Remote Sensing-Based Biomass Estimation

Over the past two decades, one of the research topics in which many works have been done is spatial modeling of biomass through synergies between remote sensing, forestry, and ecology. In order to identify satellite-derived indices that have correlation with forest structural parameters that are related with carbon storage inventories and forest monitoring, topics that are useful as environmental tools of public policies to focus areas with high environmental value. In this chapter, we present a review of different models of spatial distribution of biomass and resources based on remote sensing that are widely used. We present a case study that explores the capability of canopy fraction cover and digital canopy height model (DCHM) for modeling the spatial distribution of the aboveground biomass of two forests, dominated by Abies Religiosa and Pinus spp., located in Central Mexico. It also presents a comparison of different spatial models and products, in order to know the methods that achieved the highest accuracy through root-mean-square error. Lastly, this chapter provides concluding remarks on the case study and its perspectives in remote sensing-based biomass estimation

PROCJENA NADZEMNE BIOMASE UGLJIKA KORIŠTENJEM VRIJEDNOSTI REFLEKSIJE SATELITSKIH SNIMAKA: STUDIJA SLUČAJA U DIREKCIJI ŠUMA, CAMYAZI, TURSKA

Forest ecosystems which contain half of the terrestrial carbon deposits; play a significant role in shaping the global climate. Two different methods are used to determine the above-ground carbon stock capacity of forestlands. Direct measurement method takes a long time and requires both extensive as well as expensive field and laboratory work. One of the more indirect methods, satellite imaging on the other hand, costs less, is easier and practical compared to direct methods. It is also easier to integrate into geographic information systems (GIS). This paper provides a regression equation between the reflection values from RapidEye high resolution satellite image and sample areas where terrestrial aboveground biomass (AGB) carbon stock capacity was calculated by direct measurement method. As a result of the calculations made, using the RapidEye imagery and a “Band 4” devised equation producing R2=0.71 depending upon the data from Erzurum Camyazi Forest Directorate encompassing 9,917 ha study area, the amount of carbon stored within stands was found 285 208 tons. From this value, we can conclude that average carbon stock of the study area is 28.8 tons/ha.Postoje tri glavna nalazišta ugljika na svijetu. To su atmosfera, zemaljski i oceanski ekosustavi. Šume su najveće zemaljsko nalazište ugljika. Postoji linearni odnos između količine šumskih područja i pohranjenog ugljika. Također, u kontekstu Kyoto protokola, šumska zemljišta i količina pohranjenog ugljika jako su važni za razmjenu ugljika u nadolazećim godinama. Svrha ovog istraživanja je formalizirati jednadžbu regresije između kapaciteta pohrane nadzemnog ugljika izračunatog kroz ekstenzivni terenski pregled 344 područja uzoraka u Direkciji šuma Camyazi, Turska, te vrijednosti refleksije koje odgovaraju svakom uzorku od slika RapidEye. U istraživanju su se koristili inventarni podaci plana gospodarenja te tehnike daljinskog istraživanja za utvrđivanje količine ugljika pohranjene u sastojini unutar granica Direkcije šuma Camyazi, Turska. Kao rezultat izvršenih kalkulacija, korištenjem slika RapidEye te (Pojas 4)2 izvedene jednadžbe koja daje R2=0.71, ovisno o podacima Direkcije šuma Erzurum Camyazi, utvrđeno je da je količina ugljika pohranjena u sastojini iznosila 285 208 tona. Iz te vrijednosti možemo zaključiti da je prosječna pohrana ugljika u ispitivanom području 28.8 tona/ha. Tehnike daljinskog istraživanja korištene u ovome istraživanju pokazale su da te tehnike mogu uštedjeti vrijeme, financijska sredstva i posao kod izračuna podataka kapaciteta pohrane ugljika (koje zahtijeva prilično vremena i sredstava za izračun), a mogu se dobiti precizni rezultati. Uz to, istraživanje je pokazalo da je Red-Edge pojas (Pojas 4) satelitske slike RapidEye-a osjetljiv na biomasu i klorofil se može koristiti u istraživanjima povezanim s pohranom ugljika. Jednadžbe za biomasu i pohranu ugljika za svaku vrstu šumskog drveća još nisu dovršene. Trebaju se dovršiti što je prije moguće i kapacitet pohrane ugljika treba se točnije utvrditi. Kod izračuna kapaciteta za ovaj tip istraživanja treba uzeti u obzir financijsku stranu istraživanja uz preferiranje kombinirane metode s niskim troškovima

