Assessment of MODIS NPP algorithm-based estimates using soil fertility and forest inventory data in mixed hemiboreal forests

Lang, Mait; Kõlli, Raimo; Nikopensius, Maris; Nilson, Tiit; Neumann, Mathias; Moreno, Adam

doi:10.1515/fsmu-2017-0006

Cited by 4 publications

(4 citation statements)

References 34 publications

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“…Uncertainties originating from various inputs, such as land cover data, the fraction of the absorbed photosynthetic active radiation or the leaf area index, meteorological data, and the algorithm itself, can influence the accuracy of the GPP and NPP products [74,75]. Furthermore, the annual total GPP and the annual total NPP distributions based on the CORINE land cover classes in our study were derived from the global estimation system with a resolution of 1 km × 1 km.…”

Section: Discussionmentioning

confidence: 99%

Effects of spatial–temporal land cover distribution on gross primary production and net primary production in Schleswig-Holstein, northern Germany

2020

Carbon Balance Manage

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Background: Annual total Gross Primary Production (GPP) and Net Primary Production (NPP) and the annual total stored GPP and NPP are tightly coupled to land cover distributions because the distinct vegetation conditions of different land cover classes strongly affect GPP and NPP. Spatial and statistical analysis tools using Geographic Information Systems (GIS) were used to investigate the spatial distribution of each land cover class and the GPP and NPP based on the CORINE land cover classification in the federal state, Schleswig-Holstein, Germany for the years 2000, 2006 and 2012.Results: "Non-irrigated arable land" and "pastures" were the dominant land cover classes. Because of their large area, "non-irrigated arable land" and "pastures" had higher annual total stored GPP and NPP values than the other land cover classes. Annual total GPP and NPP hotspots were concentrated in the central-western part of Schleswig-Holstein. Cold spots were mainly located in the western and eastern Schleswig-Holstein. The distributions of the annual total GPP and NPP hotspots and cold spots were primarily determined by land cover and land cover changes among the investigated years. The average annual total NPP/GPP ratios were 0.5647, 0.5350 and 0.5573 in the years 2000, 2006 and 2012, respectively. The calculated respiration in 2006 was the highest, followed by those in 2012 and 2000. Conclusions:The land cover classes with high-ability of carbon stocks in 2000, 2006 and 2012 in Schleswig-Holstein were identified in this study. Furthermore, it is recommendable to enhance the annual total GPP and NPP and the annual total stored GPP and NPP in Schleswig-Holstein by replacing the land cover classes showing low carbon stock capabilities with the classes showing high abilities for the purpose of increasing greenhouse gas fixation.

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Section: Discussionmentioning

confidence: 99%

Effects of spatial–temporal land cover distribution on gross primary production and net primary production in Schleswig-Holstein, northern Germany

2020

Carbon Balance Manage

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“…Net Primary Productivity (NPP) is the main source of food, wood products, and fuel for human beings, which is essential for the stable development of human society and the economy [16]. Humans intervene in the natural development of the ecosystem and encroach on the NPP of vegetation under a series of activities such as production, living, and other modifications of land resources, of which the domination of land types is the most human-characterized form of activity [17].…”

Section: Introductionmentioning

confidence: 99%

Analysis of Spatial and Temporal Variability of Ecosystem Service Values and Their Spatial Correlation in Xinjiang, China

Zhang,

Wang,

et al. 2023

Remote Sensing

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Xinjiang is located in arid northwest China, which is a key area for promoting the high-quality development of the regional ecological environment. In recent years, against a background of increasing human activities and rapid natural changes, Xinjiang has faced enormous ecological challenges. This paper utilizes land-use data from 2000 to 2020 to verify the region’s current state of the ecosystem. Additionally, it uses the value equivalent factor per unit area, ecosystem service value (ESV) loss and gain matrix, and double-factor spatial autocorrelation analysis to study the spatial and temporal variabilities of ESV in Xinjiang and its attribution to spatial correlation. The results show that (1) the ESV in Xinjiang exhibits an overall increasing trend during 2000–2020, with a total increase of about CNY 18.202 billion. Regulation-service ESV takes the main position in the single-service function, accounting for about 67.18% of the total ESV. In northern Xinjiang, the ESV demonstrates a decreasing trend, dropping by about CNY 16.885 billion, while in southern Xinjiang, the ESV shows an increasing trend, rising by CNY 35.086 billion. (2) For the study period, the main loss of ESV in Xinjiang is the conversion of ecological land with a high ESV into cropland or barren land with a low ESV. The conversion of bare land to grassland led to the largest increase in ESV (about CNY 209.308 billion), whereas the conversion of grassland to barren land led to the largest loss (about CNY 183.046 billion). (3) There are positive correlations among ESV, net primary productivity (NPP), and human activity intensity (HAI). However, all of the relationships weaken year by year. The spatial agglomeration of ESV ∩ NPP is significantly greater than that of ESV ∩ HAI, so NPP is the dominant factor in the spatial correlation of ESV in Xinjiang. The findings of this study provide a scientific basis for promoting high-quality regional ecological development in China’s arid northwest.

