A novel GIS-based ensemble technique for rangeland downward trend mapping as an ecological indicator change

Abstract Rangelands provide important ecosystem services worldwide. The present study was aimed to map rangeland degradation in a critical mountainous rangeland ecosystem of Iran. The study was carried out based on seven years intensive fieldwork and recording 1147 locations with downward trends in the quality of the rangelands. Twelve conditional factors and two important ensemble algorithms including Probability density-Index of entropy (PD-IOE) and Frequency ratio-Index of entropy (FR-IOE), were used to produce rangeland downward trend (RDT) susceptibility maps. The results of validation showed that PD-IOE hybrid model with area under curve (AUC = 0.901) and standard error (SE = 0.011) is more accurate than FR-IOE hybrid model (AUC = 0.881 and SE = 0.012). In addition, our results indicate that altitude, distance to river, and distance to road are the most important factors for rangeland degradation. In addition, the places with higher altitude and less distance to roads and rivers endured more degradation and these places have downward trends. Based on the achieved results, 2% and 10% of study area fall into the very high and high classes of downward trends, respectively. Overgrazing and early grazing are two main drivers for rangeland degradation in the study area, and the rangeland managers and decision makers should define and develop strategies to reduce pressure on rangelands and promote strategies to restore these important ecosystems.

[1]  G. Pickup,et al.  A grazing gradient approach to land degradation assessment in arid areas from remotely-sensed data , 1994 .

[2]  H. Moradi,et al.  Effects of road construction on soil degradation and nutrient transport in Caspian Hyrcanian mixed forests , 2016 .

[3]  H. Pourghasemi,et al.  Performance assessment of individual and ensemble data-mining techniques for gully erosion modeling. , 2017, The Science of the total environment.

[4]  J. Isselstein,et al.  Plant biodiversity and ethnobotany of Borana pastoralists in Southern Oromia, Ethiopia , 2008, Economic Botany.

[5]  N. Peinemann,et al.  SOIL DEGRADATION RELATED TO OVERGRAZING IN THE SEMI-ARID SOUTHERN CALDENAL AREA OF ARGENTINA , 2001 .

[6]  Á. Gómez‐Gutiérrez,et al.  Selecting indicators for assessing soil quality and degradation in rangelands of Extremadura (SW Spain) , 2017 .

[7]  B. Pradhan,et al.  Application of fuzzy logic and analytical hierarchy process (AHP) to landslide susceptibility mapping at Haraz watershed, Iran , 2012, Natural Hazards.

[8]  H. Dregne.  Land Degradation in the Drylands , 2002 .

[9]  W. Ning,et al.  Changes in Livestock Migration Patterns in a Tibetan-style Agropastoral System , 2007 .

[10]  A. Cerda,et al.  Sustainable grazing , 2018, Current Opinion in Environmental Science & Health.

[11]  João Gonçalves,et al.  Improving the detection of wildfire disturbances in space and time based on indicators extracted from MODIS data: a case study in northern Portugal , 2019, Int. J. Appl. Earth Obs. Geoinformation.

[12]  Michael Dumbser,et al.  On GLM curl cleaning for a first order reduction of the CCZ4 formulation of the Einstein field equations , 2020, J. Comput. Phys..

[13]  H. Shahabi,et al.  Novel forecasting approaches using combination of machine learning and statistical models for flood susceptibility mapping. , 2018, Journal of environmental management.

[14]  Javed Mallick,et al.  GIS-based landslide susceptibility evaluation using fuzzy-AHP multi-criteria decision-making techniques in the Abha Watershed, Saudi Arabia , 2018, Environmental Earth Sciences.

[15]  M. Louhaichi,et al.  Rangelands of Central Asia: challenges and opportunities , 2016, Journal of Arid Land.

[16]  Michel Jaboyedoff,et al.  Rockfall hazard and risk assessments along roads at a regional scale: example in Swiss Alps , 2012 .

[17]  José Tuxpan-Vargas,et al.  Land subsidence by groundwater over-exploitation from aquifers in tectonic valleys of Central Mexico: A review , 2018, Engineering Geology.

[18]  Manuel Pulido Fernández,et al.  Soil Erosion Induced by the Introduction of New Pasture Species in a Faxinal Farm of Southern Brazil , 2018 .

[19]  Xiangzheng Deng,et al.  Do roads lead to grassland degradation or restoration? A case study in Inner Mongolia, China , 2011, Environment and Development Economics.