SPATIAL STATISTICS ON AMAZON RAINFOREST ASSESSMENT: SPATIALLY STRATIFIED INVENTORY PROCESSING

Biomass and wood volume estimates in forest ecosystems are fundamental to a variety of studies focusing at forest dynamics. These estimates are usually carried out through forest inventory techniques which rely upon statistical computations. This work aims at providing a new methodological approach to forest inventory processing when data is georeferenced. Specifically, geostatistical modelling is performed through ordinary co-kriging using tree basal area and tree richness as a cofactor in an Amazonian rainforest site. The spatial interpolation provided the tools for the creation of two disjoint forest strata, which are processed following the principles of Stratified Forest Inventory. The spatially stratified forest inventory processing has shown a 14.29% decrease in error as directly compared to simple random sampling processing. Only two strata have been used following spatial interpolation, albeit it is argued that theoretically any number of them could be generated. The procedure is methodologically feasible and offers a framework to future research on its development and reach. Particularly, the geometries of forest strata and the behavior of spatial interpolation along a gradient of forest vertical structures are of potential interest in future work

Estimating Aboveground Biomass Loss from Deforestation in the Savanna and Semi-arid Biomes of Brazil between 2007 and 2017

Brazilian Savannas and Semi-arid woodlands biomes exhibit high levels of aboveground biomass (AGB) associated with high rates of deforestation. The state of Minas Gerais (MG), southeast of Brazil, encompasses landscape variations ranging from Savanna and Atlantic Forest to Semiarid woodlands. The understanding of land-cover changes in these biomes is limited due to the fact that most of the efforts for estimating forest cover changes has been focused on the tropical rain forests. Hence, the question is: What is the total amount of AGB loss across Savanna and Semi-arid woodland biomes in MG state, during the period 2007–2017? We first used a total of 1914 field plots from a forest inventory to model the AGB using a combination of remote sensing and spatio-environmental predictor variables to produce a spatial-explicit AGB map. Second, from a global map of forest cover change (GFC), we obtained deforestation patches. As a result, from 2007 to 2017, the Savanna and the Semiarid woodland biomes lost together 508,042 ha of native vegetation in MG state, leading to 21,182,150 Mg of AGB loss (4.65% of total AGB). In Savannas and Semi-arid woodland biomes in MG state, conservation initiatives must be implemented to increase the forests protection and expand AGB

Spatial distribution of wood volume in brazilian savannas

Here we model and describe the wood volume of Cerrado Sensu Stricto, a highly heterogeneous vegetation type in the Savanna biome, in the state of Minas Gerais, Brazil, integrating forest inventory data with spatial-environmental variables, multivariate regression, and regression kriging. Our study contributes to a better understanding of the factors that affect the spatial distribution of the wood volume of this vegetation type as well as allowing better representation of the spatial heterogeneity of this biome. Wood volume estimates were obtained through regression models using different environmental variables as independent variables. Using the best fitted model, spatial analysis of the residuals was carried out by selecting a semivariogram model for generating an ordinary kriging map, which in turn was used with the fitted regression model in the regression kriging technique. Seasonality of both temperature and precipitation, along with the density of deforestation, explained the variations of wood volume throughout Minas Gerais. The spatial distribution of predicted wood volume of Cerrado Sensu Stricto in Minas Gerais revealed the high variability of this variable (15.32 to 98.38 m3 ha-1) and the decreasing gradient in the southeast-northwest direction914COORDENAÇÃO DE APERFEIÇOAMENTO DE PESSOAL DE NÍVEL SUPERIOR - CAPESSem informaçã

Timber production assessment of a plantation forest: An integrated framework with field-based inventory, multi-source remote sensing data and forest management history

