Evaluating the efficiency of future crop pattern modelling using the CLUE-S approach in an agricultural plain

Land Use Land Cover (LULC) change detection is an essential source of information for understanding the magnitude of environmental change to implement future development strategies. Sophisticated techniques (i.e. modelling) have been applied in the last decades worldwide for accurate LULC classification and future projections. However, using these techniques in heterogeneous agricultural regions to extract crop-related information is still challenging. This study aimed to evaluate the efficiency and applicability of crop pattern prediction for the year 2050 with the CLUE-S model in an agricultural plain. The model was calibrated and validated based on the LULC changes to model future changes of the crop pattern by 2050. Twelve driving factors were utilised to quantify the relationship of LULC classes. The statistical relationship among the factors was examined with a Binomial Logistic Regression approach. Additionally, the magnitude of change in agricultural crop patterns between 2015 and 2050 was calculated according to local/regional policies and incorporated to the model as scenario layer. Future model results indicated that the cotton would increase by % 45 whereas maize would decrease by % 10 compared to 2015. The model performance was evaluated using the ground truth from the field observations considering the agricultural policies through the ROC (Receiver Operating Characteristic) indicators. The mean ROC value for the agricultural crop patterns was calculated as 0.71, while ROC values for other LULC classes were over 0.90. Overall a 0.79 ROC value was achieved as the model accuracy.     

Comparing and Combining Landsat Satellite Imagery and Participatory Data to Assess Land-Use and Land-Cover Changes in a Coastal Village in Papua New Guinea

In regions lacking socio-economic data, pairing satellite imagery with participatory information is essential for accurate land-use/cover (LULC) change assessments. At the village scale in Papua New Guinea we compare swidden LULC classifications using remote sensing analyses alone and analyses that combine participatory information and remotely sensed data. These participatory remote sensing (PRS) methods include participatory land-use mapping, household surveys, and validation of image analysis in combination with remotely sensed data. The classifications of the swidden area made using only remote sensing analysis show swidden areas are, on average, two and a half times larger than land managers reported for 1999 and 2011. Classifications made using only remote sensing analysis are homogeneous and lack discrimination among swidden plots, fallow land, and non-swidden vegetation. The information derived from PRS methods allows us to amend the remote sensing analysis and as a result swidden areas are more similar to actual swidden area found when ground-truthing. We conclude that PRS methods are needed to understand swidden system LULC complexities.     

Largely underestimated carbon emission from land use and land cover change in the conterminous United States

Carbon (C) emission and uptake due to land use and land cover change (LULCC) are the most uncertain term in the global carbon budget primarily due to limited LULCC data and inadequate model capability (e.g., underrepresented agricultural managements). We take the commonly used FAOSTAT‐based global Land Use Harmonization data (LUH2) and a new high‐resolution multisource harmonized national LULCC database (YLmap) to drive a land ecosystem model (DLEM) in the conterminous United States. We found that recent cropland abandonment and forest recovery may have been overestimated in the LUH2 data derived from national statistics, causing previously reported C emissions from land use have been underestimated due to the definition of cropland and aggregated LULCC signals at coarse resolution. This overestimation leads to a strong C sink (30.3 ± 2.5 Tg C/year) in model simulations driven by LUH2 in the United States during the 1980–2016 period, while we find a moderate C source (13.6 ± 3.5 Tg C/year) when using YLmap. This divergence implies that previous C budget analyses based on the global LUH2 dataset have underestimated C emission in the United States owing to the delineation of suitable cropland and aggregated land conversion signals at coarse resolution which YLmap overcomes. Thus, to obtain more accurate quantification of LULCC‐induced C emission and better serve global C budget accounting, it is urgently needed to develop fine‐scale country‐specific LULCC data to characterize the details of land conversion.     

