GIS‐Based Predictive Models of Hillslope Runoff Generation Processes1

Abstract: Successful nonpoint source pollution control using best management practice placement is a complex process that requires in‐depth knowledge of the locations of runoff source areas in a watershed. Currently, very few simulation tools are capable of identifying critical runoff source areas on hillslopes and those available are not directly applicable under all runoff conditions. In this paper, a comparison of two geographic information system (GIS)‐based approaches: a topographic index model and a likelihood indicator model is presented, in predicting likely locations of saturation excess and infiltration excess runoff source areas in a hillslope of the Savoy Experimental Watershed located in northwest Arkansas. Based on intensive data collected from a two‐year field study, the spatial distributions of hydrologic variables were processed using GIS software to develop the models. The likelihood indicator model was used to produce probability surfaces that indicated the likelihood of location of both saturation and infiltration excess runoff mechanisms on the hillslope. Overall accuracies of the likelihood indicator model predictions varied between 81 and 87% for the infiltration excess and saturation excess runoff locations respectively. On the basis of accuracy of prediction, the likelihood indicator models were found to be superior (accuracy 81‐87%) to the predications made by the topographic index model (accuracy 69.5%). By combining statistics with GIS, runoff source areas on a hillslope can be identified by incorporating easily determined hydrologic measurements (such as bulk density, porosity, slope, depth to bed rock, depth to water table) and could serve as a watershed management tool for identifying critical runoff source areas in locations where the topographic index or other similar methods do not provide reliable results.     

STREAM HEALTH RANKINGS PREDICTED BY SATELLITE DERIVED LAND COVER METRICS1

ABSTRACT: Land cover and land use change have long been known to influence the chemical, physical, and biological characteristics of streams. This study makes use of land cover maps derived from fine resolution satellite imagery and an extensive stream quality dataset to determine the relationship between small watershed health rankings and land cover composition and configuration. Landscape metrics were derived from digital impervious surface area (ISA), tree cover (percent), and agricultural crop maps within Montgomery County, Maryland. Watershed rankings were developed by state and county collaborators (MD‐DNR and MCDEP) using extensive biological and chemical measurements. In stepwise logistic regression models the factors accounting for the most variation in stream health ranking were the percent ISA, followed by the percent of tree cover. Riparian buffer zone tree cover was also a significant predictor. Of the metrics that considered the spatial configuration of the landscape, a contagion index and the percent of ISA in the flow path from the ISA to the stream were also found to be significant predictors of stream health. Despite limited ability to characterize landscape configuration or narrow riparian buffer zone vegetation with coarser resolution imagery (from Landsat), model results were not significantly different from those based on the use of fine‐resolution ISA information, suggesting that broader area applications of the approach are possible. The results indicate that management practices designed to improve stream water quality should focus on the amount of ISA and tree cover in both the watershed and within the buffer zone.     

THE DEVELOPMENT OF THE NILE HYDROMETEOROLOGICAL FORECAST SYSTEM1

ABSTRACT: The National Oceanic and Atmospheric Administration is developing a river forecast system for the Nile River in Egypt. The river forecast system operates on scientific work stations using hydrometeorological models and software to predict inflows into the high Aswan Dam and forecast flow hydrographs at selected gaging locations above the dam The Nile Forecasting System (NFS) utilizes satellite imagery from the METEOSAT satellite as the input to the forecast system. Satellite imagery is used to estimate precipitation over the Blue Nile Basin using five different techniques. Observed precipitation data and climatic statistics are used to improve precipitation estimation. Precipitation data for grid locations are input to a distributed water balance model, a hill slope routing model, and a channel routing model. A customized Geographic Information System (GIS) was developed to show political boundaries, rivers, terrain elevation, and gaging network. The GIS was used to develop hydrologic parameters for the basin and is used for multiple display features.     

