science of the total environment - macaulay lab · 2017-07-31 · implications of changing spatial...

Science of the Total Environment 605–606 (2017) 445–453

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Implications of changing spatial dynamics of irrigated pasture,California's third largest agricultural water use

Matthew Shapero a,⁎, Iryna Dronova b, Luke Macaulay aa Department of Environmental Science, Planning, and Policy, University of California at Berkeley, Berkeley, CA 94720, United Statesb Department of Landscape Architecture and Environmental Planning, University of California at Berkeley, Berkeley, CA 94720, United States

H I G H L I G H T S G R A P H I C A L A B S T R A C T

• Irrigated pasture (IP) is being convertedto other land uses across California.

• The accuracy and precision of currentland cover metrics that classify IP ispoor.

• A new methodology is developed thatimproves the process of classifying IP.

• High-resolution imagery and object-based image analysis improves classifi-cation.

• Continued loss of IP will likely havebroad social and environmental conse-quences.

E-mail address: [email protected] (M. S

http://dx.doi.org/10.1016/j.scitotenv.2017.06.0650048-9697/© 2016 Elsevier B.V. All rights reserved.

a b s t r a c t
a r t i c l e i n f o
Article history:Received 25 April 2017Received in revised form 8 June 2017Accepted 8 June 2017Available online xxxx

Editor: D. Barcelo

Irrigated agriculture is practiced on680million acresworldwide. Irrigatedgrazing land is likely a significant portionofthat area but estimating an accuratefigure has remained problematic. Due to its significant contribution to agricultur-al water useworldwide, we develop amethodology to remotely sense irrigated pasture using a California case study.Irrigated pasture is the third largest agriculturalwater use in California, yet its economic returns are low. As pressuresmount for the agricultural sector to be more water efficient and for water to be directed towards its most economi-cally valuable uses, there will likely be a reduction in irrigated pasture acreage. A first step in understanding the im-portance of irrigated pasture in California is establishing a methodology to quantify baseline information about itsarea, location, and current rate of loss. This study used a novel object-based image analysis and supervised classifica-tion on publicly-available, high resolution, remote sensing National Agriculture Imaging Program (NAIP) imagery todevelop a highly accurate map of irrigated pasture in a rural county in California's Sierra foothills. Irrigated pasturewas found to have decreased by 19% during the ten-year period, 2005–2014, from 4,273 to 3,470 acres. The implica-tions of this loss include potential impacts to wetland-dependent species, groundwater recharge, game species, tra-ditional ranching culture, livestock production, and land conservation. Overall accuracy in classification across yearswas consistently over 89%. Comparing these results against available measurements of irrigated pasture providedby state and federal agencies reveals that this method significantly improves upon existing metrics and methods ofdata collection andpoints to critical needs for new targeted research andmonitoring efforts. Broadly, the analysis pre-sented here provides an improvedmethodology formapping irrigated pasture that can be extended to provide accu-rate and spatially-explicit data for other counties in California and other arid and semi-arid regions worldwide.

© 2016 Elsevier B.V. All rights reserved.

Keywords:Remote sensingObject-based image analysis (OBIA)Land-use/land-cover (LULC)NLCDRanchingSocial-ecological services

A 94720, United States.

⁎ Corresponding author at: University of California, 130 Mulford Hall #3114, Berkeley, C

hapero).

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446 M. Shapero et al. / Science of the Total Environment 605–606 (2017) 445–453

1. Introduction

Irrigated agriculture is practiced on 680 million acres worldwide.While this accounts for only 20% of cultivated land, these areas provide40% of the total food produced globally (FAO, 2016). Although studieshave attempted to model global consumptive water use of irrigatedgrazing land (Rost et al., 2008; Postel, 1998), estimating an accurate fig-ure has remained problematic, in part because of the large uncertaintiesin the global data sets distinguishing croplands and pasturelands inmo-saic landscapes (Hannerz and Lotsch, 2008). Due to its likely significantcontribution to agricultural water use worldwide, we develop a meth-odology to remotely sense irrigated pasture using a California casestudy.

California's recent drought (2013–2017) has brought renewed at-tention to the state's complex and often convoluted system of surfacewater distribution (Grantham and Viers, 2014). In particular, the irriga-tion practices of the agricultural sector—which consumes nearly 80% ofthe state's annual, non-environmental surface water flow (Mount et al.,2014; DWR, 2010)—have been strongly criticized as inefficient(Famiglietti, 2014; AghaKouchak et al., 2015). As agriculture braces forcontinued cutbacks in surface-water allocation, rising water prices,and requirements to transition to water-efficient irrigation systems orcrops (Howitt et al., 2015), the long-term economic and land-use im-pacts of these changes remain uncertain.

Irrigated pasture—or, pastureland artificially irrigated duringCalifornia's dry summers—is used to graze livestock and is a significantsource of forage for the state's cattle industry: without access to irrigat-ed pasture or other expensive supplemental feeds in the dry season,livestock lose body condition due to the low nutritional content ofnon-irrigated vegetation available on California's rangelands. Irrigatedpasture, however, is one of the state's most water intensive and leastprofitable crops. The most recent figures from the California Depart-ment of Water Resources show that irrigated pasture ranked thirdamong crops statewide in amount of water applied (DWR, 2010), butthe California County Agricultural Commissioners' Report for 2014–2015 ranks its agricultural gross value only 52nd out of 70 commoditycrops (CDFA, 2016). If considered in purely economic terms, fallowingirrigated pasture or converting it to another crop represents the mostrational response to rising water costs and restrictions (Sunding et al.,1997). Yet irrigated pasture is a critical land resource (Huntsinger etal., 2017; Richmond et al., 2010; Earl, 1950); losing substantial acres ofit across the state would have broad agricultural, environmental, andland-use consequences that economic analyses to date have not fullyconsidered.

A crucial first step in understanding irrigated pasture and its poten-tial reductions in California is measuring its statewide spatial extent,distribution, and current rate of loss. In recent decades, remote sensinghas emerged as an indispensable tool in generating land-use/land-cover(LULC) maps (Burkhard et al., 2012; Verburg et al., 2009; Friedl et al.,2002). Analyzing land cover changes across time has allowed environ-mentalmanagers tomeasure ecosystem and natural resource dynamicswith greater precision (Guida-Johnson and Zuleta, 2013; Sohl et al.,2012; Foley, 2005). The power of analysis, however, has been limitedby trade-offs between the spatial and temporal resolution of the avail-able imagery (Ellis et al., 2006). Furthermore, the varied patterningand diverse settings of irrigated pasture across the California landscapeoffers real challenges in remote identification. As such, this analysisseeks to identify the best ways to address and reconcile thesechallenges.

