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Journal of Arid Land
Journal of Arid Land  2021, Vol. 13 Issue (9): 918-933    DOI: 10.1007/s40333-021-0083-4     CSTR: 32276.14.s40333-021-0083-4
Research article     
Plant cover as an estimator of above-ground biomass in semi-arid woody vegetation in Northeast Patagonia, Argentina
Laura B RODRIGUEZ1,2, Silvia S TORRES ROBLES1,*(), Marcelo F ARTURI3, Juan M ZEBERIO1, Andrés C H GRAND4, Néstor I GASPARRI5,6
1National University of Río Negro, Atlantic Headquarters, Center for Environmental Studies from Norpatagonia (CEANPa), Viedma 8500, Argentina
2National Council of Scientific and Technical Research (CONICET), Viedma 8500, Argentina
3Ecological and Environmental Systems Research Laboratory (LISEA), National University of La Plata, La Plata 1900, Argentina
4National Institute of Agricultural Technology (INTA), AER Patagones, Carmen de Patagones 8504, Argentina
5Institute of Regional Ecology (IER), National University of Tucumán (UNT)-National Council of Scientific and Technical Research (CONICET), Tucumán 4000, Argentina
6Faculty of Natural Sciences and Miguel Lillo Institute, National University of Tucumán (UNT), Yerba Buena 4107, Argentina
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Abstract  

The quantification of carbon storage in vegetation biomass is a crucial factor in the estimation and mitigation of CO2 emissions. Globally, arid and semi-arid regions are considered an important carbon sink. However, they have received limited attention and, therefore, it should be a priority to develop tools to quantify biomass at the local and regional scales. Individual plant variables, such as stem diameter and crown area, were reported to be good predictors of individual plant weight. Stand-level variables, such as plant cover and mean height, are also easy-to-measure estimators of above-ground biomass (AGB) in dry regions. In this study, we estimated the AGB in semi-arid woody vegetation in Northeast Patagonia, Argentina. We evaluated whether the AGB at the stand level can be estimated based on plant cover and to what extent the estimation accuracy can be improved by the inclusion of other field-measured structure variables. We also evaluated whether remote sensing technologies can be used to reliably estimate and map the regional mean biomass. For this purpose, we analyzed the relationships between field-measured woody vegetation structure variables and AGB as well as LANDSAT TM-derived variables. We obtained a model-based ratio estimate of regional mean AGB and its standard error. Total plant cover allowed us to obtain a reliable estimation of local AGB, and no better fit was attained by the inclusion of other structure variables. The stand-level plant cover ranged between 18.7% and 95.2% and AGB between about 2.0 and 70.8 Mg/hm2. AGB based on total plant cover was well estimated from LANDSAT TM bands 2 and 3, which facilitated a model-based ratio estimate of the regional mean AGB (approximately 12.0 Mg/hm2) and its sampling error (about 30.0%). The mean AGB of woody vegetation can greatly contribute to carbon storage in semi-arid lands. Thus, plant cover estimation by remote sensing images could be used to obtain regional estimates and map biomass, as well as to assess and monitor the impact of land-use change on the carbon balance, for arid and semi-arid regions.

Key words： above-ground biomass      shrublands      ratio estimation      carbon storage      remote sensing      Patagonia     
Received: 07 April 2021      Published: 10 September 2021
Corresponding Authors: *Silvia S TORRES ROBLES (storresr@unrn.edu.ar)
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Laura B RODRIGUEZ
Silvia S TORRES ROBLES
Marcelo F ARTURI
Juan M ZEBERIO
Andrés C H GRAND
Néstor I GASPARRI
Cite this article:

Laura B RODRIGUEZ, Silvia S TORRES ROBLES, Marcelo F ARTURI, Juan M ZEBERIO, Andrés C H GRAND, Néstor I GASPARRI. Plant cover as an estimator of above-ground biomass in semi-arid woody vegetation in Northeast Patagonia, Argentina. Journal of Arid Land, 2021, 13(9): 918-933.