An Empirical Evaluation of Private Landowner Participation in Voluntary Forest Conservation Programs

The use of voluntary programs targeting resource conservation on private land has become increasingly prevalent in environmental policy. Voluntary programs potentially offer significant benefits over regulatory and market-based approaches. This article examines the factors affecting landowner participation in voluntary forest conservation programs using a combination of parcel-level GIS and remotely sensed data and semi-structured interviews of landowners in Monroe County, Indiana. A logistic regression model is applied to determine the probability of participation based on landowner education, membership in other non-forest voluntary programs, dominant land use activity, parcel size, distance from urban center, land resource portfolios, and forest cover. Both land use activity and the spatial configuration of a landholder’s resource portfolio are found to be statistically significant with important implications for the design and implementation of voluntary programs.

An Assessment of Forest Cover Trends in South and North Korea, From 1980 to 2010

It is generally believed that forest cover in North Korea has undergone a substantial decrease since 1980, while in South Korea, forest cover has remained relatively static during that same period of time. The United Nations Food and Agriculture Organization (FAO) Forest Resources Assessments—based on the reported forest inventories from North and South Korea—suggest a major forest cover decrease in North Korea, but only a slight decrease in South Korea during the last 30 years. In this study, we seek to check and validate those assessments by comparing them to independently derived forest cover maps compiled for three time intervals between 1990 and 2010, as well as to provide a spatially explicit view of forest cover change in the Korean Peninsula since the 1990s. We extracted tree cover data for the Korean Peninsula from existing global datasets derived from satellite imagery. Our estimates, while qualitatively supporting the FAO results, show that North Korea has lost a large number of densely forested areas, and thus in this sense has suffered heavier forest loss than the FAO assessment suggests. Given the limited time interval studied in our assessment, the overall forest loss from North Korea during the whole span of time since 1980 may have been even heavier than in our estimate. For South Korea, our results indicate that the forest cover has remained relatively stable at the national level, but that important variability in forest cover evolution exists at the regional level: While the northern and western provinces show an overall decrease in forested areas, large areas in the southeastern part of the country have increased their forest cover.     

CANOPY COVER AND SHADE DETERMINATIONS IN RIPARIAN ZONES1

ABSTRACT: Three instruments commonly used to measure stream canopy cover were evaluated: the clinometer, a modified spherical convex densiometer Model A, and a hemispherical image system. The hemispherical image system was also used to model shade. At each of five locations throughout Oregon, canopy cover above a stream was measured from the center of the stream along a series of transects. Vegetation along the reaches sampled ranged from a densely vegetated coastal forest stand to a sparsely vegetated eastern Oregon meadow. When techniques were compared within each site, canopy cover measured with the clinometer was similar to that measured with hemispherical imagery. The densiometer measurements, however, were typically lower than, though still highly correlated with, those derived from hemispherical images and from the clinometer. Because of site‐specific factors, the differences between all methods were significant in the Willamette and John Day Provinces. Canopy cover and shade were not equivalent for the streams studied.     

Computer-aided classification of forest cover types from small scale aerial photography

The US National Park Service must map forest cover types over extensive areas in order to fulfill its goal of maintaining or reconstructing presettlement vegetation within national parks and monuments. Furthermore, such cover type maps must be updated on a regular basis to document vegetation changes. Computer-aided classification of small scale aerial photography is a promising technique for generating forest cover type maps efficiently and inexpensively. In this study, seven cover types were classified with an overall accuracy of 62 percent from a reproduction of a 1120,000 color infrared transparency of a conifer-hardwood forest. The results were encouraging, given the degraded quality of the photograph and the fact that features were not centered, as well as the lack of information on lens vignetting characteristics to make corrections. Suggestions are made for resolving these problems in future research and applications. In addition, it is hypothesized that the overall accuracy is artificially low because the computer-aided classification more accurately portrayed the intermixing of cover types than the hand-drawn maps to which it was compared.     