Currently, a number of county, state, and federal agencies areattempting to measure the acreage of irrigated pasture in California.The California Department of Food and Agriculture (CDFA), (2015)keeps non-spatial records based upon producer self-reporting to countyAgricultural Commissioners' offices. The California Department ofWater Resources (DWR) provides crop maps for counties across thestate. Both the National Agricultural Statistics Service (NASS) and the

National Land Cover Database (NLCD) keep spatially-explicit recordsof agricultural land types statewide. NASS also conducts the Census ofAgriculture every five years, which gathers county-based estimatesthrough self-reporting (USDA National Agricultural Statistics Service,2014). All of these sources, however, have different but critical limita-tions. The CDFA and Census of Agriculture data are not spatially explicit,and they depend upon producer participation statewide as well as pro-ducers' accurate knowledge of the number of acres they irrigate. TheDWRdata are spatially-explicit but requires time-consuming, in-personfield work to draw crop maps. Finally, the NASS Cropland Data Layer(CDL) and NLCD spatial datasets have two limitations as they relate toirrigated pasture: 1) both classifications use imagery at course resolu-tion, which limits their accuracy and sensitivity, and 2) neither datasetincludes a classification category that would explicitly capture thekind of irrigated pasture that exists in California.

The analysis offered here improves the process of identifying irrigat-ed pasture when generating LULC data in California by using supervisedclassification on National Agriculture Imagery Program (NAIP) imageryfor multiple years. The NAIP imagery is of much finer resolution (1 m)than the data used in other classification analyses. As with other land-use studies that use hyperspatial NAIP imagery in heterogeneous land-scapes (Moskal et al., 2011, Halabisky, 2011), object-based image anal-ysis (OBIA), rather than a pixel-based approach, was employed todramatically improve classification accuracy. This study employedOBIA to measure irrigated pasture in one California County to refinethe method and to test its accuracy in a setting where its successesand uncertainties were easier to interpret. Nevada County, with its var-ied terrain, mix of high-density urban areas and low-density exurbansprawl, and range of vegetation cover types was selected as a test casebecause it offers a broad assortment of remote sensing challenges thatapply to irrigated pasture classification in California.

More broadly, however, this analysis establishes an improved andrepeatable methodology for mapping irrigated pasture that lays thegroundwork to generate reliable and accurate data at larger scales inCalifornia and ultimately internationally.

2. Materials and methods

2.1. Description of study area

Nevada County is a small, rural county located in northeastern Cali-fornia (Fig. 1). Its geographic area is 623,360 acres (974 mile2) and itstotal population was 98,877 in 2010 (U.S. Census Bureau, 2017). Thereis a steep elevational gradient as the county spans lower-elevation foot-hills in thewest to the crest of the Sierra Nevada in the east. Accordingly,it includes a diversity of vegetation types, ranging from Valley Grass-lands to Oak Woodlands to the Montane and Subalpine Vegetation ofthe Sierra Nevada Range (Barbour et al., 2007). The climate is Mediter-ranean, characterized by cool, wet winters and prolonged hot, dry sum-mers. Over 95% of annual precipitation falls between the months ofOctober andMay (CIMIS, 2017). Intensive production agriculture is lim-ited due to the county's poor foothill soils and terrain. As a result, the ag-riculture that has existed since the mid-nineteenth-century CaliforniaGold Rush has predominantly been timber and extensive livestock pro-duction in range- andwoodland settings. In 2015, its leading agricultur-al commodities were cattle–both fed heifers/steers and cull cows;rangeland and irrigated pasture leases; and truck-farm vegetables(CDFA, 2016).

Much of California's irrigation water is managed by quasi-govern-mental irrigation districts that collect, manage, and distribute waterfor agricultural and urban use (CA-LAO, 2002). Nevada Irrigation Dis-trict (NID) is the largest in the county, founded in 1921 to deliver un-treated surface water to the region's farmers and ranchers. Much ofthe complex system of earthen ditches and flumes that NID inheritedwas built in the nineteenth century to facilitate hydraulic gold mining.Early irrigation efforts within the district focused on gravity-propelled

Fig. 1.Map of Nevada County.

447M. Shapero et al. / Science of the Total Environment 605–606 (2017) 445–453

flood irrigation of alfalfa, hay, and pasture fields (Hyatt, 1931). NevadaCounty, however, experienced a profound demographic and economicshift in the second half of the twentieth century as retirees and tele-commuting urban professionals increasingly replaced resource-extrac-tion-based workers (Duane, 1999). The current landscape withinNID's 287,000-acre (448 mile2; 323 mile2 of which is within NevadaCounty) service area is a complex patchwork of small towns, low-densi-ty exurban residences, and hobby and production-oriented agriculture.Today, in addition to providing irrigation waters to ranching operationsthroughout the county, NID also provides untreated water to exurbanresidential parcels, housing associations, and golf courses (Huntsingeret al., 2017).

2.2. Data sources

This study utilized data from four sources (USDA/NRCSGeospatial Gateway, 2014; USDA NASS, 2014; Homer et al., 2015;Nevada County, California, 2015). NAIP data comes with four rasterbands representing the red, green, blue, and near-infrared electro-magnetic regions. It is offered either as a digital orthophoto quarterquad tile (DOQQs) or as compressed county mosaics (CCM). Becausethis analysis was conducted at the county scale, Nevada County'sCCM was the most appropriate data choice, and it came from USDApre-processed so that each tile in the mosaic was individually recti-fied into the UTM coordinate system, NAD 83. Subsequent filesdownloaded from other sources were then re-projected into NAD1983, UTM Zone 10.

The county NAIP file was clipped to the boundary of the local waterdistrict—the entity that provides raw, agricultural water forirrigation—in order to limit the extent of the study area to improve pro-cessing time during classification and to avoid analyzing US Forest Ser-vice land (503mile2, or, 52% of Nevada County). Although there may besome limited pasture irrigated from springs orwells in other parts of thecounty, clipping to the district boundary should not significantly affectthe results of the study, although—if anything—the estimates of irrigat-ed pasture presented here should be considered conservative or slightlylower than actual values.

2.3. Classification

Due to the composition of the landscape and the occurrence of mul-tiple green vegetation types (Fig. 2a), mapping pasture was performedusing a supervised image classification procedure, which uses trainingsamples of each class to statistically evaluate class membership for theunlabeled spatial units. The pilot analysis first considered pixel-basedMaximum Likelihood Classification (MLC) in both geospatialsoftware—ArcGIS (Esri Inc.), version 10.2 (Fig. 2b) and remote sensingsoftware—ENVI (Harris Geospatial Inc.), v. 5.2 (Fig. 2c), both of whichproduced a speckled, heterogeneous appearance to the mapped covertypes and visible inaccuracies in spatial extents and contiguity of the tar-get irrigated pasture units. For this reason we used an alternative object-based image analysis (OBIA), which first uses segmentation to delineateimage regions, or “objects,” as relatively homogeneous primitive spatialunits and then classifies them into desired land cover types based onthe user-selected approach (e.g., Blaschke, 2010). With high-resolutiondatasets such as NAIP, OBIA offers an improvement over pixel-based clas-sification approaches (Fig. 2d) because it alleviates local spectral noiseand, in addition to spectral information, also accounts for shape, form,and texture of the objects during their classification.

We implemented OBIA classification in eCognition v.8.8 (TrimbleInc.) software using the four bands of the original NAIP as the imagelayer inputs. The first step was running a multiresolution segmentationalgorithm thatmerged pixels one by one based on their spectral similar-ity to one another to form larger aggregates; the process terminatedwhen a user-defined threshold of homogeneity was exceeded. Parame-ters to determine “shape” and “compactness”were also used. These set-tings are adjusted iteratively during the segmentation process untilobjects in the image are satisfactorily grouped. These new, groupedpixels, or objects, carry not only the spectral and statistical informationof the pixels of which they consist, but also information on their texture,shape, and position (Rahman and Saha, 2008).

