Applying parcel-specific land-use data for improved monitoring of semi-natural grassland in Denmark

A major obstacle to the conservation and management of semi-natural grassland (SNG) is the general lack of consistent and precise maps showing the extent and quality of this habitat type. SNG is related to, and influenced by, agricultural land use. Both intensive land use as well as a lack of extensive land use can reduce the extent and quality of SNG. For Denmark, this paper demonstrates how parcel-specific land-use data can be applied to detect localities with spatial convergences and conflicts between SNG and agricultural land use. Based on their respective influence on SNG, land use is aggregated into five major classes and spatially overlaid with existing nationally registered SNG. Results show that almost 11 % of all SNG is mapped either on cropped land or on rotational grassland, indicating a conflict between mapped SNG and land use. Thirty percent of SNG is mapped outside any agricultural land use, pointing to a lack of management. Fifty nine percent of SNG is mapped either on land receiving payments for grazing/mowing or on other land under extensive management, indicating a convergence between SNG and land use. Finally, 30 % of land receiving payments for grazing/mowing and 62 % of other land under extensive management does not contain any semi-natural habitat. Potential, as yet unmapped, SNG might exist in these localities. Based on these results it is argued that the application of parcel-specific land-use data could significantly improve monitoring of SNG.     

Analyzing landscape changes in the Bafa Lake Nature Park of Turkey using remote sensing and landscape structure metrics

Bafa Lake Nature Park is one of Turkey’s most important legally protected areas. This study aimed at analyzing spatial change in the park environment by using object-based classification technique and landscape structure metrics. SPOT 2X (1994) and ASTER (2005) images are the primary research materials. Results show that artificial surfaces, low maqui, garrigue, and moderately high maqui covers have increased and coniferous forests, arable lands, permanent crop, and high maqui covers have decreased; coniferous forest, high maqui, grassland, and saline areas are in a disappearance stage of the land transformation; and the landscape pattern is more fragmented outside the park boundaries. The management actions should support ongoing vegetation regeneration, mitigate transformation of vegetation structure to less dense and discontinuous cover, control the dynamics at the agricultural–natural landscape interface, and concentrate on relatively low but steady increase of artificial surfaces.     

Yield and leaf area index estimations for sunflower plants using unmanned aerial vehicle images

Vegetation is commonly monitored to improve efficiency of various agricultural practices. Spatial and temporal changes in plant growth and development can be monitored with the aid of remote sensing techniques employing ground, aerial, and satellite platforms. Unmanned aerial vehicles (UAV) and multi-spectral cameras developed for UAVs have an important potential for agricultural management activities with high-resolution spatial and temporal images. However, UAV images should be assessed based on ground measurements for using these images as a decision-support tool in agriculture. This study was conducted to estimate sunflower leaf area index (LAI) and yield with the aid of Normalized Difference Vegetation Index (NDVI) images generated from raw UAV images. Furthermore, UAV-based NDVI values were compared with NDVI values calculated by using hyper-spectral measurements carried out with a ground-based spectroradiometer. Between July and August of 2017, six flight missions were conducted and spectral measurements were made simultaneously. A significant correlation (R²?=?0.77) was determined between NDVI values that belong to UAV platform and spectroradiometer. Also, regression models developed for sunflower LAI and yield estimation depending UAV-based NDVI have R² values of 0.88 and 0.91, respectively.     

The effects of biotic and abiotic factors on the spatial heterogeneity of alpine grassland vegetation at a small scale on the Qinghai–Tibet Plateau (QTP), China