Timber production is the purpose for managing plantation forests, and its spatial and quantitative information is critical for advising management strategies. Previous studies have focused on growing stock volume (GSV), which represents the current potential of timber production, yet few studies have investigated historical process-harvested timber. This resulted in a gap in a synthetical ecosystem service assessment of timber production. In this paper, we established a Management Process-based Timber production (MPT) framework to integrate the current GSV and the harvested timber derived from historical logging regimes, trying to synthetically assess timber production for a historical period. In the MPT framework, age-class and current GSV determine the times of historical thinning and the corresponding harvested timber, by using a "space-for-time" substitution. The total timber production can be estimated by the historical harvested timber in each thinning and the current GSV. To test this MPT framework, an empirical study on a larch plantation (LP) with area of 43,946 ha was conducted in North China for a period from 1962 to 2010. Field-based inventory data was integrated with ALOS PALSAR (Advanced Land-Observing Satellite Phased Array L-band Synthetic Aperture Radar) and Landsat-8 OLI (Operational Land Imager) data for estimating the age-class and current GSV of LP. The random forest model with PALSAR backscatter intensity channels and OLI bands as input predictive variables yielded an accuracy of 67.9% with a Kappa coefficient of 0.59 for age-class classification. The regression model using PALSAR data produced a root mean square error (RMSE) of 36.5 m(3) ha(-1). The total timber production of LP was estimated to be 7.27 x 10(6) m(3), with 4.87 x 10(6) m(3) in current GSV and 2.40 x 10(6) m(3) in harvested timber through historical thinning. The historical process-harvested timber accounts to 33.0% of the total timber production, which component has been neglected in the assessments for current status of plantation forests. Synthetically considering the RMSE for predictive GSV and misclassification of age-class, the error in timber production were supposed to range from -55.2 to 56.3 m(3) ha(-1). The MPT framework can be used to assess timber production of other tree species at a larger spatial scale, providing crucial information for a better understanding of forest ecosystem service. (C) 2016 Elsevier B.V. All rights reserved.ArticleINTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION.52:155-165(2016)journal articl

Incorporating canopy structure from simulated GEDI lidar into bird species distribution models

The Global Ecosystem Dynamics Investigation (GEDI) lidar began data acquisition from the International Space Station in March 2019 and is expected to make over 10 billion measurements of canopy structure and topography over two years. Previously, airborne lidar data with limited spatial coverage have been used to examine relationships between forest canopy structure and faunal diversity, most commonly bird species. GEDI’s latitudinal coverage will permit these types of analyses at larger spatial extents, over the majority of the Earth’s forests, and most importantly in areas where canopy structure is complex and/or poorly understood. In this regional study, we examined the impact that GEDI-derived Canopy Structure variables have on the performance of bird species distribution models (SDMs) in Sonoma County, California. We simulated GEDI waveforms for a two-year period and then interpolated derived Canopy Structure variables to three grid sizes of analysis. In addition to these variables, we also included Phenology, Climate, and other Auxiliary variables to predict the probability of occurrence of 25 common bird species. We used a weighted average ensemble of seven individual machine learning models to make predictions for each species and calculated variable importance. We found that Canopy Structure variables were, on average at our finest resolution of 250 m, the second most important group (32.5%) of predictor variables after Climate variables (35.3%). Canopy Structure variables were most important for predicting probability of occurrence of birds associated with Conifer forest habitat. Regarding spatial analysis scale, we found that finer-scale models more frequently performed better than coarser-scale models, and the importance of Canopy Structure variables was greater at finer spatial resolutions. Overall, GEDI Canopy Structure variables improved SDM performance for at least one spatial resolution for 19 of 25 species and thus show promise for improving models of bird species occurrence and mapping potential habitat

Applications of Remote Sensing Data in Mapping of Forest Growing Stock and Biomass

This Special Issue (SI), entitled "Applications of Remote Sensing Data in Mapping of Forest Growing Stock and Biomass”, resulted from 13 peer-reviewed papers dedicated to Forestry and Biomass mapping, characterization and accounting. The papers' authors presented improvements in Remote Sensing processing techniques on satellite images, drone-acquired images and LiDAR images, both aerial and terrestrial. Regarding the images’ classification models, all authors presented supervised methods, such as Random Forest, complemented by GIS routines and biophysical variables measured on the field, which were properly georeferenced. The achieved results enable the statement that remote imagery could be successfully used as a data source for regression analysis and formulation and, in this way, used in forestry actions such as canopy structure analysis and mapping, or to estimate biomass. This collection of papers, presented in the form of a book, brings together 13 articles covering various forest issues and issues in forest biomass calculation, constituting an important work manual for those who use mixed GIS and RS techniques

Soil Geography and Geostatistics - Concepts and Applications

Geostatistics are a useful tool for understanding and mapping the variation of soil properties across the landscapes. They can be applied at different scales regarding the initial punctual datasets the soil scientist has been provided, and regarding the target resolution of the study. This report is a collection of various studies, all dealing with geostatistical methods, which have been done in Hungary, Russia and Mexico, with the financial support of various research grants. It provides also a chapter about the general concepts of geostatistics and a discussion about limitations of geostatistics with an opening discussion on the usage of pedodiversity index. This report is then particularly recommended to soil scientists who are not so familiar with geostatistics and who need support for applying geostatistics in specific conditions.JRC.H.7-Land management and natural hazard

The global tree carrying capacity (keynote)

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