario. The CILCC forest increase compensates 90% of the global anthropogenic deforestation by 2100 in RCP8.5, but just 3% in RCP2.6. Overall, bigger land cover changes tend to originate from LULCC in the shorter term or lower radiative forcing scenarios, and from CILCC in the longer term and higher radiative forcing scenarios. The extent to which CILCC could compensate for LULCC raises difficult questions regarding global forest and biodiversity offsetting, especially at different timescales. This research shows the importance of considering the relative size of CILCC to LULCC, especially with regard to the ecological effects of the different RCPs.

Bayesian analysis of uncertainty in the GlobCover 2009 land cover product at climate model grid scale

Land cover data derived from satellites are commonly used to prescribe inputs to models of the land surface. Since such data inevitably contains errors, quantifying how uncertainties in the data affect a model’s output is important. To do so, a spatial distribution of possible land cover values is required to propagate through the model’s simulation. However, at large scales, such as those required for climate models, such spatial modelling can be difficult. Also, computer models often require land cover proportions at sites larger than the original map scale as inputs, and it is the uncertainty in these proportions that this article discusses. This paper describes a Monte Carlo sampling scheme that generates realisations of land cover proportions from the posterior distribution as implied by a Bayesian analysis that combines spatial information in the land cover map and its associated confusion matrix. The technique is computationally simple and has been applied previously to the Land Cover Map 2000 for the region of England and Wales. This article demonstrates the ability of the technique to scale up to large (global) satellite derived land cover maps and reports its application to the GlobCover 2009 data product. The results show that, in general, the GlobCover data possesses only small biases, with the largest belonging to non–vegetated surfaces. In vegetated surfaces, the most prominent area of uncertainty is Southern Africa, which represents a complex heterogeneous landscape. It is also clear from this study that greater resources need to be devoted to the construction of comprehensive confusion matrices.

Regional-scale models for relating land cover to basin surface-water quality using remotely sensed data in a GIS

Plant-based management systems implementing deep-rooted, perennial vegetation have been identified as important in mitigating the spread of secondary dryland salinity due to its capacity to influence water table depth. The Glenelg Hopkins catchment is a highly modified watershed in the southwest region of Victoria, where dryland salinity management has been identified as a priority. Empirical relationships between the proportion of native vegetation and in-stream salinity were examined in the Glenelg Hopkins catchment using a linear regression approach. Whilst investigations of these relationships are not unique, this is the first comprehensive attempt to establish a link between land use and in-stream salinity in the study area. The results indicate that higher percentage land cover with native vegetation was negatively correlated with elevated in-stream salinity. This inverse correlation was consistent across the 3 years examined (1980, 1995, and 2002). Recognising the potential for erroneously inferring causal relationships, the methodology outlined here was both a time and cost-effective tool to inform management strategies at a regional scale, particularly in areas where processes may be operating at scales not easily addressed with on-site studies.

Appraisal of random and systematic land cover transitions for regional water balance and revegetation strategies

This study describes the use of landscape transition analysis as a means for effective basin management. Land cover transitions from 1995 to 2002 were analyzed using a cross-tabulation matrix for an important economic zone in south-west Victoria, Australia. Specifically, the matrix was used to determine whether the transitions were random or systematic. Random landscape transitions occur when a land cover replaces other land covers in proportion to their availability. Systematic landscape transitions occur when there are deviations from random patterns, and land use types ‘target’ other land use types for replacement. The analysis was conducted with 11 land cover categories and showed that dryland pastures have been systematically losing area to dryland crops and blue gum (Eucalyptus globulus) plantations. Dryland crops have systematically expanded in the north-east of the catchment, an area where increasing in-stream salinization has occurred concurrently with this transition. The systematic expansion of the blue gum plantations has been predominantly in the south-west of the catchment and has the potential to reduce stream flows and groundwater recharge in an already water-stressed region, as blue gums use more water than the dryland pastures they are replacing. All other transitions were largely random. These findings have implications for land use planning in the study area for regional water balance and revegetation strategies. Landscape transition analysis is a cost-effective means of contributing to the management of water resources at a regional scale, and is highly recommended for future basin planning.

Influence of land cover on water resources

The influence of land cover on water quality and quantity was examined using a multidisciplinary approach that integrated land use maps, historical databases, and statistical modelling. Systematic changes in the Glenelg Hopkins landscape were identified, in addition to a relationship between greater proportions of vegetation and better water quality.

