Using photogrammetry to assess the recovery of a cypress forest and its impact on water-borne erosion. Case study: Guadalupe Island.

doi:10.21203/rs.3.rs-3717140/v1

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Using photogrammetry to assess the recovery of a cypress forest and its impact on water-borne erosion. Case study: Guadalupe Island.

https://doi.org/10.21203/rs.3.rs-3717140/v1

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We used photogrammetry to assess and monitor the recovery of the cypress forest on Guadalupe Island, Mexico, an ecosystem impaired by fires and overgrazing. Two drone surveys were conducted over the forest area during the summers of 2016 and 2019 with natural color (RGB) and near infrared (NIR) cameras. It is the first complete 3D reconstruction of the cypress forest on the island. The image process products were the canopy height model (CHM), digital surface model (DSM), and digital terrain model (DTM), which were used to calculate the number, density, and height of the trees. The CHM highly correlates with the forest's structure, R = 0.92, according to field measurements of the trees' heights and geo-position. Results account for ~ 67,340 trees higher than two meters in 2019 and a horizontal expansion of 134 ha from 2016 to 2019. Over 90% of the cypress population were young trees between 2 and 3 m that recovered after an extensive fire in 2008. We developed a novel method to calculate the C cover factor of the Revised Universal Soil Loss Equation (RUSLE) using CHM and DSM obtained by photogrammetry. Modeled erosion rates vary from 0 to 2 Kg m^− 2 y^− 1 in areas with trees of different ages to values greater than 15 Kg m^− 2 y^− 1 in areas with steep slopes. According to our results, the derived products from photogrammetry point clouds allow us to describe the vertical structure of the cypress forest with high accuracy and resolution and calculate the cover factor of RUSLE.

Photogrammetry

cypress forest ecosystem

canopy height model

water-borne erosion

RUSLE C-factor

Remote sensing techniques enable the assessment of changes in the landscape from diverse perspectives, therefore they have proved to be successful tools in the study of forest ecosystems: The multispectral analysis is used to detect specific types of vegetation, their change in distribution according to disturbances or seasonality (Akasheh et al., 2008; Rouse et al., n.d.; Rußwurm & Körner, 2017; Schuster et al., 2012); the laser scanner (aerial, terrestrial, or satellite) is used to model the 3-dimensional and vertical structure of vegetation and terrain with high accuracy (Andersena, 2005; Herrero-Huerta et al., 2016; Tompalski et al., 2019; Véga & St-Onge, 2008; Yépez-Rincón et al., 2021); Synthetic Aperture Radar (SAR) is used to describe changes in the landscape in places where clouds prevail over long periods, such as rainforests (Sano et al., 2021); and photogrammetry (aerial or satellite) is used to model the 3-dimensional structure of the surface (Nolan et al., 2015), the forest canopy height model (Cunliffe et al., 2016; Granholm et al., 2017; Lisein et al., 2013) its changes after a disturbance, or even to estimate the population range (Gallardo-Salazar & Pompa-García, 2020). One or more remote sensing techniques can be chosen according to the study site, the research objectives, and resources, although it is possible to combine them to improve the results in specific studies.

Photogrammetry is an accessible technique based on the processing of photographs that allows the production of 3-dimensional models of objects or surfaces (Rahaman, 2021; Schenk, 2005). Unlike laser scanning, the photogrammetry point cloud has only one return and hinders the discovery of occluded objects or penetration through the canopy. However, the color of photogrammetric point clouds adds a surface texture to the analysis. If infra-red cameras are used, vegetation indices (VI) can be combined with photogrammetry to classify vegetation (Candiago et al., 2015; Moskal et al., 2000; Viljanen et al., 2018).

The condition of a forest is the result of the constant interactions between physical and biological factors through time and space: climate (temperature, precipitation, seasonality), geology (geomorphology, soil composition, hydrology), and inter or intra-specific interactions between the organisms. This web of interactions provides goods and services: food, fuel, fibers, water supplies, carbon and water storage, soil erosion control, biodiversity, and genetic resources. Vegetation cover has an important role in controlling soil erosion by water because it can absorb the kinetic energy of raindrops, slow runoff, and keep the structure of soil by the root’s actions (Elliot et al., 2018; Pearce D & Pearce C, 2001; Toman et al., 1996).

Forest ecosystems are involved in a constant process of disturbances and regeneration that cause heterogeneity: loss of branches or trees, fires, fragmentation, overgrazing, lateral extension, harvesting, and growth of new trees. The vertical structure of a forest is the outcome of these persistent processes through space and time (Gadow et al., 2012; Granholm et al., 2017; Valverde & Silvertown, 1997). The forest structure is the vertical and horizontal projection of it and can be described by the canopy height and cover, leaf area density profile, and stem diameter (Brokaw, 1999; Donnellan A. et al., 2021). All these attributes allow us to assess managed and unmanaged ecosystems and analyze the heterogeneity according to disturbances and natural mechanisms of regeneration (Gadow et al., 2012).

