Earth Observation in Support of the City Biodiversity Index

Today, we are living in an urban world. For the first time in history, there are now more people living in cities than in rural areas. In Europe their share has reached almost three quarters. Urban areas supposedly will absorb almost all the population growth expected over the next decades. This will pose a range of challenges for cities and their surroundings, not only on resource availability and the quality of urban environments, but also on biodiversity in cities. Capturing the status and trends of biodiversity and ecosystem services in urban landscapes represents an important part of understanding whether a metropolitan area is developing along a sustainable trajectory or not. Actions to conserve biodiversity should start with stock-taking and identifying baselines, followed by regular monitoring of conservation initiatives. The City Biodiversity Index (CBI), also known as the Singapore Index on Cities‘ Biodiversity (or Singapore Index) because of Singapore‘s leadership in its development, has been adopted during COP-9 of the CBD in 2008. It is conceived as a self-assessment tool to evaluate the state of biodiversity in cities and to provide insights for improving conservation efforts. This includes an initial baseline measurement, the identification of policy priorities based on their measurements and then a monitoring at periodic intervals. Today, the CBI includes 23 indicators from three categories such as the proportion of natural areas in the city or the budget allocated to conservation projects. The CBI is designed to be applied by many cities in the world to monitor their progress in conservation efforts and their success in halting the rate of biodiversity loss. The project provides support to 4 of the 23 indicators. The results illustrated below are based on satellite earth observation data combined with local in-situ information. The output of the data analysis (i.e. percentage or an area value) can be directly used to determine the relevant CBI score

Unveiling Undercover Cropland Inside Forests Using Landscape Variables: A Supplement to Remote Sensing Image Classification

The worldwide demand for food has been increasing due to the rapidly growing global population, and agricultural lands have increased in extent to produce more food crops. The pattern of cropland varies among different regions depending on the traditional knowledge of farmers and availability of uncultivated land. Satellite images can be used to map cropland in open areas but have limitations for detecting undergrowth inside forests. Classification results are often biased and need to be supplemented with field observations. Undercover cropland inside forests in the Bale Mountains of Ethiopia was assessed using field observed percentage cover of land use/land cover classes, and topographic and location parameters. The most influential factors were identified using Boosted Regression Trees and used to map undercover cropland area. Elevation, slope, easterly aspect, distance to settlements, and distance to national park were found to be the most influential factors determining undercover cropland area. When there is very high demand for growing food crops, constrained under restricted rights for clearing forest, cultivation could take place within forests as an undercover. Further research on the impact of undercover cropland on ecosystem services and challenges in sustainable management is thus essential

Quantifying and Mapping Ecosystem Services Supplies and Demands: A Review of Remote Sensing Applications

Ecosystems provide services necessary for the livelihoods and well-being of people. Quantifying and mapping supplies and demands of ecosystem services is essential for continuous monitoring of such services to support decisionmaking. Area-wide and spatially explicit mapping of ecosystem services based on extensive ground surveys is restricted to local scales and limited due to high costs. In contrast, remote sensing provides reliable area-wide data for quantifying and mapping ecosystem services at comparatively low costs, and with the option of fast, frequent, and continuous observations for monitoring. In this paper, we review relevant remote sensing systems, sensor types, and methods applicable in quantifying selected provisioning and regulatory services. Furthermore, opportunities, challenges, and future prospects in using remote sensing for supporting ecosystem services, quantification and mapping are discussed

Undercover cropland area predicted from most influential topographic factors identified using Boosted Regression Trees.

<p>Undercover cropland area predicted from most influential topographic factors identified using Boosted Regression Trees.</p

Boosted Regression Trees fitted model showing the relative importance of influential factors of undercover cropland area calculated from field estimated percent cover.

<p>Boosted Regression Trees fitted model showing the relative importance of influential factors of undercover cropland area calculated from field estimated percent cover.</p

Influential factors of undercover cropland area (ha) calculated from field estimated percent cover derived using BRTs model with tree complexity, <i>tc</i> of 2, learning rate, <i>lr</i> of 0.008 and bag fraction, <i>bf</i> of 0.75.

<p>Influential factors of undercover cropland area (ha) calculated from field estimated percent cover derived using BRTs model with tree complexity, <i>tc</i> of 2, learning rate, <i>lr</i> of 0.008 and bag fraction, <i>bf</i> of 0.75.</p

General workflow of a) Field data sampling b) Image classification c) Validation of classification results.

<p>General workflow of a) Field data sampling b) Image classification c) Validation of classification results.</p