Ecological Land Classification Research Articles

A quantitative approach to defining soil nutrient regimes within ecosystem classifications for Northwestern Ontario

Soil nutrient regimes (SNRs) are often incorporated in ecosystem classifications. Evaluation of actual nutrient levels associated with these SNRs and the development of complementary soil chemistry regimes (SCRs) could broaden their utility. Using data from 618 forest stands in northwestern Ontario, we developed five-category SCRs using K-means clustering and examined relationships among individual nutrients, SCRs, and the SNRs of the Canadian National Vegetation Classification Associations and the Ontario Ecological Land Classification Ecosites. F, A, and B horizon samples were analyzed for organic C (OrgC), total N (TotN), C:N ratio (C:N), cation exchange capacity (CEC), exchangeable bases, base saturation (BaSat), and pH. CEC, pH, and BaSat showed good correspondence across horizons, and together with C:N accounted for much of the variation in chemical properties. There was broad agreement between Association and Ecosite SNRs and B horizon (BHorz) and All horizon (AllHorz) SCRs. C:N decreased while pH and cation metrics increased with increasing SNR and SCR richness. User's accuracies (SNRs vs. SCRs) for the classifications ranged from 31%–39% but increased to 80%–86% for SNR values within ±1 SCR class. Classification trees identified pH class, soil texture, and overstory composition as the principal field-measured factors related to BHorz SCRs.

Twenty years of ecological research in Nova Scotia wilderness areas and nature reserves: A review of studies, 2002 to 2022

The following paper is a review of the research undertaken over the last twenty years in Wilderness Areas and Nature Reserves in Nova Scotia. A brief summary is presented of the main findings of each research project conducted by the author or contributed to by the author in a significant way. Inventories have included eleven bioblitzes and over four thousand plots from systematic transects. These have revealed significant new records for species including those of conservation concern. Results suggest there are many species than have not been identified in protected areas. Geographical Information System (GIS) Ecological Land Classification was completed for Nova Scotia and this led to ecosystem gap analysis to determine ecosystems that are not well represented in the current protected areas system. Long-term monitoring, using biodiversity transects and lichens, indicates that air quality is good throughout the protected areas system. Forests are returning to a more climax condition and with the exception of a few instances, non-native plants are generally not problematic. Carbon modeling of protected areas suggests that they will be a carbon sink for the next one hundred years and would be a carbon source if managed for forestry. Protected areas are well suited to provide ideal optimal settings in which climate change adaptation and mitigation can take place. Planning for climate change within protected areas can be facilitated by a Climate Change Adaptation Framework.Research on species of special concern in protected areas has included turtles, Mainland Moose, Canada Lynx, America Marten, Lichens, Atlantic Coastal Plain Flora, forest plants and Piping Plover. Research on rare, sensitive, vulnerable ecosystems has involved predictive modeling and identification and characterization of heathlands, forest wetlands and Jack pine woodlands. Old Growth Forest research has included predictive modeling, biological inventories, dendrochronology studies and scoring using indicators. Human activities adjacent to protected areas can cause deleterious edge effects. An ongoing study in the Cloud Lake Wilderness Area is measuring the effect of adjacent forestry on birds and plants within the Wilderness Area. Several studies on connectivity have looked at the increase in connectivity caused by the establishment of protected areas in Nova Scotia. Other efforts on connectivity have identified key nodes of connectivity within the province which require protection. Human use of protected areas can lead to damage of ecosystems and so investigations on human use of protected areas has been focussed on motorized vehicles and to a lesser extent on human foot traffic. Although there have been many ecological studies in protected areas over the last twenty years, it is evident that there still is a great deal that is unknown about the biodiversity of protected areas.

