Mapping forest composition from the Canadian National Forest Inventory and land cover classification maps

Canada's National Forest Inventory (CanFI) provides coarse-grained, aggregated information on a large number of forest attributes. Though reasonably well suited for summary reporting on national forest resources, the coarse spatial nature of this data limits its usefulness in modeling applications that require information on forest composition at finer spatial resolutions. An alternative source of information is the land cover classification produced by the Canadian Forest Service as part of its Earth Observation for Sustainable Development of Forests (EOSD) initiative. This product, which is derived from Landsat satellite imagery, provides relatively high resolution coverage, but only very general information on forest composition (such as conifer, mixedwood, and deciduous). Here we link the CanFI and EOSD products using a spatial randomization technique to distribute the forest composition information in CanFI to the forest cover classes in EOSD. The resultant geospatial coverages provide randomized predictions of forest composition, which incorporate the fine-scale spatial detail of the EOSD product and agree in general terms with the species composition summaries from the original CanFI estimates. We describe the approach and provide illustrative results for selected major commercial tree species in Canada.     

Kriging for estimating spatial pattern of contaminants: Potential and problems

This paper discusses and illustrates the use of kriging techniques for estimating the spatial pattern of contaminants in environmental media, particularly soil. The assumptions underlying kriging are reviewed as are some advantages and disadvantages of the method. Lognormal kriging (kriging applied to logarithmic-transformed data) is illustrated using a set of radionuclide soil concentrations at a nuclear testing area on the Nevada Test Site. This example shows how lognormal kriging can be used to estimate average concentrations at points or for blocks of land, concentration contours over space, confidence bands about these contours, and radionuclide inventory in soil. The validity of kriging estimates depends on the accurate estimation and modeling of the spatial correlation structure of the phenomenon. Accuracy is especially important when lognormal kriging is used and estimates of means and their standard deviations are required in the original, untransformed scale. This paper illustrates the bias that can result when a changing correlation structure over space is ignored.Operated for the U.S. Department of Energy by Battelle Memorial InstituteWork supported by Nevada Applied Ecology Group, U.S. Department of Energy, Nevada Operations Office under Contract DE-AC06-76RLO 1830.     

Mapping the Spatial Variability of Plant Diversity in a Tropical Forest: Comparison of Spatial Interpolation Methods

Knowledge of the spatial distribution of plant species is essential to conservation and forest managers in order to identify high priority areas such as vulnerable species and habitats, and designate areas for reserves, refuges and other protected areas. A reliable map of the diversity of plant species over the landscape is an invaluable tool for such purposes. In this study, the number of species, the exponent Shannon and the reciprocal Simpson indices, calculated from 141 quadrat sites sampled in a tropical forest were used to compare the performance of several spatial interpolation techniques used to prepare a map of plant diversity, starting from sample (point) data over the landscape. Means of mapped classes, inverse distance functions, kriging and co-kriging, both, applied over the entire studied landscape and also applied within vegetation classes, were the procedures compared. Significant differences in plant diversity indices between classes demonstrated the usefulness of boundaries between vegetation types, mapped through satellite image classification, in stratifying the variability of plant diversity over the landscape. These mapped classes, improved the accuracy of the interpolation methods when they were used as prior information for stratification of the area. Spatial interpolation by co-kriging performed among the poorest interpolators due to the poor correlation between the plant diversity variables and vegetation indices computed by remote sensing and used as covariables. This indicated that the latter are not suitable covariates of plant diversity indices. Finally, a within-class kriging interpolator yielded the most accurate estimates of plant diversity values. This interpolator not only provided the most accurate estimates by accounting for the indices' intra-class variability, but also provided additional useful interpretations of the structure of spatial variability of diversity values through the interpretation of their semi-variograms. This additional role was found very useful in aiding decisions in conservation planning.     