Next, office-based, visual interpretation of the 1-m resolution imag-ery was used to select individual objects as training samples to definethe ruleset for each of the classes. Because the emphasis was to classifypasture, we used an abbreviated list of five major classes: 1) irrigatedpasture; 2) dry, non-irrigated rangeland; 3) oak and coniferous trees,

Fig. 2. Comparison of three classification methods. (a) Original 1 m–resolution NAIP imagery of Nevada County. (b) Maximum Likelihood Classification in ArcGIS; notice confusionbetween shadows in the oaks and irrigated pasture. (c) Maximum Likelihood Classification in ENVI. (d) Classification in eCognition with object-based image analysis.


or forest; 4) water; and 5) impervious surfaces (buildings, roads, rock,gravel, etc.). Onehundred training sampleswere selected to buildmem-bership values for each class, for a total of 500 training objects for eachimage (2005, 2014). Finally, we ran eCognition's nearest-neighbor clas-sifier, which classified the remaining undefined image objects basedupon their degree of membership in feature space to the training sam-ple classes (Baatz et al., 2004).

2.4. Accuracy assessment

Determining the quality of a classification result, the process knownas accuracy assessment demonstrates howwell the classification is ableto analyze the image and reveals potential shortcomings in themethod-ology (Congalton, 1991). Accuracy assessment for this study was per-formed in ENVI, using the tool “Confusion Matrix Using Ground-Truthed ROIs.” Separately, in ArcGIS, an office-based assessment of 1m–resolution NAIP imagery created new point vector files selectedwith expert knowledge, which were to function as test samples. Eachclass (irrigated pasture, dry range, oak, water, impervious) had at leastone hundred ground-truthed point samples, for a total of 528 test sam-ples for 2005 image and 502 for 2014. These test samples were com-pared against the classification results from eCognition in a confusionmatrix to determine overall accuracy, accuracywithin class, user's accu-racy, and producer's accuracy (Congalton, 1991; Congalton and Green,2008).

2.5. Comparison of classification techniques

The National Land Cover Database uses 30 m-Landsat imagery toprovide consistent, nationwide digital land cover classification data(Wickham et al., 2014). Although it has been shown that nationwideoverall accuracy of previous NLCD datasets has achieved upwards of79% (Wickham et al., 2013), it was unclear how this accuracy wouldhold upwithin the context of a smaller area, and by extension, howuse-ful the NLCD dataset is in measuring and mapping LULC within NevadaCounty. To achieve a comparison betweenNLCD2011 and the classifica-tion results from eCognition, the NLCD Anderson Level II class codeswere consolidated into their Level I codes to generate classes compara-ble to the five used in the eCognition analysis (Class 81, Irrigated Pas-ture; Class 71, Rangeland; Class 41, Forest; Class 11, Water; Class 21,Impervious Surfaces). A new point vector file of over 500 ground-truthed test samples was again created in ArcGIS based on 2012 1 m–resolution NAIP imagery. Finally, in ENVI, a confusionmatrixwas gener-ated for the 2011 NLCD results.

2.6. Change detection of irrigated pasture

Classification results from the two years, 2005 and 2014, wereexported from eCognition to ArcGIS as raster files. Although change oc-curred between all class types across the ten-year period, change detec-tion analysis was limited to irrigated pasture in this study. Toaccomplish this, the raster files were vectorized without smoothing, ir-rigated pasture polygons were selected and exported, and the area of

each irrigated pasture polygon was calculated. Polygons of less thanone acre in size were discarded, both to remove small areas that mayhave been erroneously misclassified in eCognition (shadows, light-col-ored oaks, ponds, riparian areas, etc.) and because polygons of lessthan one acre are likely not irrigated pasture but instead irrigationleaks, lawns, or other greenways. The resulting vector files for 2005and 2014 were unionized to create one master vector file, with threekinds of polygons for irrigated pasture: 1) present in 2005, present in2014 (no change); 2) present in 2005, absent in 2014 (loss); 3) absentin 2005, present in 2014 (gain).

3. Results

3.1. Irrigated pasture acreage

Results from theOBIA supervised classificationmeasured 4273 acresof irrigated pasture in 2005 and 3470 acres in 2014, a loss of 803 acres(19%). These acreage figures are in comparison to the non-spatial figureof 10,000 acres reported by CDFA in 2014; the spatial figure provided bythe California Department ofWater Resources of 5624 acres from 2005;the non-spatial figures reported by the NASS Census of Agriculture of4856 acres in 2007 and 4088 acres in 2012; and the 34 acres generatedfrom the NLCD spatial dataset (Class 81, Pasture/Hay). The spatially-ex-plicit Cropland Data Layer by NASS has no analogous, comparable classfor irrigated pasture (Table 1), although we combined “alfalfa,” “otherhay,” and “clover/wildflowers” as analogues for irrigated pasture, for aresult of 8 acres in 2008 and 27 acres in 2014.

3.2. Accuracy of methodological approach

Overall accuracy for eCognition classification for 2005 was 89.39%and for 2014was 89.42% (Table 2). User accuracy percentages for the ir-rigated pasture class specificallywere even higher. Towit, the algorithmcorrectly classified 96% of the irrigated pasture ground-truthed samplesin 2005 and 100% of the samples in 2014. The greatest source of confu-sion overall was in the impervious class, whichwas often confusedwith“Range” and “Oak” classes, likely due to the spectral similarities be-tween impervious surfaces, senesced grasslands, and light-coloredoaks. In comparison, overall accuracy for NLCD 2011 was 47.21%. Thegreatest source of error in the NLCD 2011 classification was in the irri-gated pasture class: not one test sample was classified correctly (77%of irrigatedpasture test sampleswere classified asRange, 17%were clas-sified as Oak, and 6% were classified as Impervious).

4. Discussion

As populations continue to rise and place increasing pressure onexisting water resources, understanding agriculture's full contributionto global water use will be of paramount importance. Irrigated pastureis a significant water use in California, which is likely true of other aridand semi-arid regions around the world. As such, it is critical both todocument its distribution and to catalogue its environmental and eco-nomic impacts. The spatially explicit quantitative analysis presented

Table 1Results.

Acres 2005–2008 Acres 2011–2014 Gain/(loss) Percent change

Classification with National Agricultural Imaging Program (NAIP) imagery 4273 (2005)a 3470 (2014)a (803)a (19%)a

CDFA Nevada County Agricultural Commissioner's Report 9700 (2005)10,000 (2006 & 2008)7986 (2007)

10,000 (2011–2014) NA NA

California Department of Water Resources 5624 (2005) – – –NASS Census of Agriculture 4856 (2007) 4088 (2012) (768) (16%)National Land Cover Database (NLCD) 20 (2006) 34 (2011) 14 70%NASS Cropland Data Layer 8 (2008)b 27 (2014)c 19 238%

a Results generated in this analysis.b Class in CDL was “alfalfa.” In 2008, CDL measured no pixels of “other hay/non-alfalfa” or “clover/wildflowers” in Nevada County.c Combined classes in CDL were “alfalfa,” “other hay/non-alfalfa,” and “clover/wildflowers.”


here is a crucialfirst step towards understanding how these systems arechanging and identifies the need for additional interdisciplinary studyof the human and environmental impacts of this change. Although theloss of irrigated pasture is not surprising given its low economic valueand the water allocation pressures created by California's recentdrought, the fallowing or conversion of irrigated pasturewillmost likelyhave agricultural, cultural, and ecosystem implications that are notaccounted for in traditional analyses. Here we provide an analysis ofthe spatial trends of this land use, the methodology we employed, thepotential for broadening to larger geographic areas, and finally discussthe implications for managers and policymakers.