Application of machine learning approaches for land cover monitoring in northern Cameroon

Machine learning (ML) models are a leading analytical technique used to monitor, map and quantify land use and land cover (LULC) and its change over time. Models such as k-nearest neighbour (kNN), support vector machines (SVM), artificial neural networks (ANN), and random forests (RF) have been used effectively to classify LULC types at a range of geographical scales. However, ML models have not been widely applied in African tropical regions due to methodological challenges that arise from relying on the coarse-resolution satellite images available for these areas. In this study, we compared the performance of four ML algorithms (kNN, SVM, ANN and RF) applied to LULC monitoring within the Mayo Rey department, North Province, Cameroon. We used satellite data from the Landsat 7 Enhanced Thematic Mapper Plus (ETM+) combined with 8 Operational Land Imager (OLI) images of northern Cameroon for November 2000 and November 2020. Our results showed that all four classification algorithms produced relatively high accuracy (overall classification accuracy >80%), with the RF model (> 90% classification accuracy) outperforming the kNN, SVM, and ANN models. We found that approximately 7% of all forested areas (dense forest and woody savanna) were converted to other land cover types between 2000 and 2020; this forest loss is particularly associated with an expansion of both croplands and built-up areas. Our study represents a novel application and comparison of statistical and ML approaches to LULC monitoring using coarse-resolution satellite images in an African tropical forest and savanna setting. The resulting land cover maps serve as an important baseline that will be useful to the Cameroon government for policy development, conservation planning, urban planning, and deforestation and agricultural monitoring.     

基于地表温度-植被指数关系的地表温度降尺度方法研究

地表温度(Land Surface Temperature, LST)在空间上和时间上均存在很大的差异性。而通过卫星遥感技术来监测地表温度存在着空间分辨率和时间分辨率上的矛盾:空间分辨率高的卫星时间分辨率低,反之亦然。为了解决这个矛盾,首先利用TsHARP (An algorithm for sharpening thermal imagery)温度降尺度方法将LST_MODIS,1km(1km MODIS(Moderate-resolution Imaging Spectroradiometer)地表温度)图像(2004年9月9日上午)降尺度为LST_MODIS,500m(500m MODIS地表温度)图像。为了对降尺度LST_MODIS,500m图像进行验证,对研究区内同一天(2004年9月9日上午)的ETM图像的第6波段的辐亮度值升尺度到500m后,再利用Sobrino ETM(Enhanced Thematic Mapper)温度反演方法反演得到LST_ETM,500m(500m ETM地表温度)图像,将LST_ETM,500m图像作为当日地表温度的实测值,对降尺度LST_MODIS,500m图像的降尺度效果进行验证。对比结果表明降尺度LST_MODIS,500m图像更加精细刻画LST_MODIS,1km图像在空间上的分布格局;定量对比3种降尺度LST_MODIS,500m和LST_ETM,500m的RMSE分别为0.786、1.002,0.754℃,降尺度结果达到预期效果。     

Estimating the ecological status and change of riparian zones in Andalusia assessed by multi-temporal AVHHR datasets

Following the European Commission's Water Framework Directive all surface waters in EU's Member States must reach a good status by 2015. The evaluation of this status will be partly based on ecological criteria, such as the hydro-morphological quality criteria which also evaluate the structure and condition of the riparian zone. Riparian zones with undisturbed or nearly undisturbed condition are given high-ecological status. The agri-environmental measures in the EU promote an extensive use of land to protect the farmed environment and its biodiversity. Recent studies in Andalusia and elsewhere suggest that extensification leads to riparian zones with higher ecological status compared to intensively used areas. We suggest that extensification and thus better ecological status of the riparian zone can be partly approximated by the amount of vegetation permanently present on the area. For this the so-called permanent vegetation fraction was derived from a multi-temporal advanced very high-resolution radiometer (AVHRR) dataset and was used (1) to classify the ecological status of the riparian zone into two classes, favourable and unfavourable, and (2) to assess the effect of agricultural practices on these areas. The classification was validated by field observations in the Guadalquivir river basin while detailed information on farming practices helped to assess the effect of agriculture on the riparian zone. The assessment was carried out in olive land cover because erosion control in olive cultivations is the most widely implemented measure in Andalusia. Results suggest that the remotely sensed permanent vegetation fraction is a good indicator of the favourable and unfavourable ecological status of the riparian zone. Furthermore, extensification of agricultural practices expressed in terms of increasing permanent vegetation cover was shown to have positive effect on the riparian zone as opposed to areas where no measures were implemented.     