Predictive Ecological Land Classification From Multi-Decadal Satellite Imagery

Ecological land classifications serve diverse purposes including sample stratification, inventory, impact assessment and environmental planning. While popular, data-driven classification approaches can require large training samples, frequently with limited robustness to rapid environmental change. We evaluate the potential to derive useful, durable ecological land classifications from a synthesis of multi-decadal satellite imagery and geospatial environmental data. Using random forests and multivariate regression trees, we analyze 1982–2000 Landsat Thematic Mapper (L45) and 2013–2020 Harmonized Landsat Sentinel (HLS) imagery to develop and then test the predictive skill of an ecological land classification for monitoring Mediterranean-climate oak woodlands at the recently established Jack and Laura Dangermond Preserve (JLDP) near Point Conception, California. Image pixels were processed using spectral and temporal mixture models. Temporal mixture model residual scores were highly correlated with oak canopy cover trends between 2012 and 2020 (r2 = 0.74, p << 0.001). The resulting topoclimatic-edaphic land classification effectively distinguished areas of systematically higher or lower oak dieback during 2012–2020 severe drought, with a fivefold difference in dieback rates between land classes. Our results highlight the largely untapped potential for developing predictive ecological land classifications from multi-decadal satellite imagery to guide scalable, ground-supported monitoring of rapid environmental change.

(Retracted) Application of remote sensing image ecological land classification method in crop planting area extraction under the background of data fusion

Data mining is a common basic function in humans and many other intellectual systems. The popularization of computers has created a good research environment for the advanced application of remote sensing satellites. With China’s economic and population growth, the demand for food is also increasing. Obtaining the planting area is one of the most important tasks in calculating harvest costs. However, traditional cost analysis methods lack information. The traditional remote sensing image classification method is based on the pixel classification method, which cannot effectively extract the spatial texture information in an image. The pixel-based classification method also has the problem of salt and pepper phenomenon in the classification results, which generates a large number of invalid broken patches and ultimately leads to low classification accuracy. Here, we investigated the application of remote state system mapping for the extraction of the planting area. We analyzed the basic principles of remote image processing and remote image sharing methods and introduced an audio system and optical algorithm. Here, two separation methods are selected, long-distance traction measurements are performed in three training areas, and the two results are compared and analyzed. Experimental results show that the method of objective orientation increases the working speed compared with the method of manuscript translation. The benefit of speed measurement becomes more apparent in both seasons as the measuring range increases. Computer automatic classification and the method of artificial visual interpretation are tested sequentially, and the total time taken is 49 and 13.5 days, respectively.

Proposed new soil order — Leptosolic order for Canadian System of Soil Classification

Shallow soils occur throughout the world and are recognized as Leptosols at the highest level in the World Reference Base. These soils are notionally characterized as having a lithic contact close to the soil surface. Within the Canadian System of Soil Classification (CSSC), shallow soils are currently handled at the family level according to the depth at which the lithic contact is encountered. At the series level, these soils are usually designated as a shallow phase of a non-shallow soil series, ignoring the hierarchical structure of the CSSC. Shallow soils occur almost anywhere in Canada where the glacial drift is thin. The presence of bedrock close to the surface impacts drainage, the amount of available moisture, depth for rooting, and has a major influence on soil formation. Consequently, it is proposed that the importance of shallow soils be elevated to the order level, to be consistent with the frequency of their occurrence in the Canadian landscape and for consistency with other soil classification systems of the world. This requires integration at the great group and subgroup levels within all orders of the CSSC, as well as changes to the current formal definition of soil. These proposed modifications include nullifying the minimum 10 cm depth requirement as part of the current definition of soil in the CSSC for closer consistency with ecological land classification and other soil classification systems of the world. Proposed modifications to the current key to the soil orders, great groups, and subgroups are presented and discussed.

Decision tree-based classification in coastal area integrating polarimetric SAR and optical data

PurposeAs one of the world's most productive ecosystems, ecological land plays an important role in regional and global environments. Utilizing advanced optical and synthetic aperture radar (SAR) data for land cover/land use research becomes increasingly popular. This research aims to investigate the complementarity of fully polarimetric SAR and optical imaging for ecological land classification in the eastern coastal area of China.Design/methodology/approachFour polarimetric decomposition methods, namely, H/Alpha, Yamaguchi3, VanZyl3 and Krogager, were applied to Advanced Land Observing Satellite (ALOS) SAR image for scattering parameter extraction. These parameters were merged with ALOS optical parameters for subsequent classification using the object-based quick, unbiased, efficient statistical tree decision tree method.FindingsThe experimental results indicate that an improved classification performance was obtained in the decision level when merging the two data sources. In fact, unlike classification using only optical images, the proposed approach allowed to distinguish ecological land with similar spectrum but different scattering. Moreover, unlike classification using only polarimetric information, the integration of polarimetric and optical data allows to accurately distinguish reed from artemisia and sand from salt field and therefore achieve a detailed classification of the coastal area characteristics.Originality/valueThis research proposed an integrated classification method for coastal ecological land with polarimetric SAR and optical data. The object-based and decision-level fusion enables effective ecological land classification in coastal area was verified.

