Mapping high-resolution percentage canopy cover using a multi-sensor approach

Sunde, Michael G.; Diamond, David D.; Elliott, Lee F.; Hanberry, Phillip; True, Diane

doi:10.1016/j.rse.2020.111748

Cited by 10 publications

(5 citation statements)

References 25 publications

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“…We estimated canopy cover using 20‐m resolution imagery from Sentinel Hub (2022) to create canopy rasters via object‐based image analysis and supervised classification in ArcGIS Pro (Esri, 2011) for each year (2016–2020). These rasters were visually verified using fine‐scale, 3‐m resolution imagery via Planet Labs (Planet Team, 2017; Sunde et al, 2020; Tilahun, 2015). A full description of methods used to create and verify canopy rasters can be found in Appendix S1: Tables S1 and S2.…”

Section: Methodsmentioning

confidence: 99%

Mammalian resistance to megafire in western U.S. woodland savannas

et al. 2023

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Increasingly frequent megafires are dramatically altering landscapes and critical habitats around the world. Across the western United States, megafires have become an almost annual occurrence, but the implication of these fires for the conservation of native wildlife remains relatively unknown. Woodland savannas are among the world's most biodiverse ecosystems and provide important food and structural resources to a variety of wildlife, but they are threatened by megafires. Despite this, the great majority of fire impact studies have only been conducted in coniferous forests. Understanding the resistance and resilience of wildlife assemblages following these extreme perturbations can help inform future management interventions that limit biodiversity loss due to megafire. We assessed the resistance of a woodland savanna mammal community to the short‐term impacts of megafire using camera trap data collected before, during, and after the fire. Specifically, we utilized a 5‐year camera trap data set (2016–2020) from the Hopland Research and Extension Center to examine the impacts of the 2018 Mendocino Complex Fire, California's largest recorded wildfire at the time, on the distributions of eight observed mammal species. We used a multispecies occupancy model to quantify the effects of megafire on species' space use, to assess the impact on species size and diet groups, and to create robust estimates of fire's impacts on species diversity across space and time. Megafire had a negative effect on the detection of certain mammal species, but overall, most species showed high resistance to the disturbance and returned to detection and site use levels comparable to unburned sites by the end of the study period. Following megafire, species richness was higher in burned areas that retained higher canopy cover relative to unburned and burned sites with low canopy cover. Fire management that prevents large‐scale canopy loss is critical to providing refugia for vulnerable species immediately following fire in oak woodlands, and likely other mixed‐forest landscapes.

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Section: Methodsmentioning

confidence: 99%

Mammalian resistance to megafire in western U.S. woodland savannas

et al. 2023

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“…The pixel-based method treats a single pixel as an analysis unit. The simplest implementation of the pixel-based method is shown by Path-#1 in Figure 2, which utilizes the pixel values for all the bands in this pixel location (purple color indicates pixels of concern) to form a feature vector, and employs the traditional classifiers such as random forest (RF) [39,40], support vector machine (SVM) [41,42], artificial neural network (ANN), and maximum likelihood classification (MLC) to perform the classification [43]. In this scenario, one feature vector leads to one scalar value (often an integer), which represents one landcover type as a classification result and is assigned to this pixel location.…”

Section: Pixel-based Mapping Methodsmentioning

confidence: 99%

A Review of Landcover Classification with Very-High Resolution Remotely Sensed Optical Images—Analysis Unit, Model Scalability and Transferability

Qin

Liu

2022

Remote Sensing

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As an important application in remote sensing, landcover classification remains one of the most challenging tasks in very-high-resolution (VHR) image analysis. As the rapidly increasing number of Deep Learning (DL) based landcover methods and training strategies are claimed to be the state-of-the-art, the already fragmented technical landscape of landcover mapping methods has been further complicated. Although there exists a plethora of literature review work attempting to guide researchers in making an informed choice of landcover mapping methods, the articles either focus on the review of applications in a specific area or revolve around general deep learning models, which lack a systematic view of the ever advancing landcover mapping methods. In addition, issues related to training samples and model transferability have become more critical than ever in an era dominated by data-driven approaches, but these issues were addressed to a lesser extent in previous review articles regarding remote sensing classification. Therefore, in this paper, we present a systematic overview of existing methods by starting from learning methods and varying basic analysis units for landcover mapping tasks, to challenges and solutions on three aspects of scalability and transferability with a remote sensing classification focus including (1) sparsity and imbalance of data; (2) domain gaps across different geographical regions; and (3) multi-source and multi-view fusion. We discuss in detail each of these categorical methods and draw concluding remarks in these developments and recommend potential directions for the continued endeavor.

