National Forest Monitoring Systems Research Articles

Low-Cost Non-Contact Forest Inventory: A Case Study of Kieni Forest in Kenya

Forests are a vital source of food, fuel, and medicine and play a crucial role in climate change mitigation. Strategic and policy decisions on forest management and conservation require accurate and up-to-date information on available forest resources. Forest inventory data such as tree parameters, heights, and crown diameters must be collected and analysed to monitor forests effectively. Traditional manual techniques are slow and labour-intensive, requiring additional personnel, while existing non-contact methods are costly, computationally intensive, or less accurate. Kenya plans to increase its forest cover to 30% by 2032 and establish a national forest monitoring system. Building capacity in forest monitoring through innovative field data collection technologies is encouraged to match the pace of increase in forest cover. This study explored the applicability of low-cost, non-contact tree inventory based on stereoscopic photogrammetry in a recently reforested stand in Kieni Forest, Kenya. A custom-built stereo camera was used to capture images of 251 trees in the study area from which the tree heights and crown diameters were successfully extracted quickly and with high accuracy. The results imply that stereoscopic photogrammetry is an accurate and reliable method that can support the national forest monitoring system and REDD+ implementation.

Accuracy of the Copernicus High-Resolution Layer Forest Type (HRL FTY) assessed with domestic NFI sampling plots in Poland

Abstract Over the past years, several remote sensing maps of land cover have been produced, but they still exhibit certain differences compared to the real land use that reduce their value for climate and carbon cycle modelling as well as for national estimates of forest carbon stocks and their change. This paper outlines a straightforward framework for evaluating map accuracy and estimating uncertainty in land cover area, specifically for forest-related land cover maps in Poland for the year 2018. The study compares stratified field-based data from the National Forest Inventory (NFI) with remote sensing data on forest variables, at the pixel level, in order to identify suitable methods for accuracy and area uncertainty estimation. Additionally, the paper introduces and presents a variety of accuracy metrics applicable to assess overall uncertainties in GHG inventories. The results indicate that the High-Resolution Layer Forest Type (HRL FTY) product (part of the broader Copernicus Land Monitoring Service [CLMS] portfolio), assessed using NFI field-based information, achieved an overall accuracy (OA) of 69.2%. This metric varies among particular nature protection forms, with the highest observed ones in Natura 2000 sites of 70.45%. The primary source of map errors was associated with distinguishing between broad-leaved and coniferous forest areas. Improving future maps necessitates more precise differentiation between species to better support national forest monitoring systems for the purpose of greenhouse gas (GHG) inventories where information on the spatial distribution and variability of forests sources, biodiversity assessment, threat prevention, estimation of carbon content is becoming an important part of the associated reporting system.

MAPPING LAND COVER TIME SERIES USING LANDSAT-8 AND SENTINEL-1 IN SOUTH KALIMANTAN

Abstract. Remote sensing has been widely used for forest monitoring. However, most forest monitoring systems largely rely on optical images that limit temporal analyses due to cloud cover, particularly in the tropics. The current study used integration of optical Landsat-8 and Sentinel-1 SAR to produce individual year land cover classification maps for Kalimantan, Indonesia that differentiate between native forest and tree plantations, such as oil palm and rubber. We applied a Bayesian network to produce a time series of land cover classification that improved accuracy of individual year land cover maps. Accuracy assessment using a confusion matrix showed that final map had overall accuracy of 90%, while user's and producer's accuracy for each land cover class was above 85%, except non–forest, which had 76% producer's accuracy due to errors in the classification between young rubber plantations and non–forest. Improved maps will support Indonesia's national forest monitoring system and sustainable forest management.

