Effects of grazing and topography on long-term vegetation changes in a Mediterranean ecosystem in Israel

The dynamics of Mediterranean vegetation over 28 years was studied in the Northern Galilee Mountains, Israel, in order to identify and quantify the major factors affecting it at the landscape scale. Image analysis of historical and current aerial photographs was used to produce high resolution digital vegetation maps (pixel size = 30 cm) for an area of 4 km² in the Galilee Mountains, northern Israel. GIS tools were used to produce corresponding maps of grazing regime, topographic indices and other relevant environmental factors. The effects of those factors were quantified using a multiple regression analyses. Major changes in the vegetation occurred during the period studied (1964–1992); tree cover increased from 2% in 1964 to 41% in 1992, while herbaceous vegetation cover decreased from 56% in 1964 to 24% in 1992. Grazing, topography and initial vegetation cover were found to significantly affect present vegetation patterns. Both cattle grazing and goat grazing reduced the rate of increase in tree cover, yet even intensive grazing did not halt the process. Grazing affected also the woody-herbaceous vegetation dynamics, reducing the expansion of woody vegetation. Slope, aspect, and the interaction term between these two factors, significantly affected vegetation pattern. Altogether, 56% and 72% of the variability in herbaceous and tree cover, respectively, was explained by the regression models. This study indicates that spatially explicit Mediterranean vegetation dynamics can be predicted with fair accuracy using few biologically important environmental variables.     

Automatic detection of woody vegetation in repeat landscape photographs using a convolutional neural network

Repeat photography is an efficient method for documenting long-term landscape changes. So far, the usage of repeat photographs for quantitative analyses is limited to approaches based on manual classification. In this paper, we demonstrate the application of a convolutional neural network (CNN) for the automatic detection and classification of woody regrowth vegetation in repeat landscape photographs. We also tested if the classification results based on the automatic approach can be used for quantifying changes in woody vegetation cover between image pairs. The CNN was trained with 50 × 50 pixel tiles of woody vegetation and non-woody vegetation. We then tested the classifier on 17 pairs of repeat photographs to assess the model performance on unseen data. Results show that the CNN performed well in differentiating woody vegetation from non-woody vegetation (accuracy = 87.7%), but accuracy varied strongly between individual images. The very similar appearance of woody vegetation and herbaceous species in photographs made this a much more challenging task compared to the classification of vegetation as a single class (accuracy = 95.2%). In this regard, image quality was identified as one important factor influencing classification accuracy. Although the automatic classification provided good individual results on most of the 34 test photographs, change statistics based on the automatic approach deviated from actual changes. Nevertheless, the automatic approach was capable of identifying clear trends in increasing or decreasing woody vegetation in repeat photographs. Generally, the use of repeat photography in landscape monitoring represents a significant added value to other quantitative data retrieved from remote sensing and field measurements. Moreover, these photographs are able to raise awareness on landscape change among policy makers and public as well as they provide clear feedback on the effects of land management.     

Mapping the invasive species Phragmites australis in linear wetland corridors

The detection and monitoring of invasive species at the initial stage of invasion is often critical to control/eradication efforts. In the case of Phragmites australis, anthropogenic linear wetlands such as roadside and agricultural ditches are believed to play a key role in invasion patterns. Accurate remote sensing of an aquatic macrophyte in such narrow habitats, however, remains a challenge. We used large-scale (1/8000) panchromatic and color aerial photographs to produce different distribution maps of P. australis in a network of linear wetlands. Accuracy assessments were conducted to compare the two classifications and sources of errors were identified using logistic regressions. Different thresholds of stem abundance (1%, 5%, 20%, and 40%) were used in the error matrices to determine the stem abundance at which our classification is optimized. Results show that color images are much better in enabling the detection of P. australis. Producer's accuracy ranges from 44% to 71% (depending on the selected threshold of stem abundance) for color images and from 16% to 28% for panchromatic images. User's accuracy ranges from 84% to 55% for color photographs and from 51% to 28% for panchromatic photographs. Generally, the mapping of vigorous populations is more accurate. The presence of Typha sp. is the main source of commission errors. Landscape context also affects the mapping accuracy. We discuss the relevance of our results for mapping invasion patterns in narrow linear wetlands.     