[20]  Mario G. Manzano,et al.  Processes of desertification by goats overgrazing in the Tamaulipan thornscrub (matorral) in north-eastern Mexico. , 2000 .

[21]  A. Mahdavi.  Forests and rangelands? wildfire risk zoning using GIS and AHP techniques , 2012 .

[22]  L. W. Zevenbergen,et al.  A METHODOLOGY FOR PREDICTING CHANNEL MIGRATION NCHRP PROJECT NO. 24-26 , 2001 .

[23]  Boris Schröder,et al.  How can statistical models help to determine driving factors of landslides , 2012 .

[24]  E. J. Milner-Gulland,et al.  Rangeland degradation in Kazakhstan during the Soviet era: re-examining the evidence , 2003 .

[25]  M. López‐Vicente,et al.  Soil water content and temporal stability in an arid area with natural and planted grasslands , 2018, Hydrological Processes.

[26]  K. Yin,et al.  Statistical prediction model for slope instability of metamorphosed rocks , 1988 .

[27]  Roland Geerken,et al.  Assessment of rangeland degradation and development of a strategy for rehabilitation , 2004 .

[28]  S. Ayoubi,et al.  Near-saturated soil hydraulic properties as influenced by land use management systems in Koohrang region of central Zagros, Iran , 2014 .

[29]  K. Takeuchi,et al.  Spatially heterogeneous impacts on rangeland after social system change in Mongolia , 2007 .

[30]  S. Keesstra,et al.  Using hydrological connectivity to detect transitions and degradation thresholds: Applications to dryland systems , 2020 .

[31]  C. Marzban.  The ROC Curve and the Area under It as Performance Measures , 2004 .

[32]  H. Pourghasemi,et al.  Changes in morphometric meander parameters identified on the Karoon River, Iran, using remote sensing data , 2016 .

[33]  S. Milton,et al.  A Conceptual Model of Arid Rangeland Degradation , 1994 .

[34]  M. Erfanian,et al.  Effects of preservation policy on land use changes in Iranian Northern Zagros forests , 2019, Land Use Policy.

[35]  Lukas W. Lehnert,et al.  A hyperspectral indicator system for rangeland degradation on the Tibetan Plateau: A case study towards spaceborne monitoring , 2014 .

[36]  J. Friedman.  Special Invited Paper-Additive logistic regression: A statistical view of boosting , 2000 .

[37]  A. Cerdà.  Seasonal variability of infiltration rates under contrasting slope conditions in southeast Spain. , 1996 .

[38]  Brent N. Holben,et al.  Fraction images derived from NOAA AVHRR data for studying the deforestation in the Brazilian Amazon , 1994 .

[39]  N. Tsendbazar,et al.  Understanding transportation-caused rangeland damage in Mongolia. , 2013, Journal of environmental management.

[40]  V. Singh,et al.  Novel Hybrid Evolutionary Algorithms for Spatial Prediction of Floods , 2018, Scientific Reports.

[41]  Yichun Xie,et al.  Quantitative Analysis of Driving Factors of Grassland Degradation: A Case Study in Xilin River Basin, Inner Mongolia , 2012, TheScientificWorldJournal.

[42]  Hamid Reza Pourghasemi,et al.  How do machine learning techniques help in increasing accuracy of landslide susceptibility maps? , 2020 .

[43]  Vasilios P. Papanastasis,et al.  Plant cover as a tool for monitoring desertification in mountain Mediterranean rangelands , 2003 .

[44]  Karim C. Abbaspour,et al.  Assessing the impact of climate change on water resources in Iran , 2009 .

[45]  Sarfraz Ahmad,et al.  RANGELAND DEGRADATION AND MANAGEMENT APPROACHES IN BALOCHISTAN, PAKISTAN , 2012 .

[46]  A. Ayoub.  Extent, severity and causative factors of land degradation in the Sudan , 1998 .

[47]  H. Pourghasemi,et al.  Gully Erosion Modeling Using GIS-Based Data Mining Techniques in Northern Iran: A Comparison Between Boosted Regression Tree and Multivariate Adaptive Regression Spline , 2018, Advances in Natural and Technological Hazards Research.

[48]  D. Engle,et al.  Conservation of Pattern and Process: Developing an Alternative Paradigm of Rangeland Management , 2012 .

[49]  Donatello Telesca,et al.  Machine learning models accurately predict ozone exposure during wildfire events. , 2019, Environmental pollution.

[50]  Yonten Nyima.  Political‐economic factors in official reports on rangeland degradation: A critical case study from the Tibet Autonomous Region , 2019 .

