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Remote Sensing of Forest Growth in a Changing Climate

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Forest Remote Sensing".

Deadline for manuscript submissions: closed (31 December 2019) | Viewed by 17968

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Dr. Damiano Gianelle

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Research and Innovation Center, Fondazione E. Mach, Via E. Mach, 1, 38010 S. Michele all'Adige, TN, Italy
Interests: remote sensing; biogeochemical cycles; climate change; forest ecology; ecosystems functioning; carbon balance; plant traits

Special Issue Information

Dear Colleagues,

Most of the scientific literature agrees with the observation that forest biomass has been increasing in the last few decades, with higher growth rates than expected. Even if the increased growth rates strongly differ among forest types, age classes, and geographical regions, this pattern was measured in many different forest ecosystems, and it seems to be synchronized in different biomes at broad spatial scales. Increased growth rates have been attributed to climate change, but there remains a great deal of uncertainty regarding the drivers of such changes.

Several climatic drivers have been explored (i.e., temperature, carbon dioxide, radiation, nitrogen deposition) and multiple-sometimes contrasting- mechanisms (i.e., increase of growing season, more efficient use of water, increase of diffuse radiation, fertilization effect, changes in plant phenology) have been proposed to explain this phenomenon. Furthermore, changes in land use and structure within different forest types have also been demonstrated to strongly influence forest dynamics and growth. Remote sensing data, when combined with high temporal resolution (i.e., Fluxnet) or wide spatial cover in situ observations (national forest inventories, EU-Forest) can provide new insights to investigate this aspect. This Special Issue on "Remote Sensing of Forest Growth in a Changing Climate" calls for manuscripts that demonstrate successful combinations of field and remote-sensing data on forcing and feedbacks between climate changes and forest growth. All types of original research contributions will be considered, including analyses at all temporal and spatial scales, making use of both empirical and biogeochemical models supported by remote sensing data.

Dr. Damiano Gianelle
Guest Editor

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Keywords

forest growth
climate change
forcing and feedbacks between climate changes and forest ecology
biogeochemical models
temporal and spatial scales

Published Papers (4 papers)

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Research

29 pages, 4262 KiB

Open AccessArticle

Predicting Carbon Accumulation in Temperate Forests of Ontario, Canada Using a LiDAR-Initialized Growth-and-Yield Model

by Paulina T. Marczak, Karin Y. Van Ewijk, Paul M. Treitz, Neal A. Scott and Donald C.E. Robinson

Remote Sens. 2020, 12(1), 201; https://doi.org/10.3390/rs12010201 - 06 Jan 2020

Cited by 8 | Viewed by 5044

Abstract

Climate warming has led to an urgent need for improved estimates of carbon accumulation in uneven-aged, mixed temperate forests, where high uncertainty remains. We investigated the feasibility of using LiDAR-derived forest attributes to initialize a growth and yield (G&Y) model in complex stands at the Petawawa Research Forest (PRF) in eastern Ontario, Canada; i.e., can G&Y models based on LiDAR provide accurate predictions of aboveground carbon accumulation in complex forests compared to traditional inventory-based estimates? Applying a local G&Y model, we forecasted aboveground carbon stock (tons/ha) and accumulation (tons/ha/yr) using recurring plot measurements from 2012–2016, FVS1. We applied statistical predictors derived from LiDAR to predict stem density (SD), stem diameter distribution (SDD), and basal area distribution (BA_dist). These data, along with measured species abundance, were used to initialize a second model (FVS2). A third model was tested using LiDAR-initialized tree lists and photo-interpreted estimates of species abundance (i.e., FVS3). The carbon stock projections for 2016 from the inventory-based G&Y model) were equivalent to validation carbon stocks measured in 2016 at all size-class levels (p < 0.05), while LiDAR-based G&Y models were not. None of the models were equivalent to validation data for accumulation (p > 0.05). At the plot level, LiDAR-based predictions of carbon accumulation over a nine-year period did not differ when using either inventory or photo-interpreted species (p < 0.05). Using a constant mortality rate, we also found statistical equivalency of inventory and photo-interpreted accumulation models for all size classes ≥17 cm. These results suggest that more precise information is needed on tree characteristics than we could derive from LiDAR, but that plot-level species information is not as critical for predictions of carbon accumulation in mixed-species forests. Further work is needed on the use of LiDAR to quantify stand properties before this technique can be used to replace recurring plot measurements to quantify carbon accumulation. Full article

(This article belongs to the Special Issue Remote Sensing of Forest Growth in a Changing Climate)

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14 pages, 2692 KiB

Open AccessArticle

Spatial Upscaling of Tree-Ring-Based Forest Response to Drought with Satellite Data

by Peipei Xu, Wei Fang, Tao Zhou, Xiang Zhao, Hui Luo, George Hendrey and Chuixiang Yi

Remote Sens. 2019, 11(20), 2344; https://doi.org/10.3390/rs11202344 - 10 Oct 2019