URL:

http://jal.xjegi.com/10.1007/s40333-021-0083-4     OR     http://jal.xjegi.com/Y2021/V13/I9/918

Fig. 1 Geographical location of the study area and distribution of sampling sites. Structural survey sites: 1?42; harvest sites for biomass estimation: 1?21. The solid line indicates the limit between ''Monte'' (southwest) and ''Espinal'' (northeast) ecoregions according to Morello et al. (2012), and the dotted line indicates the boundary of the transitional vegetation unit ''Transition Monte'', according to Oyarzabal et al. (2018).
Fig. 2 Woody vegetation with different total coverages in the study region. (a), site 19 (cover of 95.2%); (b), site 12 (cover of 68.6%); (c), site 17 (cover of 71.4%); (d), site 2 (cover of 26.0%).
Model n b0 b1 b2 b3 AIC R2
logAGB=b0+b1logCoverT# 21 ‒4.62*** 1.92*** - 31.9 0.80***
logAGB=b0+b1logCoverT+b2BA 21 ‒4.03** 1.75*** 0.16NS - 32.6 0.80***
logAGB=b0+b1logCoverT+b2Mean height 21 ‒4.26** 1.75*** 0.33NS - 32.1 0.81***
logAGB=b0+b1logCoverT+b2Max height 21 ‒4.18** 1.75*** 0.11NS - 33.1 0.80***
logAGB=b0+b1Cover>10+b2Cover5‒10+b3Cover<5 21 1.26** 0.03*** 0.04** 0.02NS 38.4 0.75**
Table 1 Models used to estimate above-ground biomass (AGB) based on structure variables
Fig. 3 Relationship between log-transformed total plant cover and log-transformed above-ground biomass. The selected model is plotted as a solid line.
Date
(dd/mm/yy)		 B1 B2 B3 B4 B5 B7 NDVI SAVI EVI
19/09/10 ‒0.41** ‒0.68*** ‒0.61*** ‒0.62*** ‒0.66*** ‒0.59*** ‒0.06 NS ‒0.29* ‒0.23NS
10/10/10 ‒0.64*** ‒0.68*** ‒0.68*** ‒0.75*** ‒0.66*** ‒0.58** 0.05NS ‒0.33** ‒0.27*
21/12/10 ‒0.63*** ‒0.69*** ‒0.76*** ‒0.54** ‒0.69*** ‒0.67*** 0.77*** 0.68*** 0.73***
23/01/11 ‒0.32** ‒0.37** ‒0.39** ‒0.16NS ‒0.43** ‒0.39** 0.41** 0.28* 0.31**
17/03/07 ‒0.64*** ‒0.68*** ‒0.67*** ‒0.17NS ‒0.62*** ‒0.63*** 0.51** 0.39** 0.41**
Table 2 Pearson's correlation coefficient for the bands (LANDSAT TM) and green indices (NDVI, SAVI, and EVI) in relation to the biomass on different dates
Model n b0 b1 b2 AIC R2
logAGB=b0+b1logB2+b2logB3# 42 3.64NS 11.77** ‒11.30*** 62.3 0.62***
AGB=b0+b1NDVI 42 ‒31.23*** 226.48*** - 75.1 0.58***
logAGB=b0+b1logB4 42 ‒5.70*** ‒4.34*** - 93.1 0.56***
AGB=b0+b1EVI 42 ‒34.12** 304.37*** - 76.7 0.52***
Table 3 Regression models of AGB based on spectral data
Fig. 4 Predicted vs. observed values of the regression of log-transformed AGB based on bands 2 and 3. The solid line indicates a linear model with intercept=0 and slope=1.
Fig. 5 AGB mapped for the entire study area based on LANDSAT TM bands 2 and 3 (a), and cut-offs of AGB along the geographic gradient (b?e). Isohyets reflects the mean annual precipitation.
Fig. 6 Predicted vs. observed plots of the model of AGB based on bands 2 and 3 in 1000 permutations of the bootstrap procedure. Solid and discontinuous lines exhibit 95% upper and lower confidence limits (5 and 95 quantiles, respectively).