A quantitative analysis of photometric data from aerial photographs for vegetation survey

This paper describes the application of quantitative density analysis to black and white aerial photographs for vegetation survey using a Quantimet 720 image analyser. The photometric data are analysed using both a supervised and an unsupervised classification strategy. Floristic data collected from an independent ground survey, are used to categorise those vegetation classes of interest against which the photometric data classifications are assessed. The preliminary results obtained suggest that broad classes of vegetation types may be distinguished automatically from their grey scale distribution patterns.     

Mapping the invasive species Phragmites australis in linear wetland corridors

The detection and monitoring of invasive species at the initial stage of invasion is often critical to control/eradication efforts. In the case of Phragmites australis, anthropogenic linear wetlands such as roadside and agricultural ditches are believed to play a key role in invasion patterns. Accurate remote sensing of an aquatic macrophyte in such narrow habitats, however, remains a challenge. We used large-scale (1/8000) panchromatic and color aerial photographs to produce different distribution maps of P. australis in a network of linear wetlands. Accuracy assessments were conducted to compare the two classifications and sources of errors were identified using logistic regressions. Different thresholds of stem abundance (1%, 5%, 20%, and 40%) were used in the error matrices to determine the stem abundance at which our classification is optimized. Results show that color images are much better in enabling the detection of P. australis. Producer's accuracy ranges from 44% to 71% (depending on the selected threshold of stem abundance) for color images and from 16% to 28% for panchromatic images. User's accuracy ranges from 84% to 55% for color photographs and from 51% to 28% for panchromatic photographs. Generally, the mapping of vigorous populations is more accurate. The presence of Typha sp. is the main source of commission errors. Landscape context also affects the mapping accuracy. We discuss the relevance of our results for mapping invasion patterns in narrow linear wetlands.     

Object-oriented image analysis to extract landscape elements in urban fringes, Central Japan

A method enabling the object-oriented image analysis of landscape elements incorporating topographic data was designed and tested on a Japanese countryside target area. IKONOS data (four multispectral bands with a spatial resolution of 4 m and a panchromatic band with a spatial resolution of 1 m) acquired on 23 April 2001 were used. Definiens v.5 software (Definiens AG, München, Germany) was employed for the classification. The initial segmentation was multiresolution and bottom-up, and each segment identified was considered to be one object. Two classifications employing the same landscape elements and ground truth data were implemented. One classification adopted an object-based image analysis classification method based on spectral characteristics; the other utilized an object-oriented image analysis (OOIA) that allows for a suitable scale parameter to be selected independently for each landscape element. In addition, topographic data derived from field surveys (walking surveys) and topographic maps were used to create a topographic database delineating the boundary between valley bottoms and the adjacent slopes (elevation: about 10 m). These data were then integrated into the OOIA analysis. The accuracies of the two classifications were assessed by comparing the results to a master landscape map produced directly from aerial photographs and on-site observations. The object-oriented method using the topographic data resulted in a higher overall kappa coefficients (0.63–0.47) than the object-based method. These results indicate that object-oriented image analysis of very high resolution data used in combination with topographic data can be an effective tool for landscape classification in Japan, where historical land-use patterns have resulted in finely dissected landscapes.     

基于可见光植被指数的面向对象湿地水生植被提取方法

利用ESP分割工具确定最佳分割尺度,通过多尺度分割算法创建最优分割影像,基于微型无人机影像数据生成可见光植被指数,从一系列可见光植被指数中选取一组最优植被指数,建立决策树规则,利用隶属度函数对研究区自动分类,生成水生植被分布图.结果表明: 监督分类法的总体精度为53.7%,面向对象分类法总体精度为91.7%,与基于像元的监督分类法相比,面向对象分类法显著改善了影像分类结果,并大大提高了水生植被提取精度,监督分类法的Kappa系数为0.4,而面向对象分类法的Kappa系数为0.9.这表明利用微型无人机数据生成的可见光植被指数结合面向对象分类方法提取水生植被在该研究区是可行的,并能够应用到其他类似区域.     

Reaching new heights: can drones replace current methods to study plant population dynamics?