Natural disturbance regimes for implementation of ecological forestry: a review and case study from Nova Scotia, Canada

Ecological forestry is based on the idea that forest patterns and processes are more likely to persist if harvest strategies produce stand structures, return intervals, and severities similar to those from natural disturbances. Taylor et al. (2020) reviewed forest natural disturbance regimes in Nova Scotia, Canada, to support implementation of ecological forestry. In this follow-up paper, we (i) review the use of natural disturbance regimes to determine target harvest rotations, age structures, and residual stand structures; and (ii) describe a novel approach for use of natural disturbance regimes in ecological forestry developed for Nova Scotia. Most examples of ecological forestry consider only the local, dominant disturbance agent, such as fire in boreal regions. Our approach included: (i) using current ecological land classification to map potential natural vegetation (PNV) community types; (ii) determining cumulative natural disturbance effects of all major disturbances, in our case fire, hurricanes, windstorm, and insect outbreaks for each PNV; and (iii) using natural disturbance regime parameters to derive guidelines for ecological forestry for each PNV. We analyzed disturbance occurrence and return intervals based on low, moderate, and high severity classes (<30, 30–60, and >60% of biomass of living trees killed, respectively), which were used to determine mean annual disturbance rates by severity class. Return intervals were used to infer target stand age-class distributions for high, moderate, and low severity disturbances for each PNV. The range of variation in rates of high severity disturbances among PNVs was from 0.28%·year–1 in Tolerant Hardwood to 2.1%·year–1 in the Highland Fir PNV, equating to return intervals of 357 years in Tolerant Hardwood to 48 years in Highland Fir PNVs. As an example, this return interval for the Tolerant Hardwood PNV resulted in target rotation lengths of 200 years for 35% of the PNV area, 500 years for 40%, and 1000 years for 25%. The proposed approach of determining natural disturbance regimes for PNV communities and calculating target disturbance rates and corresponding harvest rotation lengths or entry times appears to be a feasible method to guide ecological forestry in any region with a strong ecological land classification system and multiple disturbance agents.

DETERMINATION OF REGIONS SUITABLE FOR AGRICULTURE IN THE GORDON COSENS FOREST OF ONTARIO BY MEANS OF ANALYTICAL HIERARCHY PROCESS WITH FUZZY LOGIC INFERENCE

Abstract. Analytical Hierarchy Process (AHP) with fuzzy logic inference on attributes was employed to determine areas most suitable for agriculture in the Gordon Cosens Forest (GCF) region within the District of Cochrane in northern Ontario, Canada. Attribute layers considered were soil texture, ELC (Ecological Land Classification) moisture regime, slope, canopy height model (CHM), distance to existing road networks and distance to water bodies. Fuzzy logic inference was utilized to rescale the attributes to a normalized range, taking into account preferability, which was then subjected to pairwise comparisons via AHP to determine the attribute layers' weightings. For the study area, the localities identified as most compatible for agricultural development include the southeastern section of the GCF at approximately 30 km south of the community of Fauquier and the westernmost area of the GCF at about 10 km east of Mattice.

Improvement of soil property mapping in the Great Clay Belt of northern Ontario using multi-source remotely sensed data

This study aimed to improve the accuracy in modelling the properties of forest soil by employing a variety of remotely sensed data. In addition to the commonly used multispectral satellite imagery, airborne LiDAR (light detection and ranging) data were exploited to derive detailed vegetation properties and topographic variables. Random forest (RF) and support vector machine (SVM) approaches were applied to classify soil types in terms of texture, calcareous substrate reaction to hydrochloric acid, and ELC (Ecological Land Classification) moisture regime. The developed methods were tested on data acquired over a boreal region (49° - 50° N, 81° - 84° W) with a combined area of 4,085 km2 in the Great Clay Belt (GCB) region, Ontario, Canada. Compared with the field-collected data, the overall accuracies and kappa coefficients of the retrieved soil properties were greater than 0.7 and 0.5, respectively. The accuracies attained between the RF and SVM approaches were similar, but in general the highest accuracies were achieved by the RF method. The models developed for the whole GCB regions generated accuracies comparable to those for the three sub-regions. The lowest modelling uncertainties occurred in areas dominated by peatland, whereas the highest modelling uncertainties existed in the regions with dry moisture regime or clayey soil at the surface. The results also showed that environmental covariates corresponding to vegetation were most important in the prediction of soil properties. Specifically, canopy height model (CHM) and gap fraction derived from LiDAR data, were among the most important variables.The inclusion of LiDAR-derived covariates demonstrated potential, applied in addition with topographic and climatic covariates and optical imagery. CHM pertains to the vertical dimension, and gap fraction relates to the density of the canopy layer, respectively; both covariates that offer supplemental detail that is not necessarily ascertained for the canopy by optical imagery.