Understanding the complex effects of biotic and abiotic factors on the composition of vegetation is very important for developing and implementing strategies for promoting sustainable grassland development. The vegetation–disturbance–environment relationship was examined in degraded alpine grasslands in the headwater areas of three rivers on the Qinghai–Tibet Plateau in this study. The investigated hypotheses were that (1) the heterogeneity of the vegetation of the alpine grassland is due to a combination of biotic and abiotic factors and that (2) at a small scale, biotic factors are more important for the distribution of alpine vegetation. On this basis, four transects were set along altitudinal gradients from 3,770 to 3,890 m on a sunny slope, and four parallel transects were set along altitudinal gradients on a shady slope in alpine grasslands in Guoluo Prefecture of Qinghai Province, China. It was found that biological disturbances were the major forces driving the spatial heterogeneity of the alpine grassland vegetation and abiotic factors were of secondary importance. Heavy grazing and intensive rat activity resulted in increases in unpalatable and poisonous weeds and decreased fine forages in the form of sedges, forbs, and grasses in the vegetation composition. Habitat degradation associated with biological disturbances significantly affected the spatial variation of the alpine grassland vegetation, i.e., more pioneer plants of poisonous or unpalatable weed species, such as Ligularia virgaurea and Euphorbia fischeriana, were found in bare patches. Environmental/abiotic factors were less important than biological disturbances in affecting the spatial distribution of the alpine grassland vegetation at a small scale. It was concluded that rat control and light grazing should be applied first in implementing restoration strategies. The primary vegetation in lightly grazed and less rat-damaged sites should be regarded as a reference for devising vegetation restoration measures in alpine pastoral regions.     

Cover estimation versus density counting in species-rich pasture under different grazing intensities

Two methods for monitoring of grassland vegetation were compared: visual estimation of plant cover (C) and plant densities counting (D). C and D were performed in monthly intervals for three vegetation growing seasons after imposing different grazing regimes on abandoned grassland in 1998. Species scores obtained from paired redundancy analyses (RDA) of C and D data were compared and Spearman's rank correlations were used to show if the two methods give comparable results. Results of C and D were highly correlated in the first two growing seasons only. In the third season, correlation was substantially lower as the sward structure was more heterogeneous due to creation of differently defoliated patches especially under extensive grazing. Presence of the same plant species with different habit in frequently and in infrequently grazed patches, reduced significance of Spearman's rank correlations. Cover estimation can fully substitute plant density counting in grassland with lower proportion of frequently and infrequently grazed patches only, but caution should be used when comparing different management regimes in long term analyses.     

A new multiscale approach for monitoring vegetation using remote sensing-based indicators in laboratory, field, and landscape

Remote sensing is an important tool for studying patterns in surface processes on different spatiotemporal scales. However, differences in the spatiospectral and temporal resolution of remote sensing data as well as sensor-specific surveying characteristics very often hinder comparative analyses and effective up- and downscaling analyses. This paper presents a new methodical framework for combining hyperspectral remote sensing data on different spatial and temporal scales. We demonstrate the potential of using the “One Sensor at Different Scales” (OSADIS) approach for the laboratory (plot), field (local), and landscape (regional) scales. By implementing the OSADIS approach, we are able (1) to develop suitable stress-controlled vegetation indices for selected variables such as the Leaf Area Index (LAI), chlorophyll, photosynthesis, water content, nutrient content, etc. over a whole vegetation period. Focused laboratory monitoring can help to document additive and counteractive factors and processes of the vegetation and to correctly interpret their spectral response; (2) to transfer the models obtained to the landscape level; (3) to record imaging hyperspectral information on different spatial scales, achieving a true comparison of the structure and process results; (4) to minimize existing errors from geometrical, spectral, and temporal effects due to sensor- and time-specific differences; and (5) to carry out a realistic top- and downscaling by determining scale-dependent correction factors and transfer functions. The first results of OSADIS experiments are provided by controlled whole vegetation experiments on barley under water stress on the plot scale to model LAI using the vegetation indices Normalized Difference Vegetation Index (NDVI) and green NDVI (GNDVI). The regression model ascertained from imaging hyperspectral AISA-EAGLE/HAWK (DUAL) data was used to model LAI. This was done by using the vegetation index GNDVI with an R ² of 0.83, which was transferred to airborne hyperspectral data on the local and regional scales. For this purpose, hyperspectral imagery was collected at three altitudes over a land cover gradient of 25 km within a timeframe of a few minutes, yielding a spatial resolution from 1 to 3 m. For all recorded spatial scales, both the LAI and the NDVI were determined. The spatial properties of LAI and NDVI of all recorded hyperspectral images were compared using semivariance metrics derived from the variogram. The first results show spatial differences in the heterogeneity of LAI and NDVI from 1 to 3 m with the recorded hyperspectral data. That means that differently recorded data on different scales might not sufficiently maintain the spatial properties of high spatial resolution hyperspectral images.     

