Guide to bamboo carbon inventory and estimation in non-forest areas of Nepal: Insights from global practices

ABSTRACTWe believe that global health practice and evaluation operate with misleading assumptions about lack of reliability of small population-based health surveys (district level and below), leading managers and decision-makers to under-use this valuable information and programmatic tool and to rely on health information from large national surveys when neither timing nor available data meet their needs. This paper uses a unique opportunity for comparison between a knowledge, practice, and coverage (KPC) household survey and Rwanda Demographic and Health Survey (RDHS) carried out in overlapping timeframes to disprove these enduring suspicions. Our analysis shows that the KPC provides coverage estimates consistent with the RDHS estimates for the same geographic areas. We discuss cases of divergence between estimates. Application of the Lives Saved Tool to the KPC results also yields child mortality estimates comparable with DHS-measured mortality. We draw three main lessons from the study and conclude with recommendations for challenging unfounded assumptions against the value of small household coverage surveys, which can be a key resource in the arsenal of local health programmers.

Carbon (C) emissions from forest fires in the Amazon during extreme droughts may correspond to more than half of the global emissions resulting from land cover changes. Despite their relevant contribution, forest fire-related C emissions are not directly accounted for within national-level inventories or carbon budgets. A fundamental condition for quantifying these emissions is to have a reliable estimation of the extent and location of land cover types affected by fires. Here, we evaluated the relative performance of four burned area products (TREES, MCD64A1 c6, GABAM, and Fire_cci v5.0), contrasting their estimates of total burned area, and their influence on the fire-related C emissions in the Amazon biome for the year 2015. In addition, we distinguished the burned areas occurring in forests from non-forest areas. The four products presented great divergence in the total burned area and, consequently, total related C emissions. Globally, the TREES product detected the largest amount of burned area (35,559 km2), and consequently it presented the largest estimate of committed carbon emission (45 Tg), followed by MCD64A1, with only 3% less burned area detected, GABAM (28,193 km2) and Fire_cci (14,924 km2). The use of Fire_cci may result in an underestimation of 29.54 ± 3.36 Tg of C emissions in relation to the TREES product. The same pattern was found for non-forest areas. Considering only forest burned areas, GABAM was the product that detected the largest area (8994 km2), followed by TREES (7985 km2), MCD64A1 (7181 km2) and Fire_cci (1745 km2). Regionally, Fire_cci detected 98% less burned area in Acre state in southwest Amazonia than TREES, and approximately 160 times less burned area in forests than GABAM. Thus, we show that global products used interchangeably on a regional scale could significantly underestimate the impacts caused by fire and, consequently, their related carbon emissions.

Forest biomass and its change over time have been measured at both local and large scales, an example for the latter being forest greenhouse gas inventories. Currently used methodologies to obtain stock change estimates for large forest areas are mostly based on forest inventory information as well as various factors, referred to as biomass factors, or biomass equations, which transform diameter, height or volume data into biomass estimates. However, while forest inventories usually apply statistically sound sampling and can provide representative estimates for large forest areas, the biomass factors or equations used are, in most cases, not representative, because they are based on local studies. Moreover, their application is controversial due to the inconsistent or inappropriate use of definitions involved. There is no standardized terminology of the various factors, and the use of terms and definitions is often confusing. The present contribution aims at systematically summarizing the main types of biomass factors (BF) and biomass equations (BE) and providing guidance on how to proceed when selecting, developing and applying proper factors or equations to be used in forest biomass estimation. The contribution builds on the guidance given by the IPCC (Good practice guidance for land use, land-use change and forestry, 2003) and suggests that proper application and reporting of biomass factors and equations and transparent and consistent reporting of forest carbon inventories are needed in both scientific literature and the greenhouse gas inventory reports of countries.

The study reports estimates of above ground phytomass carbon pools in Indian forests for 1992 and 2002 using two different methodologies. The first estimate was derived from remote sensing based forest area and crown density estimates, and growing stock data for 1992 and 2002 and the estimated pool size was in the range 2,626–3,071 Tg C (41 to 48 Mg C ha − 1) and 2,660–3,180 Tg C (39 to 47 Mg C ha − 1) for 1992 and 2002, respectively. The second methodology followed IPCC 2006 guidelines and using an initial 1992 pool of carbon, the carbon pool for 2002 was estimated to be in the range of 2,668–3,112 Tg C (39 to 46 Mg C ha − 1), accounting for biomass increment and removals for the period concerned. The estimated total biomass increment was about 458 Tg over the period 1992–2002. Removals from forests include mainly timber and fuel wood, whereby the latter includes large uncertainty as reported extraction is lower than actual consumption. For the purpose of this study, the annual extraction values of 23 million m3 for timber and 126 million m3 for fuel wood were used. Out of the total area, 10 million ha are plantation forests with an average productivity (3.2 Mg ha − 1 year − 1) that is higher than natural forests, a correction of 408 Tg C for the 10 year period was incorporated in total estimated phytomass carbon pool of Indian forests. This results in an estimate for the net sink of 4 Tg C year − 1. Both approaches indicate Indian forests to be sequestering carbon and both the estimates are in agreement with recent studies. A major uncertainty in Indian phytomass carbon pool dynamics is associated with trees outside forests and with soil organic carbon dynamics. Using recent remote-sensing based estimates of tree cover and growing stock outside forests, the estimated phytomass carbon pool for trees outside forests for the year 2002, is 934 Tg C with a national average tree carbon density of 4 Mg C ha − 1 in non-forest area, in contrast to an average density of 43 Mg C ha − 1 in forests. Future studies will have to consider dynamics in both trees outside forests and soil for total terrestrial carbon dynamics.