Forest Cover Change,Physiography, Local Economy,and Institutions in a Mountain Watershed in Nepal

This study assessed changes in forest cover in a mountain watershed in central Nepal between 1976 and 2000 by comparing classified satellite images coupled by GIS analyses, and examined the association of forest change with major physiographic, economic, and local forest governance parameters. The results showed an increase in forested area (forest plus shrublands) by 7.6% during 1976–2000. Forest dynamism (changes including improvement, deterioration, gain, and loss) was highest in low-elevation, south-facing and less-steep slopes that were closer to roads. Proportionately the highest net improvement and gain to forested area also took place in those locations. Forest degradation occurred at twice the rate of improvement in high elevation areas (> 2300 m). Forests located in urban and semiurban areas (i.e., a market-oriented economy) experienced a proportionately higher amount of net improvement and gain than forests in rural areas (i.e., a subsistence economy). Among the three governance arrangements, proportionately the highest net improvement and gain took place in semigovernment forests (forested area legally under the forest department but with de facto control and claim of ownership by local communities and/or municipality) followed by formalized community forests (including leasehold). Government forests, which were mostly found in the southern high mountains and had virtually open access, remained relatively stable during the study period. Over 50% of the watershed forests have not come under community-based management despite favorable policy and more than two decades of government intervention with continuous donor support. The findings indicate that the present one size fits all approach of community forest handover policy in Nepal needs rethinking to accommodate biophysical and socioeconomic variations across the country.     

Prediction and uncertainty source analysis of the spatial and temporal disturbance from off-road vehicular traffic in a complex ecosystem

Vehicle use during military training activities results in soil disturbance and vegetation loss. The capacity of lands to sustain training is a function of the sensitivity of lands to vehicle use and the pattern of land use. The sensitivity of land to vehicle use has been extensively studied. Less well understood are the spatial patterns of vehicle disturbance. Since disturbance from off-road vehicular traffic moving through complex landscapes varies spatially, a spatially explicit nonlinear regression model (disturbance model) was used to predict the pattern of vehicle disturbance across a training facility. An uncertainty analysis of the model predictions assessed the spatial distribution of prediction uncertainty and the contribution of different error sources to that uncertainty.For the most part, this analysis showed that mapping and modeling process errors contributed more than 95% of the total uncertainty of predicted disturbance, while satellite imagery error contributed less than 5% of the uncertainty. When the total uncertainty was larger than a threshold, modeling error contributed 60% to 90% of the prediction uncertainty. Otherwise, mapping error contributed about 10% to 50% of the total uncertainty. These uncertainty sources were further partitioned spatially based on other sources of uncertainties associated with vehicle moment, landscape characterization, satellite imagery, etc.     

Assessing slope stability in unplanned settlements in developing countries

Unplanned housing in developing countries is often located on steep slopes. Frequently no building code is enforced for such housing and mains water is provided with no drainage provision. Both of these factors can be particularly significant in terms of landslide risk if, as is so often the case, such slopes lack any planned drainage provision. There is thus a need to develop a model that facilitates the assessment of slope stability in an holistic context, incorporating a wide range of factors (including surface cover, soil water topographic convergence, slope loading and point source water leakage) in order that appropriate advice can be given as to the general controls on slope stability in such circumstances. This paper outlines a model configured for this specific purpose and describes an application to a site in St. Lucia, West Indies, where there is active slope movement in an unplanned housing development on relatively steep topography. The model findings are in accord with the nature of the current failure at the site, provide guidance as to the significance of slope drainage and correspond to inferences drawn from an application of resistance envelope methods to the site. In being able to scenario test a uniquely wide range of combinations of factors, the model structure is shown to be highly valuable in assessing dominant slope stability process controls in such complex environments.     

Assessing the impact of urbanization on regional net primary productivity in Jiangyin County, China

Urbanization is one of the most important aspects of global change. The process of urbanization has a significant impact on the terrestrial ecosystem carbon cycle. The Yangtze Delta region has one of the highest rates of urbanization in China. In this study, carried out in Jiangyin County as a representative region within the Yangtze Delta, land use and land cover changes were estimated using Landsat TM and ETM+ imagery. With these satellite data and the BEPS process model (Boreal Ecosystem Productivity Simulator), the impacts of urbanization on regional net primary productivity (NPP) and annual net primary production were assessed for 1991 and 2002. Landsat-based land cover maps in 1991 and 2002 showed that urban development encroached large areas of cropland and forest. Expansion of residential areas and reduction of vegetated areas were the major forms of land transformation in Jiangyin County during this period. Mean NPP of the total area decreased from 818 to 699 gCm(-2)yr(-1) during the period of 1991 to 2002. NPP of cropland was only reduced by 2.7% while forest NPP was reduced by 9.3%. Regional annual primary production decreased from 808 GgC in 1991 to 691 GgC in 2002, a reduction of 14.5%. Land cover changes reduced regional NPP directly, and the increasing intensity and frequency of human-induced disturbance in the urbanized areas could be the main reason for the decrease in forest NPP.     