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“…Considering the uneven spatial distribution of a forest, a large number of training samples need to be selected and labeled [32]. In FFC research, although the method based on field sampling can be used to obtain more accurate sample data, this method is expensive and can only be used in small research areas [43,44]; UAV RS can be used to obtain a larger range of sample data, but it is limited by the availability and timeliness of the data [20,[45][46][47]. High-resolution satellite remote sensing imagery (HRSRS) is characterized by a high spatial resolution and wide imaging range and has obvious advantages in terms of ground object recognition.…”

Section: Introductionmentioning

confidence: 99%

“…High-resolution satellite remote sensing imagery (HRSRS) is characterized by a high spatial resolution and wide imaging range and has obvious advantages in terms of ground object recognition. HRSRS is widely used to obtain reference datasets in the research involving estimating the FFC [22,23,34,[47][48][49]. For example, Baumann, et al [23], Godinho et al [34] used high-resolution Google Earth images to collect reference datasets, while Gessner, et al [22] used the results of classification and aggregation of IKONOS and QuickBird as a reference dataset.…”

Section: Introductionmentioning

confidence: 99%

Mapping 30 m Fractional Forest Cover over China’s Three-North Region from Landsat-8 Data Using Ensemble Machine Learning Methods

Liu

Liang

et al. 2021

Remote Sensing

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The accurate monitoring of forest cover and its changes are essential for environmental change research, but current satellite products for forest coverage carry many uncertainties. This study used 30-m Landsat-8 data, and aggregated 1-m GaoFen-2 (GF-2) satellite images to construct the training samples and used multiple machine learning algorithms (MLAs) to estimate the fractional forest cover (FFC) in China’s Three North Region (TNR). In this study, multiple MLAs were merged to construct stacked generalization (SG) models based on the idea of SG, and the performances of the MLAs in the FFC estimation were evaluated. The results of the 10-fold cross-validation showed that all non-linear algorithms had a good performance, with an R2 value of greater than 0.8 and a root-mean square error (RMSE) of less than 0.05. In the bagging ensemble, the random forest (RF) (R2 = 0.993, RMSE = 0.020) model performed the best and in the boosting ensemble, the light gradient boosted machine (LGBM) (R2 = 0.992, RMSE = 0.022) performed the best. Although the evaluation index of the RF is slightly better than that of the LGBM, the independent validation results show that the two models have similar performances. The model evaluation results of the independent datasets showed that, in the SG model, the performance of the SG(LGBM) (R2 = 0.991, RMSE = 0.034) was better than that of the single or non-ensemble model. Comparing the FFC estimates of our model with those of existing datasets showed that our model exhibited more forest spatial distribution details and higher accuracy in complex landscapes. Overall, in this study, the method of using high-resolution remote sensing (RS) images to extract samples for FFC estimation is feasible. Our results demonstrate the potential of the ensemble MLAs to map the FFC. The research results also show that among many MALs, the RF algorithm is the most suitable algorithm for estimating FFC, which provides a reference for future research.

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Mapping high-resolution percentage canopy cover using a multi-sensor approach

Cited by 10 publications

References 25 publications

Mammalian resistance to megafire in western U.S. woodland savannas

Mammalian resistance to megafire in western U.S. woodland savannas

A Review of Landcover Classification with Very-High Resolution Remotely Sensed Optical Images—Analysis Unit, Model Scalability and Transferability

Mapping 30 m Fractional Forest Cover over China’s Three-North Region from Landsat-8 Data Using Ensemble Machine Learning Methods

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