Spatially explicit data on heterogeneously distributed plant populations are difficult to quantify using either traditional field-based methods or remote sensing techniques alone. Unmanned Aerial Vehicles (UAVs) offer new means and tools for baseline monitoring of such populations. We tested the use of vegetation classification of UAV-acquired photographs as a method to capture heterogeneously distributed plant populations, using Jacobaea vulgaris as a model species. Five sites, each containing 1–4 pastures with varying J. vulgaris abundance, were selected across Schleswig–Holstein, Germany. Surveys were conducted in July 2017 when J. vulgaris was at its flowering peak. We took aerial photographs at a 50 m altitude using three digital cameras (RGB, red-edge and near-infrared). Orthomosaics were created before a pixel-based supervised classification. Classification results were evaluated for accuracy; reliability was assessed with field data collected for ground verification. An ANOVA tested the relationship between field-based abundance estimations and the supervised classifications. Overall accuracy of the classification was very high (90.6%,?±?1.76 s.e.). Kappa coefficients indicated substantial agreement between field data and image classification (≥?0.65). Field-based estimations were a good predictor of the supervised classifications (F?=?7.91, df?=?4, P?=?0.007), resulting in similar rankings of J. vulgaris abundance. UAV-acquired images demonstrated the potential as an objective method for data collection and species monitoring. However, our method was more time consuming than field-based estimations due to challenges in image processing. Nonetheless, the increasing availability of low-cost consumer-grade UAVs is likely to increase the use of UAVs in plant ecological studies.     

Classifying the Neotropical savannas of Belize using remote sensing and ground survey

Aim This paper evaluates a method of combining data from GPS ground survey with classifications of medium spatial resolution LANDSAT imagery to distinguish variations within Neotropical savannas and to characterize the boundaries between savanna areas and the associated gallery forests, seasonally dry forests and wetland communities. Location Rio Bravo Conservation Area, Orange Walk District, Belize, Central America. Methods Dry season LANDSAT data for 10 April 1993 and 9 March 2001 covering a conservation area of 240,000 acres (97,459 ha), were rectified to sub‐pixel accuracy using ground control points positioned by GPS ground survey. The 1993 image was used to assess the accuracy with which the boundaries between the savanna matrix and gallery forests, high forests, wetlands and water bodies could be discriminated. The image was classified by a maximum likelihood (ML) classifier and the shapes and areas of forest and wetland classes were compared with an interpretation of these land cover types from 1 : 24,000 aerial photography, mapped at 1 : 50,000 scale in 1993. The 2001 image was used to assess whether different subtypes of savanna could be distinguished from LANDSAT data. This required the creation of a reference (‘ground truth’) data set for testing classifications of the image. One hundred and sixty sample patches (650 ha, distributed over an area of 7000 ha) of ten sub‐types of savanna vegetation and associates identified using a physiognomic classification scheme, were delineated on the ground by GPS and divided into two subsets for training and testing. Continuous classifications of LANDSAT data covering the savannas were developed that estimated potential contributions from up to five sub‐types of land cover (grassland, wetland, pine woodland, gallery forest and palmetto). The accuracy of each classification was assessed by comparison against ground data. An ML classification was also produced for the 2001 image using the same areas for training. This allowed a comparison of the relative accuracy of both continuous and Boolean ML methods for classifying savanna areas. Results The boundary between savannas and evergreen forests, gallery forests and open water in the study region could be delineated by the ML classifier to within 2 pixels (60 m) using LANDSAT imagery. However, the constituent sub‐types within the savanna were poorly discriminated. Whilst the shape and extent of closed canopy forest, gallery forest, wetlands and water bodies agreed closely with the distributions interpreted from aerial photography, classes such as ‘open pine savanna’ or ‘grassland’ were only 45–65% accurate when tested against ground data. A continuous classification, estimating the proportions of three savanna vegetation subtypes (grassland, marshland and woodland) present in each pixel, correctly classified more of the ground data for these cover types than the comparable ML result. Proportional mixtures of the land cover estimated by the continuous classifier also compared realistically with the vegetation formations observed along ground transects. Main conclusions By using GPS, a ground survey of vegetation cover was accurately matched to remotely sensed imagery and the accuracy of delineating boundaries and classifying areas of savanna was assessed directly. This showed that ML classification techniques can reliably delineate the boundaries of savannas, but continuous classifiers more accurately and realistically represent the distribution of the subtypes comprising savanna land cover. By combining these ground survey and image classification methods, medium spatial resolution satellite sensor data can provide an affordable means for land managers to assess the nature, extent and distribution of savanna formations. Over time, using the archives of LANDSAT (and SPOT) data together with marker sites surveyed in the field, quantitative changes in the extents and boundaries of savannas in response to both natural (e.g. fire, hurricane and drought) and anthropogenic (e.g. cutting and disturbance) factors can be assessed.     

