Tree Species Abundance Predictions in a Tropical Agricultural Landscape with a Supervised Classification Model and Imbalanced Data

Abstract

Abstract:Mapping species through classification of imaging spectroscopy data is facilitating research to understand tree species distributions at increasingly greater spatial scales. Classification requires a dataset of field observations matched to the image, which will often reflect natural species distributions, resulting in an imbalanced dataset with many samples for common species and few samples for less common species. Despite the high prevalence of imbalanced datasets in multiclass species predictions, the effect on species prediction accuracy and landscape species abundance has not yet been quantified. First, we trained and assessed the accuracy of a support vector machine (SVM) model with a highly imbalanced dataset of 20 tropical species and one mixed-species class of 24 species identified in a hyperspectral image mosaic (350-2500 nm) of Panamanian farmland and secondary forest fragments. The model, with an overall accuracy of 62%˘2.3% and F-score of 59%˘2.7%, was applied to the full image mosaic (23,000 ha at a 2-m resolution) to produce a species prediction map, which suggested that this tropical agricultural landscape is more diverse than what has been presented in field-based studies. Second, we quantified the effect of class imbalance on model accuracy. Model assessment showed a trend where species with more samples were consistently over predicted while species with fewer samples were under predicted. Standardizing sample size reduced model accuracy, but also reduced the level of species over-and under-prediction. This study advances operational species mapping of diverse tropical landscapes by detailing the effect of imbalanced data on classification accuracy and providing estimates of tree species abundance in an agricultural landscape. Species maps using data and methods presented here can be used in landscape analyses of species distributions to understand human or environmental effects, in addition to focusing conservation efforts in areas with high tree cover and diversity.

Classification of Tree Species in a Diverse African Agroforestry Landscape Using Imaging Spectroscopy and Laser Scanning

“…However, when species with fewer than 20 samples were combined, we reached higher OA and Kappa than in a recent study conducted in a similar landscape in Panama [10]. However, our data had fewer species with more than 20 samples.…”

“…In the previous studies with a high number of species, a mixed group of species with fewer samples has been commonly used [10,56]. However, combining all the species under a fixed limit (e.g., 20 samples) creates large and highly heterogeneous mixed class.…”

“…We tested three different approaches to group species. First, we grouped all species with less than 20 samples together to group "other" and classified the rest of the species separately as done earlier in a similar setting [10]. Next, we classified each of the species separately against a mixed group of all other species.…”

“…In total 61 species were observed for 543 crowns, while 19 of the species had only 1 observation and 10 had 2-3 observations. These were excluded from the classification as done in a similar setting earlier [10]. The classifications included in total 499 crowns from 31 species (Table 1).…”

Feature Selection Solution with High Dimensionality and Low-Sample Size for Land Cover Classification in Object-Based Image Analysis

“…As mentioned in Section 2.2, classifiers tend to result in a classification preference towards the major class with uneven class sizes [39,51]. In a feature selection process, the selected features may be more conducive to the separation of the major class.…”