Impact of land use land cover change on a sand dune ecosystem in Northwest Beach, Point Pelee National Park, Canada

This paper analyses Land use land cover (LULC) change in the Northwest (NW) Beach, Point Pelee National Park (PPNP) to understand its effect on sediment transport dynamics for sand dune restoration. Due to development of infrastructure, beginning in the 1960s, sand dunes were completely removed from parts of NW beach. Spatial-temporal changes for LULC were assessed using aerial photos and images for 1959, 1977, 2006 and 2015. Based on the Ecological Land Classification System (Southern Ontario), object based image analysis and feature extraction methods were used to generate classified maps. The photos were the highest quality available in the Parks Canada, PPNP archive. LULC classes included Shoreline Vegetation, Deciduous Thicket, Sand Barren and Dune Type, and Infrastructure. Aerial photographs of LULC change for 1959–1977 and South Western Ontario Orthoimagery Project (SWOOP) images for 2006–2015 were analysed. A large gap exists between 1977 and 2006 as no images were available for that time period. Results indicated a significant increase in the Deciduous Thicket in 2015, acting as a barrier for sand movement to the parking lots. Decrease in the Shoreline Vegetation Type along with an increase in the lake level indicate a decrease in beach width and supply area for sediment transport. Based on the analysis, active management through the removal of cottonwood trees, and policy changes are recommended for dune restoration in Point Pelee. Accuracy assessment of the 2015 classification using an error matrix resulted in an overall accuracy for the LULC classification of 88%.

Effect of climate data uncertainty on ecological land classification: a case study from Argentina

CR Climate Research Contact the journal Facebook Twitter RSS Mailing List Subscribe to our mailing list via Mailchimp HomeLatest VolumeAbout the JournalEditorsSpecials CR 78:261-270 (2019) - DOI: https://doi.org/10.3354/cr01574 Effect of climate data uncertainty on ecological land classification: a case study from Argentina M. R. Derguy1,2,3,*, A. A. Drozd1,2, S. Martinuzzi4, J. L. Frangi1, M. F. Arturi1 1Laboratorio de Investigación de Sistemas Ecológicos y Ambientales (LISEA), Universidad Nacional de La Plata, Diagonal 113 No. 469, La Plata, Argentina 2Departamento de Ciencias Ambientales y Turismo, Universidad Nacional de Avellaneda (UNDAV), España 350, Avellaneda, Argentina 3Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Godoy Cruz 2290, CABA C1425FQB, Buenos Aires, Argentina 4SILVIS Lab, Department of Forest and Wildlife Ecology, University of Wisconsin-Madison, 1630 Linden Drive, Madison, Wisconsin 53706, USA *Corresponding author: mderguy@fcnym.unlp.edu.ar ABSTRACT: Ecological studies based on gridded climate data are increasingly common, but the effect of the uncertainty of climate data on ecological estimations is rarely assessed. Here, we assessed the effect of the uncertainty of local and global climate data (LCD and GCD, respectively) on ecological land classifications, using the Holdridge life zones and the country of Argentina as a case study. We quantified the uncertainty in LCD using information from meteorological stations and a simulation approach, which allowed us to create an altered LCD (aLCD), and to quantify the agreement between LCD and aLCD through confidence limits. Then, we quantified the agreement between the life zone maps derived from GCD and LCD—while accounting for LCD uncertainties—and assessed the effects of spatial resolution, environmental resolution, and topographic heterogeneity. We found that the mean agreement between LCD and aLCD for the whole country was about 75%, but there were important variations in the amplitude of the confidence intervals depending on the region. In addition, the mean agreement between ecological maps derived from LCD and GCD ranged between 40 and 83%. However, the LCD-GCD agreement fell inside the confidence limits of the LCD, and decreasing the spatial resolution from 1 to 50 km did not change the results. Decreased thematic resolution improved the agreement in the tropical and antiboreal regions. Overall, our study shows that uncertainties in ecological applications of climate data are higher in environmentally heterogeneous lands, and highlights the need for incorporating climate data uncertainties into ecological studies. KEY WORDS: Agreement · Climate data · Land classification · Holdridge life zones · WorldClim Full text in pdf format PreviousCite this article as: Derguy MR, Drozd AA, Martinuzzi S, Frangi JL, Arturi MF (2019) Effect of climate data uncertainty on ecological land classification: a case study from Argentina. Clim Res 78:261-270. https://doi.org/10.3354/cr01574 Export citation RSS - Facebook - Tweet - linkedIn Cited by Published in CR Vol. 78, No. 3. Online publication date: October 17, 2019 Print ISSN: 0936-577X; Online ISSN: 1616-1572 Copyright © 2019 Inter-Research.