National REDD+ initiatives in Caribbean SIDS to enhance NDC implementation

The objective of this paper is to explore the potential of Reducing Emissions from Deforestation and Forest degradation, and Foster conservation, Sustainable management of forests, and Enhancement of forest carbon stocks (REDD+) to support Caribbean Small Island Developing States (SIDS) efforts in achieving their Nationally Determined Contributions (NDCs) under the 2015 Paris Climate Agreement. A comprehensive overview of the region's perspective on REDD+ in meeting their climate targets in the forestry sector was conducted. The methodology adopted a content analysis of Caribbean countries' NDCs and REDD+ Readiness Preparation Proposals (R-PP). The data used for the content analysis were obtained from initial and updated NDCs for sixteen Caribbean SIDS and REDD+ R-PP for four countries. The results demonstrate that Caribbean countries have high interest in adopting deforestation and forest degradation into their NDC targets. However, REDD+ does not appear to be a high priority in these countries and has been adopted in only four countries. REDD+ systems and mechanisms including measurement, reporting and verification (MRV) in forests, forest and land governance and land rights, and safeguards and financing for REDD+ are hardly considered in Caribbean SIDS NDCs except for the four countries that have already adopted REDD+. These four countries have paid considerable attention to adhere to REDD+ rules as demonstrated in their R-PPs. They are however still in the process of developing MRV forest systems and yet to develop national forest monitoring systems, and details on financing REDD+ and implementation are scarce.

Alert-Driven Community-Based Forest Monitoring: A Case of the Peruvian Amazon

Community-based monitoring (CBM) is one of the- most sustainable ways of establishing a national forest monitoring system for successful Reduce Emissions from Deforestation and Forest Degradation (REDD+) implementation. In this research, we present the details of the National Forest Conservation Program (PNCB—Programa Nacional de Conservación de Bosques para la Mitigación del Cambio Climático), Peru, from a satellite-based alert perspective. We examined the community’s involvement in forest monitoring and investigated the usability of 1853 CBM data in conjunction with 445 satellite-based alerts. The results confirm that Peru’s PCNB contributed significantly to the REDD+ scheme, and that the CBM data provided rich information on the process and drivers of forest change. We also identified some of the challenges faced in the existing system, such as delays in satellite-based alert transfer to communities, sustaining community participation, data quality and integration, data flow, and standardization. Furthermore, we found that mobile devices responding to alerts provided better and faster data on land-use, and a better response rate, and facilitated a more targeted approach to monitoring. We recommend expanding training efforts and equipping more communities with mobile devices, to facilitate a more standardized approach to forest monitoring. The automation and unification of the alert data flow and incentivization of the participating communities could further improve forest monitoring and bridge the gap between near-real-time (NRT) satellite-based and CBM systems.

Developing Multi-Source Indices to Discriminate between Native Tropical Forests, Oil Palm and Rubber Plantations in Indonesia

Over the last 18 years, Indonesia has experienced significant deforestation due to the expansion of oil palm and rubber plantations. Accurate land cover maps are essential for policymakers to track and manage land change to support sustainable forest management and investment decisions. An automatic digital processing (ADP) method is currently used to develop land cover change maps for Indonesia, based on optical imaging (Landsat). Such maps produce only forest and non-forest classes, and often oil palm and rubber plantations are misclassified as native forests. To improve accuracy of these land cover maps, this study developed oil palm and rubber plantation discrimination indices using the integration of Landsat-8 and synthetic aperture radar Sentinel-1 images. Sentinel-1 VH and VV difference (>7.5 dB) and VH backscatter intensity were used to discriminate oil palm plantations. A combination of Landsat-8 NDVI, NDMI with Sentinel-1 VV and VH were used to discriminate rubber plantations. The improved map produced four land cover classes: native forest, oil palm plantation, rubber plantation, and non-forest. High-resolution SPOT 6/7 imagery and ground truth data were used for validation of the new classified maps. The map had an overall accuracy of 92%; producer’s accuracy for all classes was higher than 90%, except for rubber (65%), and user’s accuracy was over 80% for all classes. These results demonstrate that indices developed from a combination of optical and radar images can improve our ability to discriminate between native forest and oil palm and rubber plantations in the tropics. The new mapping method will help to support Indonesia’s national forest monitoring system and inform monitoring of plantation expansion.