4.1. Irrigated pasture distribution and trends

The present analysis establishes more accurate measures of the totalnumber of acres of irrigated pasture, figures which differ substantiallyfrom three of the five currently-available metrics: 1) the statistics pub-lished by CDFA and the Nevada County Agricultural Commissioner's Re-port, 2) the LULC data generated by NLCD, and 3) the NASS CroplandData Layer. Only statistics from the California Department of Water

Table 2Confusion matrices.

Classification Ground truth (pixels)

Irri. past. Range Water Oak Impervious Total

a. eCognition classification (2005)Unclassified 0 0 0 0 0 0Irri. past. 97 3 0 5 3 108Range 2 93 0 1 11 107Water 0 0 94 5 0 99Oak 2 0 7 99 10 118Impervious 0 4 0 3 89 96Total 101 100 101 113 113 528Overall accuracy (472/528) 89.39%

b. eCognition classification (2014)Unclassified 0 0 0 0 0 0Irri. past. 100 2 0 4 5 111Range 0 97 0 0 10 107Water 0 0 84 2 0 86Oak 0 0 11 93 12 116Impervious 0 1 5 1 74 81Total 100 100 100 100 101 501Overall accuracy (448/501) 89.42%

c. NCLD (2011) classification (using 2012 NAIP test samples)Unclassified 0 0 0 0 0 0Class 81 (irri. past.) 0 0 0 0 0 0Class 71 (range) 77 90 9 11 41 228Class 11 (water) 0 0 32 0 0 32Class 41 (oak) 17 11 51 89 34 202Class 21 (impervious) 6 1 5 2 26 40Total 100 102 97 102 101 502Overall accuracy (237/502) 47.21%

Resources (DWR) and the NASS Census of Agriculture align with thepresent classification (Table 1). Furthermore, results in Nevada Countyacross time demonstrate that there has been a substantial loss of irrigat-ed pasture (19%) in the ten-year period 2005–2014 (see Fig. 3). Thistrend is corroborated by the NASS Census of Agriculture data, which re-ported a 16% loss of irrigated pasture in the period between 2007 and2012. The DWR and Census of Agriculture figures, however, are bothlimited but for different reasons. The DWR uses in-person, field-basedvisits to map cover types in each California county. This approach,while accurate, prevents timely production of LULCdata: some counties,for example, have not been mapped since 1999 (18 years ago). In com-parison, our remote sensing methodology offers substantial time andbudgetary savings and the ability to perform change detection acrossrelevant time scales. The Census of Agriculture is limited in that its sta-tistics are non-spatial. The spatially explicit results presented here arean improvement in that they allow for much broader future analysis:in addition to knowing the amount of irrigated pasture that exists inthe county and its rate of decline, this study allows managers andpolicymakers to know where it exists, where losses are happening,and by extension, to begin to answer why and to what it is converting.Furthermore, this information will be useful for managers to better as-sess how the loss of irrigated pasture impacts other natural resourceconcerns, such as changes in the hydrologic cycle or threats to sensitiveplant or animal species that may rely on irrigated pasture.

4.2. Strengths and limitations of a remote sensing approach

Analysis of irrigated pasture distributionwith remote sensing is onlyas sensitive as the available imagery, and classification using remotelysensed images must account for the inherent tradeoffs in the spatial,temporal, and spectral resolutions of data sources. One purpose of thepresent study was to understand how to reconcile these tradeoffswhen classifying irrigated pasture. Compared to NLCD, for example,whose spatial resolution is 30 m as in Landsat imagery, the finer 1 m-resolution of NAIP allowed for significantly more detailed and accurateclassification (Fig. 4). Alternatively, in comparison to other missions orpopular satellite imagery, temporal and spectral resolution for NAIP ismore limited. The program's data collection frequency is 1–4 yearsand images are only taken once at the height of the summer growingseason, not necessarily at the same phenological stage across the state.However, because the objectives of this study were to classify pastureand detect change over a decade, NAIP's limited temporal resolutiondid not adversely impact this analysis. In fact, the within-year timingof the image capture–at the height of the growing season–took full ad-vantage of plant phenology in California. By summer, annual vegetationacross the state is senesced, which accentuates spectral differenceswiththe vibrant green of irrigated pastures. And given the strengths ofmulti-resolution segmentation and OBIA analysis, we were able to overcomeNAIP's limited spectral resolution, as the algorithm was able to classifysuccessfully with only the four bands offered (red, green, blue, near-infrared).

Fig. 3. Change detection in western Nevada County (2005–2014).


There were three potential sources of error in these analyses. Thefirst one was positional error that could result from different accuracyof geo-registration among various datasets. Although the NAIP com-pressed county mosaics (CCMs) were pre-processed and orthorectifiedby USDA to be in a consistent coordinate system before download,pixels between years (2005, 2014) did not align precisely. After classifi-cation in eCognition, the raster outcomes were resampled in ArcGIS toachieve alignment, however a certain degree of errormay have been in-troduced into the change detection results. Second, classification error,or the incorrect assignment of objects into the candidate cover types,could be a problem, especially between classeswith similar spectral sig-natures (light-colored oaks, irrigated pasture, lawns, riparian areas,etc.). However, overall accuracy figures of close to 90% and producer ac-curacy figures in the “Irrigated Pasture” class of 96% (2005) and 100%(2014) demonstrate that the level of classification error in this analysiswas acceptable. The class confusion of early results that used a pixel-based approach (Fig. 2) further reveals that the success of this classifica-tion process relies heavily on OBIA's capacity to enhance class contrastswhile smoothing local noise (e.g., Blaschke, 2010; Dronova et al., 2012).Classification for all classes could potentially be improved in the futureby refining the segmentation process to achieve greater match of prim-itive objects to pasture units, increasing the number of classes, or in-creasing the number of training samples per class. Finally, some of theuncertainly likely resulted from limited temporal availability of theNAIP imagery of California, as previously discussed, which precluded acomprehensive interpretation of land cover and land use transition inthe years between 2005 and 2014. Although NLCD classification wasperformed on Landsat imagery from 2011, the test samples generatedin this analysis to perform classification accuracy assessment used

NAIP imagery from 2012. This gap between classification date and accu-racy assessment date may have introduced some degree of error.