Assessing impact of forest landscape dynamics on migratory corridors: a case study of two protected areas in Himalayan foothills

Contiguity of protected areas (PAs) is a critical factor to promote well being of the native flora, fauna and life support system to humans. Such contiguity cannot be guaranteed without providing a path or ‘a corridor’ through forested landscapes that includes natural land cover and undisturbed patches. Incidentally, the Himalayan foothills have greater pressure on these landscapes due to high human dependence for livelihood. This pressure is expected to increase in the coming years altering the potential corridors between PAs. The PA managers need flexible processing, modeling and decision tools to propose a range of acceptable corridors between the PAs and ensure their sustainable health. Such flexible tools can be utilized in future to modify for taking decision to conserve the patches connecting patches and adapt as per changing landscapes. This article describes utility of geospatial modeling tools to assess the status of corridors in light of changing landscapes between Rajaji and Jim Corbett National Park, the two most important PAs in the Himalayan foothills. The work has been carried out in four stages, first—using satellite data land use land cover (LULC) maps were prepared for year 1990, 2000 and 2005, second—Land Change Modeler (LCM) was used for LULC change analysis, third—Multi Layer Perceptron Neural Network (MLPNN) was used to predict the status of LULC for 2015 and 2020, and fourth—using temporal morphology of the areas behaving both as barrier and easiness, friction surface cost was calculated to identify least cost pathways (LCPs)/migratory corridors between the PAs. The LULC maps for 1990, 2000 and 2005 were evaluated using accuracy assessment (80%) and Khat statistics (>0.79). The change prediction model was validated by comparing actual LULC of 2005 with predicted LULC of 2005 and the agreement was 71%. The LCP has shifted with the predicted change in the classes. The corridor has shifted by 0.5–3 km towards the south and has come closer to the agriculture fields and river channels.     

Modeling the ecological niche of long-term land use changes: The role of biophysical factors

Land use/land cover changes (LULCCs) represent the result of the complex interaction between biophysical factors and human activity, acting over a wide range of temporal and spatial scales. The aim of this work is to quantify the role of biophysical factors in constraining the trajectories of land abandonment and urbanization in the last 50 years. A habitat suitability model borrowed from animal ecology was used to analyze the ecological niche of the following LULCC trajectories occurred in Emilia-Romagna (northern Italy) during 1954–2008: (i) land abandonment (LA) and (ii) urbanization (URB), both from agricultural areas (URB_agr) and from semi-natural areas (URB_for). Results showed that the different LULCC trajectories were driven by different combinations of biophysical factors, such as climate, topography and soil quality. In particular, slope and elevation resulted as the main driving factors for rural processes, while slope and temperatures resulted as the main constraints underlying urban processes. This approach may represent a conceptual and technical step toward the systematic assessment of LULCC processes, thus providing an effective support tool to inform decision makers about land use transformations, their underlying causes, as well as their possible implications.     

Land use and cover with intensity of agriculture for Canada from satellite and census data

Aim To develop the first national databases on land use and agricultural land use intensity in Canada for a wide variety of environmental monitoring applications. Location Canada. Methods In this paper, we describe a new system for the construction of both land use and land use intensity (within agricultural regions) called LUCIA (land use and cover with intensity of agriculture). Our methodology combines the highly detailed Canadian Census of Agriculture and recent growing season composites derived from the SPOT4/VEGETATION sensor. Census data are of much coarser resolution than the remotely sensed data but, by removing non‐agricultural pixels from each census sampling area, we were able to refine the census data sufficiently to allow their use as ground truth data in some areas. The ‘refined’ census data were then used in the final step of an unsupervised classification of the remotely sensed data. Results and main conclusions The results of the land use classification are generally consistent with the input census data, indicating that the LUCIA output reflects actual land use trends as determined by national census information. Land use intensity, defined as the principal component of census variables that relate to agricultural inputs and outputs (e.g. chemical inputs, fertilizer inputs and manure outputs), is highest in the periphery of the great plains region of central Canada but is also very high in southern Ontario and Québec.     