Random Forest characterization of upland vegetation and management burning from aerial imagery

Aim The upland moorlands of Great Britain form distinctive landscapes of international conservation importance, comprising mosaics of heathland, acid grassland, blanket bog and bracken. Much of this landscape is managed by rotational burning to create gamebird habitat and there is concern over whether this is driving long‐term changes in upland vegetation communities. However, the inaccessibility and scale of uplands means that monitoring changes in vegetation and burning practices is difficult. We aim to overcome this problem by developing methods to classify aerial imagery into high‐resolution maps of dominant vegetation cover, including the distribution of burns on managed grouse moors. Location  Peak District National Park, England, UK. Methods Colour and infrared aerial photographs were classified into seven dominant land‐cover classes using the Random Forest ensemble machine learning algorithm. In addition, heather (Calluna vulgaris) was further differentiated into growth phases, including sites that were newly burnt. We then analysed the distributions of the vegetation classes and managed burning using detrended correspondence analysis. Results Classification accuracy was c. 95% and produced a 5‐m resolution map for 514 km² of moorland. Cover classes were highly aggregated and strong nonlinear effects of elevation and slope and weaker effects of aspect and bedrock type were evident in structuring moorland vegetation communities. The classification revealed the spatial distribution of managed burning and suggested that relatively steep areas may be disproportionately burnt. Main conclusions Random Forest classification of aerial imagery is an efficient method for producing high‐resolution maps of upland vegetation. These may be used to monitor long‐term changes in vegetation and management burning and infer species–environment relationships and can therefore provide an important tool for effective conservation at the landscape scale.     

全色型及彩色红外型航空像片解译方法在植被制图中的应用

在编制大、中比例尺植被图的过程中,热红外的,多光谱的、彩色红外的和全色型的航空像片都可以利用。我们在云南省腾冲进行了航空遥感试验。根据直接解译标志和间接解译标志的不同组合可以进行植被类型的解译。这种综合的方法用于遥感图像的解译可以获得良好的效果。文中提出以下的直接解译标志,即植物群落影像的颜色、色调及其影纹结构,植物群落、群落片断或单株的形状、大小和高度以及它们的投影形状。间接解译标志包括植物生长地所处的海拔高度、纬度位置和地貌部位以及成土母岩的性质。间接标志还有人类活动对植被分布影响的程度。为了便于进行自动的植被分类,可以用检索表将各植被类型的详细解译标志表示出来。本文列出了位于常绿阔叶林区的云南省腾冲植被类型解译检索表。所用全色型航空像片的比例尺是1:35000,而彩色红外型航空像片的比例尺是1:34000。     

UAV-based classification of maritime Antarctic vegetation types using GEOBIA and random forest

Development of vegetation communities in areas of Antarctica without permanent ice cover emphasizes the need for effective remote sensing techniques for proper monitoring of local environmental changes. Detection and mapping of vegetation by image classification remains limited in the Antarctic environment due to the complexity of its surface cover, and the spatial heterogeneity and spectral homogeneity of cryptogamic vegetation. As ultra-high resolution aerial images allow a comprehensive analysis of vegetation, this study aims to identify different types of vegetation cover (i.e., algae, mosses, and lichens) in an ice-free area of  Hope Bay, on the northern tip of the Antarctic Peninsula. Using the geographic object-based image analysis (GEOBIA) approach, remote sensing data sets are tested in the random forest classifier in order to distinguish vegetation classes within vegetated areas. Because species of algae, mosses, and lichens may have similar spectral characteristics, subclasses are established. The results show that when only the mean values of green, red, and NIR bands are considered, the subclasses have low separability. Variations in accuracy and visual changes are identified according to the set of features used in the classification. Accuracy improves when multilayer information is used. A combination of spectral and morphometric products and by-products provides the best result for the detection and delineation of different types of vegetation, with an overall accuracy of 0.966 and a Kappa coefficient of 0.946. The method allowed for the identification of units primarily composed of algae, mosses, and lichens as well as differences in communities. This study demonstrates that ultra-high spatial resolution data can provide the necessary properties for the classification of vegetation in Maritime Antarctica, even in images obtained by sensors with low spectral resolution.     