Estimating Stand Age from Airborne Laser Scanning Data to Improve Models of Black Spruce Wood Density in the Boreal Forest of Ontario

Spatial models that provide estimates of wood quality enable value chain optimization approaches that consider the market potential of trees prior to harvest. Ecological land classification units (e.g., ecosite) and structural metrics derived from Airborne Laser Scanning (ALS) data have been shown to be useful predictors of wood quality attributes in black spruce stands of the boreal forest of Ontario, Canada. However, age drives much of the variation in wood quality among trees, and has not been included as a predictor in previous models because it is poorly represented in inventory systems. The objectives of this study were (i) to develop a predictive model of mean stem age of black spruce-dominated stands, and (ii) refine models of black spruce wood density by including age as a predictor variable. A non-parametric model of stand age that used a k nearest neighbor (kNN) classification based on a random forests (rf) distance metric performed well, producing a root mean square difference (RMSD) of 15 years and explaining 62% of the variance. The subsequent random forests model of black spruce wood density generated from age and ecosite predictors was useful, with a root mean square error (RMSE) of 59.1 kg·m−3. These models bring large-scale wood quality prediction closer to becoming operational by including age and site effects that can be derived from inventory data.

The Production–Living–Ecological Land Classification System and Its Characteristics in the Hilly Area of Sichuan Province, Southwest China Based on Identification of the Main Functions

Production–living–ecological land (PLEL) is one of the research focuses of land planning and regional sustainable development in China. This paper builds a three-level classification system of PLEL based on the identification of the main land use functions (LUFs). Taking 215 typical towns in the hilly area of Sichuan Province, Southwest China as samples, the quantitative, spatial, and functional characteristics and impact factors of PLEL were studied. The results showed that (1) production land holds a dominant role in the hilly area of Sichuan Province, and production land (PL), living land (LL) and ecological land (EL) account for 66.06%, 7.60%, and 26.34% of the area, respectively. The area of agricultural production land is the largest; forestland and rural living land rank second and third. (2) The spatial patterns of PLEL in different regions of hilly area have differences. The proportion of PL gradually decreases from north to south, while the proportion of EL gradually increases from north to south, and the difference in LL is not obvious. The EL is mainly distributed in the upper and middle parts of hills, and the PL and LL are mainly distributed in the foot slopes and valleys. (3) The main functions of PLEL in the hilly area of Sichuan are production and ecology. The production function is mainly for agricultural and forestry products, and the living function is mainly for cultural leisure and residential functions. There are little differences among the ecological sub-functions. (4) There is a strong correlation between PLEL and natural–social–economic factors in the hilly area of Sichuan. Natural conditions such as latitude, relative height, and surface roughness have significant impacts on PL and EL. Social and economic factors such as population density, location and total industrial output value have a significant impact on LL. The results of this study provide valuable implications for the spatial planning and sustainable development in the Sichuan Basin and upstream of the Yangtze River.