Soil erosion is a natural process based on the transport of soil particles by the action of air, gravity, or water. Water-borne erosion is the most common and widespread of these processes and has increased around the world due to human activity, generating the loss of soil in crop areas, human settlements, and other degraded ecosystems (Gudino-Elizondo et al., 2018; Lal, 2003; Li & Fang, 2016; Phinzi & Ngetar, 2019; Vrieling, 2006).

There are several models to predict water-borne erosion; among them, the Revised Universal Soil Loss Equation (RUSLE) is the most used (Borrelli et al., 2021). This model considers five environmental factors that contribute to water-borne soil erosion: rainfall erosivity (R), soil erodibility (K), slope length and steepness (LS), conservation practices (P), and the cover factor (C). RUSLE model can be implemented with Geographical Information Systems (GIS) to evaluate soil erosion, mainly by computing the LS factor (Phinzi & Ngetar, 2019; Renard et al., 1997). Because it uses digital terrain models, this method allows the assessment of large areas considering the topography variations. The result obtained after applying this model is an estimate of the amount of soil lost in a year over a specific area expressed in units of mass/area/time (ton ha-¹ y-¹).

The C factor models the effect of land cover on erosion rates, where vegetation cover plays an important role in modulating soil erosion; (Renard et al., 1997). Different methods to compute this factor using remote sense techniques have been published, based mainly on multi-spectral images, and describing the distribution of vegetation using vegetation indices (Almagro et al., 2019; Biddoccu et al., 2020; Pacheco et al., 2019; Puente et al., 2011; Tanyaş et al., 2015). In this paper, we incorporate vegetation height to model the C factor.

Guadalupe Island of volcanic origin, the 7th largest island of Mexico with 243.6 Km² is a Biosphere Reserve located in the eastern north Pacific near the Baja California coast, Mexico (Fig. 1). This island is home to many endemic and native species of flora and fauna (Aguirre-Muñoz et al., 2003; Junak et al., 2005; Moran R., 1996; Oberbauer et al., 2009; Santos del Prado & Peters, 2005a). This reserve has been affected by human activity; in the terrestrial ecosystem, the introduction of exotic invasive species caused a decrease in biodiversity, plant coverage, and loss of soil (Junak et al., 2005; Luna Mendoza et al., 2005; Oberbauer et al., 2009; Santos del Prado & Peters, 2005a; Yépez-Rincón et al., 2021). The NGO Grupo de Ecología y Conservación de Islas (GECI), Mexican government institutions (CONANP, CONABIO, SEMARNAT) and Mexican and international donors have worked together during the last decades making remarkable restoration and conservation efforts in this place. The eradication of feral goats was one of the most important management practices and was completed in 2007. This activity resulted in the recovery of plants (both native and non-native) in large areas (Keitt et al., 2005; Rodriguez et al., 2006; Santos del Prado & Peters, 2005a).

The island presents a climatic gradient due to its elevation (1,298 m), its north-south orientation, and the influence of dominant northwestern winds and the California Ocean current. These prevailing conditions bring humidity preferably to the northwest of the island which is captured by vegetation in the higher parts; the extrusion of the island and its attitude against predominant winds, fosters the recurrent formation of Kármán vortexes at the lee side of the island (Horváth et al., 2020). The persistent humidity allowed the establishment of forests of Guadalupe Monterey Pine (Pinus radiata var. binata), Island Oak (Quercus tomentella), and Guadalupe cypress (Cupressus guadalupensis) in the higher areas (Santos del Prado & Peters, 2005b; Villanueva Díaz et al., 2015).

The cypress forest is the most extended tree community on the island. It is distributed between 800 and 1200 m above sea level, forming three patches of different sizes and densities (Ramírez-Serrato, 2014; Rodriguez et al., 2006). The community showed an evident recovery after the goat eradication (Keitt et al., 2005), at least 7,307 mature cypress individuals were reported in 2006 (Rodriguez et al., 2006), and the growth of hundreds of seedlings around those trees (Aguirre Muñoz et al., 2006). However, in 2008, an extensive fire killed 60% of the adults and new recruits leaving a substantial extension of burned surface and few live individuals (Ramírez-Serrato, 2014) (Fig. 1). Despite the fire, the forest had a significant recovery due to the cones of these trees; they are serotinous and are sealed until a fire or heat opens them. Also, they had beneficial conditions for the growth of new individuals because of soil components after the fire (Oberbauer et al., 2009). Currently, the Guadalupe Island cypress forest has a significant number of individuals that grew after 2008. Aguirre Muñoz (2009) reported 120,000 new trees in the south patch.