[51]  Dieu Tien Bui,et al.  A comparative study between popular statistical and machine learning methods for simulating volume of landslides , 2017 .

[52]  D. Bui,et al.  Shallow landslide susceptibility assessment using a novel hybrid intelligence approach , 2017, Environmental Earth Sciences.

[53]  J. Hill,et al.  Coupling spectral unmixing and trend analysis for monitoring of long-term vegetation dynamics in Mediterranean rangelands , 2003 .

[54]  O. Korup,et al.  Landslide prediction from machine learning , 2014 .

[55]  K. Paudel,et al.  Assessing rangeland degradation using multi temporal satellite images and grazing pressure surface model in Upper Mustang, Trans Himalaya, Nepal , 2010 .

[56]  Kanwar L. Sahrawat,et al.  Pasture degradation effects on soil quality indicators at different hillslope positions in a semiarid region of western Iran , 2017, Environmental Earth Sciences.

[57]  G. N. Smit,et al.  Cattle-rangeland management practices and perceptions of pastoralists towards rangeland degradation in the Borana zone of southern Ethiopia. , 2007, Journal of environmental management.

[58]  B. Cousins,et al.  Invisible capital: The contribution of communal rangelands to rural livelihoods in South Africa , 1999 .

[59]  A. Darvishsefat,et al.  Land use change modeling through an integrated Multi-Layer Perceptron Neural Network and Markov Chain analysis (case study: Arasbaran region, Iran) , 2019, Journal of Forestry Research.

[60]  Nadhir Al-Ansari,et al.  GIS Based Hybrid Computational Approaches for Flash Flood Susceptibility Assessment , 2020, Water.

[61]  O. Sala,et al.  Rangeland Ecosystem Services: Nature’s Supply and Humans’ Demand , 2017 .

[62]  Swades Pal,et al.  Application of frequency ratio and logistic regression models for assessing physical wetland vulnerability in Punarbhaba river basin of Indo-Bangladesh , 2018 .

[63]  Artemi Cerdà,et al.  Changes in overland flow and infiltration after a rangeland fire in a Mediterranean scrubland , 1998 .

[64]  Wei Chen,et al.  Gully headcut susceptibility modeling using functional trees, naïve Bayes tree, and random forest models , 2019, Geoderma.

[65]  J. Kigel,et al.  Vegetation response to grazing management in a Mediterranean herbaceous community: a functional group approach , 2000 .

[66]  A-Xing Zhu,et al.  Flood susceptibility assessment in Hengfeng area coupling adaptive neuro-fuzzy inference system with genetic algorithm and differential evolution. , 2018, The Science of the total environment.

[67]  F. Escobedo,et al.  Exploring management objectives and ecosystem service trade-offs in a semi-arid rangeland basin in southeast Iran , 2019, Ecological Indicators.

[68]  S. Ayoubi,et al.  Erodibility of calcareous soils as influenced by land use and intrinsic soil properties in a semiarid region of central Iran , 2018, Environmental Monitoring and Assessment.

[69]  M. Clarke,et al.  Long-term erosion and surface roughness change of rain-forest terrain following selective logging, Danum Valley, Sabah, Malaysia , 2006 .

[70]  David W. Hosmer,et al.  Applied Logistic Regression , 1991 .

[71]  J. Rockström,et al.  Policy: Sustainable development goals for people and planet , 2013, Nature.

[72]  Bayes Ahmed,et al.  Application of Bivariate and Multivariate Statistical Techniques in Landslide Susceptibility Modeling in Chittagong City Corporation, Bangladesh , 2017, Remote. Sens..

[73]  W. Baethgen,et al.  Drivers, Process, and Consequences of Native Grassland Degradation: Insights from a Literature Review and a Survey in Río de la Plata Grasslands , 2019, Agronomy.

[74]  Thomas Oommen,et al.  Machine Learning Based Predictive Modeling of Debris Flow Probability Following Wildfire in the Intermountain Western United States , 2017, Mathematical Geosciences.

[75]  S. Sadeghi,et al.  Comparison between effects of open grazing and manual harvesting of cultivated summer rangelands of northern Iran on infiltration, runoff and sediment yield , 2007 .

[76]  Kurt J. Marfurt,et al.  Using machine learning as an aid to seismic geomorphology, which attributes are the best input? , 2019, Interpretation.