Pocket wetlands as additions to stormwater treatment train systems: a case study from a restored stream in Brampton, ON, Canada

Ontario urban development necessitates stream restoration projects for existing watercourses that flow through a property under development. This is part of the planning process, mandated by local conservation authorities to conserve the natural heritage and ecological function of natural areas identified under Ontario’s Ecological Land Classification. Stream restoration is commonly incorporated into larger stormwater management upgrades in the re-developed landscape. This study evaluates flow attenuation and water quality mitigation of a pocket wetland (PW), a shallow constructed wetland located at the outlet of a stormwater management system, within a major channel realignment and stream restoration project in Brampton, Ontario, Canada. The PW was incorporated into the floodplain of the natural channel design, between the outflow of the stormwater pond and a tributary of Churchville Creek. The latter contains habitat for redside dace (Clinostomus elongates), a designated species-at-risk under the Ontario Endangered Species Act. The addition of the PW to the stormwater treatment train was designed to mitigate the impacts that poor water quality has on local biota. Runoff, water temperature, total suspended solids and conductivity were monitored during 21 rainfall events between May and October 2014. Average PW event residence time was ~2 h and the overall changes in water quality between instream monitoring sites were negligible. Instream water temperature changes were minor (< 1°C) during flow events, with small increases in water temperature (< 0.5°C) observed during baseflow. The change in suspended sediment between the stream and PW flow was −22–31 mg/L. Suspended sediment inputs were greatest in the fall, which coincides with larger rainfall events. This study demonstrates PWs provide additional water storage time, and have added value in stormwater management. Although PWs are not formally included in current policy requirements, evidence from this study suggests the inclusion of PWs in future projects is worthwhile.

Exploring dust emission responses to land cover change using an ecological land classification

Abstract Despite efforts to quantify the impacts of land cover change on wind erosion, assessment uncertainty remains large. We address this uncertainty by evaluating the application of ecological site concepts and state-and-transition models (STMs) for detecting and quantitatively describing the impacts of land cover change on wind erosion. We apply a dust emission model over a rangeland study area in the northern Chihuahuan Desert, New Mexico, USA, and evaluate spatiotemporal patterns of modelled horizontal sediment mass flux and dust emission in the context of ecological sites and their vegetation states; representing a diversity of land cover types. Our results demonstrate how the impacts of land cover change on dust emission can be quantified, compared across land cover classes, and interpreted in the context of an ecological model that encapsulates land management intensity and change. Results also reveal the importance of established weaknesses in the dust model soil characterisation and drag partition scheme, which appeared generally insensitive to the impacts of land cover change. New models that address these weaknesses, coupled with ecological site concepts and field measurements across land cover types, could significantly reduce assessment uncertainties and provide opportunities for identifying land management options.

Modelling and mapping heavy metal and nitrogen concentrations in moss in 2010 throughout Europe by applying Random Forests models

ObjectiveThis study explores the statistical relations between the concentration of nine heavy metals (HM) (arsenic (As), cadmium (Cd), chromium (Cr), copper (Cu), mercury (Hg), nickel (Ni), lead (Pb), vanadium (V), zinc (Zn)), and nitrogen (N) in moss and potential explanatory variables (predictors) which were then used for mapping spatial patterns across Europe. Based on moss specimens collected in 2010 throughout Europe, the statistical relation between a set of potential predictors (such as the atmospheric deposition calculated by use of two chemical transport models (CTM), distance from emission sources, density of different land uses, population density, elevation, precipitation, clay content of soils) and concentrations of HMs and nitrogen (N) in moss (response variables) were evaluated by the use of Random Forests (RF) and Classification and Regression Trees (CART). Four spatial scales were regarded: Europe as a whole, ecological land classes covering Europe, single countries participating in the European Moss Survey (EMS), and moss species at sampling sites. Spatial patterns were estimated by applying a series of RF models on data on potential predictors covering Europe. Statistical values and resulting maps were used to investigate to what extent the models are specific for countries, units of the Ecological Land Classification of Europe (ELCE), and moss species. ResultsLand use, atmospheric deposition and distance to technical emission sources mainly influence the element concentration in moss. The explanatory power of calculated RF models varies according to elements measured in moss specimens, country, ecological land class, and moss species. Measured and predicted medians of element concentrations agree fairly well while minima and maxima show considerable differences. The European maps derived from the RF models provide smoothed surfaces of element concentrations (As, Cd, Cr, Cu, N, Ni, Pb, Hg, V, Zn), each explained by a multivariate RF model and verified by CART, and thereby more information than the dot maps depicting the spatial patterns of measured values. ConclusionsRF is an eligible method identifying and ranking boundary conditions of element concentrations in moss and related mapping including the influence of the environmental factors.