4.3. Towards a broad-scale regional framework

Most critically, these preliminary results suggest that themethodol-ogy established here can be repeated to provide accurate and ongoingmeasurements of irrigated pasture at larger regional scales. Some keychallenges, however, will need to be addressed before expanding thisframework to broader regional scopes. First, processing time for OBIAsegmentation and supervised classification in our study area was long,and substantially increasing the area of interest to include all potentialirrigated pastures statewide may prove computationally prohibitive.Solutions to this include: 1) conducting analyses regionally, and therebydecreasing thefile size and pixel count of the imagery; 2) exploring par-allel processing resources, which would relieve computation strain; or3) opting to use imagerywith lower resolution but greater temporal fre-quency and spectral sensitivity to irrigated pasture, e.g. 30m-resolutionLandsat. Although these results suggest that NLCD—which uses Landsatimagery—lacked sufficient detail to classify irrigated pasture, a refinedOBIA-assisted methodology that uses Landsat imagery and specificallyaccounts for the unique shape, texture, and spectral characteristics of ir-rigated pasture would likely improve accuracy. Furthermore, despite itsreduced spatial resolution, Landsat's sixteen-day return interval adds atemporal dimension to classification that may also improve accuracy,if, for example, identification of irrigated pasture can be assisted byusing differences in seasonal phenology. A second challenge to extend-ing classification statewide is the varied shape, size, and appearance ofirrigated pasture. A singular feature class created with test samples

Fig. 4. Detail of classification results and change detection. (a) 2005 1 m–resolution NAIP imagery, selected area of Nevada County, California. (b) 2014 1 m–resolution NAIP imagery ofsame selected area. (c) eCognition OBIA classification result of 2005 NAIP image (green: oaks; red: irrigated pasture; grey: impervious surfaces; blue: water; yellow: range). (d)eCognition OBIA classification result of 2014 NAIP image. (e) NLCD classification of same selected area having used 30 m–resolution 2011 Landsat imagery (for comparison). (f)Change detection of irrigated pasture (green: no change; red: loss; blue: gain) having used eCognition classification results. (For interpretation of the references to colour in this figurelegend, the reader is referred to the web version of this article.)


from the whole state may not be able to adequately account for the va-riety across regions. As discussed above, conducting regional analyses toimprove processing time would have the added benefit of allowing forregion-specific test samples.

4.4. Implications for management and policy

The inaccuracy of existingmeasurements—with the exception of theCensus of Agriculture and the Department of WaterResources—highlights how irrigated pasture is not sufficiently moni-tored or its loss prioritized by many government agencies. Becausewater is such a crucial resource for California, this study provides amechanism to better understand the implications of changes to irriga-tion on pasture. From a purely economic perspective, irrigating pastureas a land practice may appear an unwise use of water (Sunding et al.,1997) but reducing acreage statewide would likely have significant en-vironmental, agricultural, and policy repercussions that demand furtherconsideration.

Previous research has catalogued the important ways that irrigatedpasture supports or facilitates wetland-dependent wildlife species(Richmond et al., 2010; Swolgaard et al., 2008; Ivey and Herziger,2001), groundwater recharge and riparian area promotion (Peck andLovvorn, 2001; Wiener et al., 2008), game species (Earl, 1950), andwildfire abatement (Huntsinger et al., 2017). However, there has beenno systematic effort to quantify the value of these ecosystem servicesor incorporate them into an economic model of water use in California.To this end, irrigated pasture is an excellent example of a “social-ecolog-ical service,” in which the complex and frequently unrecognized ser-vices it provides are cogenerated by human activity and ecosystemprocess (Huntsinger & Oviedo, 2014; Hruska et al., 2015).

Additionally, land managers and policy experts have not adequatelyidentified the economic, social, and policy dimensions of lost irrigatedpasture, more specifically the anticipated impacts and disruption toranching livelihoods, livestock production, and land conservation ofworking landscapes. Ranchers rely on irrigated pasture as a forage re-source, which fills a critical gap in the livestock production calendarwhen non-irrigated rangelands elsewhere in the state are unable tosupport the operation's stock numbers or the nutritional demands ofthe animals. As a result, many ranching operations migrate seasonally

between non-irrigated rangelands in the wetter winter months to irri-gated pasture or montane meadows during the dry summer months(Huntsinger et al., 2010). The loss of irrigated pasture is likely to erodea rancher's ability to productively and profitably operate in the state,and could mean a major reduction in production capacity for livestockoperations. Irrigated pasture loss would also result in elevated grazingpressure on rangelands or increased need to truck cattle out of state,both of which would upset the current balance achieved by rancherswhomigrate seasonally between irrigated pasture and rangelands. Irri-gated pasture acreage then has an amplification effect: if an acre of irri-gated pasture can support two cows for the summer, those same twocows will then graze twenty to forty acres of non-irrigated rangelandin the winter (Drake and Phillips, 2006). If land managers and land-use planners prioritized conserving irrigated pasture, it could have theindirect benefit of ensuring appropriate levels of continued grazing onrangelands across the state. While poorly managed grazing can havedetrimental impacts to an ecosystem, well-managed grazing providesimportant ecosystem benefits to rangelands in California, includingthe prevention of invasive plant establishment and spread, the promo-tion of floral and faunal biodiversity, and the reduction of fuels to sup-port fire prevention efforts (Hayes and Holl, 2003; Marty, 2005; Sulakand Huntsinger, 2007).

Finally, the advances in remote sensing provide an opportunity forgovernments and international development agencies to gather precisestatistics on irrigated pasture use without the major investment of re-sources that is required to implement a survey or census of agriculture.While this kind of traditional statistics gathering remains an importantpart of understanding the agricultural sector in many developed coun-tries, other developing countries may not have the resources to imple-ment sophisticated efforts to gather high-quality data. This remotesensingmethodology provides policymakers, administrators, and inter-national development practitioners the ability in certain contexts toleap-frog traditional data gathering methods and to achieve similar ifnot more reliable results (Van Eekelen et al., 2015).

5. Conclusions

The present analysis reveals that irrigated pasture as a land use is onthe decline in this region,which likely portends a broader statewide and


potentially global trend. Additional interdisciplinary research is neededto determine the implications of this loss to the environment, the econ-omy, and to agriculture. Successful management in the future of thishuman-natural system will require timely and accurate data on landuse practices across large spatial scales, a methodology for which is of-fered here. The inaccuracies evident in existing products such as NLCDhighlights a critical point: namely, that many remotely-sensed productsadequately identify landscape features, or land cover, but have inherentlimitations identifying human activity, or land use (Irwin andGeoghegan, 2001). Traditional maps and remote sensing analysesalone are often a “snapshot” and are thus are less powerful in informingdynamic, on-the-ground management over time. The effort here to es-tablish a management-informed methodology that captures LULC anddetects change of a particular land use across time, in this case irrigatedpasture, is a first step towards a new type of map that is able to illumi-nate the agricultural, economic, and environmental dimensions at workin land and water use politics in California. To that end, the approachhere followsNagabhatla et al. (2015)who argue that geospatial datasetscan serve as a foundational and unifying platform for researchers frommultiple disciplines in order to address otherwise complex humanand natural resource dilemmas. A next step towards understandingthe implications of irrigated pasture conversion in California and be-yondwill require a “transdisciplinary” approach, one that brings togeth-er policymakers, ecologists, ranchers, and economists.

Funding

This research received funding support from the University of Cali-fornia Division of Agricultural and Natural Resources. Additional sup-port was provided by the Department of Environmental Science,Policy, and Management at the University of California, Berkeley,through its Graduate Student in Extension fellowship program.