Mapping Soil Gas Radon Concentration: A Comparative Study of Geostatistical Methods

Understanding soil gas radon spatial variations can allow the constructor of a new house to prevent radon gas flowing from the ground. Indoor radon concentration distribution depends on many parameters and it is difficult to use its spatial variation to assess radon potential. Many scientists use to measure outdoor soil gas radon concentrations to assess the radon potential. Geostatistical methods provide us a valuable tool to study spatial structure of radon concentration and mapping. To explore the structure of soil gas radon concentration within an area in south Italy and choice a kriging algorithm, we compared the prediction performances of four different kriging algorithms: ordinary kriging, lognormal kriging, ordinary multi-Gaussian kriging, and ordinary indicator cokriging. Their results were compared using an independent validation data set. The comparison of predictions was based on three measures of accuracy: (1) the mean absolute error, (2) the mean-squared error of prediction; (3) the mean relative error, and a measure of effectiveness: the goodness-of-prediction estimate. The results obtained in this case study showed that the multi-Gaussian kriging was the most accurate approach among those considered. Comparing radon anomalies with lithology and fault locations, no evidence of a strict correlation between type of outcropping terrain and radon anomalies was found, except in the western sector where there were granitic and gneissic terrain. Moreover, there was a clear correlation between radon anomalies and fault systems.     

Comparison of ordinary and lognormal kriging on skewed data of total cadmium in forest soils of Sweden

Spatial statistical analysis of georeferenced data of total cadmium (TCd) in forest soils of Sweden was assumed to providemore advantageous maps than traditional interpolated maps. However, 264 measurements of TCd in O-horizon of forest soils displayed skewed frequency distribution. Since atypicalobservations affect badly the variogram, outliers wereidentified, different data transformations were tested andordinary (OK) and lognormal kriging (LK) scenarios werecompared based on cross-validation. Results were comparedusing overall measures of predictors, e.g. traditionalmean squared prediction error (MSPE), mean of krigingvariances, variance ratio, median of internallystandardised residuals, and assessments of classificationaccuracy, such as percentage of correctly predictedsamples and within-class MSPE.One outlier was identified based on the absolute value of skewness of value differences less or equal to one in data pairs separated at certain lag classes. Mapping categories characterised by percentage of correct classification and within-class MSPE were found to be essential in comparison of kriging results additionally to the overall measures. In comparison of kriging methods, OK predicted high values moreaccurately and LK was more effective to predict low and mediumvalues. Thus, OK was suggested for mapping high concentration of TCd and other pollutants. Percentage of correctly predictedsamples and within-class MSPE were found to be dependent on kriging method, as well as on the number and limits of categories.     

Spatial dynamics of carbon storage: a case study from Turkey

Forest ecosystems have an important role in carbon cycle at both regional and global scales as an important carbon sink. Forest degradation and land cover changes, caused by deforestation and conversion to non-forest area, have a strong impact on carbon storage. The carbon storage of forest biomass and its changes over time in the Hartlap planning unit of the southeastern part of Turkey have been estimated using the biomass expansion factor method based on field measurements of forests plots with forest inventory data between 1991 and 2002. The amount of carbon storage associated with land use and land cover changes were also analyzed. The results showed that the total forested area of the Hartlap planning unit slightly increased by 2.1 %, from 27,978.7 ha to 28,282.6 ha during the 11-year period, and carbon storage increased by 9.6 %, from 390,367.6 to 427,826.9 tons. Carbon storage of conifer and mixed forests accounted for about 70.6 % of carbon storage in 1991, and 67.8 % in 2002 which increased by 14,274.6 tons. Land use change and increasing forest area have a strong influence on increasing biomass and carbon storage.     

Fine-resolution mapping of wildfire fuel types for Canada: Fuzzy logic modeling for an Alberta pilot area

The Canadian Forest Fire Danger Rating System (CFFDRS) is used daily across Canada for evaluating forest fire danger. Fuel-type information is one of the inputs required by the models used in the CFFDRS. In this project, three fuel-type maps with a 25 m resolution were produced for a pilot study area located in Alberta using land cover only; land cover and biomass; and, land cover, biomass and leaf area index data derived from satellite imagery. The relationships between inputs and fuel types were determined mainly by the opinions of forest fire scientists and incorporated into a computer program using fuzzy set methodology. Not all the CFFDRS fuel types could be distinguished using these inputs; three of the coniferous types had to be grouped into one common fuel type. Overall accuracy was between 74 and 83% based on ground-truth comparisons. The most accurate map resulted from land cover and biomass data. Detailed accuracy assessment indicated that the overall accuracy increased up to 86% if ambiguous fuel type identification was considered. No combination of inputs was able to define a fuel type with absolute certainty, which is a reflection of differing expert opinions and the small number of inputs used to produce the maps.     