Classifying Northern New England Landscapes for Improved Conservation

On the Ground Ecological land classification enables improved conservation by linking land types to vegetation, ecosystem services, disturbance regimes, and conservation practices. Defining landscape-scale ecological site groups allows for the development of generalized state-and-transition models for summarizing the major ecological dynamics and associated conservation practices within a region. We defined nine ecological site groups for northern New England (MLRA 143) by identifying the fewest number of ecological classes as possible while retaining maximum utility of state-and-transition models for each class. Ecological site groups provide scalability of ecological site information and simplify the development of ecological concepts and the application of appropriate conservation practices.

GIS-fuzzy logic technique in modeling soil depth classes: Using parts of the Clay Belt and Hornepayne region in Ontario, Canada as a case study

Globally, there is a growing desire to predict and map the spatial distribution of soil depth on the landscape due to its numerous roles in areas such as agriculture, forestry, hydrology and ecological land classification. The aim of this study was to develop a technique to model and map soil depth classes based on GIS-fuzzy logic modeling approach and to examine the mapping accuracy of this technique with the accuracy of soil depth classes' derived using randomForest modeling approach. This was carried out for a portion of the Clay Belt and Hornepayne region in Ontario, Canada as a case study. The GIS-fuzzy logic approach was performed based on the soil-environment model (case-based reasoning and rule-based reasoning) using a 10m LiDAR-derived Digital Elevation Model (DEM) and derivatives such as curvature, slope, aspect, slope position classification, smooth multi-path wetness index and surface roughness in combination with mode of deposition and spatial observations of soil depth information. A digital soil depth map with classes of deep (>120cm), moderately deep (>60 and ≤120cm), shallow (>15 and ≤60cm) and very shallow (≤15cm) was predicted across 430,076ha. An overall mapping accuracy of 94% in validation was obtained from the GIS-fuzzy logic modeling approach relative to 88% derived from the randomForest modeling technique. The GIS-fuzzy logic modeling approach could be readily implemented to predict and map the spatial distribution of soil depth classes on various landscapes.

Can ecological land classification increase the utility of vegetation monitoring data?

Vegetation dynamics in rangelands and other ecosystems are known to be mediated by topoedaphic properties. Vegetation monitoring programs, however, often do not consider the impact of soils and other sources of landscape heterogeneity on the temporal patterns observed. Ecological sites (ES) comprise a land classification system based on soil, topographic, and climate variations that can be readily applied by land managers to classify topoedaphic properties at monitoring locations. We used a long-term (>40y) vegetation record from southeastern Arizona, USA to test the utility of an ES classification for refining interpretations of monitoring data in an area of relatively subtle soil differences. We focused on two phenomena important to rangeland management in the southeastern Arizona region: expansion of the native tree velvet mesquite (Prosopis velutina Woot.) and spread of the introduced perennial grass Lehmann lovegrass (Eragrostis lehmanniana Nees). Specifically, we sought to determine if a quantitative, ES-specific analysis of the long-term record would (1) improve detection of changes in plant species having heightened ecological or management importance and (2) further clarify topoedaphic effects on vegetation trajectories. We found that ES class membership was a significant factor explaining spatiotemporal variation in velvet mesquite canopy cover, Lehmann lovegrass basal cover, and Lehmann lovegrass density measurements. In addition, we observed that the potential magnitude of velvet mesquite and Lehmann lovegrass increases varied substantially among ES classes. Our study brings attention to a practical land management tool that might be called upon to increase the effectiveness of vegetation-based indicators of ecosystem change.