References

AghaKouchak, A., Feldman, D., Hoerling, M., Huxman, T., Lund, J., 2015. Water and cli-mate: recognize anthropogenic drought. Nature 524 (7566):409–411. http://dx.doi.org/10.1038/524409a.

Baatz, M., Benz, U., Dehghani, S., Heynen, M., Höltje, A., Hofmann, P., ... Willhauck, G., 2004.eCognition User Guide. Definiens Imaging GmbH, Munich, Germany.

Barbour, M.G., Keeler-Wolf, T., Schoenherr, A.A., 2007. Terrestrial Vegetation of California.University of California Press.

Blaschke, T., 2010. Object based image analysis for remote sensing. ISPRS J. Photogramm.Remote Sens. 65 (1):2–16. http://dx.doi.org/10.1016/j.isprsjprs.2009.06.004.

Burkhard, B., Kroll, F., Nedkov, S., Müller, F., 2012. Mapping ecosystem service supply, de-mand and budgets. Ecol. Indic. 21:17–29. http://dx.doi.org/10.1016/j.ecolind.2011.06.019.

California Department of Food and Agriculture, 2015. California County Agricultural Com-missioners' Reports, Crop Year 2012–2013.

California Department of Food and Agriculture, 2016. California County Agricultural Com-missioners' Crop Year 2014–2015.

California Department ofWater Resources (DWR), 2010. Agricultural Land andWater UseEstimates. Retrieved March 3, 2017, from. http://www.water.ca.gov/landwateruse/anlwuest.cfm.

California Irrigation Management Information System (CIMIS), 2017. California Depart-ment of Water Resources. Browns Valley, California. http://www.cimis.water.ca.gov.

California Legislative Analyst's Office (CA-LAO), 2002. Water Special Districts: A Look atGovernance and Public Participation. Retrieved March 3, 2017, from. http://www.lao.ca.gov/2002/water_districts/special_water_districts.html.

Congalton, R.G., 1991. A review of assessing the accuracy of classifications of remotelysensed data. Remote Sens. Environ. 37 (1), 35–46.

Congalton, R.G., Green, K., 2008. Assessing the Accuracy of Remotely Sensed Data: Princi-ples and Practices. CRC Press.

Drake, D.J., Phillips, R.L., 2006. Fundamentals of Beef Management. vol. 3495. UCANRPublications.

Dronova, I., Gong, P., Clinton, N., Wang, L., Fu,W., Qi, S., Liu, Y., 2012. Landscape analysis ofwetland plant functional types: the effects of image segmentation scale, vegetationclasses and classification methods. Remote Sens. Environ. 127, 357–369.

Duane, T.P., 1999. Shaping the Sierra: Nature, Culture, and Conflict in the ChangingWest.Univ of California Press.

Earl, J.P., 1950. Production of mallards on irrigated land in the Sacramento Valley, Califor-nia. J. Wildl. Manag. 14 (3), 332–342.

Ellis, E.C., Wang, H., Xiao, H.S., Peng, K., Liu, X.P., Li, S.C., ... Yang, L.Z., 2006. Measuring long-term ecological changes in densely populated landscapes using current and historical

high resolution imagery. Remote Sens. Environ. 100 (4):457–473. http://dx.doi.org/10.1016/j.rse.2005.11.002.

Famiglietti, J.S., 2014. The global groundwater crisis. Nat. Clim. Chang. 4 (11):945–948.http://dx.doi.org/10.1038/nclimate2425.

FAO, 2016. AQUASTAT Main Database - Food and Agriculture Organization of the UnitedNations (FAO) (Website accessed on [05/04/2017 19:14]).

Foley, J.A., 2005. Global consequences of land use. Science 309 (5734):570–574. http://dx.doi.org/10.1126/science.1111772.

Friedl, M.A., McIver, D.K., Hodges, J.C.F., Zhang, X.Y., Muchoney, D., Strahler, A.H.,Woodcock, C.E., Gopal, S., Schneider, A., Cooper, A., Baccini, A., Gao, F., C. S., 2002.Global land cover mapping from MODIS: algorithms and early results. Remote Sens.Environ. 83.

Grantham, T.E., Viers, J.H., 2014. 100 years of California's water rights system: patterns,trends and uncertainty. Environ. Res. Lett. 9 (8):10. http://dx.doi.org/10.1088/1748-9326/9/8/084012.

Guida-Johnson, B., Zuleta, G.A., 2013. Land-use land-cover change and ecosystem loss inthe Espinal ecoregion, Argentina. Agric. Ecosyst. Environ. 181, 31–40.

Halabisky, M., 2011. Object-based classification ofsemi-arid wetlands. J. Appl. Remote.Sens. 5 (1):53511. http://dx.doi.org/10.1117/1.3563569.

Hannerz, F., Lotsch, A., 2008. Assessment of remotely sensed and statistical inventories ofAfrican agricultural fields. Int. J. Remote Sens. 29 (13), 3787–3804.

Hayes, G.F., Holl, K.D., 2003. Cattle grazing impacts on annual forbs and vegetation com-position of mesic grasslands in California. Conserv. Biol. 17 (6), 1694–1702.

Homer, C.G., Dewitz, J.A., Yang, L., Jin, S., Danielson, P., Xian, G., Coulston, J., Herold, N.D.,Wickham, J.D., Megown, K., 2015. Completion of the 2011 National Land Cover Data-base for the conterminous United States-representing a decade of land cover changeinformation. Photogramm. Eng. Remote Sens. vol. 81 (5), 345–354.

Howitt, R., Medellín-azuara, J., Macewan, D., 2015. Economic Analysis of the 2015 Droughtfor California Agriculture. Center for Watershed Sciences. University of California,Davis, California (20 pp.) Retrieved from. http://watershed.ucdavis.edu.

Hruska, T.V., Huntsinger, L., Oviedo, J.L., 2015. An accidental resource: the social ecologicalsystem framework applied to small wetlands in Sierran foothill oak woodlands. In:Standiford, Richard B., Purcell, Kathryn L. (Eds.), Proceedings of the Seventh CaliforniaOak Symposium: Managing Oak Woodlands in a Dynamic World (tech. cords. Gen.Tech. Rep. PSW-GTR-251).

Huntsinger, L., Forero, L.C., Sulak, A., 2010. Transhumance and pastoralist resilience in theWestern United States. Pastor. Res. Policy Pract. 1 (1):9–36. http://dx.doi.org/10.3362/2041-7136.2010.002.

Huntsinger, L., Oviedo, J.L., 2014. Ecosystem services are social-ecological services in a tra-ditional pastoral system: the case of California’s Mediterranean rangelands. Ecol. Soc.19 (1), 8.

Huntsinger, L., Hruska, T.V., Oviedo, J.L., Shapero, M.W.K., Nader, G.A., Ingram, R.S., 2017.Save water or save wildlife? Water use and conservation in the Central Sierran foot-hills oak woodlands of California, USA. Ecol. Soc. 22 (2).

Hyatt, E., 1931. Report on Irrigation Districts in California For the Year 1930, Bulletin No.21-B. Publications of the Division of Water Resources.

Irwin, E.G., Geoghegan, J., 2001. Theory, data, methods: developing spatially explicit eco-nomic models of land use change. Agric. Ecosyst. Environ. 85 (1), 7–24.