An assessment of the optimal scale for monitoring of MODIS and FIA NPP across the eastern USA

Robust monitoring of carbon sequestration by forests requires the use of multiple data sources analyzed at a common scale. To that end, model-based Moderate Resolution Imaging Spectroradiometer (MODIS) and field-based Forest Inventory and Analysis (FIA) data of net primary productivity (NPP) were compared at increasing levels of spatial aggregation across the eastern USA. A total of 52,167 FIA plots and colocated MODIS forest cover NPP pixels were analyzed using a hexagonal tiling system. A protocol was developed to assess the optimal scale as an optimal size of landscape patches at which to map spatially explicit estimates of MODIS and FIA NPP. The optimal mapping resolution (hereafter referred to as optimal scale) is determined using spatially scaled z-statistics as the tradeoff between increased spatial agreement as measured by Pearson’s correlation coefficient and decreased details of coverage as measured by the number of hexagons. Spatial sensitivity was also assessed using land cover assessment and forest homogeneity using spatially scaled z-statistics. Pearson correlations indicate that MODIS and FIA NPP are most highly correlated when using large hexagons, while z-statistics indicate an optimal scale at an intermediate hexagon size of 390 km². This optimal scale had more spatial detail than was obtained for larger hexagons and greater spatial agreement than was obtained for smaller hexagons. The z-statistics for land cover assessment and forest homogeneity also indicated an optimal scale of 390 km².     

Fire risk evaluation using multicriteria analysis—a case study

Forest fires are one of the major causes of ecological disturbance and environmental concerns in tropical deciduous forests of south India. In this study, we use fuzzy set theory integrated with decision-making algorithm in a Geographic Information Systems (GIS) framework to map forest fire risk. Fuzzy set theory implements classes or groupings of data with boundaries that are not sharply defined (i.e., fuzzy) and consists of a rule base, membership functions, and an inference procedure. We used satellite remote sensing datasets in conjunction with topographic, vegetation, climate, and socioeconomic datasets to infer the causative factors of fires. Spatial-level data on these biophysical and socioeconomic parameters have been aggregated at the district level and have been organized in a GIS framework. A participatory multicriteria decision-making approach involving Analytical Hierarchy Process has been designed to arrive at a decision matrix that identified the important causative factors of fires. These expert judgments were then integrated using spatial fuzzy decision-making algorithm to map the forest fire risk. Results from this study were quite useful in identifying potential “hotspots” of fire risk, where forest fire protection measures can be taken in advance. Further, this study also demonstrates the potential of multicriteria analysis integrated with GIS as an effective tool in assessing “where and when” forest fires will most likely occur.     

Patterns of Spatial Autocorrelation in Stream Water Chemistry

Geostatistical models are typically based on symmetric straight-line distance, which fails to represent the spatial configuration, connectivity, directionality, and relative position of sites in a stream network. Freshwater ecologists have explored spatial patterns in stream networks using hydrologic distance measures and new geostatistical methodologies have recently been developed that enable directional hydrologic distance measures to be considered. The purpose of this study was to quantify patterns of spatial correlation in stream water chemistry using three distance measures: straight-line distance, symmetric hydrologic distance, and weighted asymmetric hydrologic distance. We used a dataset collected in Maryland, USA to develop both general linear models and geostatistical models (based on the three distance measures) for acid neutralizing capacity, conductivity, pH, nitrate, sulfate, temperature, dissolved oxygen, and dissolved organic carbon. The spatial AICC methodology allowed us to fit the autocorrelation and covariate parameters simultaneously and to select the model with the most support in the data. We used the universal kriging algorithm to generate geostatistical model predictions. We found that spatial correlation exists in stream chemistry data at a relatively coarse scale and that geostatistical models consistently improved the accuracy of model predictions. More than one distance measure performed well for most chemical response variables, but straight-line distance appears to be the most suitable distance measure for regional geostatistical modeling. It may be necessary to develop new survey designs that more fully capture spatial correlation at a variety of scales to improve the use of weighted asymmetric hydrologic distance measures in regional geostatistical models.     