CLUE-S模型在南京市土地利用变化研究中的应用

土地利用/覆盖变化模型是研究区域景观动态并解释其驱动机制的重要技术手段.应用CLUE-S模型,在Landsat TM影像等相关数据支持下,对南京地区1998-2006年土地利用的时空动态变化进行了研究.结果表明:各土地利用类型变化受地形因素影响最大,人均GDP与城镇用地和农业用地的分布呈显著相关,城乡主干道对土地利用变化的贡献显著大于省级及以上道路;海拔较高区域林地的发生比率较高,而地形低平区域农田、城建用地的发生比率较高.经检验,在300 m空间分辨率水平,对南京地区2003年、2006年土地利用状况模拟的精度分别达到了85.7%和84.1%;而通过将研究区分成若干子区,分别修正模型参数并重新模拟,准确率提高到89.7%和88.3%,分区赋值法有效地提高了模拟精度.研究表明,CLUE-S模型对城市发展的空间结构也有较强的预测能力,对指导城市规划、分析景观动态的驱动机制有重要参考价值.     

The Relative Impacts of Climate and Land-Use Change on Conterminous United States Bird Species from 2001 to 2075

Species distribution models often use climate data to assess contemporary and/or future ranges for animal or plant species. Land use and land cover (LULC) data are important predictor variables for determining species range, yet are rarely used when modeling future distributions. In this study, maximum entropy modeling was used to construct species distribution maps for 50 North American bird species to determine relative contributions of climate and LULC for contemporary (2001) and future (2075) time periods. Species presence data were used as a dependent variable, while climate, LULC, and topographic data were used as predictor variables. Results varied by species, but in general, measures of model fit for 2001 indicated significantly poorer fit when either climate or LULC data were excluded from model simulations. Climate covariates provided a higher contribution to 2001 model results than did LULC variables, although both categories of variables strongly contributed. The area deemed to be “suitable” for 2001 species presence was strongly affected by the choice of model covariates, with significantly larger ranges predicted when LULC was excluded as a covariate. Changes in species ranges for 2075 indicate much larger overall range changes due to projected climate change than due to projected LULC change. However, the choice of study area impacted results for both current and projected model applications, with truncation of actual species ranges resulting in lower model fit scores and increased difficulty in interpreting covariate impacts on species range. Results indicate species-specific response to climate and LULC variables; however, both climate and LULC variables clearly are important for modeling both contemporary and potential future species ranges.     

Area-based scenario development in land-use change modeling: A system dynamics-assisted approach for mixed agricultural-residential landscapes

This study aimed to enhance land use and land cover (LULC) change models by addressing their main limitations, which include the lack of accountability and temporal stability of driving forces. Additionally, the study aimed to create area-based scenarios to forecast future LULCs, rather than solely relying on distribution-based scenarios. To accomplish this goal, the study developed a coupled System Dynamics (SD) and Cellular Automata (CA) modeling system to simulate possible LULC changes in the Gavkhooni Basin, central Iran. The study utilized LULC maps from Landsat images in 2001, 2011, and 2021 to analyze spatio-temporal land use changes in the region. Agricultural and residential transition suitability layers were produced using a spatial Multi-Criteria Evaluation procedure and applied to inform the CA model in the proper allocation of LULC changes. Three interconnected water supply, agricultural, and residential area projection subsystems were developed using system dynamics method to determine land requirements for LULC conversions from 2020 to 2041, taking into account factors such as water availability, land suitability, agricultural labor force, and economic development. Ten scenarios were developed based on changes in the key variables affecting the limiting factors, such as climatic conditions and water management policies, to project agricultural and residential areas in the future. The CA's spatial allocation informed by transition suitability layers was found to be satisfactory with a Kappa-location value of 0.85. The subsystems were competent in projecting water supply with Mean Absolute Error (MAE) values of 6.57% and the dynamics of agricultural and residential areas with MAE values of 2.94%, whereas those of the Markovian Chain model were found to be 23.02% and 7.5% for agricultural and residential areas, respectively. The study found that available agricultural areas varied significantly between 86.53 and 1480 sq.km under different climatic conditions, irrigation efficiency, and agricultural water assignment coefficients between 2024 and 2033. Residential area demand was found to be increasing with different rates under the scenarios between 47.40 and 73.01 sq.km. The SD-CA coupled framework presented in this research can be viewed as a decision support system to develop compensatory strategies for better management and planning of agricultural and residential lands.     