Ivey, G.L., Herziger, C.P., 2001. Distribution of Greater Sandhill Crane Pairs in California,2000. California Department of Fish and Game.

Marty, J.T., 2005. Effects of cattle grazing on diversity in ephemeral wetlands. Conserv.Biol. 19 (5), 1626–1632.

Moskal, L.M., Styers, D.M., Halabisky, M., 2011. Monitoring urban tree cover using object-based image analysis and public domain remotely sensed data. Remote Sens. 3 (10):2243–2262. http://dx.doi.org/10.3390/rs3102243.

Mount, J., Freeman, E., Lund, J., 2014. Water Use in California. Public Policy Institute of Cal-ifornia. http://dx.doi.org/10.5066/F7KD1VXV (July).

Nagabhatla, N., Padmanabhan, M., Kühle, P., Vishnudas, S., Betz, L., Niemeyer, B., 2015.LCLUC as an entry point for transdisciplinary research - reflections from an agricul-ture land use change study in South Asia. J. Environ. Manag. 148:42–52. http://dx.doi.org/10.1016/j.jenvman.2014.03.019.

Nevada County, California, 2015. GIS Open Data Portal. Retrieved December 1, 2015 from.https://www.mynevadacounty.com/nc/igs/gis/Pages/OpenData.aspx.

Peck, D.E., Lovvorn, J.R., 2001. The importance of flood irrigation in water supply to wet-lands in the Laramie Basin, Wyoming, USA. Wetlands 21 (3):370–378. http://dx.doi.org/10.1672/0277-5212(2001)021[0370:TIOFII]2.0.CO;2.

Postel, S.L., 1998. Water for food production: will there be enough in 2025? Bioscience 48(8), 629–637.

Rahman, M.R., Saha, S.K., 2008. Multi-resolution segmentation for object-based classifica-tion and accuracy assessment of land use/land cover classification using remotelysensed data. J. Indian Soc. Remote Sens. 36 (2):189–201. http://dx.doi.org/10.1007/s12524-008-0020-4.

Richmond, O.M.W., Chen, S.K., Risk, B.B., Tecklin, J., Beissinger, S.R., 2010. California blackrails depend on irrigation-fed wetlands in the Sierra Nevada foothills. Calif. Agric. 64(2):85–93. http://dx.doi.org/10.3733/ca.v064n02p85.

Rost, S., Gerten, D., Bondeau, A., Lucht, W., Rohwer, J., Schaphoff, S., 2008. Agriculturalgreen and blue water consumption and its influence on the global water system.Water Resour. Res. 44 (9).

Sohl, T.L., Sleeter, B.M., Sayler, K.L., Bouchard, M.A., Reker, R.R., Bennett, S.L., ... Zhu, Z.,2012. Spatially explicit land-use and land-cover scenarios for the Great Plains of theUnited States. Agric. Ecosyst. Environ. 153, 1–15.

Sulak, A., Huntsinger, L., 2007. Public land grazing in California: untapped conservationpotential for private lands? Working landscapes may be linked to public lands.Rangelands 29 (3), 9–12.

Sunding, D., Zilberman, D., Howitt, R., Dinar, A., MacDougall, N., 1997. Modeling the im-pacts of reducing agricultural water supplies: lessons from California's Bay Delta

http://dx.doi.org/10.1038/524409ahttp://refhub.elsevier.com/S0048-9697(17)31469-9/rf0010http://refhub.elsevier.com/S0048-9697(17)31469-9/rf0015http://refhub.elsevier.com/S0048-9697(17)31469-9/rf0015http://dx.doi.org/10.1016/j.isprsjprs.2009.06.004http://dx.doi.org/10.1016/j.ecolind.2011.06.019http://dx.doi.org/10.1016/j.ecolind.2011.06.019http://refhub.elsevier.com/S0048-9697(17)31469-9/rf0030http://refhub.elsevier.com/S0048-9697(17)31469-9/rf0030http://refhub.elsevier.com/S0048-9697(17)31469-9/rf0035http://refhub.elsevier.com/S0048-9697(17)31469-9/rf0035http://www.water.ca.gov/landwateruse/anlwuest.cfmhttp://www.water.ca.gov/landwateruse/anlwuest.cfmhttp://www.cimis.water.ca.govhttp://www.lao.ca.gov/2002/water_districts/special_water_districts.htmlhttp://www.lao.ca.gov/2002/water_districts/special_water_districts.htmlhttp://refhub.elsevier.com/S0048-9697(17)31469-9/rf0060http://refhub.elsevier.com/S0048-9697(17)31469-9/rf0060http://refhub.elsevier.com/S0048-9697(17)31469-9/rf0065http://refhub.elsevier.com/S0048-9697(17)31469-9/rf0065http://refhub.elsevier.com/S0048-9697(17)31469-9/rf0070http://refhub.elsevier.com/S0048-9697(17)31469-9/rf0070http://refhub.elsevier.com/S0048-9697(17)31469-9/rf0075http://refhub.elsevier.com/S0048-9697(17)31469-9/rf0075http://refhub.elsevier.com/S0048-9697(17)31469-9/rf0075http://refhub.elsevier.com/S0048-9697(17)31469-9/rf0080http://refhub.elsevier.com/S0048-9697(17)31469-9/rf0080http://refhub.elsevier.com/S0048-9697(17)31469-9/rf0085http://refhub.elsevier.com/S0048-9697(17)31469-9/rf0085http://dx.doi.org/10.1016/j.rse.2005.11.002http://dx.doi.org/10.1038/nclimate2425http://refhub.elsevier.com/S0048-9697(17)31469-9/rf0100http://refhub.elsevier.com/S0048-9697(17)31469-9/rf0100http://dx.doi.org/10.1126/science.1111772http://refhub.elsevier.com/S0048-9697(17)31469-9/rf0110http://refhub.elsevier.com/S0048-9697(17)31469-9/rf0110http://dx.doi.org/10.1088/1748-9326/9/8/084012http://dx.doi.org/10.1088/1748-9326/9/8/084012http://refhub.elsevier.com/S0048-9697(17)31469-9/rf0120http://refhub.elsevier.com/S0048-9697(17)31469-9/rf0120http://dx.doi.org/10.1117/1.3563569http://refhub.elsevier.com/S0048-9697(17)31469-9/rf0130http://refhub.elsevier.com/S0048-9697(17)31469-9/rf0130http://refhub.elsevier.com/S0048-9697(17)31469-9/rf0135http://refhub.elsevier.com/S0048-9697(17)31469-9/rf0135http://refhub.elsevier.com/S0048-9697(17)31469-9/rf0140http://refhub.elsevier.com/S0048-9697(17)31469-9/rf0140http://refhub.elsevier.com/S0048-9697(17)31469-9/rf0140http://watershed.ucdavis.eduhttp://refhub.elsevier.com/S0048-9697(17)31469-9/rf0150http://refhub.elsevier.com/S0048-9697(17)31469-9/rf0150http://refhub.elsevier.com/S0048-9697(17)31469-9/rf0150http://refhub.elsevier.com/S0048-9697(17)31469-9/rf0150http://refhub.elsevier.com/S0048-9697(17)31469-9/rf0150http://dx.doi.org/10.3362/2041-7136.2010.002http://dx.doi.org/10.3362/2041-7136.2010.002http://refhub.elsevier.com/S0048-9697(17)31469-9/rf9100http://refhub.elsevier.com/S0048-9697(17)31469-9/rf9100http://refhub.elsevier.com/S0048-9697(17)31469-9/rf9100http://refhub.elsevier.com/S0048-9697(17)31469-9/rf0160http://refhub.elsevier.com/S0048-9697(17)31469-9/rf0160http://refhub.elsevier.com/S0048-9697(17)31469-9/rf0165http://refhub.elsevier.com/S0048-9697(17)31469-9/rf0165http://refhub.elsevier.com/S0048-9697(17)31469-9/rf0170http://refhub.elsevier.com/S0048-9697(17)31469-9/rf0170http://refhub.elsevier.com/S0048-9697(17)31469-9/rf0175http://refhub.elsevier.com/S0048-9697(17)31469-9/rf0175http://refhub.elsevier.com/S0048-9697(17)31469-9/rf0180http://refhub.elsevier.com/S0048-9697(17)31469-9/rf0180http://dx.doi.org/10.3390/rs3102243http://dx.doi.org/10.5066/F7KD1VXVhttp://dx.doi.org/10.1016/j.jenvman.2014.03.019https://www.mynevadacounty.com/nc/igs/gis/Pages/OpenData.aspxhttp://dx.doi.org/10.1672/0277-5212(2001)021[0370:TIOFII]2.0.CO;2http://refhub.elsevier.com/S0048-9697(17)31469-9/rf0210http://refhub.elsevier.com/S0048-9697(17)31469-9/rf0210http://dx.doi.org/10.1007/s12524-008-0020-4http://dx.doi.org/10.1007/s12524-008-0020-4http://dx.doi.org/10.3733/ca.v064n02p85http://refhub.elsevier.com/S0048-9697(17)31469-9/rf0225http://refhub.elsevier.com/S0048-9697(17)31469-9/rf0225http://refhub.elsevier.com/S0048-9697(17)31469-9/rf0225http://refhub.elsevier.com/S0048-9697(17)31469-9/rf0230http://refhub.elsevier.com/S0048-9697(17)31469-9/rf0230http://refhub.elsevier.com/S0048-9697(17)31469-9/rf0235http://refhub.elsevier.com/S0048-9697(17)31469-9/rf0235http://refhub.elsevier.com/S0048-9697(17)31469-9/rf0235http://refhub.elsevier.com/S0048-9697(17)31469-9/rf0240http://refhub.elsevier.com/S0048-9697(17)31469-9/rf0240


