**Author details**

**ANOVA Pairwise comparisons between chlorophyll estimation methods (Holm adjustment method) p-value SPAD vs. reflectance SPAD vs. PROSPECT Reflectance vs. PROSPECT**

**Table 5.** ANOVA and pairwise comparison between the three chlorophyll methods for chlorophyll estimation based on

**Table 5** shows ANOVA and pairwise comparison between the three chlorophyll methods for

**Figure 10** presented that the chlorophyll estimations at leave level (SPAD-502, reflectance indices, and PROSPECT model) and estimations at regional level (satellite images) applied in this study show strong correlations between them. This finding demonstrates that a combination of field-based methods at leaf level with remote sensing methods at regional level may provide a good opportunity to evaluate forest health caused by land use changes. As it was stated in the introduction, forest degradation and its related changes in ecosystem services have not been fully assessed using remote sensing techniques, especially in high diverse tropical forest. The estimations of MTCI index in Site 1 and Site 2 shown in **Figure 12** have demonstrated lower levels of chlorophyll content caused by land use changes, specifically due the influence of petroleum facilities cause forest degradation. Therefore, in those areas accurate estimations of photosynthetic activity of forested areas are needed to quantify forest degra-

dation and evaluate environmental services provided by flora in the tropical forest.

Three optical methods for estimation of chlorophyll content at leaf level were applied to the collection of over 1100 leaf samples collected in the Ecuadorian Amazon rainforest, which represents a wide range of vegetation species growing in a disturbed and a pristine lowland rainforest. The first method is based on transmittance from the SPAD-502 chlorophyll meter index, the second method is based on reflectance measurements collected by a spectroradiometer, and the third method estimates chlorophyll content from the radiative transfer PROSPECT model. For the first method, seven models that account for a wide range of

All dataset \*\*\* \*\*\* \*\*\* \* <28 ns ns ns ns 28–40 \*\*\* \*\* . \*\*\* 40–50 \*\*\* ns \*\*\* \*\*\* 50–60 \*\*\* \*\*\* \*\*\* \*\*\* 60–70 \*\*\* \*\*\* \*\*\* ns 70–80 \*\*\* \*\*\* \*\*\* \*\* 80> \*\*\* \*\*\* \*\*\* \*\*\*

70 Tropical Forests - The Challenges of Maintaining Ecosystem Services while Managing the Landscape

chlorophyll estimation based on the binned SPAD-502 index.

ns, nonsignificant.

\*\*Highly significant (1%). \*Significant (5%).

the binned SPAD-502 index.

**6. Conclusion**

\*\*\*Strongly significant (0.1%) or lowest significant (10%).

Paul Arellano1,2,3\*, Kevin Tansey3 and Heiko Balzter3,4

\*Address all correspondence to: parellano@yachaytech.edu.ec; pa134@le.ac.uk

1 Yachay Tech University, School of Geological Sciences & Engineering, San Miguel de Urcuquí, Imbabura, Ecuador

2 Centre of Earth Observation, Yachay Tech University, San Miguel de Urcuquí, Hacienda, Imbabura, Ecuador

3 University of Leicester, Department of Geography, Centre of Landscape and Climate Research, Leicester, UK

4 National Centre for Earth Observation, University of Leicester, Leicester, UK