Agricultural land-use change and ash (<Emphasis Type="Italic">Fraxinus excelsior</Emphasis> L.) colonization in Pyrenean landscapes: an interdisciplinary case study

Changes in agricultural land use are responsible for significant modifications in mountain landscapes. This study is part of an interdisciplinary research on the processes and consequences of spontaneous afforestation of Pyrenean landscapes by ash, and the possibilities for its management. We address the relationships between vegetation dynamics and land-use change from the combination of an agricultural study of change in farm management and an ecological study of grassland colonization by ash. In the framework of a village case study, we characterized parcels management and land-use histories, and analyzed the dynamics of the composition of grassland vegetation communities. From a joint analysis of the results obtained in each discipline, we discuss the limitations and complementarities of the two approaches for the interdisciplinary assessment of the afforestation process.     

Modeling and Measurement of Structural Changes at a Landscape Scale in Dryland Areas

We present a technique to quantify and model the intensity of structural changes produced by management of dry grazing lands at a landscape scale. The technique is illustrated with the analysis of digitized black–white (b/w) imagery and an application to the study of changes induced by grazing gradients. Structural changes in patchy vegetation canopies were studied in the Patagonian Monte (Chubut, Argentina) at two resolution scales by means of linear transects in the field (50 m) and others drawn on aerial b/w photographs (2–5 km) of grazed paddocks. Spatial series of plant cover values along transects in the field and on photographs were analyzed with standard techniques of spectral analyses, including auto-correlation spectra and Fourier transforms. In order to test the internal consistency of the techniques used, synthetic plant canopies with patches of varying cover and size were generated by means of a stochastic model of plant growth under different stocking rates or after varying periods of recovery. The behavior of the simulation model is consistent with the observed dynamics of plant canopies in semiarid environments. There is a consistent relation between the number and geometric properties of plant patches (patch number, patch size, patch connectivity) and the signal/noise ratios of the Fourier decomposition describing plant density data. Signal/noise ratios corresponding to plant cover data in paddocks with different grazing treatment are consistent with the assumptions derived from modeled canopies, as well as those estimated from optical density of b/w aerial photographs of paddocks. We tested the hypotheses that patch arrangements as quantified by the signal/noise ratios vary in accordance with grazing gradients in paddocks with a permanent corner-located watering point. The use of digitized b/w images allows inspecting permanent changes over time periods when other types of images were not yet available.     

An Ecosystem Approach to Combat Desertification on the Colorado Plateau

Desertification of shrub and grassland into pinyon-juniper woodland is occurring over much of the Colorado Plateau in the southwestern United States. As trees invade, they out-compete shrubs and grasses, increasing erosion rates and reducing infiltration of moisture into the soil. This has caused habitat problems for wildlife, and reduced forage for livestock. These impacts also affect the human communities that rely on ranching and tourism related to hunting. Past land use and management practices including heavy livestock grazing, fire suppression and introduction of exotic annual plants are believed to have led to current conditions. The Montrose office of the Bureau of Land Management has implemented an ecosystem-based program to reverse the desertification process on public land. The program is centered on detailed landscape objectives describing the desired vegetation mosaic on 360 000 ha of public land. The objectives outline proportions of plant seral stages and arrays of patch sizes for each planning unit. These objectives are based on priority management issues and the need to replicate a natural vegetation mosaic. Where the existing mosaic does not meet objectives, mechanical vegetation treatments and prescribed fire are used to create early and mid-seral patches on the ground. This restored vegetation pattern and type should be sustained over time through a natural fire regime and improved livestock management. Because many uncertainties exist, an adaptive management process is being used that allows mosaic objectives to be changed or processes modified where monitoring or scientific research indicate a need.     

基于Sentinel-2卫星数据的中山市植被叶面积指数反演及空间分析

以广东省中山市为例,基于SNAP软件和2景Sentinel-2卫星数据,反演并分析中山市全域及特定区域内植被叶面积指数。结果表明,反演结果与中山市生态空间格局和植被保护状况相一致,且与其他研究报道中对水库库区林地采用现场实测方法获得的数据高度一致,表明该方法反演城镇植被叶面积指数具有良好的适用性和准确性。     

A comparison of nutrient losses from two simulated pastureland management scenarios