Application of QuickBird and aerial imagery to detect Pinus radiata in remnant vegetation

The invasion of Pinus radiata from long‐term established plantations is contributing to the degradation of fragmented and isolated remnants of native vegetation. Within the south‐east of South Australia, the 20 vegetation communities that occur within 500 m of a plantation edge are at risk, including nine state threatened communities. To plan effective mitigation strategies, the current extent and distribution of P. radiata needs to be ascertained. High spatial resolution, multispectral QuickBird imagery and aerial photography were used to classify P. radiata within eucalypt and acacia woodlands, melaleuca shrubland, modified pasture and an Eucalyptus globulus plantation. Unsupervised classification of aerial photography gave the best result showing reasonable conformity with the observed distribution of P. radiata at the site scale. However, the 9.4 ± 13.5 (SD) cover classified in the quadrats sampled for the accuracy assessment exceeded the 1.4 ± 2.4 (SD) P. radiata cover determined from an independent dataset. Only 30.1 ± 37.9% (SD) of trees within the quadrats and 9.40 ± 13.49% (SD) of their foliage cover were classified. Trees detected by partial classification of canopy were positively correlated with both tree height and canopy diameter. Overall, the low detection rates were attributed to insufficient spectral resolution. Using higher resolution imagery, together with an object‐based image analysis or combination of multispectral and airborne digital image classification, restricted to large emergent adult trees using LiDAR analysis, is likely to improve adult P. radiata detection accuracy.     

Estimating the relative abundance of emperor penguins at inaccessible colonies using satellite imagery

Emperor penguin (Aptenodytes forsteri) populations are useful environmental indicators due to the bird’s extreme reliance on sea ice. We used remote sensing technology to estimate relative adult bird abundance at two inaccessible emperor penguin colonies in the Ross Sea, Antarctica. We performed supervised classification of 12 panchromatic satellite images of the seven known Ross Sea colonies. We used regression to predict adult bird counts at the inaccessible colonies by relating the number of pixels classified as “penguin” in the satellite images of the accessible colonies to corresponding known adult bird counts from aerial photographs or ground counts. While our analysis was hampered by excessive guano and shadows, we used satellite imagery to differentiate between relatively small (<3,000 adult birds) and larger colonies (>5,000 adult birds). Remote sensing technology is logistically less intense and less costly than aerial or ground censuses when the objective is to document penguin presence and/or large emperor penguin population changes (e.g., catastrophic changes). Improvements expected soon in the resolution of the satellite images should allow for more accurate abundance estimates.     

基于多时相中巴资源卫星影像的冬小麦分类精度

中巴资源卫星2号星（CBERS-02）具有较高的空间分辨率和较丰富的光谱信息,对植被有较强的探测能力.利用2006—2007年北京地区冬小麦生育期早期的5景CBERS-02卫星影像,计算了各时相和不同时相组合的主要地物类型及冬小麦的光谱可分性距离,进行了监督分类,同时,结合高分辨率航空和卫星遥感影像,构建了训练样本和验证样本,对利用CBERS-02卫星提取的生育早期的冬小麦进行了时相分析和精度评价,并与同期TM影像提取结果进行对比.结果表明：时相是影响冬小麦分类的主要因素,不同光学传感器的遥感影像也会影响分类精度;多时相组合有利于提高冬小麦的提取精度,与单时相冬小麦提取的最高精度相比,最佳时相组合的制图精度提高了20.0%、用户精度提高了7.83%;与TM数据相比, CBERS-02卫星影像的冬小麦分类精度略低.     

SCAPEVIEWER: preliminary results of a landscape perception classification system based on neural network technology

In this paper, the implementation of a pilot computerized system for the classification of landscape images (SCAPEVIEWER) is presented. A total of 108 landscape photographs have been organized, according to the mean estimation of scenic beauty from seven experts, into three classes: indistinctive (C1), typical or common (C2), and distinctive (C3). For each of the landscape photographs, 10 indices are estimated. These indices are then fed to a classifier based on neural network (NN) technology. In order to examine whether NNs are suitable for this specific application, two different approaches have been tested and compared against a linear discrimination method (LDM) classifier. The first approach is a feed forward NN (Classic-NN), while the second approach (Hybrid-NN) is based on the Classic-NN modified by using genetic algorithms (GAs). The correct classification performances achieved by the Classic-NN and the Hybrid-NN were 87% and 84%, respectively, while the classification performance of the LDM classifier was only 68%. Although the Classic-NN achieved slightly better results than the Hybrid-NN, the latter is preferred due to its ability of index selection and automatical adjustment of internal NN parameters. The pilot system has shown the feasibility for classifying landscape photographs according to scenic beauty by means of a computerized system combining the knowledge of an expert with a NN classifier.     