Estimating land cover map accuracy and area uncertainty using a confusion matrix: A case study in Kalimantan, Indonesia

Remote sensing is widely used to generate land cover maps, but the maps derived from remote sensing often produce accuracy below expectations for map error. Therefore, quantifying map accuracy is essential for reporting the precision of an estimated area. This study describes a simple framework for assessing map accuracy and estimating land cover area uncertainty for a land cover changes map for Kalimantan in 2012-2018. This study compared simple random sampling and stratified random sampling to determine suitable procedures for estimating accuracy and area uncertainty. The validation relies on the visual assessment of high spatial resolution images such as SPOT 6/7 and high-resolution temporal images from Open Foris Collect Earth. Our results showed that the land cover change map assessed using random sampling had an overall accuracy of 74% while using stratified random sampling had an overall accuracy of 75%. Thus, for tropical regions with high cloud cover, we recommend using stratified random sampling. The major source of map error was in differentiating between native forest and plantation areas. Future map improvement requires more accurate differentiation between forest and plantation to better support national forest monitoring systems for sustainable forest management.

Assessment of REDD+ MRV Capacity in Developing Countries and Implications under the Paris Regime

The United Nations Framework Convention on Climate Change (UNFCCC) recognized the importance of forests in combating climate change and agreed upon financial support for REDD+ activities in developing countries through the Warsaw REDD+ Framework (WRF). The REDD+ activities for conserving carbon stored in forests to reduce GHG emissions and to enhance the carbon sink function of forests are expected to serve as an important means for achieving the climatic goal. In this study, a set of criteria was devised to assess the REDD+ Measuring, Reporting, and Verifying (MRV) implementation capabilities of developing countries, which was applied to analyze the REDD+ MRV levels in REDD+ countries. Based on the Forest Reference Emission Level/Forest Reference Level (FREL/FRL), National Strategy (NS), National Forest Monitoring System (NFMS) and Safeguard Information Summary (SIS) the countries submitted, 36 REDD+ countries submitted only the FREL/FRL (Group I), while 5 countries fulfilled the WRF requirements and registered REDD+ reduction results (Group II), and 6 received Results-Based Payments (RBP) (Group III). From longest to shortest, the periods for which the International Fund provided support were arranged in the order of Group III, II, and I, verifying the relative importance of international support. From highest to lowest, the overall MRV capability was also arranged in the order of Group III, II, and I, although Group I or Group II was at a higher level than the other groups in some elements. REDD+ countries in the Readiness Phase (Group I) would aim to have the MRV capabilities of Groups II and III to receive RBP, and international support for REDD+ MRV capacity building could enable them to do. However, in addition to the receipt of RBP, REDD+ should be reflected in the Nationally Determined Contribution (NDC) as consistent reduction results at the national GHG inventory level, and the advancement of REDD+ MRV is expected to be a necessary and sufficient condition for REDD+ cooperation under the Paris Agreement cooperative approach framework. For the following groups, international cooperation is essential. Countries in the Readiness Phase need to be supported with the establishment of an MRV framework, which will enable them to achieve REDD+ to receive RBP and be reflected in NDCs. For REDD+ countries that have thus far met the WRF requirements, the REDD+ scope needs to be upscaled to national levels, and the MRV system should be further advanced to establish a cooperative approach system that can achieve more ambitious reduction targets through forests.

Assessing Accuracy of Land Cover Change Maps Derived from Automated Digital Processing and Visual Interpretation in Tropical Forests in Indonesia

This study assessed the accuracy of land cover change (2000–2018) maps compiled from Landsat images with either automated digital processing or with visual interpretation for a tropical forest area in Indonesia. The accuracy assessment used a two-stage stratified random sampling involving a confusion matrix for assessing map accuracy and by estimating areas of land cover change classes and associated uncertainty. The reference data were high-resolution images from SPOT 6/7 and high-resolution images finer than 5 m obtained from Open Foris Collect Earth. Results showed that the map derived from automated digital processing had lower accuracy (overall accuracy 73–77%) compared to the map based on visual interpretation (overall accuracy 80–84%). The automated digital processing map error was in differentiating between native forest and plantation areas. While the visual interpretation map had a higher accuracy, it did not consistently differentiate between native forest and shrub areas. Future improvement of the digital map requires more accurate differentiation between forest and plantation to better support national forest monitoring systems for sustainable forest management.