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“…Wood volume data are important for the estimation of carbon storage and estimation of timber, but tree species composition data are required for biodiversity assessment (Laarmann et al, 2009;McRoberts et al, 2012), satellite-based estimation of net primary production (Zhao et al, 2011;Lang et al, 2017), ecosystem models (Duveneck et al, 2015), and also for purposes of monitoring and forest industry planning. Tree species composition of a forest stand is a vector of the relative proportions of individual species stemwood volume from total stemwood volume of the stand.…”

Section: Introductionmentioning

confidence: 99%

“…Statistilise metsainventuuri vaatluste põhjal saab üldistusi teha kõige rohkem maakonna tasandil. Teisalt kasvab nõudlus pidevalt ülepinnaliselt uuendatavate andmete järele uut tüüpi interaktiivse metsakorralduse süsteemide arenedes (Kor jus et al, 2017), seoses vajadusega korraldada seiret või rakendada taimkatte primaarproduktsiooni mudeleid (Zhao et al, 2011;Lang et al, 2017). Ühe lahendusena saab kasutada kogu ala katvaid kirjeldavaid tunnuseid multispektraalsetelt satelliidipiltidelt ning mõnda näidistel põhinevat masinõppemeetodit, mille realisatsioone on testitud ka Eestis (Tamm & Remm, 2009…”

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Construction of tree species composition map of Estonia using multispectral satellite images, soil map and a random forest algorithm

et al. 2018

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Landsat-8 OLI and Sentinel-2 MSI images from years 2015 and 2016, a 1:10,000 digital soil map and a large number of reference samples were used with a random forest machine learning implementation in GRASS GIS to construct a tree species map for the entire territory of Estonia (42,755 km2). Class probabilities for seven main tree species, an extra class for other species and probability of the forest cover not conforming to the forest definition were assigned for each pixel. Validation of dominant species distribution by area showed very strong correlation at county level both in state forests (R2 = 0.98) and in private forests (R2 = 0.93). Validation of tree species composition using harvester measurement data from 2,045 regeneration felling areas showed also very strong correlation (R2 = 0.75) with the measured values of the proportion of coniferous trees. There was some tendency to underestimate the proportion of more common species and overestimation was found for the species with smaller proportion in the mixture. The accuracy for the proportion of deciduous species that were present in a smaller number of reference observations was substantially smaller. Validation of the results by using data from 659 large sample plots from the database of the Estonian Network of Forest Research Plots and 3,002 small sample plots from the National Forest Inventory (NFI) data base confirmed the findings based on harvester data. The NFI data revealed also a decrease of estimation error with the increase of forest age. Cohen’s kappa index of agreement for main species for NFI sample plots with main species proportion equal to or greater than 75% decreased from 0.69 to 0.66 when observations with forests younger than 20 years were included in the comparison. Overall, the constructed map provides valuable data about tree species composition for the forests where no up to date inventory data are available or for the projects that require continuous cover of tree species data of known quality over the entire Estonia.

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Assessment of MODIS NPP algorithm-based estimates using soil fertility and forest inventory data in mixed hemiboreal forests

Cited by 4 publications

References 34 publications

Effects of spatial–temporal land cover distribution on gross primary production and net primary production in Schleswig-Holstein, northern Germany

Effects of spatial–temporal land cover distribution on gross primary production and net primary production in Schleswig-Holstein, northern Germany

Analysis of Spatial and Temporal Variability of Ecosystem Service Values and Their Spatial Correlation in Xinjiang, China

Construction of tree species composition map of Estonia using multispectral satellite images, soil map and a random forest algorithm

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