Fecal deposits by grazing animals on pasturelands have the potential to leach nutrients to runoff during rainfall events. Unlike croplands, grazing systems such as pasturelands or rangelands have little opportunity to ameliorate nutrient runoff through in-field or edge-of-field management practices. Thus, we investigated the amounts and concentrations of nutrients in overland flow from simulated grazing lands. Two grazing management scenarios were simulated: continuous grazing represented by two sparsely vegetated (SV) plots and rotational grazing represented by two densely vegetated (DV) plots. In addition, there were two control plots. The plots were treated with standard cowpats and rainfall was simulated until overland flow at the edge of the plots reached steady-state. Higher runoff was observed from DV plots (9.97 mm) than SV plots (7.05 mm), but the average total suspended solids concentration in runoff from SV plots was approximately 17 times the concentration observed in runoff from the DV plots. The average total phosphorus (TP) concentrations were highest in plots simulating continuous grazing (5.91 mg L(-1)). In both DV and SV plots at least 83% of the TP was found to be in the dissolved form. The average total Kjeldhal nitrogen (TKN) and total nitrogen concentrations observed in runoff samples from SV plots were 1.25 and 1.46 mg L(-1), respectively. Organic nitrogen comprised 95% of the TKN observed in runoff samples from SV plots. The SV plots have relatively higher loads for those nutrients in the particle associated form compared to DV plots, whereas DV plots had higher loads for those nutrients in the dissolved form. Grazing lands without any additional manure applications were found to release nutrients in high levels and vegetation did not show any effect on removing dissolved nutrients from runoff. These results are useful to inform selection of appropriate management practices to reduce nutrient transport to surface waters in watersheds dominated by grazed lands.     

Vegetation recovery patterns assessment at landslides caused by catastrophic earthquake: A case study in central Taiwan

The catastrophic earthquake, 7.3 on the Richter scale, occurred on September 21, 1999 in Central Taiwan. Much of standing vegetation on slopes was eliminated and massive, scattered landslides were induced at the Jou-Jou Mountain area of the Wu-Chi basin in Nantou County. We evaluated three methods for assessing landslide hazard and vegetation recovery conditions. (1) Self-organizing map (SOM) neural network coupled with fuzzy technique was used to quickly extract the landslide. (2) The NDVI-based vegetation recovery index derived from multi-temporal SPOT satellite images was used to evaluate vegetation recovery rate in the denudation sites. (3) The spatial distribution index (SDI) based on land-cover topographic location was employed to analyze vegetation recovery patterns, including the invading, surviving and mixed patterns at the Jou-Jou Mountain area. On September 27, 1999, there were 849.20 ha of landslide area extracted using the self-organizing map and fuzzy technique combined model. After six years of natural vegetation succession, the landslide has gradually restored, and vegetation recovery rate reached up to 86%. On-site observation shows that many native pioneer plants have invaded onto the denudation sites even if disturbed by several typhoons. Two native surviving plants, Arundo formosana Hack and Pinus taiwanensis Hayata, play a vital role in natural vegetation succession in this area, especially for the sites on ridgeline and steep slopes.     

Development of a Grassland Integrity Index Based on Breeding Bird Assemblages

We utilized landscape and breeding bird assemblage data from three Breeding Bird Survey (BBS) routes sampled from 1965–1995 to develop and test a grassland integrity index (GII) in a mixed-grass prairie area of Oklahoma. The overall study region is extensively fragmented from long-term agricultural activity, and native habitat remnants have been degraded by recent encroachment of woody vegetation, namely eastern redcedar (Juniperus virginiana L.). The 50 individual bird survey points along the BBS routes, known as stops, were used as sample sites. Our process first focused on developing a grassland disturbance index (GDI) as a measure of cumulative landscape disturbances for these sites. The GDI was based on five key landscape variables identified in an earlier species-level study of long-term avian community dynamics: total tree, shrub, and herbaceous vegetation cover indices, overall mean landscape patch size, and grassland patch core size. The GII was then developed based on breeding bird assemblage data. Assemblages were based on commonly used response guilds reflective of five avian life history parameters: foraging mode/location, nesting location, habitat specificity, migratory pattern, and dietary guild. We tested the response of 78 candidate assemblage metrics to the GDI, and eliminated those with no or poor response or with high correlations (redundant), resulting in 13 metrics for use in the final index. Individual metric scores were scaled to fall between 0 and 10, and the cumulative index to range from 0 to 100. Although broader application and refinement are possible, the avian-based GII has an advantage over labor-intensive, habitat-based monitoring in that the GII is derived from readily available long-term BBS data. Therefore, the GII shows promise as an inexpensive tool that could easily be applied over other areas to monitor changes in regional grassland conditions.     