Monitoring Regional Riparian Forest Cover Change Using Stratified Sampling and Multiresolution Imagery1

Claggett, Peter R., Judy A. Okay, and Stephen V. Stehman, 2010. Monitoring Regional Riparian Forest Cover Change Using Stratified Sampling and Multiresolution Imagery. Journal of the American Water Resources Association (JAWRA) 46(2):334-343. DOI: 10.1111/j.1752-1688.2010.00424.x Abstract: The Chesapeake Bay watershed encompasses 165,760 km² of land area with 464,098 km of rivers and streams. As part of the Chesapeake Bay restoration effort, state and federal partners have committed to restoring 26,000 miles (41,843 km) of riparian forest buffers. Monitoring trends in riparian forest buffers over large areas is necessary to evaluate the efficacy of these restoration efforts. A sampling approach for estimating change in riparian forest cover from 1993/1994 to 2005 was developed and implemented in Anne Arundel County, Maryland, to exemplify a method that could be applied throughout the Bay watershed. All stream reaches in the county were stratified using forest cover change derived from Landsat imagery. A stratified random sample of 219 reaches was selected and forest cover change within the riparian buffer of each sampled reach was interpreted from high-resolution aerial photography. The estimated footprint of gross change in riparian forest cover (i.e., the sum of gross gain and gross loss) for the county was 1.83% (SE = 0.22%). Stratified sampling taking advantage of a priori knowledge of locations of change proved to be a practical and efficient protocol for estimating riparian forest buffer change at the county scale and the protocol would readily extend to much broader scale monitoring.     

Modelling the conversion of Colombian lowland ecosystems since 1940: drivers, patterns and rates

In biologically mega-diverse countries that are undergoing rapid human landscape transformation, it is important to understand and model the patterns of land cover change. This problem is particularly acute in Colombia, where lowland forests are being rapidly cleared for cropping and ranching. We apply a conceptual model with a nested set of a priori predictions to analyse the spatial and temporal patterns of land cover change for six 50-100 km(2) case study areas in lowland ecosystems of Colombia. Our analysis included soil fertility, a cost-distance function, and neighbourhood of forest and secondary vegetation cover as independent variables. Deforestation and forest regrowth are tested using logistic regression analysis and an information criterion approach to rank the models and predictor variables. The results show that: (a) overall the process of deforestation is better predicted by the full model containing all variables, while for regrowth the model containing only the auto-correlated neighbourhood terms is a better predictor; (b) overall consistent patterns emerge, although there are variations across regions and time; and (c) during the transformation process, both the order of importance and significance of the drivers change. Forest cover follows a consistent logistic decline pattern across regions, with introduced pastures being the major replacement land cover type. Forest stabilizes at 2-10% of the original cover, with an average patch size of 15.4 (+/-9.2)ha. We discuss the implications of the observed patterns and rates of land cover change for conservation planning in countries with high rates of deforestation.     

A remote sensing tool for near real-time monitoring of harmful algal blooms and turbidity in reservoirs

Harmful algal blooms (HABs) diminish the utility of reservoirs for drinking water supply, irrigation, recreation, and ecosystem service provision. HABs decrease water quality and are a significant health concern in surface water bodies. Near real-time monitoring of HABs in reservoirs and small water bodies is essential to understand the dynamics of turbidity and HAB formation. This study uses satellite imagery to remotely sense chlorophyll-a concentrations (chl-a), phycocyanin concentrations, and turbidity in two reservoirs, the Grand Lake O′ the Cherokees and Hudson Reservoir, OK, USA, to develop a tool for near real-time monitoring of HABs. Landsat-8 and Sentinel-2 imagery from 2013 to 2017 and from 2015 to 2020 were used to train and test three different models that include multiple regression, support vector regression (SVR), and random forest regression (RFR). Performance was assessed by comparing the three models to estimate chl-a, phycocyanin, and turbidity. The results showed that RFR achieved the best performance, with R² values of 0.75, 0.82, and 0.79 for chl-a, turbidity, and phycocyanin, while multiple regression had R² values of 0.29, 0.51, and 0.46 and SVR had R² values of 0.58, 0.62, and 0.61 on the testing datasets, respectively. This paper examines the potential of the developed open-source satellite remote sensing tool for monitoring reservoirs in Oklahoma to assess spatial and temporal variations in surface water quality.     