Direct Measurement of Forest Degradation Rates in Malawi: Toward a National Forest Monitoring System to Support REDD+

Spatial time-series measurements of forest degradation rates are important for estimating national greenhouse gas emissions but have been challenging for open forests and woodlands. This lack of quantitative data on forest degradation rates, location and biomass is an important constraint to developing national REDD+ policy. In Malawi, and in most countries in Africa, most assessments of forest cover change for carbon emissions monitoring tend to report only deforestation in the public forest estate managed by the government, even when important forest degradation also occurs in agricultural areas, such as customary forests and other tree-based systems. This study has resulted in: (a) a new robust forest map for Malawi, (b) spatial and quantitative measurements of both forest degradation and deforestation, and (c) a demonstration of the approach through the introduction of a tool that maps across the broad landscape of forests and trees outside of forests. The results can be used to support REDD+ National Forest Monitoring Systems. This analysis produces new estimates of landscape-wide deforestation rates between 2000–2009 (22,410 ha yr−1) and 2009–2015 (38,937 ha yr−1). We further produce new estimates of the rate of forest degradation between 2000–2009 (42,961 ha yr−1) and 2009–2015 (71,878 ha yr−1). The contribution of these new tools and estimates to capacities for calculating carbon emissions are important, increasing prospects for full REDD+ readiness across semi-arid Africa.

Requirements and Realities of REDD+ Implementation in Nigeria

Negotiations and decisions of the 2007 and 2010 Conference of the Parties (COP) sessions at Bali and Cancun enabled participants to develop pilot projects leading into a national strategy on Reducing Emissions from Deforestation and Forest Degradation (REDD+) ‘Readiness’. REDD+ provides incentives for countries to receive payments for restraining greenhouse gas emissions through forest conservation, sustainable forest management and enhancement of forest carbon stocks. The overarching Warsaw Framework on REDD+ set at COP19 in 2013 provided guidelines for how countries can work towards eligibility for results-based payments for carbon savings from REDD+. It included seven points covering governance issues, approaches to reference emissions level estimation, forest inventory and reporting obligations. In this paper we examine the case of REDD+ in Nigeria by assessing how effectively that nation has implemented the Warsaw Framework requirements. We determine that the country’s over-ambitious political motivation for REDD+ affected not only the practical design of its National Strategy but also distracted management from establishing adequate safeguards for the program. Lack of capacity in advanced modeling hindered the establishment of the forest emissions reference level. That step was essential to enabling payments for reported and verified emission reduction outcomes. We reveal that national circumstances like forest governance, capacity, and political factors significantly influenced the effectiveness of pilot programs. From the Nigerian case, we maintain that effective REDD+ pilot programs should ensure that there is: full and active engagement with relevant stakeholders; a comprehensive review of forest management policies and regulations to establish a reliable forest reference level; a rigorous and robust national forest monitoring system; and safeguards to assist implementation and distribute carbon benefit-sharing rights.

A Multi Sensor Approach to Forest Type Mapping for Advancing Monitoring of Sustainable Development Goals (SDG) in Myanmar

Monitoring forests is important for measuring overall success of the 2030 Agenda because forests play an essential role in meeting many Sustainable Development Goals (SDG), especially SDG 15. Our study evaluates the contribution of three satellite data sources (Landsat-8, Sentinel-2 and Sentinel-1) for mapping diverse forest types in Myanmar. This assessment is especially important because Myanmar is currently revising its classification system for forests and it is critical that these new forest types can be accurately mapped and monitored over time using satellite imagery. Our results show that using a combination of Sentinel-1 and Sentinel-2 yields the highest accuracy (89.6% ± 0.16 percentage point(pp)), followed by Sentinel-2 alone (87.97% ± 0.11 pp) and Landsat-8 (82.68% ± 0.13 pp). The higher spatial resolution of Sentinel-2 Blue, Green, Red, Narrow Near Infrared and Short Wave Infrared bands enhances accuracy by 4.83% compared to Landsat-8. The addition of the Sentinel-2 Near Infrared and three Vegetation Red Edge bands further improve accuracy by 0.46% compared to using only Sentinel-2 Blue, Green, Red, Narrow Near Infrared and Short Wave Infrared bands. Adding the radar information from Sentinel-1 further increases the accuracy by 1.63%. We were able to map the two major forest types, Upper Moist and Upper Dry Mixed Deciduous Forest, which comprise 90% of our study area. Accuracies for these forest types ranged from 77 to 96% depending on the sensors used, demonstrating the feasibility of using satellite data to map forest categories from a newly revised classification system. Our results advance the ongoing development of the National Forest Monitoring System (NFMS) by the Myanmar Forest Department and United Nations-Food and Agriculture Organization (UN-FAO) and facilitates future monitoring of progress towards the SDGs.