A new approach for crop identification with wavelet variance and JM distance

This paper develops a new crop mapping method through combined utilization of both time and frequency information based on wavelet variance and Jeffries–Matusita (JM) distance (CIWJ for short). A two-dimensional wavelet spectrum was obtained from datasets of daily continuous vegetation indices through a continuous wavelet transform using the Mexican hat and the Morlet mother wavelets. The time-average wavelet variance (TAWV) and the scale-average wavelet variance (SAWV) were then calculated based on the wavelet spectrum of the Mexican hat and the Morlet wavelet, respectively. The class separability based on the JM distance was evaluated to discriminate the proper period or scale range applied. Finally, a procedure for criteria quantification was developed using the TAWV and SAWV as the major metrics, and the similarity between unclassified pixels and established land use/cover types was calculated. The proposed CIWJ method was applied to the middle Hexi Corridor in northwest China using 250-m 8-day composite moderate-resolution imaging spectroradiometer (MODIS) enhanced vegetation index (EVI) time series datasets in 2012. The CIWJ method was shown to be efficient in crop field mapping, with an overall accuracy of 83.6 % and kappa coefficient of 0.7009, assessed with 30 m Chinese Environmental Disaster Reduction Satellite (HJ-1)-derived data. Compared with methods utilizing information on either frequency or time, the CIWJ method demonstrates tremendous potential for efficient crop mapping and for further applications. This method could be applied to either coarse or high spatial resolution images for agricultural crop identification, as well as other more general or specific land use classifications.     

Vegetation NDVI Linked to Temperature and Precipitation in the Upper Catchments of Yellow River

Vegetation in the upper catchment of Yellow River is critical for the ecological stability of the whole watershed. The dominant vegetation cover types in this region are grassland and forest, which can strongly influence the eco-environmental status of the whole watershed. The normalized difference vegetation index (NDVI) for grassland and forest has been calculated and its daily correlation models were deduced by Moderate Resolution Imaging Spectroradiometer products on 12 dates in 2000, 2003, and 2006. The responses of the NDVI values with the inter-annual grassland and forest to three climatic indices (i.e., yearly precipitation and highest and lowest temperature) were analyzed showing that, except for the lowest temperature, the yearly precipitation and highest temperature had close correlations with the NDVI values of the two vegetation communities. The value of correlation coefficients ranged from 0.815 to 0.951 (p?<?0.01). Furthermore, the interactions of NDVI values of vegetation with the climatic indicators at monthly interval were analyzed. The NDVI of vegetation and three climatic indices had strong positive correlations (larger than 0.733, p?<?0.01). The monthly correlations also provided the threshold values for the three climatic indictors, to be used for simulating vegetation growth grassland under different climate features, which is essential for the assessment of the vegetation growth and for regional environmental management.     

A Model for Simulating the Soil Organic Carbon Pool of Steppe Ecosystems

In this research, the improved Terrestrial Ecosystem Regional (TECO-R) model was adapted to steppe ecosystems and then utilized to simulate the soil organic carbon pool in the period from 1989 to 2011 (excluding 1994, 2002, 2009, and 2010) for a typical steppe in Xilingol League of Inner Mongolia in China. The improved TECO-R model is an ecological model in combination of remote sensing data, which allows the spatial scale for the analysis of soil organic carbon which is not limited to vegetation or soil type. The spatial and temporal resolution advantages of remote sensing image can be well utilized in this model. The results indicate that in addition to an accurate simulation of the soil carbon pool of a steppe ecosystem, the vegetation aboveground carbon pool, grazing intensity of herbivores, mowing coefficient, litter carbon pool, root carbon pools of different vegetation layers, root-shoot ratio, actual residence time of different carbon pools, and allocation coefficients of different carbon pools in corresponding years are also available from the TECO-R model. Some of the above data are difficult to obtain through macro-observation but can be simulated with the TECO-R model by combining the model with input data; this is very important for a correct understanding of the feedback relationships between the steppe ecosystem’s carbon cycle and climate change (e.g., global warming) and human activities such as grazing.     

