Estimation of Tree Cover in an Agricultural Parkland of Senegal Using Rule-Based Regression Tree Modeling

Stefanie Herrmann,Andrew Wickhorst,Stuart Marsh

doi:10.3390/rs5104900

Abstract

Field trees are an integral part of the farmed parkland landscape in West Africa and provide multiple benefits to the local environment and livelihoods. While field trees have received increasing interest in the context of strengthening resilience to climate variability and change, the actual extent of farmed parkland and spatial patterns of tree cover are largely unknown. We used the rule-based predictive modeling tool Cubist® to estimate field tree cover in the west-central agricultural region of Senegal. A collection of rules and associated multiple linear regression models was constructed from (1) a reference dataset of percent tree cover derived from very high spatial resolution data (2 m Orbview) as the dependent variable, and (2) ten years of 10-day 250 m Moderate Resolution Imaging Spectrometer (MODIS) Normalized Difference Vegetation Index (NDVI) composites and derived phenological metrics as independent variables. Correlation coefficients between modeled and reference percent tree cover of 0.88 and 0.77 were achieved for training and validation data respectively, with absolute mean errors of 1.07 and 1.03 percent tree cover. The resulting map shows a west-east gradient from high tree cover in the peri-urban areas of horticulture and arboriculture to low tree cover in the more sparsely populated eastern part of the study area. A comparison of current (2000s) tree cover along this gradient with historic cover as seen on Corona images reveals dynamics of change but also areas of remarkable stability of field tree cover since 1968. The proposed modeling approach can help to identify locations of high and low tree cover in dryland environments and guide ground studies and management interventions aimed at promoting the integration of field trees in agricultural systems.

Highlights

Our primary objective is to test a locally calibrated, rule-based modeling approach and produce a regionally valid map of current (2000s) tree cover for the west central agricultural region of Senegal, which is accurate enough to be useful for land use/management planning purposes and to function as a baseline for monitoring changes in tree cover
Our results demonstrate that rule-based regression tree modeling has potential to estimate tree cover in dryland environments with acceptable accuracy, if high spatial resolution data can be obtained for a subset of the area of interest
We produced a map of tree cover for the west-central agricultural region of Senegal by training a rule-based model with an Orbview-derived reference dataset to estimate percent tree cover from coarser resolution eMODIS data

Summary

Introduction

Agricultural parklands are a traditional land use system in semi-arid West Africa that is characterized by deliberate retention of scattered trees on cultivated land [1,2]. These field trees constitute an integral part of the ecological and livelihood systems. There has been a growing awareness of the importance of field trees as integral components of dryland agricultural systems, in the contexts of preventing or reversing land degradation and strengthening resilience to climate variability and change [5,6]. A systematic assessment of current field tree cover is required to identify target areas for promoting regeneration of field trees, allocating funds for interventions, and monitoring long term program success

Objectives

Results

Conclusion