基于能值分析和生态用地分类的中国生态系统生产总值核算研究

PDF HTML阅读 XML下载 导出引用 引用提醒 基于能值分析和生态用地分类的中国生态系统生产总值核算研究 DOI: 10.5846/stxb201408211657 作者: 作者单位: 国土资源部信息中心,国土资源部信息中心,国土资源部信息中心,中国科学院生态环境研究中心,国土资源部信息中心 作者简介: 通讯作者: 中图分类号: 基金项目: 国土资源部国土资源战略研究重点实验室开放课题(KFS201304);国土资源部公益性行业科研专项(20101101) Accounting of Gross Ecosystem Product based on emergy analysis and ecological land classification in China Author: Affiliation: information center of land resource ministry,information center of land resource ministry,,, Fund Project: 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:生态系统生产总值是生态系统为人类提供的产品与服务价值的总和,研究与建立一个独立的核算一个国家或地区的生态系统生产总值的方法与体系,对科学评价与合理利用自然资源、助推生态文明建设等具有重要意义。将生态系统生产总值分为供给价值、文化价值、承载价值与调节价值等四大类,基于能值分析方法和生态用地分类体系,计算得出全国及各地区生态系统生产总值,并将其与国内生产总值进行比较,为自然资源资产负债核算和综合生态系统管理等研究提供理论和方法借鉴。研究结果表明:(1)生态用地分类体系包括湿地、森林、草地和其他生态土地4个一级类型、19个二级类型。其中,湿地、森林、草地可统称为基础性生态用地。2008年,中国生态用地总量为763.95万 km2,约占陆域国土面积的80%,其中基础性生态用地约为530.8万km2。(2)2008年,中国国土生态系统生产总值约为19万亿美元,是当年国内生产总值4.71万亿美元的4倍,人均生态系统总值约为1.45万美元/人。从生态系统生产总值来看,广东、山东、河南位居前三,西藏最低;从人均生态系统生产总值来看,西藏和内蒙古居前,甘肃最低。 Abstract:Gross Ecosystem Product (GEP) can be defined as the value of products and services provided by ecosystems to human beings. It is significant in considering the value of final goods and services produced by the population of a country or region in a given period. In general, the natural environment, upon which human beings rely for their existence and development, should be viewed within the compound economy-society-nature system. Thus, the value of goods and services provided by ecosystems should be incorporated within economic accounting systems. Recently, there have been a number of attempts to refine social development evaluation using measures such as Gross National Happiness Index and Human Development Index. In addition, many researchers have estimated the value of ecosystem services using approaches such as the market value method, shadow price assessment, alternative engineering method, and opportunity cost approach. However, their studies lack unified accounting indicators and accounting systems that are matched with the framework of national economic statistics. In this paper, an integrated method was proposed for estimating the value of GEP, taking into account ecosystem supplies, ecological cultural values, adjustable land values, and carrying capacities. A detailed emergy analysis was introduced into the assessment of resource supplies, cultural values, and carrying capacities. A numerical model was adopted to estimate adjustable values based on the classification and size of ecological land, as well as the corresponding adjustable value of an individual type of ecological land. Moreover, the results of gross national and regional ecosystem production were compared with the Gross Domestic Product (GDP). The results indicate that this method could offer theoretical and technical insights in assessing natural resource assets and liabilities for business accounting and integrated ecosystem management purposes. It is also of great significance for the scientific evaluation and rational utilization of natural resources and in promoting the creation of an ecologically sustainable civilization. Based on the main functions of ecosystem services, ecological land can be classified into 4 types, namely wetland, forest, grassland, and other ecological land, as well as 19 sub-types. Among these types, wetland, forest, and grassland should be regarded as fundamental ecological land. The results indicated that in 2008, the area of ecological land in China was 7.6395 million km2, accounting for about 80 percent of the entire land area, and the extent of other ecological land was approximately 5.308 million km2. Meanwhile, the total value of land ecological supplies, ecological cultural values, and ecological carrying capacity in 2008 was 18.16 trillion dollars in China. The above ecosystem services accounted for 18.8, 8.4, and 72.8 percent respectively of the total value. The value of land ecology adjusting measured by basic ecological land was 835.12 billion dollars. The total value of GEP in China was about 19 trillion dollars in 2008. Compared with the GDP for that year, namely, 4.71 trillion dollars, GEP was four times as much as GDP. GEP per capita was about 14500 dollars. The values of GEP in different provinces of China were found to vary. The values of GEP in Guangdong, Shandong, and Henan provinces were bigger than those in other provinces, while the value of GEP in Tibet was the lowest. However, the values of per capita GEP in Tibet and Inner Mongolia were the top two among all of the provinces. In contrast, the per capita value in Gansu province was the lowest. 参考文献 相似文献 引证文献