With the increase of technological capacities, many forest inventory techniques are supported by remote sensing data. Despite the variety of methods used for land use land cover classification, the lack of specific methods for developing countries is still a concern. Canopy cover mapping methods are an important element in the monitoring activities of Reducing Emissions from Deforestation and forest Degradation (REDD+) programmes for the United Nations. As a worldwide initiative, REDD+ focuses on environmental issues while also considering social factors, hence, by its nature, many REDD+ programmes focus on developing countries. The enhancement of a country’s existing national forest monitoring systems (NFMS) to obtain more accurate information of its forest resources is a common theme amongst REDD+
supported projects (GOFC-GOLD 2016).
Due to working in the context of sustainable development, the programme has wide scale political support within such developing countries, and involves work with local communities to aid public level understanding of individual actions in the context of a whole community.
These system has two functions, such as monitoring and MRV ((monitoring, reporting and verification) (GOFC-GOLD 2016). According to the Global Forest Observation Initiative’s report of year 2016 the monitoring function underlines that many domestic instruments for forest resource assessment already exist, and are able to provide the information necessary for REDD+, though, specific improvements are often necessary, in addition adjustments to the aims of the program and countries specific features. The other function - MRV - is relaxed to the implementation of monitoring tools and the data acquisition for reporting (GOFC-GOLD 2016).
Remote sensing data for forest inventory allow expanding of spatial coverage of large area biomass estimates. In the same time, remotely sensed data are necessary to fill spatial gaps when an inventory project is conducted. This type of hybrid approach is particularly required for natural forests where basic inventory data for biomass estimation are lacking. Minimum mapping units depend on the data quality and resolution. Nowadays, remotely sensed data have become an key data source for biomass estimation. Generally, two ways to estimate biomass exist – through a direct relationship between spectral response (or backscatter for SAR) and biomass using statistical methods; or via indirect relationships. Attributes are derived from leaf area index (LAI), structure (crown closure and height) or shadow fraction.
(GOFC-GOLD 2016).
A variety of remotely sensed data sources of coarse spatial resolution are important and those as utilised for biomass mapping (data, such as SPOT VEGETATION, AVHRR, and MODIS).
In order to establish a good linkage of field measurements and coarse resolution remote sensing data (e.g., MODIS, AVHRR, IRS - WiFS), several studies have introduced multiscale imagery with moderate spatial resolution imagery (e.g., Landsat, ASTER) in their methods. The most frequently utilised imagery in these studies is data of Landsat TM and ETM+. Deriving stand attributes from LIDAR data and inserting them into allometric biomass equations became an additional way in many studies. Other studies found the use of multispectral, LIDAR and RADAR data integrating spectral response, image texture and backscatter together as supplementary variables in multivariate regression models (GOFC-GOLD 2016).
Geographical information system (GIS) based methods with ancillary data exclusively ( i.e.
climate normals, precipitation, topography, and vegetation zones) are applicable in biomass estimation (GOFC-GOLD 2016). Geostatistical approaches, for instance, kriging are utilised in different research works (Simard et al., 1992). Frequently, GIS models are used to combine multiple data sources for biomass estimation (e.g., forest inventory and remotely sensed data).
For instance, mapping of Amazonian forest AGB was conducted using MODIS, JERS 1 SAR, QuickSCAT, SRTM, and climate and vegetation data (GOFC-GOLD 2016).
The major question in the use of remotely sensed data for forest biomass mapping is consistency in results from different sources and methods with the respect to relationships respect to both time and space. Additional work is needed to improve uncertainty in biomass estimation for ground-based methodologies – it is important to learn about sources of uncertainty and introduce remote sensing methods, which are reliable and are equally suitable across time and space (GOFC-GOLD 2016). Table 1 represents data availability, which can be used for biomass estimation.
Table 1. Current availability of fine scale satellite data sources (GOFC-GOLD 2016)
DMS program Probably Commercially
ALOS /
Note: dark blue means it is common or fully existing, light blue means it is partially existing and several examples were found, white – rare or no applications or examples.
The implementation and development of different earth observation techniques is highly important for climate change mitigation within the REDD+ program. Earth observation from space first started in 1972 (Jones and Vaughan, 2011), when the first data about our planet was acquired. The importance of the developments in aerial color and color infrared in the 1950s is also outlined by this text, as it enabled the first opportunities to assess vegetation cover. In the
same decade, Robert Colwell first referenced the introduction of photogrammetry and photointerpretation (Colwell, 1956). In 1972 NASA implemented a new technology for this period (later renamed as Landsat), and those can be seem as the starting point for modern land use land cover classification (Murayama et al., 2015). Some other early publications were published in 70s (for instance, 1976, Anderson et al. (1976)). Tokola et al. (1999) provide an example about how to monitor deforestation and forest degradation over time using old Landsat data. Some historical events had a big impact on methodological development. The year 2005 is famous for the first launch of Google Earth, and in 2008 NASA announced that their data would become freely available (Jones and Vaughan 2011), opening previously unavailable possibilities for scientists across over the world.
During recent years the Food and Agricultural Organisation (FAO) of the United Nations has been active in developing operational MRV tools. The Open Foris approach for environmental monitoring based on canopy cover record and classification using Google Earth was introduced in 2009 (openforis.org).
Vegetation indexes is a tool to map land use classes and assess vegetation cover. One of the official sources for land use land cover classification is the guidelines rom the Intergovernmental Panel on Climate Change (IPCC) (IPCC Guidelines for National Greenhouse Gas Inventories, 2006). This includes the following classes for greenhouse gas inventories: (i) Forest land; (ii) Cropland; (iii) Grassland; (iv) Wetlands; (v) Settlements; (vi) Other land (Penman et al., 2012). Having unique ecosystems, countries need local approaches to assess their forest resources. Moreover, each area has different density of forest on its territory and this has to be taken into account when selecting a method. Due to the impact of forest degradation and deforestation having a significant effect, countries have a need for accurate information about their forests.
The main objective of this master’s thesis is to study different ways to measure forest degradation using optical satellite imagery in tropical arid forests of Western Africa. The target area is located in Senegal and partly includes the Niokolo-Koba National Park; it is a part of the REDD+ Piloting project “Monitoring and Non-Wood Forest Product Value Chains to Mitigate Green House Gas Emissions in the Rural Communities of Bandafassi, Senegal”. The projects was co-funded by Nordic Climate Facility (NCF), the facility was financed by the Nordic Development Fund (NDF) and NCF.
The research question is as follows:
What are the methods of canopy cover estimation using optical satellite imagery and how accurate are they?
Which can be split into two specific questions:
1. What are suitable vegetation indexes for canopy cover estimation?
2. How well can forest classes be predicted using modern remote sensing methods of canopy cover estimation and what are applicable thresholds?
The target area is a part of project within the REDD+ programme of the United Nations.
Understanding the importance of measuring forest cover for further improvement and adjustment of methods for specific conditions of African countries is highly important within climate change actions. This gives a strong motivation to focus on the topic of land use land cover classification for the case of Senegal. This country has not only unique natural characteristics and rare inhabitants, but cultural features with people living in this land and using this forest.