Using unmanned aerial vehicles for vegetation mapping and identification of botanical species in wetlands

High-resolution aerial photographs have important applications in vegetation mapping, especially in environments, such as wetlands, which are not easily accessible by ground operators. Unmanned aerial vehicles (UAVs), equipped with cameras capable of taking photographs of <?1 cm pixel resolution, are promising not only for the vegetation mapping but also for the identification of plant species. This paper illustrated the results of three different flight heights (5 m?=?3.5126 mm/pixel; 10 m?=?7.0252 mm/pixel; 25 m?=?17.5630 mm/pixel), using 12MP images and their magnification, on the identification of vegetation and botanical species in a rewetted peatland populated mainly by Phragmites australis and Myriophyllum aquaticum within the Massaciuccoli Lake basin (Northern Tuscany, Italy). Among the obtained images, we selected the best flight height for the vegetation mapping and the botanical identification of the plant species using both visual and automated image analyses. Images taken from flights at 25 m of height proved to be useful for a sufficiently detailed mapping, while those from 10 m of height were more suitable for the detection of plant microcommunities. However, the most accurate identification of the species (at the taxonomic level of genus/species) was possible only with the images taken from 5 m of height.

     

Remote sensing of upland vegetation: the potential of high spatial resolution satellite sensors

Aim Traditional methodologies of mapping vegetation, as carried out by ecologists, consist primarily of field surveying or mapping from aerial photography. Previous applications of satellite imagery for this task (e.g. Landsat TM and SPOT HRV) have been unsuccessful, as such imagery proved to have insufficient spatial resolution for mapping vegetation. This paper reports on a study to assess the capabilities of the recently launched remote sensing satellite sensor Ikonos, with improved capabilities, for mapping and monitoring upland vegetation using traditional image classification methods. Location The location is Northumberland National Park, UK. Methods Traditional remote sensing classification methodologies were applied to the Ikonos data and the outputs compared to ground data sets. This enabled an assessment of the value of the improved spatial resolution of satellite imagery for mapping upland vegetation. Post‐classification methods were applied to remove noise and misclassified pixels and to create maps that were more in keeping with the information requirements of the NNPA for current management processes. Results The approach adopted herein for quick and inexpensive land cover mapping was found to be capable of higher accuracy than achieved with previous approaches, highlighting the benefits of remote sensing for providing land cover maps. Main conclusions Ikonos imagery proved to be a useful tool for mapping upland vegetation across large areas and at fine spatial resolution, providing accuracies comparable to traditional mapping methods of ground surveys and aerial photography.     

High‐resolution riparian vegetation mapping to prioritize conservation and restoration in an impaired desert river

In highly impaired watersheds, it is critical to identify both areas with desirable habitat as conservation zones and impaired areas with the highest likelihood of improvement as restoration zones. We present how detailed riparian vegetation mapping can be used to prioritize conservation and restoration sites within a riparian and instream habitat restoration program targeting 3 native fish species on the San Rafael River, a desert river in southeastern Utah, United States. We classified vegetation using a combination of object‐based image analysis (OBIA) on high‐resolution (0.5 m), multispectral, satellite imagery with oblique aerial photography and field‐based data collection. The OBIA approach is objective, repeatable, and applicable to large areas. The overall accuracy of the classification was 80% (Cohen's κ = 0.77). We used this high‐resolution vegetation classification alongside existing data on habitat condition and aquatic species' distributions to identify reaches' conservation value and restoration potential to guide management actions. Specifically, cottonwood (Populus fremontii) and tamarisk (Tamarix ramosissima) density layers helped to establish broad restoration and conservation reach classes. The high‐resolution vegetation mapping precisely identified individual cottonwood trees and tamarisk thickets, which were used to determine specific locations for restoration activities such as beaver dam analogue structures in cottonwood restoration areas, or strategic tamarisk removal in high‐density tamarisk sites. The site prioritization method presented here is effective for planning large‐scale river restoration and is transferable to other desert river systems elsewhere in the world.