How to Achieve Effective Participation of Communities in the Monitoring of REDD+ Projects: A Case Study in the Democratic Republic of Congo (DRC)

Developing countries that implement the Reducing emissions from deforestation and forest degradation (REDD+) mechanism under the United Nations Framework Convention on Climate Change are required to ensure the effective participation of all stakeholders including indigenous peoples and local communities. Community-based monitoring (CBM) of REDD+ projects could contribute to meeting REDD+ monitoring, reporting, and verification requirements and to ensuring effective community participation. The Democratic Republic of Congo (DRC) is the most advanced country in REDD+ implementation in the Congo Basin region, but the role of forest communities in REDD+ monitoring has not been adequately defined. Based on a Delphi survey, this study aimed to explore the factors that are crucial in achieving effective community participation in the monitoring of REDD+ projects. Out of 65 experts with in-depth knowledge of REDD+ and CBM in the DRC and elsewhere, 35 agreed to participate in the study. In three rounds, 19 feedbacks were received from the first round, 17 from the second and 14 from the third. Data were analyzed in a qualitative (MAXQDA) and quantitative (Microsoft Excel) manner. There was consensus among experts that, per definition, effective participation of communities in the monitoring of REDD+ projects must be a process characterized by a free and prior informed consent (FPIC), recognition of traditional knowledge and community rights, and involvement of communities in all steps of the monitoring process. In practice, the latter point poses several challenges as it requires capacity building, careful selection of indicators, adequate local institutional arrangements and a benefit-sharing system. Ideally, local CBM systems should be nested within the national forest monitoring system, but this will require more strategic efforts at the national level in the DRC, including a framework concept for the role of communities and CBM in REDD+ that can be further adapted to particular circumstances on the ground.

Combining remote sensing techniques and participatory mapping to understand the relations between forest degradation and ecosystems services in a tropical rainforest

Forest degradation is an environmental problem that falls in the domains of different disciplines. Therefore, it is necessary to tackle this issue from a multidisciplinary perspective to support the design of appropriate forest monitoring systems and policies. The overall goal of this study was to combine remote sensing (RS) and ecosystem services (ES) data to better understand how the demand for ES is related to forest degradation. The study area was the Sumaco Biosphere Reserve located in central-northern Ecuador. First, participatory mapping (208 respondents) was employed to identify the demand and location for five predefined ES categories in tropical forest. Next, RS data were used to delineate degraded forest areas based on an empirically derived canopy cover value of 30–86%. The results show that the majority of ES locations was situated in areas with a canopy cover higher than 90%, which corresponds to undisturbed forest, while in degraded forest there were fewer ES locations. Considering the land-use pattern in the study area, there is a great danger that the undisturbed forest encountered at the time of the study will be degraded in future if uncontrolled forest use continues. The results of this study will be useful to local authorities for developing specific forest management strategies, and the methodology can support national and subnational forest monitoring systems.

Applying a systems approach to assess carbon emission reductions from climate change mitigation in Mexico’s forest sector

The Paris Agreement of the United Nation Framework Convention on Climate Change calls for a balance of anthropogenic greenhouse emissions and removals in the latter part of this century. Mexico indicated in its Intended Nationally Determined Contribution and its Climate Change Mid-Century Strategy that the land sector will contribute to meeting GHG emission reduction goals. Since 2012, the Mexican government through its National Forestry Commission, with international financial and technical support, has been developing carbon dynamics models to explore climate change mitigation options in the forest sector. Following a systems approach, here we assess the biophysical mitigation potential of forest ecosystems, harvested wood products and their substitution benefits (i.e. the change in emissions resulting from substitution of wood for more emissions-intensive products and fossil fuels), for policy alternatives considered by the Mexican government, such as a net zero deforestation rate and sustainable forest management. We used available analytical frameworks (Carbon Budget Model of the Canadian Forest Sector and a harvested wood products model), parameterized with local input data in two contrasting Mexican states. Using information from the National Forest Monitoring System (e.g. forest inventories, remote sensing, disturbance data), we demonstrate that activities aimed at reaching a net-zero deforestation rate can yield significant CO2e mitigation benefits by 2030 and 2050 relative to a baseline scenario (‘business as usual’), but if combined with increasing forest harvest to produce long-lived products and substitute more energy-intensive materials, emissions reductions could also provide other co-benefits (e.g. jobs, illegal logging reduction). We concluded that the relative impact of mitigation activities is locally dependent, suggesting that mitigation strategies should be designed and implemented at sub-national scales. We were also encouraged about the ability of the modeling framework to effectively use Mexico’s data, and showed the need to include multiple sectors and types of collaborators (scientific and policy-maker communities) to design more comprehensive portfolios for climate change mitigation.