SPATIAL DISTRIBUTION OF POINT SOIL MOISTURE ESTIMATES USING LANDSAT TM DATA AND FUZZY-C CLASSIFICATION1

ABSTRACT: Many hydrologic models have input data requirements that are difficult to satisfy for all but a few well-instrumented, experimental watersheds. In this study, point soil moisture in a mountain watershed with various types of vegetative cover was modeled using a generalized regression model. Information on sur-ficial characteristics of the watershed was obtained by applying fuzzy set theory to a database consisting of only satellite and a digital elevation model (DEM). The fuzzy-c algorithm separated the watershed into distinguishable classes and provided regression coefficients for each ground pixel. The regression model used the coefficients to estimate distributed soil moisture over the entire watershed. A soil moisture accounting model was used to resolve temporal differences between measurements at prototypical measurement sites and validation sites. The results were reasonably accurate for all classes in the watershed. The spatial distribution of soil moisture estimates corresponded accurately with soil moisture measurements at validation sites on the watershed. It was concluded that use of the regression model to distribute soil moisture from a specified number of points can be combined with satellite and DEM information to provide a reasonable estimation of the spatial distribution of soil moisture for a watershed.     

Impact of Fencing on the Recovery of the Ground Flora on Heavily Eroded Slopes of a Deciduous Forest

This paper seeks to outline early stages in the recovery of forest ground flora on eroded slopes impacted by recreation activities and to suggest how these data might be applied in the formulation of management policies for forest recreation areas. Based on a fencing experiment in the Sonian Forest near Brussels, we investigated whether, over a 6-year period, the vegetation was able to recover after having been destroyed by recreation use. Short-term trends in overall species composition were already observable during this 6-year study. Species recovery on eroded hills was related to slope, aspect, and soil type. During the considered time scale, the proportion of hemicryptophytes and the number of ancient forest species increased significantly. A downward trend was detected for Ellenbergs nitrogen and temperature indexes and for the proportion of therophytes and pioneer plants of disturbed places. Changes in species frequencies suggest six recovery strategies: early, late, expanding, disappearing, transient, and fluctuating species. Aside from seedling reproduction from overstory influences, Luzula sylvatica appeared to be the most resilient of the species identified in the study since this species has the highest global frequency in our sampling plots and has increased its cover during the study period. Study results indicate that (1) protection from recreation has initiated the recovery of species in the herb layer, but (2) it may take a long time before vegetation previously present in the ground flora may recover in both density and species composition.     

Topographic Effects on Soil Organic Carbon in Louisiana Watersheds

Terrestrial carbon storage is influenced by a number of environmental factors, among which topographic and geomorphological features are of special significance. This study was designed to examine the relationships of soil organic carbon (SOC) density to various terrain parameters and watershed characteristics across Louisiana, USA. A polygon data set of 484 watersheds and 12 river drainage basins for Louisiana was used to form the landscape units. SOC densities were calculated for each soil map unit using the State Soil Geographic (STATSGO) database. Average drainage densities and average slopes at watershed and basin scales were quantified with the 1:24 K Digital Elevation Models (DEM) data, and the Louisiana hydrographic water features. Correlation and regression analyses were performed to determine relationships among drainage density, slope, elevation, and SOC. The study found an average watershed drainage density of 1.6 km/km² and an average watershed slope of 2.9 degrees in Louisiana. The results revealed that SOC density at both watershed and basin scales was closely related to drainage density, slope, and elevation. SOC density was positively correlated with watershed drainage density, but negatively correlated with watershed slope gradient and elevation. Regression models were developed for predicting SOC density at watershed and basin scales, obtaining regression coefficients (r ²) ranging from 0.43 to 0.83. The study showed that estimation of SOC at watershed and drainage basin scales combining DEM data can be a feasible approach to improve the understanding of the relationships among SOC, topographic, and geomorphological features.     