problem. Decentralization and Coordination of Water Resources Management.Swolgaard, C.A., Reeves, K.A., Bell, D.A., 2008. Foraging by Swainson's Hawks in a vine-

yard-dominated landscape. J. Raptor Res. 42 (3):188–196. http://dx.doi.org/10.3356/JRR-07-15.1.

U.S. Census Bureau, 2017. Quick Facts, Nevada County, California, Population estimatesbase, July 1, 2015. Retrieved March 3, 2017, from. https://www.census.gov/quickfacts/table/PST045215/06057.

USDA National Agricultural Statistics Service, 2014. Census of Agriculture. Retrieved Feb-ruary 25, 2017 from. https://www.agcensus.usda.gov/Publications/2012/.

USDA National Agricultural Statistics Service Cropland Data Layer, 2014. National Crop-land Data Layer. Retrieved December 1, 2015 from. https://nassgeodata.gmu.edu/CropScape/ USDA-NASS, Washington, DC.

USDA/NRCS Geospatial Gateway, 2014. National Ag. Imagery Program Mosaic. RetrievedDecember 1, 2015 from. https://datagateway.nrcs.usda.gov/GDGOrder.aspx.

Van Eekelen, M.W., Bastiaanssen, W.G., Jarmain, C., Jackson, B., Ferreira, F., Van der Zaag,P., ... Dost, R.J.J., 2015. A novel approach to estimate direct and indirect water with-drawals from satellite measurements: a case study from the Incomati basin. Agric.Ecosyst. Environ. 200, 126–142.

Verburg, P.H., van de Steeg, J., Veldkamp, A., Willemen, L., 2009. From land cover changeto land function dynamics: a major challenge to improve land characterization.J. Environ. Manag. 90 (3):1327–1335. http://dx.doi.org/10.1016/j.jenvman.2008.08.005.

Wickham, J.D., Stehman, S.V., Gass, L., Dewitz, J., Fry, J.A., Wade, T.G., 2013. Accuracy as-sessment of NLCD 2006 land cover and impervious surface. Remote Sens. Environ.130:294–304. http://dx.doi.org/10.1016/j.rse.2012.12.001.

Wickham, J., Homer, C., Vogelmann, J., McKerrow, A., Mueller, R., Herold, N., Coulston, J.,2014. The multi-resolution land characteristics (MRLC) consortium—20 years of de-velopment and integration of USA national land cover data. Remote Sens. 6 (8),7424–7441.

Wiener, J.D., Dwire, K.A., Skagen, S.K., Crifasi, R.R., Yates, D., 2008. Riparian ecosystem con-sequences of water redistribution along the Colorado Front Range. Water Resour. IM-PACT 10 (3), 18–21.

http://refhub.elsevier.com/S0048-9697(17)31469-9/rf0240http://dx.doi.org/10.3356/JRR-07-15.1http://dx.doi.org/10.3356/JRR-07-15.1https://www.census.gov/quickfacts/table/PST045215/06057https://www.census.gov/quickfacts/table/PST045215/06057https://www.agcensus.usda.gov/Publications/2012/https://nassgeodata.gmu.edu/CropScapehttps://nassgeodata.gmu.edu/CropScapehttps://datagateway.nrcs.usda.gov/GDGOrder.aspxhttp://refhub.elsevier.com/S0048-9697(17)31469-9/rf0270http://refhub.elsevier.com/S0048-9697(17)31469-9/rf0270http://refhub.elsevier.com/S0048-9697(17)31469-9/rf0270http://dx.doi.org/10.1016/j.jenvman.2008.08.005http://dx.doi.org/10.1016/j.jenvman.2008.08.005http://dx.doi.org/10.1016/j.rse.2012.12.001http://refhub.elsevier.com/S0048-9697(17)31469-9/rf0285http://refhub.elsevier.com/S0048-9697(17)31469-9/rf0285http://refhub.elsevier.com/S0048-9697(17)31469-9/rf0285http://refhub.elsevier.com/S0048-9697(17)31469-9/rf0290http://refhub.elsevier.com/S0048-9697(17)31469-9/rf0290http://refhub.elsevier.com/S0048-9697(17)31469-9/rf0290

Implications of changing spatial dynamics of irrigated pasture, California's third largest agricultural water use1. Introduction2. Materials and methods2.1. Description of study area2.2. Data sources2.3. Classification2.4. Accuracy assessment2.5. Comparison of classification techniques2.6. Change detection of irrigated pasture

3. Results3.1. Irrigated pasture acreage3.2. Accuracy of methodological approach

4. Discussion4.1. Irrigated pasture distribution and trends4.2. Strengths and limitations of a remote sensing approach4.3. Towards a broad-scale regional framework4.4. Implications for management and policy

5. ConclusionsFundingReferences

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