Carbon storage and emissions offset potential in an African dry forest, the Arabuko-Sokoke Forest, Kenya

Concerns about rapid tropical deforestation, and its contribution to rising atmospheric concentrations of greenhouse gases, increase the importance of monitoring terrestrial carbon storage in changing landscapes. Emerging markets for carbon emission offsets may offer developing nations needed incentives for reforestation, rehabilitation, and avoided deforestation. However, relatively little empirical data exists regarding carbon storage in African tropical forests, particularly for those in arid or semi-arid regions. Kenya's 416 km(2) Arabuko-Sokoke Forest (ASF) is the largest remaining fragment of East African coastal dry forest and is considered a global biodiversity hotspot (Myers et al. 2000), but has been significantly altered by past commercial logging and ongoing extraction. Forest carbon storage for ASF was estimated using allometric equations for tree biomass, destructive techniques for litter and herbaceous vegetation biomass, and spectroscopy for soils. Satellite imagery was used to assess land cover changes from 1992 to 2004. Forest and thicket types (Cynometra webberi dominated, Brachystegia spiciformis dominated, and mixed species forest) had carbon densities ranging from 58 to 94 Mg C/ha. The ASF area supported a 2.8-3.0 Tg C carbon stock. Although total forested area in ASF did not change over the analyzed time period, ongoing disturbances, quantified by the basal area of cut tree stumps per sample plot, correlated with decreased carbon densities. Madunguni Forest, an adjoining forest patch, lost 86% of its forest cover and at least 76% of its terrestrial carbon stock in the time period. Improved management of wood harvesting in ASF and rehabilitation of Madunguni Forest could substantially increase terrestrial carbon sequestration in the region.     

Analysing land cover changes for understanding of forest dynamics using temporal forest management plans

This study analyses forest dynamics and land use/land cover change over a 43-year period using spatial-stand-type maps of temporal forest management plans of Karaisal? Forest Enterprise in the Eastern Mediterranean Region of Turkey. Stand parameters (tree species, crown closures and developmental stages) of the dynamics and changes caused by natural or artificial intervention were introduced and mapped in a Geographic Information System (GIS) and subjected to fragmentation analysis using FRAGSTATS. The Karaisal? Forest Enterprise was first planned in 1969 and then the study area was planned under the Mediterranean Forest Use project in 1991 and five-term forest management plans were made. In this study, we analysed only four periods (excluding 1982 revision plans): 1969, 1991, 2002 and 2012. Between 1969 and 2012, overall changes included a net increase of 3,026 ha in forested areas. Cumulative forest improvement accounted for 2.12 % and the annual rate of total forest improvement averaged 0.08 %. In addition, productive forest areas increased from 36,174 to 70,205 ha between 1969 and 2012. This translates into an average annual productive forest improvement rate of 1.54 %. At the same time, fully covered forest areas with crown closure of “3” (>70 %) increased about 21,321 ha, and young forest areas in developmental stage of “a” (diameter at breast height (dbh)?<?8 cm) increased from 716 to 13,305 ha over the 43-year study period. Overall changes show that productive and fully covered forest areas have increased egregiously with a focus on regenerated and young developmental stages. A spatial analysis of metrics over the 43-year study period indicated a more fragmented landscape resulting in a susceptible forest to harsh disturbances.     

Spatial Prediction of Stream Temperatures Using Top-Kriging with an External Drift

Top-kriging is a method for estimating stream flow and stream flow-related variables on a river network. Top-kriging treats these variables as emerging from a two-dimensional spatially continuous process in the landscape. The top-kriging weights are estimated by a family of variogram models (regularisations) for different catchment areas (kriging support), which accounts for the different scales and the nested nature of the catchments. This assures that kriging weights are distributed to both hydrologically connected and unconnected sites of the stream network according to the data situation: top-kriging gives most weight to close-by sites at the same river system, but when the next hydrologically connected site is far away, more weight is given to a close-by site at an adjacent river system. The distribution of weights is in contrast to ordinary kriging and stream distance-based kriging which does not account for both spatial proximity and network connectivity. We extend the top-kriging method by incorporating an external drift function to account for the deterministic patterns of the spatial variable. We test the method for a comprehensive Austrian stream temperature dataset. The drift is modelled by exponential regression with catchment altitude. Top-kriging is then applied to the regression residuals. The variogram used in top-kriging is fitted by a semiautomatic optimisation procedure. A leave-one-out cross-validation analysis shows that the model performs well for the study domain. The residual mean squared error (cross-validation) decreases by 20 % when using top-kriging in addition to the regression model. For regions where the observed stream temperatures deviate from the expected value of the drift model, top-kriging corrects these regional biases. By exploiting the topological information of the stream network, top-kriging is able to improve the local adjustment of the drift model for the main streams and the tributaries.     

