Modelling and Mapping Above Ground Biomass Using Sentinel 2 and Planet Scope Remotely Sensed Data in West Usambara Tropical Rainforests, Tanzania

Abstract

Forest biomass estimation using field -based inventories at a large scale is challenging and generally entails large uncertainty in tropical regions. With their wall-to-wall coverage ability, optical remote sensing signals had gained a wide acceptance for larger scale estimation of AGB at different spatial scales, ranging from local to global. However, their applicability in tropical forests is still limited. In this study, we investigated the performance of Sentinel 2 and Planet Scope remotely sensed data for AGB modelling, predicting and mapping in the tropical rainforest of Tanzania. A total of 296 field inventory plots were measured across the west Usambara mountain forests. AGB values were computed for each of the field plot in Mg/ha, and related with remotely sensed predictor variables using parametric and non- parametric statistical methods. Band values, vegetation indices and texture based variables were derived from each of the remotely sensed data. The AGB models were developed and validated using k-fold cross validation and their relative root mean square error (cvRMSEr%) were used to judge their accuracies. Relative efficiency (RE) of each dataset as compared to pure field inventory was also computed. The results showed that, Sentinel 2 based model fitted using generalized linear models (RMSEr = 67.00 % and pseudo-R2= 20%) had better performance as compared to Planet Scope based models (cvRMSEr = 72.1 % and pseudo-R2= 5.2%). Overall GLMs resulted into a models with less prediction error as compared to random forest when using Sentinel 2 data. However, for the Planet Scope, there was marginal improvement of using random forest (cvRMSEr = 72.0%) as compared to GLMs. Models, that in cooperated texture variables resulted into better prediction accuracy as compared to those with band values and indices only. The R.E values for Sentinel2 and Planet Scope were 1.2 and 1.1 respectively. Our study had demonstrated that Sentinel 2 and Planet Scope remotely sensed data can be used to develop cost-effective method for AGB estimation within the context of tropical rainforests of Tanzania. Further studies are however encouraged to look more on the best way of optimizing the efficiency of the two data sources in AGB estimations.

Fault structure mapping using satellite-based remote sensing technology in Mindoro

“…The study utilizes a combination of high resolution remote sensing data and other relevant textual materials, optical data from the PlanetScope. Planet's constellation of Dove satellites offers daily imagery with a spatial resolution of approximately 3-5 meters [6][7] (Table 1), which is sufficient for detailed observation of surface features that may indicate the presence of fault structures.…”

Estimating Forest Variables for Major Commercial Timber Plantations in Northern Spain Using Sentinel-2 and Ancillary Data

“…This is because texture measures increase the spatial information about the stand and therefore better capture their structural characteristics [92]. Many authors have increased the accuracy of forest models by adding texture measures to the spectral bands and indexes (e.g., [93][94][95]).…”

Total Stem Biomass Estimation Using Sentinel-1 and -2 Data in a Dense Coniferous Forest of Complex Structure and Terrain

“…Considering that a close relationship was found in previous studies between optical sensors' spectral products (e.g., vegetation indices) and forest AGB [16][17][18][19][20]64], eight broadand narrow-band greenness vegetation indices, one water content vegetation index, and three biophysical parameters (i.e., leaf area index (LAI), fraction of absorbed photosynthetically active radiation (FAPAR), fractional cover (FCOVER)) were computed and employed as additional predictor variables (Table 4). Spectral feature selection was based on their merits for biophysical parameter retrieval, as identified in recent studies [9,11,19,32]. Vegetation index computation was performed within the GGE environment, while biophysical parameters were produced using the biophysical processor tool of the SNAP toolbox.…”

“…Landsat and MODIS are among the most common data sources for AGB estimation for continental scales, providing a reasonable accuracy [10]. Contrary to other optical sensors (e.g., Landsat 8 OLI), the improved spatial resolution of Sentinel-2 data enables small-scale AGB estimation at a plot-or stand-level [11]. Moreover, Sentinel-2 offers three additional vegetation spectral bands in the red edge (RE) region and one narrow near infrared (NNIR) band, both at 20 m spatial resolution [12], which are expected to contribute to improved biomass estimation and mapping [13][14][15].…”