[77]  George L. W. Perry,et al.  Using Machine Learning to Predict Geomorphic Disturbance: The Effects of Sample Size, Sample Prevalence, and Sampling Strategy , 2018, Journal of Geophysical Research: Earth Surface.

[78]  H. Azadi,et al.  Rangeland degradation in North China: perceptions of pastoralists. , 2010, Environmental research.

[79]  G. Destouni,et al.  GIS-Based Site Selection for Check Dams in Watersheds: Considering Geomorphometric and Topo-Hydrological Factors , 2019, Sustainability.

[80]  S. Schnabel,et al.  The Impact of Heavy Grazing on Soil Quality and Pasture Production in Rangelands of SW Spain , 2018 .

[81]  Huimin Zhu,et al.  Overgrazing leads to soil cracking that later triggers the severe degradation of alpine meadows on the Tibetan Plateau , 2019, Land Degradation & Development.

[82]  G. Oba,et al.  Efficacy of Integrating Herder Knowledge and Ecological Methods for Monitoring Rangeland Degradation in Northern Kenya , 2009 .

[83]  Raed J Al-Tabini,et al.  Livestock, medicinal plants and rangeland viability in Jordan’s Badia: through the lens of traditional and local knowledge , 2012, Pastoralism: Research, Policy and Practice.

[84]  Mario G. Manzano,et al.  Overgrazing and desertification in northern Mexico: highlights on north eastern region. , 2000 .

[85]  J. Hanley,et al.  The meaning and use of the area under a receiver operating characteristic (ROC) curve. , 1982, Radiology.

[86]  E. Mwangomo,et al.  Wildlife water utilization and importance of artificial waterholes during dry season at Ruaha National Park, Tanzania , 2008, Wetlands Ecology and Management.

[87]  Ayed G. Mohammad,et al.  The impact of vegetative cover type on runoff and soil erosion under different land uses , 2010 .

[88]  Eve McDonald-Madden,et al.  Using Landsat observations (1988–2017) and Google Earth Engine to detect vegetation cover changes in rangelands - A first step towards identifying degraded lands for conservation , 2019, Remote Sensing of Environment.

[89]  S. Keesstra,et al.  Physically‐Based Modelling of the Post‐Fire Runoff Response of a Forest Catchment in Central Portugal: Using Field versus Remote Sensing Based Estimates of Vegetation Recovery , 2016 .

[90]  Tien-Dat Pham,et al.  Multi-Hazard Exposure Mapping Using Machine Learning Techniques: A Case Study from Iran , 2019, Remote. Sens..

[91]  M. Marschalko,et al.  Landslide susceptibility assessment of the Kraľovany–Liptovský Mikuláš railway case study , 2010 .

[92]  C. E. SHANNON,et al.  A mathematical theory of communication , 1948, MOCO.

[93]  Wei Chen,et al.  Landslide spatial modeling: Introducing new ensembles of ANN, MaxEnt, and SVM machine learning techniques , 2017 .

[94]  B. Pradhan,et al.  Spatial prediction of gully erosion using ALOS PALSAR data and ensemble bivariate and data mining models , 2019, Geosciences Journal.

[95]  G. Mountrakis,et al.  Integrating Traditional Ecological Knowledge and Remote Sensing for Monitoring Rangeland Dynamics in the Altai Mountain Region , 2019, Environmental Management.

[96]  L. Bruijnzeel,et al.  Production of runoff and sediment by rural roads, trails and settlements in the Upper Konto catchment, East Java, Indonesia , 2007 .

[97]  C. M. Waters,et al.  Assessing resilience to underpin implementation of Land Degradation Neutrality: A case study in the rangelands of western New South Wales, Australia , 2019, Environmental Science & Policy.

[98]  R. Leemans,et al.  Effects of different management regimes on soil erosion and surface runoff in semi-arid to sub-humid rangelands , 2015 .

[99]  H. Pourghasemi,et al.  Evaluating the influence of geo-environmental factors on gully erosion in a semi-arid region of Iran: An integrated framework. , 2017, The Science of the total environment.

[100]  Qingbo Zhou,et al.  Mapping dynamics of soil organic matter in croplands with MODIS data and machine learning algorithms. , 2019, The Science of the total environment.

[101]  G. Ghaffari,et al.  SWAT‐simulated hydrological impact of land‐use change in the Zanjanrood basin, Northwest Iran , 2010 .

[102]  Hassan Abolghasemi,et al.  International Medical Response to a Natural Disaster: Lessons Learned from the Bam Earthquake Experience , 2006, Prehospital and Disaster Medicine.