Using remotely sensed imagery to monitor savanna rangeland deterioration through woody plant proliferation: a case study from communal and biodiversity conservation rangeland sites in Mokopane, South Africa

Healthy rangelands are of economic and biodiversity conservation importance in the savannas of northern South Africa. The proliferation of woody plant species on the rangelands, known as bush encroachment, constitutes a degradation of rangeland quality, given the non-palatability of the encroaching species. Though the causes are not fully understood, heavy grazing and fire suppression are thought to be primary causes of bush encroachment. This study utilised multitemporal (1994, 2000 and 2008) SPOT images of two rangeland sites in Mokopane, South Africa in monitoring and assessing bush encroachment. The study sites were a fenced biodiversity conservation rangeland with game species in which fire is suppressed and an open access communal rangeland grazed by livestock. Field plot-derived encroachment categories of heavy encroachment, moderate encroachment, low encroachment and non-encroached were used in hybrid classification of the images, following radiometric normalisation and geometric registration. GIS overlay analysis using the non-encroached category enabled the quantification and mapping of change in the preferable open grass rangeland typifying savannas. The biodiversity conservation area was undergoing a trend of reduction in open grass rangeland, whereas the communal rangelands were getting more opened up by livestock trampling. Rangeland management practices of fire utilisation, stocking levels and stock concentration account for the differing trends. Lightly grazed and heavily grazed wild game-utilised rangelands under a fire suppression rangeland management regime had bush encroachment rates of approximately 34% and 56%, respectively, in 6 years. Multitemporal remote sensing proved to be useful for monitoring bush encroachment as indicator of state of savanna rangelands.     

Using Landsat image time series to study a small water body in Northern Spain

Ramsar Convention and EU Water Framework Directive are two international agreements focused on the conservation and achievement of good ecological and chemical status of wetlands. Wetlands are important ecosystems holding many plant and animal communities. Their environmental status can be characterised by the quality of their water bodies. Water quality can be assessed from biophysical parameters (such as Chlorophyll-a concentration ([Chla]), water surface temperature and transparency) in the deeper or lacustrine zone, or from bioindicators (as submerged aquatic vegetation) in the shallow or palustrine zone. This paper proves the use of Landsat time series to measure the evolution of water quality parameters and the environmental dynamics of a small water body (6.57 ha) in a Ramsar wetland (Arreo Lake in the North of Spain). Our results show that Landsat TM images can be used to describe periodic behaviours such as the water surface temperature or the phenologic state of the submerged vegetation (through normalized difference vegetation index, NDVI) and thus detect anomalous events. We also show how [Chla] and transparency can be measured in the lacustrine zone using Landsat TM images and an algorithm adjusted for mesotrophic Spanish lakes, and the resulting values vary in time in accordance with field measurements (although these were not synchronous with the images). The availability of this algorithm also highlights anomalies in the field data series that are found to be related with the concentration of suspended matter. All this potential of Landsat imagery to monitor small water bodies in wetlands can be used for hindcasting of past evolution of these wetlands (dating back to 1970s) and will be also useful in the future thanks to the Landsat continuity mission and the Operational Land Imager.     

Retrospective assessment of dryland soil stability in relation to grazing and climate change

Accelerated soil erosion is an aspect of dryland degradation that is affected by repeated intense drought events and land management activities such as commercial livestock grazing. A soil stability index (SSI) that detects the erosion status and susceptibility of a landscape at the pixel level, i.e., stable, erosional, or depositional pixels, was derived from the spectral properties of an archived time series (from 1972 to 1997) of Landsat satellite data of a commercial ranch in northeastern Utah. The SSI was retrospectively validated with contemporary field measures of soil organic matter and erosion status that was surveyed by US federal land management agencies. Catastrophe theory provided the conceptual framework for retrospective assessment of the impact of commercial grazing and soil water availability on the SSI. The overall SSI trend was from an eroding landscape in the early drier 1970s towards stable conditions in the wetter mid-1980s and late 1990s. The landscape catastrophically shifted towards an extreme eroding state that was coincident with the “The Great North American Drought of 1988”. Periods of landscape stability and trajectories toward stability were coincident with extremely wet El Niño events. Commercial grazing had less correlation with soil stability than drought conditions. However, the landscape became more susceptible to erosion events under multiple droughts and grazing. Land managers now have nearly a year warning of El Niño and La Niña events and can adjust their management decisions according to predicted landscape erosion conditions.