US army land condition-trend analysis (LCTA) program

The US Army Land Condition-Trend Analysis (LCTA) program is a standardized method of data collection, analysis, and reporting designed to meet multiple goals and objectives. The method utilizes vascular plant inventories, permanent field plot data, and wildlife inventories. Vascular plant inventories are used for environmental documentation, training of personnel, species identification during LCTA implementation, and as a survey for state and federal endangered or threatened species. The permanent field plot data documents the vegetational, edaphic, topographic, and disturbance characteristics of the installation. Inventory plots are allocated in a stratified random fashion across the installation utilizing a geographic information system that integrates satellite imagery and soil survey information. Ground cover, canopy cover, woody plant density, slope length, slope gradient, soil information, and disturbance data are collected at each plot. Plot data are used to: (1) describe plant communities, (2) characterize wildlife and threatened and endangered species habitat, (3) document amount and kind of military and nonmilitary disturbance, (4) determine the impact of military training on vegetation and soil resources, (5) estimate soil erosion potential, (6) classify land as to the kind and amount of use it can support, (7) determine allowable use estimates for tracked vehicle training, (8) document concealment resources, (9) identify lands that require restoration and evaluate the effectiveness of restorative techniques, and (10) evaluate potential acquisition property. Wildlife inventories survey small and midsize mammals, birds, bats, amphibians, and reptiles. Data from these surveys can be used for environmental documentation, to identify state and federal endangered and threatened species, and to evaluate the impact of military activities on wildlife populations. Short- and long-term monitoring of permanent field plots is used to evaluate and adjust land management decisions.     

Forest and Farmland Conservation Effects of Oregon’s (USA) Land-Use Planning Program

Oregons land-use planning program is often cited as an exemplary approach to forest and farmland conservation, but analyses of its effectiveness are limited. This article examines Oregons land-use planning program using detailed spatial data describing building densities in western Oregon. An empirical model describes changes in building densities on forest and agricultural lands from 1974 to 1994, as a function of a gravity index of lands commuting distance to cities of various sizes, topographic characteristics, and zoning adopted under Oregons land-use planning program. The effectiveness of Oregons land-use planning program is evaluated based on the statistical significance of zoning variables and by computing estimated areas of forest and agricultural lands falling into undeveloped, low-density developed, and developed building density categories, with and without land-use zoning in effect. Results suggest that Oregons land-use planning program has provided a measurable degree of protection to forest and agricultural lands since its implementation.     

Feature Extraction Techniques for Measuring Piñon and Juniper Tree Cover and Density,and Comparison with Field-Based Management Surveys

Western North America is experiencing a dramatic expansion of piñon (Pinus spp.) and juniper (Juniperus spp.) (P-J) trees into shrub-steppe communities. Feature extracted data acquired from remotely sensed imagery can help managers rapidly and accurately assess this land cover change in order to manage rangeland ecosystems at a landscape-scale. The objectives of this study were to: (1) develop an effective and efficient method for accurately quantifying P-J tree canopy cover and density directly from high resolution photographs and (2) compare feature-extracted data to typical in-situ datasets used by land managers. Tree cover was extracted from aerial-photography using Feature Analyst^®. Tree density was calculated as the sum of the total number of individual polygons (trees) within the tree cover output file after isolation using a negative buffer post-processing technique. Feature-extracted data were compared to ground reference measurements from Utah’s Division of Wildlife Resources Range Trend Project (DWR-RTP). We found that the proposed feature-extraction techniques used for measuring cover and density were highly correlated to ground reference and DWR-RTP datasets. Feature-extracted measurements of cover generally showed a near 1:1 relationship to these data, while tree density was underestimated; however, after calibration for juvenile trees, a near 1:1 relationship was realized. Feature-extraction techniques used in this study provide an efficient method for assessing important rangeland indicators, including: density, cover, and extent of P-J tree encroachment. Correlations found between field and feature-extracted data provide evidence to support extrapolation between the two approaches when assessing woodland encroachment.