Spatial assessment of water quality parameters in Jhelum city (Pakistan)

In this study, we assess the drinking water quality of Jhelum city. Two hundred and ninety-two drinking water samples were randomly collected in the study area. These samples were chemically analyzed for three key toxic (in excess) elements such as pH, total dissolved solids (TDS), and calcium. Geostatistical techniques such as variogram and kriging were used to investigate the spatial variations of these minerals across the city. The spatial structure for each element was found to be anisotropic, and thus, anisotropic variograms were used. The kriging predictions revealed significant concentrations of the above-stated elements at some locations in the study area. While comparing with the World Health Organization, United States Environmental Protection Agency, and Pakistan Environmental Protection Agency standards, the water samples were found to be unsatisfactory for drinking. We conclude that the drinking water in this region is of poor quality and needs proper treatment to make it palatable.     

Changes in forest biomass carbon stock in Northern Turkey between 1973 and 2006

New forest management and planning approaches are designed to optimize forest structure. Optimal forest structure was determined using newly established growth models while considering primary timber production objectives as well as non-timber objectives for inaccessible areas and social and political pressures on land management. With currently planned management the forests of the Ormanüstü Planning Unit (OPU) in the Black Sea region of northern Turkey are likely to become an important C sink. To quantify this potential C sink and understand its implication to the regional carbon budget and future forest management, we estimated the changes in the OPU between 1973 and 2006. Based on four periods of data for the OPU forests obtained from the Forest Management and Planning Office of Turkey, we used allometric biomass and C regression equations along with biomass expansion factors to estimate the forest biomass carbon pool for each of four inventory years 1973, 1984, 1997, and 2006. Since 1973, OPU forests have accumulated 110.2?×?10³ tons of C as a result of forest expansion and the growth of extant forests, increasing by 50.8 % from 217?×?10³ tons in 1973 to 327.2?×?10³ tons C in 2006. Hardwood and softwood forests accounted for 44 and 56 % of carbon accumulation during this period, respectively. From 1973 through 2006, forest C accumulated at a rate of 3.3?×?10³ tons C year^?1. Carbon density of the OPU forests in the Black Sea region increased by 48.2 % from 5,679 to 8,419 tons/ha.     

Determination of the compositional change (1999–2006) in the pine forests of British Columbia due to mountain pine beetle infestation

The current mountain pine beetle (Dendroctonus ponderosae Hopkins) outbreak in British Columbia and Alberta is the largest recorded forest pest infestation in Canadian history. We integrate a spatial hierarchy of mountain pine beetle and forest health monitoring data, collected between 1999 and 2006, with provincial forest inventory data, and generate three information products representing 2006 forest conditions in British Columbia: cumulative percentage of pine infested by mountain pine beetle, percentage of pine uninfested, and the change in the percentage of pine on the landscape. All input data were formatted to a standardized spatial representation (1 ha minimum mapping unit), with preference given to the most detailed monitoring data available at a given location for characterizing mountain pine beetle infestation conditions. The presence or absence of mountain pine beetle attack was validated using field data (n?=?2054). The true positive rate for locations of red attack damage over all years was 92%. Classification of attack severity was validated using the Kruskal gamma statistic (γ?=?0.49). Error between the survey data and field data was explored using spatial autoregressive (SAR) models, which indicated that percentage pine and year of infestation were significant predictors of survey error at α?=?0.05. Through the integration of forest inventory and infestation survey data, the total area of pine infested is estimated to be between 2.89 and 4.14 million hectares. The generated outputs add value to existing monitoring data and provide information to support management and modeling applications.     

Multi-scale spatial structure of heavy metals in agricultural soils in Beijing

To effectively investigate the spatial variability of heavy metals in soil, produce a higher quality spatial distribution map, and identify the potential pollution sources of heavy metals, geostatistics was employed to evaluate the effect of scale on spatial variability of heavy metals in Beijing agricultural soils. The results revealed that spatial variability of Cr, Ni, Zn, and Hg was dependent on scale. Validation of the optimality of theoretical semivariance and comparative analysis of the estimation accuracy demonstrated that the multi-scale nested model can reveal the spatial structure of heavy metals effectively and improve the estimation accuracy better than the single-scale method, thereby enabling production a higher quality spatial interpolation map. Thus, the multi-scale kriging nested model is a useful tool for revealing spatial variability of heavy metals in soils, while the spatial distribution maps allow the identification of hot spots with high concentrations of heavy metals.     