Land use changes and associated environmental impacts on the Mediterranean shallow Lake Stymfalia, Greece

Land cover and land use changes affect ecological landscape functions and processes. Land use changes mainly caused by human activities, is a common reason for wetlands degradation worldwide. Lake Stymfalia, located at Peloponnese, southern Greece, is an ancient wetland with a great ecological value. Lake Stymfalia has been severely degraded and transformed during the past 60 years due to agricultural activities in the surrounding areas and watercourses alterations. In this context, we investigated the land cover/ use changes and the role of the reed beds in the terrestrialization process of this shallow wetland. This particular effort utilized remotely sensed data and Geographical Information Systems (GIS) techniques to estimate land use alterations for the period 1945–1996. Patch related landscape indices were generated to analyze impacts on landscape features. Spatial and thematic information concerning the surface area and the major land cover types of the lake for years 1945, 1960, 1972, 1987, 1992, and 1996 was obtained from aerial photographs and land surveys of the area, and was stored in the GIS database. The 1996 map was ground verified, corrected and updated to 2004 conditions. From the spatio-temporal analysis of the stored data, a permanent decrease of the open water surface has been observed between the years 1945 and 1996. The results indicated that the reed beds expanded dramatically, increasing by 89.3%, and is the predominant aquatic vegetation of the whole wetland. Open water areas and wet meadows decreased by 53.7 and 96.5% respectively. Landscape analyses and, in particular, the use of selected landscape metrics, proved useful for detecting and quantitatively characterising dynamic ecological processes. As land cover/use analysis of the wetland has shown much serious environmental degradation, conservation measures should be undertaken urgently.     

Land use/cover change and its drivers: a case in the watershed of Lake Kasumigaura, Japan

Land use/land cover (LULC) changes in the watershed (2,157 km²) of Lake Kasumigaura during 1979–1996 (Period-1: 1979–1990, Period-2: 1990–1996) were analyzed, and their socio-economic and biophysical drivers were compared using time-series, high-quality GIS datasets in order to examine the characteristics of a model forecasting the future LULC. The changes occurred over an area of more than 90 km² during the overall period at changing rates of 0.22% year⁻¹ in Period-1 and 0.25% year⁻¹ in Period-2. Forestland decreased most in both periods at changing rates of 0.45% year⁻¹ in Period-1 and 0.61% year⁻¹ in Period-2. However, predominant changing patterns differed, i.e., from forest to golf course in Period-1 and from forest to artificial field in Period-2. Particularly in Period-2, a significant LULC change was observed in an area of high population increase on the edge of an already high-population area. Relationships examined among LULC change, population, and rate of population change suggested that the urbanized area was highly resistant to LULC change, and that such change was less frequent in areas of population decline. Statistical analyses indicated that the most influential drivers for total LULC changes were population in Period-1 and distance from the Tokyo Station in Period-2. Since the change potentials differed between the periods, we could not assume a stationary process for the corresponding drivers. Somewhat low S values (indices for demonstrability) show that LULC changes in the watershed of Lake Kasumigaura occurred rather randomly, probably resulting in fragmentation of the landscape.     

Remotely sensed landscape heterogeneity as a rapid tool for assessing local biodiversity value in a highly modified New Zealand landscape

The widespread conversion of natural habitats to agricultural land has created a need to integrate intensively managed landscapes into conservation management priorities. However, there are no clearly defined methods for assessing the conservation value of managed landscapes at the local scale. We used remotely sensed landscape heterogeneity as a rapid practical tool for the assessment of local biodiversity value within a predominantly agricultural landscape in Canterbury, New Zealand. Bird diversity was highly significantly correlated with landscape heterogeneity, distance from rivers and the Christchurch central business district, altitude and average annual household income, indicating that remotely sensed landscape heterogeneity is a good predictor of local biodiversity patterns. We discuss the advantages and limitations of using geographic information systems to determine local areas of high conservation value.     

