The Potential of High Resolution (5 m) RapidEye Optical Data to Estimate Above Ground Biomass at the National Level over Tanzania

Abstract

In this paper, we review the potential of high resolution optical satellite data to reduce the significant investment in resources required for a national field survey for producing estimates of above ground biomass (AGB). We use 5 m resolution RapidEye optical data to support a country wide biomass inventory with the objective of bringing to the attention of the traditional forestry sector the advantages of integrating remote sensing data in the planning and execution of field data acquisition. We analysed the relationship between AGB estimates from a subset of the national survey field plot data collected by the Tanzania Forest Service, with a set of remote sensing biophysical parameters extracted from a sample of fine spatial (5 m) resolution RapidEye images using a regression estimator. We processed RapidEye data using image segmentation for 76 sample sites each of 20 km by 20 km (covering 2.3% of the land area of the country) to image objects of 1 ha. We extracted reflectance and texture information from those objects which overlapped with the field plot data and tested correlations between the two using four different models: Two models from inferential statistics and two models from machine learning. The best results were found using the random forests algorithm (R2 = 0.69). The most important explicative factor extracted from the remote sensing data was the shadow index, measuring the absorption of light in the visible bands. The model was then applied to all image objects on the RapidEye images to obtain AGB for each of the 76 sample sites, which were then interpolated to estimate the AGB stock at the national scale. Using the relative efficiency measure, we assessed the improvement that the introduction of remote sensing data brings to obtain an AGB estimate at the national level, with the same precision as the full survey. The improvement in the precision of the estimate (by reducing its variance) resulted in a relative efficiency of 3.2. This demonstrates that the introduction of remote sensing data at this fine resolution can substantially reduce the number of field plots required, in this case threefold.

Monitoring tropical forest carbon stocks and emissions using Planet satellite data

“…Hojas Gascon et al . 21 used RapidEye (5 m) optical images to reduce the efforts in collecting national field data for estimating AGB over Tanzania. Hirata et al .…”

“…22 developed an object-based approach to map the AGB in tropical forests of Cambodia using a combination of airborne LiDAR with QuickBird images. While medium and lower resolution satellite images have global coverage and are usually free of charge, the usage of high and very-high resolution satellite images in estimating ACD is limited to smaller study areas, due to their costs and availability 21 .…”

“…Part of this success is because RF is non-parametric, robust to a high number of input variables and insensitive to data skew 36 . RF regression techniques have been intensively used to map carbon stocks 8,35 , biomass 21,31 or tree canopy height 37,38 , for a broad range of spatial resolutions of satellite images. When dealing with high and very high resolution images, besides spectral reflectance, band ratios and indices derived from these, common features used in an RF regression are related to image texture, which detects forest canopy structural heterogeneity and ultimately predicts variations in ACD 20 .…”

“…Two very popular textural measures used as a remotely sensed vegetation structure feature are the grey-level co-occurrence matrix (GLCM) texture 39–42 and Fourier transform textural ordination (FOTO) 20,43–45 . Although textural features have been applied to various sensors such as IKONOS-2 42 , Cartosat-1a 46 , SPOT-5 41 , QuickBird 47,48 , WorldView-2 49 , or RapidEye 21 , it was not tested on how it performs using Planet Dove images for large scale mapping of ACD.…”

Landscape Dynamics in an Iconic Watershed of Northwestern Mexico: Vegetation Condition Insights Using Landsat and PlanetScope Data

“…As satellite data increase in their availability and temporal repertoire, future applications for higher resolution data are promising [103,104]. Our results show that high spatial resolution data are ideal to generate detailed maps of spatially restricted habitats, such as riparian ecosystems in arid and semi-arid regions.…”