Assessment of spatiotemporal varying relationships between rainfall, land cover and surface water area using geographically weighted regression

Traditional regression techniques such as ordinary least squares (OLS) are often unable to accurately model spatially varying data and may ignore or hide local variations in model coefficients. A relatively new technique, geographically weighted regression (GWR) has been shown to greatly improve model performance compared to OLS in terms of higher R 2 and lower corrected Akaike information criterion (AICC). GWR models have the potential to improve reliabilities of the identified relationships by reducing spatial autocorrelations and by accounting for local variations and spatial non-stationarity between dependent and independent variables. In this study, GWR was used to examine the relationship between land cover, rainfall and surface water habitat in 149 sub-catchments in a predominately agricultural region covering 2.6 million ha in southeast Australia. The application of the GWR models revealed that the relationships between land cover, rainfall and surface water habitat display significant spatial non-stationarity. GWR showed improvements over analogous OLS models in terms of higher R 2 and lower AICC. The increased explanatory power of GWR was confirmed by the results of an approximate likelihood ratio test, which showed statistically significant improvements over analogous OLS models. The models suggest that the amount of surface water area in the landscape is related to anthropogenic drainage practices enhancing runoff to facilitate intensive agriculture and increased plantation forestry. However, with some key variables not present in our analysis, the strength of this relationship could not be qualified. GWR techniques have the potential to serve as a useful tool for environmental research and management across a broad range of scales for the investigation of spatially varying relationships.

Historical land-cover/use in different slope and riparian buffer zones in watersheds of the state of São Paulo, Brazil

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Land cover change and environmental quality assessment using GIS techniques: a case study in Brazilian Southeastern region for the period 1988-2003

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Land-use and land-cover change in Atlantic Forest landscapes

The Atlantic Forest is one of the most threatened tropical biomes, with much of the standing forest in small (less than 50 ha), disturbed and isolated patches. The pattern of land-use and land-cover change (LULCC) which has resulted in this critical scenario has not yet been fully investigated. Here, we describe the LULCC in three Atlantic Forest fragmented landscapes (Sao Paulo, Brazil) between 1960-1980s and 1980-2000s. The three studied landscapes differ in the current proportion of forest cover, having 10%, 30% and 50% respectively. Between the 1960s and 1980s. forest cover of two landscapes was reduced while the forest cover in the third landscape increased slightly. The opposite trend was observed between the 1980s and 2000s: forest regeneration was greater than deforestation at the landscapes with 10% and 50% of forest cover and, as a consequence, forest cover increased. By contrast, the percentage of forest cover at the landscape with 30% of forest cover was drastically reduced between the 1980s and 2000s. LULCC deviated from a random trajectory, were not constant through time in two study landscapes and were not constant across space in a given time period. This landscape dynamism in single locations over small temporal scales is a key factor to be considered in models of LULCC to accurately simulate future changes for the Atlantic Forest. In general, forest patches became more isolated when deforestation was greater than forest regeneration and became more connected when forest regeneration was greater than deforestation. As a result of the dynamic experienced by the study landscapes, individual forest patches currently consist of a mosaic of different forest age classes which is likely to impact bio-diversity. Furthermore, landscape dynamics suggests the beginning of a forest transition in some Atlantic Forest regions, what could be of great importance for biodiversity conservation due to the potential effects of young secondary forests in reducing forest isolation and maintaining a significant amount of the original biodiversity. (C) 2012 Elsevier B.V. All rights reserved.

A new information fusion method for land-use classification using high resolution satellite imagery

Satellite image classification involves designing and developing efficient image classifiers. With satellite image data and image analysis methods multiplying rapidly, selecting the right mix of data sources and data analysis approaches has become critical to the generation of quality land-use maps. In this study, a new postprocessing information fusion algorithm for the extraction and representation of land-use information based on high-resolution satellite imagery is presented. This approach can produce land-use maps with sharp interregional boundaries and homogeneous regions. The proposed approach is conducted in five steps. First, a GIS layer - ATKIS data - was used to generate two coarse homogeneous regions, i.e. urban and rural areas. Second, a thematic (class) map was generated by use of a hybrid spectral classifier combining Gaussian Maximum Likelihood algorithm (GML) and ISODATA classifier. Third, a probabilistic relaxation algorithm was performed on the thematic map, resulting in a smoothed thematic map. Fourth, edge detection and edge thinning techniques were used to generate a contour map with pixel-width interclass boundaries. Fifth, the contour map was superimposed on the thematic map by use of a region-growing algorithm with the contour map and the smoothed thematic map as two constraints. For the operation of the proposed method, a software package is developed using programming language C. This software package comprises the GML algorithm, a probabilistic relaxation algorithm, TBL edge detector, an edge thresholding algorithm, a fast parallel thinning algorithm, and a region-growing information fusion algorithm. The county of Landau of the State Rheinland-Pfalz, Germany was selected as a test site. The high-resolution IRS-1C imagery was used as the principal input data.