Estimating state-wide biomass carbon stocks for a REDD plan in Acre, Brazil

A canopy transpiration model, RM-CWU, was used for estimating consumptive water use (CWU) of subalpine forest stands in the Rocky Mountains. Annual CWU was calculated for nine subunits in two watersheds on the Fraser Experimental Forest near Fraser, CO. Calculated CWU of the forested portions of the subunits ranged from 305 to 460 mm • year−1. After combining these rates on an area-weighted basis with evapotranspiration estimates of the nonforested areas of each subunit, annual subunit CWU rates varied from 159 to 388 mm • year−1. Annual CWU for the Lexen Creek watershed was 352 mm • year−1; for the East St. Louis Creek watershed CWU was 264 mm • year−1. Annual CWU calculated for each watershed with RM-CWU was compared with completely independent estimates of evapotranspiration (ET) determined as the difference between precipitation input and watershed runoff. Using two different estimates of precipitation input, two ET estimates were obtained for each watershed. For both watersheds, CWU calculated with RM-CWU was within several percent of the ET estimates expected to be the most accurate. This suggests that CWU estimates from a physiologically based canopy transpiration model may be used to determine water vapor loss from watersheds in the Rocky Mountains.

National forest inventories (NFIs) have a long history, although their current major features date only to the early years of the twentieth century. Recent issues such as concern over the effects of acid deposition, biodiversity, forest sustainability, increased demand for forest data, international reporting requirements and climate change have led to the expansion of NFIs to include more variables, greater diversity in sampling protocols and a generally more holistic approach. This review focuses on six selected topics: (1) a brief historical review; (2) a summary of common structural features of NFIs; (3) a brief review of international reporting requirements using NFI data with an emphasis on approaches to harmonized estimation; (4) an overview of inventory estimation methods that can be enhanced with remotely sensed data; (5) an overview of nearest neighbors prediction and estimation techniques; and (6) a brief overview of several emerging issues including carbon inventories in developing countries and use of lidar data. Although general inventory principles will remain unchanged, sampling designs, plot configurations and measurement protocols will require modification before they can be applied in countries with tropical forests. Technological advances, particularly in the use of remotely sensed data, including lidar data, have led to greater inventory efficiencies, better maps and accurate estimation for small areas.

High deforestation rates in Amazonia have motivated considerable efforts to monitor forest changes with satellite images, but mapping forest distribution and monitoring change at a regional scale remain a challenge. This article proposes a new approach based on the integrated use of Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat Thematic Mapper (TM) images to rapidly map forest distribution in Rondônia, Brazil. The TM images are used to differentiate forest and non-forest areas and the MODIS images are used to extract three fraction images (vegetation, shade and soil) with linear spectral mixture analysis (LSMA). A regression model is built to calibrate the MODIS-derived forest results. This approach is applied to the MODIS image in 2004 and is then transferred to other MODIS images. Compared to INPE PRODES (Brazil's Instituto Nacional de Pesquisas Espaciais – Programme for the Estimation of Deforestation in the Brazilian Amazon) data, the errors for total forest area estimates in 2000, 2004 and 2006 are −0.97%, 0.81% and −1.92%, respectively. This research provides a promising approach for mapping fractional forest (proportion of forest cover area in a pixel) distribution at a regional scale. The major advantage is that this procedure can rapidly provide the spatial and temporal patterns of fractional forest cover distribution at a regional scale by the integrated use of MODIS images and a limited number of Landsat images.

Examination of the physical background underlying the ERS response of forest and analysis of time series of ERS data indicates that the greater temporal stability of forest compared with many other types of land cover presents a means of mapping forest area. The processing chain necessary to make such area estimations involves reconstruction of an optimal estimate of the backscattering coefficient at each pixel using temporal and spatial filtering so that classification rules derived from large scale averaging are applicable. The rationale behind the filtering strategy and the level of averaging needed is explained in terms of the observed multitemporal behavior of forest and nonforest areas, much of this analysis is generic and applicable to a wide range of situation in which significant information is carried by multitemporal features of the data. The choice of decision rules is based on the forest observations, with the added requirement for robustness. The performance of a classifier based only on change is assessed on a range of test sites in the UK, Finland, and Poland. Error sources in this classifier are identified, and the possibility of improving performance by including radiometric information in the mapping strategy is discussed. Brief discussions of how the classification is affected by the addition of coherence and how the processing chain would need to be modified for other forms of satellite data are included.