n Mean height
(m)		 Maximum height (m) Cover>5
(%)		 Cover<5
(%)		 CoverT (%) BA
(m2)
3 0.78 1.50 0.0 18.7 (100.0) 18.7
5 1.64 2.50 4.0 (18.0) 18.2 (82.0) 22.3 4.6
42 2.38 4.00 10.9 (46.0) 13.0 (54.0) 23.9 4.3
2 0.57 1.50 0.0 26.0 (100.0) 26.0
9 1.28 2.00 0.0 26.7 (100.0) 26.7
29 1.09 1.80 0.0 27.8 (100.0) 27.8
41 1.56 2.00 0.0 28.5 (100.0) 28.6
4 0.88 2.50 0.9 (3.0) 27.8 (97.0) 28.6 0.4
35 0.78 1.50 0.0 32.9 (100.0) 32.9
8 0.77 1.50 0.0 36.0 (100.0) 36.0
10 0.73 1.50 0.0 36.3 (100.0) 36.3
22 1.11 1.70 0.0 36.6 (100.0) 36.6
36 0.85 2.00 0.0 37.3 (100.0) 37.3
34 0.91 2.00 0.0 41.8 (100.0) 41.8
26 0.84 1.50 0.0 44.3 (100.0) 44.3
6 1.03 2.00 0.0 44.3 (100.0) 44.3
1 1.01 2.00 0.0 45.3 (100.0) 45.3
31 1.03 4.00 19.8 (43.0) 26.0 (57.0) 45.8 2.2
11 1.07 1.50 0.0 48.3 (100.0) 48.3
13 1.04 2.00 0.4 (1.0) 48.1 (99.0) 48.4 0.2
27 0.68 1.50 0.7 (1.0) 50.2 (99.0) 50.9 0.4
24 0.75 1.50 0.0 52.5 (100.0) 52.5
32 1.01 2.00 0.0 54.1 (100.0) 54.1
30 1.21 3.00 3.1 (6.0) 51.8 (94.0) 54.9 1.7
40 1.53 2.00 0.0 56.1 (100.0) 56.1
23 1.13 2.10 0.0 57.1 (100.0) 57.1
7 1.69 3.50 19.1 (33.0) 38.6 (67.0) 57.8 9.6
16 1.71 5.00 16.3 (28.0) 41.7 (72.0) 57.8 18.0
28 0.94 1.75 0.0 59.4 (100.0) 59.4
15 1.25 2.00 0.0 60.4 (100.0) 60.4
14 1.63 4.00 11.9 (19.0) 49.3 (81.0) 61.2 4.3
33 1.56 3.50 9.5 (14.0) 57.9 (86.0) 67.4 8.6
12 1.52 3.00 4.6 (7.0) 64.0 (93.0) 68.6 17.7
25 1.43 3.00 11.1 (16.0) 58.0 (84.0) 69.1 5.2
20 1.12 2.00 0.8 (1.0) 69.9 (99.0) 70.7 0.4
17 1.71 4.00 26.2 (37.0) 45.2 (63.0) 71.4 11.2
38 1.12 3.00 4.7 (6.0) 71.6 (94.0) 76.3 2.5
39 1.94 4.00 37.1 (48.0) 40.4 (52.0) 77.5 20.5
37 1.82 3.00 28.6 (36.0) 50.5 (64.0) 79.1 111.2
18 1.49 2.50 17.4 (21.0) 64.9 (79.0) 82.3 1.0
21 2.08 5.00 40.6 (44.0) 52.5 (56.0) 93.0 7.3
19 1.91 4.50 50.6 (53.0) 44.6 (47.0) 95.2 24.6
Table S1 Estimates of the structural variables for the analyzed sites
n CoverT (%) Mean height (m) Maximum height (m) BA
(m2)		 Cover>10 (%) Cover5‒10
(%)		 Cover<5 (%) AGB (Mg/hm2)
1 19.2 0.5 1.2 0.0 0.0 0.0 19.2 5.3
2 22.3 0.5 0.7 0.0 0.0 0.0 22.3 2.7
3 24.0 1.1 1.4 0.0 0.0 2.8 21.2 2.0
4 32.7 0.8 1.8 0.0 0.0 0.0 32.7 7.5
5 34.6 1.1 1.1 0.0 0.0 23.6 11.1 14.8
6 45.1 1.1 1.7 0.0 0.0 16.6 28.5 14.0
7 45.1 2.5 3.5 0.9 31.6 13.6 0.0 34.7
8 46.8 1.3 1.7 0.0 0.0 46.8 0.0 20.2
9 47.5 0.7 2.5 0.0 0.0 0.0 47.5 12.4
10 48.3 1.2 2.5 0.0 0.0 12.0 36.3 15.4
11 50.4 1.5 1.4 0.5 18.1 21.7 10.6 27.1
12 51.2 1.2 1.7 0.0 0.0 12.2 39.0 14.7
13 56.2 1.8 1.5 0.0 0.0 43.0 13.2 18.2
14 57.9 1.0 1.1 0.0 46.4 0.0 11.5 10.9
15 69.0 1.5 2.2 0.3 63.6 0.0 5.4 70.8
16 70.7 1.8 4.0 1.1 50.0 0.0 20.7 20.3
17 77.0 2.0 4.3 1.3 48.3 14.8 13.9 54.8
18 78.5 1.4 2.5 0.0 78.5 0.0 0.0 29.3
19 91.0 2.2 3.5 0.9 62.5 0.0 28.4 51.7
20 92.7 1.2 1.7 0.0 44.2 47.8 0.8 58.9
21 144.7 1.4 5.0 7.4 128.0 0.0 16.7 161.1
Table S2 Structural variables in the Monte-Espinal transition of NE Patagonia
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