Forest inventory and analysis in Gilgit-Baltistan

PurposeThe purpose of the study is to analyse the occurrence and distribution of different tree species in Gilgit-Baltistan, Pakistan, as a baseline for further inventories, and estimate the biomass per species and plot. Furthermore, it aims to measure forest biodiversity using established formulae for tree species diversity index, richness, evenness and accumulative curve.Design/methodology/approachField data were collected, including stratification of forest sample plots. Statistical analysis of the data was carried out, and locally appropriate allometric equations were applied for biomass estimation.FindingsRepresentative circular 556 forest sample plots of 1,000 m2 contained 13,135 trees belonging to nine tree species with a total aboveground biomass of 12,887 tonnes. Sixty-eight per cent of the trees were found between 2,600 and 3,400 masl; approximately 63 per cent had a diameter at breast height equal to 30 cm, and 45 per cent were less than 12 m in height. The Shannon diversity index was 1.82, and Simpson’s index of diversity was 0.813.Research limitations/implicationsRough terrain, long distances, harsh weather conditions and location of forest in steep narrow valleys presented challenges for the field crews, and meant that fieldwork took longer than planned.Practical implicationsEstimating biomass in Gilgit-Baltistan’s forests using locally developed allometric equations will provide transparency in estimates of forest reference levels, National Forest Monitoring System in Pakistan and devising Reducing Emissions from Deforestation and Forest Degradation national strategies and for effective implementation.Originality/valueThis paper presents the first detailed forest inventory carried out for the dry temperate and semi-arid cold region of Gilgit-Baltistan, Pakistan.

Monitoreo de la superficie de los bosques nativos de Chile: un desafío pendiente

Forest monitoring is important for decision making of forest management and conservation. In Chile, the forest monitoring system was initiated in 1994, which has been continued through the implementation of regional updates. This has enabled a temporal monitoring of the distribution and surface covered by native forest. However, while regional updates have reported increase in the surface covered by native forest, other studies have shown an opposite trend. Therefore, the capacity of the forest monitoring system to measure the temporal variation in forest areas was evaluated. Specifically, a review of reports and official data of the national forest monitoring system was carried out through the fulfillment of three basic criteria: i) comparability, ii) replicability and iii) quality. According to our results, the Chilean forest monitoring system does not fulfill their basic requirements because: (i) methodologies have not been consistent over time; (ii) it does not provide a baseline of land cover or forest loss that allows comparisons with changing forest area; iii) there is not adequate error estimation and how it can affect the results and analysis of monitoring. The national forest monitoring system requires a redefinition of its aims and methods, guiding them to the long term by convening different stakeholders looking for a national agreement.

Types and rates of forest disturbance in Brazilian Legal Amazon, 2000-2013.