Cited by 17 | Viewed by 3542

Abstract

We have integrated the observational capability of satellite remote sensing with plot-scale tree-ring data to upscale the evaluation of forest responses to drought. Satellite data, such as the normalized difference vegetation index (NDVI), can provide a spatially continuous measure with limited temporal coverage, while tree-ring width index (RWI) provides an accurate assessment with a much longer time series at local scales. Here, we explored the relationship between RWI and NDVI of three dominant species in the Southwestern United States (SWUS) and predicted RWI spatial distribution from 2001 to 2017 based on Moderate Resolution Imaging Spectroradiometer (MODIS) 1-km resolution NDVI data with stringent quality control. We detected the optimum time windows (around June–August) during which the RWI and NDVI were most closely correlated for each species, when the canopy growth had the greatest effect on growth of tree trunks. Then, using our upscaling algorithm of NDVI-based RWI, we were able to detect the significant impact of droughts in 2002 and in 2011–2014, which supported the validity of this algorithm in quantifying forest response to drought on a large scale. Full article

(This article belongs to the Special Issue Remote Sensing of Forest Growth in a Changing Climate)

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15 pages, 16821 KiB

Open AccessArticle

Predicting Selected Forest Stand Characteristics with Multispectral ALS Data

by Michele Dalponte, Liviu Theodor Ene, Terje Gobakken, Erik Næsset and Damiano Gianelle

Remote Sens. 2018, 10(4), 586; https://doi.org/10.3390/rs10040586 - 10 Apr 2018

Cited by 28 | Viewed by 4603

Abstract

In this study, the potential of multispectral airborne laser scanner (ALS) data to model and predict some forest characteristics was explored. Four complementary characteristics were considered, namely, aboveground biomass per hectare, Gini coefficient of the diameters at breast height, Shannon diversity index of the tree species, and the number of trees per hectare. Multispectral ALS data were acquired with an Optech Titan sensor, which consists of three scanners, called channels, working in three wavelengths (532 nm, 1064 nm, and 1550 nm). Standard ALS data acquired with a Leica ALS70 system were used as a reference. The study area is located in Southern Norway, in a forest composed of Scots pine, Norway spruce, and broadleaf species. ALS metrics were extracted for each plot from both elevation and intensity values of the ALS points acquired with both sensors, and for all three channels of the ALS multispectral sensor. Regression models were constructed using different combinations of metrics. The results showed that all four characteristics can be accurately predicted with both sensors (the best R² being greater than 0.8), but the models based on the multispectral ALS data provide more accurate results. There were differences regarding the contribution of the three channels of the multispectral ALS. The models based on the data of the 532 nm channel seemed to be the least accurate. Full article

(This article belongs to the Special Issue Remote Sensing of Forest Growth in a Changing Climate)

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15457 KiB

Open AccessArticle

The Effects of Forest Area Changes on Extreme Temperature Indexes between the 1900s and 2010s in Heilongjiang Province, China

by Lijuan Zhang, Tao Pan, Hongwen Zhang, Xiaxiang Li and Lanqi Jiang

Remote Sens. 2017, 9(12), 1280; https://doi.org/10.3390/rs9121280 - 09 Dec 2017

Cited by 10 | Viewed by 4138

Abstract

Land use and land cover changes (LUCC) are thought to be amongst the most important impacts exerted by humans on climate. However, relatively little research has been carried out so far on the effects of LUCC on extreme climate change other than on regional temperatures and precipitation. In this paper, we apply a regional weather research and forecasting (WRF) climate model using LUCC data from Heilongjiang Province, that was collected between the 1900s and 2010s, to explore how changes in forest cover influence extreme temperature indexes. Our selection of extreme high, low, and daily temperature indexes for analysis in this study enables the calculation of a five-year numerical integration trail with changing forest space. Results indicate that the total forested area of Heilongjiang Province decreased by 28% between the 1900s and 2010s. This decrease is most marked in the western, southwestern, and northeastern parts of the province. Our results also reveal a remarkable correlation between change in forested area and extreme high and low temperature indexes. Further analysis enabled us to determine that the key factor explaining increases in extreme high temperature indexes (i.e., calculated using the number of warm days, warm nights, as well as tropical nights, and summer days) is decreasing forest area; data also showed that this factor caused a decrease in extreme low temperature indexes (i.e., calculated using the number of cold days and cold nights, as well as frost days, and ice days) and an increase in the maximum value of daily minimum temperature. Spatial data demonstrated that there is a significant correlation between forest-to-farmland conversion and extreme temperature indexes throughout most of our study period. Spatial data demonstrated that there is a significant correlation between forest-to-farmland conversion and extreme temperature indexes throughout most of our study period. Positive correlations are also present between decreasing forest area, the more frequent occurrence of extreme high temperature events, and a rise in the maximum value of daily minimum temperature. At the same time, we found clear negative correlations between decreasing forest area and less frequent occurrence of extreme low temperature events. Full article

(This article belongs to the Special Issue Remote Sensing of Forest Growth in a Changing Climate)

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