Predicting saltwater intrusion into aquifers in vicinity of deserts using spatio-temporal kriging

The primary objective of this study was to provide a detailed framework to use the spatio-temporal kriging to model the spatio-temporal variations of salinity data and predict saltwater intrusion into freshwater aquifers in the vicinity of deserts. EC data, measured in extraction wells in the Mahvelat plain located in the Northeastern part of Iran, available from 2007 to 2013, were used to demonstrate the developed framework. The source of data was not a well-designed measurement network. Therefore, to homogenize the data, spatial analysis was used to find EC distribution in the area in each year of study. To conduct the spatial analysis, a guideline and a systematic process were developed to select an appropriate kriging method and optimize its parameters. This process can be applied to different variables. After spatial analysis of EC data for all the years of the analysis period using empirical Bayesian kriging (EBK) method with manually optimized parameters, spatio-temporal and corresponding variogram analysis was conducted using R software. This process was based on a separable product-sum model applied to the data from 2007 to 2012. The data of 2013 and the data available for the years 1999 and 2006 were used for evaluating the performance of the spatio-temporal model. The EC distribution maps, developed for different years until 2021, show a high level of EC in the north, south, and west of the study area and growing saltwater intrusion into the central freshwater aquifer. This result can be attributed to the over-exploitation of the aquifer and hydraulic head and gradient distribution in the area. The framework provided in this study for spatio-temporal analysis of unstructured EC data is useful for groundwater managers in making proper decisions.     

SPATIAL ANALYSIS AND EUTROPHICATION ASSESSMENT FOR CHLOROPHYLL a IN TAIHU LAKE

Spatial structure analysis and kriging analysis have been identified to be useful tools in illustrating the spatial patterns of variables. Taihu Lake is one of the largest fresh water lakes in China, and has suffered serious eutrophication in recent years due to the rapid economic development and growing environmental pollution in the Taihu Catchment. In this paper, spatial structural analysis, kriging interpolation and eutrophication assessment were carried out for chlorophyll a in the lake. Studies show that spherical model could be applied to fit all experimental variograms. Positive nuggets were observed for three directions except NE–SW direction. The variograms show some anisotropy with anisotropic ratio falling within 1.76. The spatial structural patterns of chlorophyll a in the lake were affected by factors such as distribution of pollution sources, water flow and wind. Two-dimensional ordinary block kriging was applied for interpolation process. An eutrophication assessment map was also made based on a water-quality evaluation standard. Results show that the content of chlorophyll a in Taihu Lake was quite high. The whole lake has suffered serious eutrophication. However, the eutrophic situation varied in space. Higher contents of chlorophyll a appeared mainly in the northern part of the lake.     

Effects of soil mechanical resistance on nematode community structure under conventional sugarcane and remaining of Atlantic Forest

Nematodes present high potential as a biological indicator of soil quality. In this work, it was evaluated relations between soil physical properties and nematode community under sugarcane cropping and remaining of Atlantic Forest areas in Northeastern Pernambuco, Brazil. Soil samples were collected from September to November 2009 along two 200-m transects in both remaining of Atlantic Forest and sugarcane field at deeps of 0–10, 10–20, 20–30, 30–40, and 40–50 cm. For soil characterization, it was carried out analysis of soil size, water content, total porosity, bulk density, and particle density. The level of soil mechanical resistance was evaluated through a digital penetrometer. Nematodes were extracted per 300 cm³ of soil through centrifugal flotation in sucrose being quantified, classified according trophic habit, and identified in level of genus or family. Data were analyzed using Pearson correlation at 5% of probability. Geostatistical analysis showed that the penetration resistance, water content, total porosity, and bulk density on both forest and cultivated area exhibited spatial dependence at the sampled scale, and their experimental semivariograms were fitted to spherical and exponential models. In forest area, the ectoparasites and free-living nematodes exhibited spherical model. In sugarcane field, the soil nematodes exhibited pure nugget effect. Pratylenchus sp. and Helicotylenchus sp. were prevalent in sugarcane field, but in forest, there was prevalence of Dorylaimidae and Rhabditidae. Total amount of nematode did not differ between environments; however, community trophic structure in forest presented prevalence of free-living nematodes: omnivores followed by bacterial-feeding soil nematodes, while plant-feeding nematodes were prevalent in sugarcane field. The nematode diversity was higher in the remaining of Atlantic Forest. However, the soil mechanical resistance was higher under sugarcane cropping, affecting more directly the free-living nematodes; especially Dorylaimidae which was the most sensible to changes in soil physical properties.