Deforestation rates in primary humid tropical forests of the Brazilian Legal Amazon (BLA) have declined significantly since the early 2000s. Brazil's national forest monitoring system provides extensive information for the BLA but lacks independent validation and systematic coverage outside of primary forests. We use a sample-based approach to consistently quantify 2000-2013 tree cover loss in all forest types of the region and characterize the types of forest disturbance. Our results provide unbiased forest loss area estimates, which confirm the reduction of primary forest clearing (deforestation) documented by official maps. By the end of the study period, nonprimary forest clearing, together with primary forest degradation within the BLA, became comparable in area to deforestation, accounting for an estimated 53% of gross tree cover loss area and 26 to 35% of gross aboveground carbon loss. The main type of tree cover loss in all forest types was agroindustrial clearing for pasture (63% of total loss area), followed by small-scale forest clearing (12%) and agroindustrial clearing for cropland (9%), with natural woodlands being directly converted into croplands more often than primary forests. Fire accounted for 9% of the 2000-2013 primary forest disturbance area, with peak disturbances corresponding to droughts in 2005, 2007, and 2010. The rate of selective logging exploitation remained constant throughout the study period, contributing to forest fire vulnerability and degradation pressures. As the forest land use transition advances within the BLA, comprehensive tracking of forest transitions beyond primary forest loss is required to achieve accurate carbon accounting and other monitoring objectives.

Estimation of Tree Height and Forest Biomass Using Airborne LiDAR Data: A Case Study of Londiani Forest Block in the Mau Complex, Kenya

Tactical decisions on natural resource management require accurate and up to date spatial information for sustainable forest management. Remote sensing devices by the use of multispectral data obtained from satellites or airborne sensors, allow substantial data acquisition that reduce cost of data collection and satisfy demands for continuous precise data. Forest height and Diameter at Breast Height (DBH) are crucial variables to predict volume and biomass. Traditional methods for estimation of tree heights and biomass are time consuming and labour intensive making it difficult for countries to carry out periodic National forest inventories to support forest management and REDD+ activities. This study assessed the applicability of LiDAR data in estimating tree height and biomass in a variety of forest conditions in Londiani Forest Block. The target forests were natural forest, plantation forests and other scattered forests analysed in a variety of topographic conditions. LiDAR data were collected by an aircraft flying at an elevation of 1550 m. The LIDAR pulses hitting the forest were used to estimate the forest height and the density of the vegetation, which implied biomass. LiDAR data were collected in 78 sampling plots of 15 m radius. The LiDAR data were ground truthed to compare its accuracy for above ground biomass (AGB) and height estimation. The correlation coefficients for heights between LiDAR and field data were 0.92 for the pooled data, 0.79 in natural forest, 0.95 in plantation forest and 0.92 in other scattered forest. AGB estimated from LiDAR and ground truthed data had a correlation coefficient of 0.86 for the pooled data, 0.78 in natural forest, 0.84 in plantation forest and 0.51 in other scattered forests. This implied 62%, 84% and 89% accuracy of AGB estimation in natural forests, other scattered forests and plantation forests respectively. The even aged conditions of plantation forests might have resulted to better estimates of height and AGB as compared to uneven aged natural forests and scattered forests. The results imply the reliability of using Airborne LIDAR scanning in forest biomass estimates in Kenya and are an option for supporting a National Forest Monitoring System for REDD+.

Comparison of Different Resolution Satellite Imageries for Forest Carbon Quantification

The current trend of the monitoring of the forest involves the measurement of aboveground forest biomass carbon using the multisource forest inventory techniques. The multisource forest inventory techniques involve the multiple data inputs such as GIS, Remote sensing, GPS, field measurement and existing information. The remote sensing data are useful for the quantification of aboveground forest carbon using the spectral and spatial characteristics of the data. The application of remote sensing data for the forest carbon quantification may enhance the efficiency in terms of resource allocation, time spent and interoperability and ultimately support the efficient National Forest Monitoring System as a basis for the REDD (+) implementation in future. The study tried to compare the methods and results from the multispatial resolution satellite imageries for the quantification of forest biomass carbon i.e. Landsat TM(30m), RapidEye (5/6.5m) and WorldView PAN (0.5m). The medium resolution imageries like Landsat and RapidEye images have scope to process at the plot level where as WorldView PAN has scope to process upto the tree by tree level. Thus the methods of operation involves mainly two stream i.e. NDVI extraction for the plot average for Landsat and RapidEye data and CPA analysis for the individual tree for WorldView data. The result shows the higher R2 (0.6) relation in CPA method rather than the NDVI relation with the total forest carbon if we see the linear relations. While we see the polynomial relation, we get the R2 value of 0.77 from the NDVI value of RapidEye which support conclude the use of WorldView PAN image and RapidEye image for the quantification of forest carbon.