Mapping soil organic matter in low-relief areas based on land surface diurnal temperature difference and a vegetation index

Accurate estimates of the spatial variability of soil organic matter (SOM) are necessary to properly evaluate soil fertility and soil carbon sequestration potential. In plains and gently undulating terrains, soil spatial variability is not closely related to relief, and thus digital soil mapping (DSM) methods based on soil–landscape relationships often fail in these areas. Therefore, different predictors are needed for DSM in the plains. Time-series remotely sensed data, including thermal imagery and vegetation indices provide possibilities for mapping SOM in such areas. Two low-relief agricultural areas (Peixian County, 28 km × 28 km and Jiangyan County, 38 km × 50 km) in northwest and middle Jiangsu Province, east China, were chosen as case study areas. Land surface diurnal temperature difference (DTD) extracted from moderate resolution imaging spectroradiometer (MODIS) land surface temperature (LST), and soil-adjusted vegetation index (SAVI) at the peak of growing season calculated from Landsat ETM+ image were used as predictors. Regression kriging (RK) with a mixed linear model fitted by residual maximum likelihood (REML) and residuals interpolated by simple kriging (SK) were used to model and map SOM spatial distribution; ordinary kriging (OK) was used as a baseline comparison. The root mean squared error, mean error and mean absolute error calculated from leave-one-out cross-validation were used to assess prediction accuracy. Results showed that the proposed covariates provided added value to the observations. SAVI aggregated to MODIS resolution was able to identify local highs and lows not apparent from the DTD imagery alone. Despite the apparent similarity of the two areas, the spatial structure of residuals from the linear mixed models were quite different; ranges on the order of 3 km in Jiangyan but 16 km in Peixian, and accuracy of best models differed by a factor of two (3.3 g/kg and 6.3 g/kg SOM, respectively). This suggests that time-series remotely sensed data can provide useful auxiliary variable for mapping SOM in low-relief agricultural areas, with three important cautions: (1) image dates must be carefully chosen; (2) vegetation indices should supplement diurnal temperature differences, (3) model structure must be calibrated for each area.     

The ecological condition of geographically isolated wetlands in the southeastern United States: The relationship between landscape level assessments and macrophyte assemblages

Geographically isolated wetlands (GIWs) are common features of the Dougherty Plain physiographic region in southwestern Georgia. Due to lack of protection at the state and federal levels, these wetlands are threatened by intensive agricultural and silvicultural land uses common in the region. Recently, the ecological condition of such GIWs was assessed for the southeastern United States using the Landscape Development Intensity Index (LDI), a practical assessment tool that relies on remotely sensed land use and land cover (LULC) data surrounding isolated wetlands to rapidly predict wetland condition. However, no assessments have been attempted for GIWs in the Dougherty Plain specifically. Our goal was to develop a framework to guide and refine remote assessment of wetland condition within this agriculturally intense region of the southeastern USA. In this study, we characterized human disturbances associated with isolated wetlands in the Dougherty Plain, and paired the rapid assessment of GIWs using LDI with an intensive assessment of wetland plant communities. Specifically, we: (1) examined how macrophyte assemblages and vegetation metrics vary across a human disturbance gradient in the Dougherty Plain; (2) compared multiple condition assessment outcomes using variations of the LDI method that differed in spatial extent and resolution of LULC categories; and (3) determined the predicted condition of GIWs in the Dougherty Plain as indexed by LDI and compared with region-wide assessments of GIWs of the southeastern USA. Generally, the relationship between wetland plant communities and surrounding land use supported the assumptions of the LDI index in that wetlands surrounded by agricultural land use classes featured distinct plant communities relative to those surrounded by forested land use classes. Our results indicated that finer spatial resolution of LULC data improved the predictive ability of LDI. However, based on incongruence between wetland vegetation composition and LDI scores in some forested landscapes, this study identified limitations of the LDI assessment method, particularly when applied in regions in which prescribed fire is an important ecological driver of vegetation and habitat. Thus, we conclude that LDI may be biased toward an overestimation of reference condition GIWs, even though the habitat may be functionally degraded by the absence of natural processes such as fire. Regardless, relative to the assessment of the entire southeastern US, a greater proportion of total GIWs of the Dougherty Plain were identified as impaired due to the intensity of irrigated agricultural land use.     