Holocene land-cover reconstructions for studies on land cover-climate feedbacks

The major objectives of this paper are: (1) to review the pros and cons of the scenarios of past anthropogenic land cover change (ALCC) developed during the last ten years, (2) to discuss issues related to pollen-based reconstruction of the past land-cover and introduce a new method, REVEALS (Regional Estimates of VEgetation Abundance from Large Sites), to infer long-term records of past land-cover from pollen data, (3) to present a new project (LANDCLIM: LAND cover – CLIMate interactions in NW Europe during the Holocene) currently underway, and show preliminary results of REVEALS reconstructions of the regional land-cover in the Czech Republic for five selected time windows of the Holocene, and (4) to discuss the implications and future directions in climate and vegetation/land-cover modeling, and in the assessment of the effects of human-induced changes in land-cover on the regional climate through altered feedbacks. The existing ALCC scenarios show large discrepancies between them, and few cover time periods older than AD 800. When these scenarios are used to assess the impact of human land-use on climate, contrasting results are obtained. It emphasizes the need for methods such as the REVEALS model-based land-cover reconstructions. They might help to fine-tune descriptions of past land-cover and lead to a better understanding of how long-term changes in ALCC might have influenced climate. The REVEALS model is demonstrated to provide better estimates of the regional vegetation/land-cover changes than the traditional use of pollen percentages. This will achieve a robust assessment of land cover at regional- to continental-spatial scale throughout the Holocene. We present maps of REVEALS estimates for the percentage cover of 10 plant functional types (PFTs) at 200 BP and 6000 BP, and of the two open-land PFTs "grassland" and "agricultural land" at five time-windows from 6000 BP to recent time. The LANDCLIM results are expected to provide crucial data to reassess ALCC estimates for a better understanding of the land suface-atmosphere interactions.

Assessing land use and land cover change in tropical dry forest or Northern Chinandega, Nicaragus from 1985 to 2011

The objective of this study is to gain a quantitative understanding of land use and land cover change (LULCC) that have occurred in a rural Nicaraguan municipality by analyzing Landsat 5 Thematic Mapper (TM) images. By comparing the potential extent of tropical dry forest (TDF) with Landsat 5 TM images, this study analyzes the loss of this forest type on a local level for the municipality of San Juan de Cinco Pinos (63.5 km2) in the Department of Chinandega. Change detection analysis shows where and how land use has changed from 1985 to the present. From 1985 to 2011, nearly 15% of the TDF in San Juan de Cinco Pinos was converted to other land uses. Of the 1434.2 ha of TDF that was present in 1985, 1223.64 ha remained in 2011. The deforestation is primarily a result of agricultural expansion and fuelwood extraction. If current rates of TDF deforestation continue, the municipality faces the prospect of losing its forest cover within the next few decades.

Pollen-based quantitative reconstructions of Holocene regional vegetation cover (plant-functional types and land-cover types) in Europe suitable for climate modelling

We present quantitative reconstructions of regional vegetation cover in north-western Europe, western Europe north of the Alps, and eastern Europe for five time windows in the Holocene around 6k, 3k, 0.5k, 0.2k, and 0.05k calendar years before present (bp)] at a 1 degrees x1 degrees spatial scale with the objective of producing vegetation descriptions suitable for climate modelling. The REVEALS model was applied on 636 pollen records from lakes and bogs to reconstruct the past cover of 25 plant taxa grouped into 10 plant-functional types and three land-cover types evergreen trees, summer-green (deciduous) trees, and open land]. The model corrects for some of the biases in pollen percentages by using pollen productivity estimates and fall speeds of pollen, and by applying simple but robust models of pollen dispersal and deposition. The emerging patterns of tree migration and deforestation between 6k bp and modern time in the REVEALS estimates agree with our general understanding of the vegetation history of Europe based on pollen percentages. However, the degree of anthropogenic deforestation (i.e. cover of cultivated and grazing land) at 3k, 0.5k, and 0.2k bp is significantly higher than deduced from pollen percentages. This is also the case at 6k in some parts of Europe, in particular Britain and Ireland. Furthermore, the relationship between summer-green and evergreen trees, and between individual tree taxa, differs significantly when expressed as pollen percentages or as REVEALS estimates of tree cover. For instance, when Pinus is dominant over Picea as pollen percentages, Picea is dominant over Pinus as REVEALS estimates. These differences play a major role in the reconstruction of European landscapes and for the study of land cover-climate interactions, biodiversity and human resources.