The United Nation’s Program for Reducing Emissions from Deforestation and Forest Degradation (REDD+) aims to reduce the 20% contribution to global emissions of greenhouse gases from the forest sector, offering a financial value of the carbon stored in forests as an incentive for local communities. The pre-requisite for the setup of a participatory REDD + Program is the monitoring, reporting and verification (MRV) of baseline carbon stocks and their changes over time. In this study, we investigated miombo woodland’s dynamics in terms of composition, structure and biomass over a 4-year period (2005–2009), and the Carbon Stock Density (CSD) for the year 2009. The study was conducted in the Niassa National Reserve (NNR) in northern Mozambique, which is the 14th largest protected area in the world.

The advances of remote sensing techniques allow for the generation of dense point clouds to detect detailed surface changes up to centimeter/millimeter levels. However, there is still a need for an easy method to derive such surface changes based on digital elevation models generated from dense point clouds while taking into consideration spatial varied uncertainty. We present a straightforward method, Las2DoD, to quantify surface change directly from point clouds with spatially varied uncertainty. This method uses a cell-based Welch’s t-test to determine whether each cell of a surface experienced a significant elevation change based on the points measured within the cell. Las2DoD is coded in Python with a simple graphic user interface. It was applied in a case study to quantify hillslope erosion on two plots: one dominated by rill erosion, and the other by sheet erosion, in southeastern United States. The results from the rilled plot indicate that Las2DoD can estimate 90% of the total measured sediment, in comparison to 58% and 70% from two other commonly used methods. The Las2DOD-derived result is less accurate (65%) but still outperforms the other two methods (30% and 48%) for the plot dominated by sheet erosion. Las2DoD captures more low-magnitude changes and is particularly useful where surface changes are small but contribute significantly to the total surface change when summed.

The purpose of this study was to identify the ecological effects of drought on the vegetation resources of subsistence agropastoral communities on the Bolivian Altiplano. The objectives of the study were to (1) characterize vegetation cover response during a typical year, and (2) identify vegetation cover type's response to drought using an image differencing change detection technique. A geographical information system (GIS), which included multi-temporal (from 1972-1987) Landsat satellite imagery, was used as the assessment tool. Vegetation index difference images showed that (1) all vegetation types were impacted by drought, but the wet meadow vegetation cover type had the least response, and (2) approximately 90 per cent of vegetation cover had not changed between 1972 and 1987. Crop and animal production in agropastoral systems are dependant on the availability of vegetation resources. The wet meadow vegetation type was the most resistant to drought, which suggests that during drought periods it is a key resource upon which the livestock of the community would depend. Little change in vegetation cover over the 15-year span of the study, and the rapid recovery of this parameter following the drought of 1983-84, suggest that agricultural practices (especially livestock grazing) are not contributing to resource degradation when measured only in terms of change in vegetation cover.

Aboveground biomass (AGB) estimation plays a crucial role in forest management and carbon emission reporting, especially for developing countries wishing to address REDD+ projects. Both passive and active remote-sensing technologies can provide spatially explicit information of AGB by using a limited number of field samples, thus reducing the substantial budgetary cost of field inventories. The aim of the current study was to estimate AGB in the Niassa Special Reserve (NSR) using fusion of optical (Landsat 8/OLI and Sentinel 2A/MSI) and radar (Sentinel 1B and ALOS/PALSAR-2) data. The performance of multiple linear regression models to relate ground biomass with different combinations of sensor data was assessed using root-mean-square error (RMSE), and the Akaike and Bayesian information criteria (AIC and BIC). The mean AGB and carbon stock (CS) estimated from field data were estimated at 56 Mg ha⁻¹ (ranging from 11 to 95 Mg ha⁻¹) and 28 MgC ha⁻¹, respectively. The best model estimated AGB at 63 ± 20.3 Mg ha⁻¹ for NSR, ranging from 0.6 to 200 Mg ha⁻¹ (r² = 87.5%, AIC = 123, and BIC = 51.93). We obtained an RMSE % of 20.46 of the mean field estimate of 56 Mg ha⁻¹. The estimation of AGB in this study was within the range that was reported in the existing literature for the miombo woodlands. The fusion of vegetation indices derived from Landsat/OLI and Sentinel 2A/MSI, and backscatter from ALOS/PALSAR-2 is a good predictor of AGB.

A critical component of environmental education is to ensure student understanding and use of available technologies to better experience and analyze spatially distributed features of the environment. Combining mobile technologies with geographic information systems in field data collection may provide a unique opportunity for students to feel engaged in what they are learning and take ownership of their learning process. We customized an open access data collection application using Collector for ArcGIS and investigated its impacts on student engagement and perception of the incorporation of technology within an environmental science curriculum. Analyses of pre- and post-surveys indicate that the inclusion of geospatial technologies as a part of environmental curricula allows students to take the lead on their own research, view field data interactively as opposed to looking at a database in hindsight and analyze multiscale data as it is presented during field data collection. The findings of this study are consistent with previous studies, suggesting a strong association between the inclusion of geospatial technologies as a part of curricula and student engagement.