Quantifying the biophysical and socioeconomic drivers of changes in forest and agricultural land in South and Southeast Asia

South and Southeast Asia (SSEA) has been a hotspot for land use and land cover change (LULCC) in the past few decades. The identification and quantification of the drivers of LULCC are crucial for improving our understanding of LULCC trends. So far, the biophysical and socioeconomic drivers of forest change have not been quantified at the regional scale, particularly for SSEA. In this study, we quantify the biophysical and socioeconomic drivers of forest change on a country‐by‐country basis in SSEA using an integrated quantitative methodology, which systematically accounts for previously published driver information and regional datasets. We synthesize more than 200 publications to identify the drivers of the forest change at different spatial scales in SSEA. Subsequently, we collect spatially explicit proxy data to represent the identified drivers. We quantify the dynamics of forest and agricultural land from 1992 to 2015 using the Climate Change Initiative (CCI) land cover data developed by the European Space Agency (ESA). A geographically weighted regression method is employed to quantify the spatially heterogeneous drivers of forest change. Our results show that socioeconomic drivers are more important than biophysical drivers for the conversion of forest to agricultural land in South Asia and maritime Southeast Asia. In contrast, biophysical drivers are more important than socioeconomic drivers for the conversion of agricultural land to forest in maritime Southeast Asia and less important in South Asia. Both biophysical and socioeconomic drivers contribute approximately equally to both changes in the mainland Southeast Asia region. By quantifying the dynamics of forest and agricultural land and the spatially explicit drivers of their changes in SSEA, this study provides a solid foundation for LULCC modeling and projection.     

Spatial modeling of deforestation processes in the Central Peruvian Amazon

This study examines the relation between primary forest loss and landscape characteristics in the Ucayali region, Peru. Seven variables (rivers, elevation, annual precipitation, soil suitability for agriculture, population density, paved roads, and unpaved roads), were identified as potential deforestation drivers. The variables were converted into spatially explicit layers of continuous data and divided into a 9 km² grid. A multiple regression analysis was conducted to determine variable significance. Distance to paved and unpaved roads were strongly associated with deforestation, followed by distance to rivers, annual precipitation and elevation. All significant variables were negatively correlated with deforestation. Variables excluded from the model were population density and soil suitability for agriculture, suggesting that the influence of population density on forest clearing across the study area was not significant, and that deforestation activities were undertaken regardless whether soils are suitable for agriculture or not. Based on the linear regression analysis, the significant variables were selected and added to the Land Change Modeler in order to project primary forest coverage by 2025. The modeling results predict extensive deforestation along the Aguaytia River and at the forest/non-forest interface along the paved highway. The rate of primary forest removal is expected to increase from 4783 ha y⁻¹ (for the 2007–2014 period) to 5086 ha y⁻¹ (for the 2015–2025 period). A preliminary survey questionnaire conducted to explore deforestation intentions by farmers in the region, partly confirmed the overall deforestation trends as projected by the model.     

Spatial complexity in fragmenting Amazonian rainforests: Do feedbacks from edge effects push forests towards an ecological threshold?

Deforestation and resulting landscape fragmentation are important concerns in many tropical areas. Deforestation is a complex process with many potential feedback loops, many of which are ignored in models that attempt to interpolate forest loss based on past deforestation rates. In addition, most ecological studies of the impacts of deforestation have focused on landscapes that are already fragmented. These studies ignore the fact that edge effects, such as anthropogenic fire, reach their maximum well before habitat connectivity is lost and may create positive feedbacks that result in further fragmentation. We developed a simple model to explore the potential influence of edge effects on fragmentation rates and used remotely sensed data from the MAP (Madre de Dios, Acre, and Pando) region of the Brazilian Amazon to parameterize the relationships of interest. Under reasonable real-world parameter combinations, edge effects can have a significant impact on deforestation rates, supporting the hypothesis that the true tipping point in a forest to pasture regime shift occurs earlier (i.e., ∼50% forest loss) than analysis of a loss in connectivity would suggest (i.e., ∼60% forest loss). Our results have important implications for understanding deforestation, edge-driven processes, regime shifts, and the management of complex pattern-process relationships.