Other authors have studied the cypress forest of Guadalupe Island using remote sensing techniques. Rodriguez (2006) did a multispectral analysis with a Quickbird (2004/04/24) high-resolution satellite image to estimate the number of trees and described the distribution of the forest patches. In 2014 Ramirez-Serrato calculated the cypress forest’s extent using multispectral satellite images from 2001 to 2012, estimating different VI to locate healthy vegetation and burned areas; as the opened and closed spaces within the forest patches. Ramírez-Serrato (2014) reported three forest patches and described their extension after the fire: north, central, and south patches (Fig. 1D). The north patch did not have live trees after the fire and had an area of 9.4 hectares; the central patch had 56.3 hectares with only the 7% of the extent not burned; and the south patch had 131.5 hectares of extension, about 44% of it is covered with living trees (Fig. 1). Yépez-Rincón et al. (2021) developed a 3D Structural Classification Method (3D-SCM) using Terrestrial Laser Scanner (TLS) data. This study describes the vertical structure of two sites in the cypress forest, incorporating a crown envelope descriptor mentioned as individual shape index, and with high accuracy results between field measurements and laser data. They reported a coefficient of determination R² = 0.949 for Diameter Breast Height (DBH), R² = 0.974 for the crown, and R² = 0.97 for heights between maximum laser pulse of tree heights and crowns in relation to field measurements.

In addition to forest distribution studies, there are descriptive works about soil and erosion on Guadalupe Island; these give information about the classification, structure, and soil composition (Hoeksema et al., 2012; Leathem B. T. & Z.T., 2003). Ramos-Franco (2007) estimated the water-borne erosion in the cypress forest area by monitoring gullies of the south patch before and after the wet season, describing the erosion susceptibility from low to high according to the slope variation. His estimations had a min of 43.56 ton ha-¹ y-¹ and a max of 142.45 ton ha-¹ y-¹.

The present work is a contribution to the study of forest ecosystems using 3D photogrammetry. Its objectives are 1) to prove that photogrammetry is a suitable technique to model the three-dimensional structure of Guadalupe Island Cypress Forest; 2) to assess the recovery and changes of the forest after the 2008 fire using photogrammetry and multispectral analysis, and 3) to predict the annual loss of soil rate using high-resolution digital models obtained by photogrammetry.

Guadalupe Island has a template-dry climate, with wet-cold conditions during the winter and warm-dry during the summer. There is seasonality in the landscape of the island, during the winter with higher humidity it turns green due to the growth of annual vegetation, while during the summer, there is a contrast between dry and evergreen plants. Because Guadalupe Cypress is an evergreen plant, it is easy to distinguish among other plants during the summer, except Calystegia macrostegia ssp. macrostegia (an evergreen shrub distributed in the north of the island). For this reason, we used a multi-spectral image of August 26, 2016 (summer) from the Planet.com constellation to delimit forest extent and delineate the flight survey zones.

Two fieldwork campaigns were made to collect the aerial photographs used to model the forest, one in July 2016 and the other in June 2019. Flight missions were programmed with a 75% lateral and along-track overlap. In the 2016 fieldwork campaign, an RGB and a NIR cameras were used. The flights were programmed with crossed lines for an enhanced reconstruction. The 2019 flights were made using an RGB camera. Details for each survey are described in Fig. 2 and Table 1.

Table 1

**Unmanned aerial vehicle (UAV) flights made over the cypress forest in 2016 and 2019 (GCP = ground control points**).
Date	UAV	Camera	Flight area	Number of flights	GCP	Height of flight over the ground
July 2016	Fixed wing drone - eBee Classic senseFly	SONY WX, 18 MP RGB	1,000 ha	11	19	130 m Terrain awareness cross lines/grid
July 2016	Fixed wing drone - eBee Classic senseFly	CANON S110, 12 MP NIR	1,000 ha	10 10	19	130 m Terrain awareness cross lines/grid
June 2019	Multirotor DJI Phantom 4 Pro	1CMOS 20 MP RGB	600 ha	12	13	100 m

In June 2019, 24 aleatory plots were traced over the forest area (Fig. 2) to count trees taller than two m and register their heights. Each plot had a circular form with a radio of 12 m (~ 450 m² area). The reproductive status of every tree was also registered (presence of male and female cones).

During the initial growth stage, the Guadalupe Cypress has a conical crown; when they reach their maximum height, their crowns are rounded or irregular (Aljos-Farjon, 2017; Moran R., 1996; Yépez-Rincón et al., 2021). Considering this fact, we classified trees into two groups according to the crown shape: Mature trees (that survived the fire) with rounded or sub-rounded crowns and young trees (that grew after the fire) with conical crowns (Fig. 3).

Photogrammetry

Aerial images were processed using Pix4D mapper software version 3.2.23 (Pix4D SA, Switzerland), incorporating the GCPs into the data flow for each campaign. The results were colored point clouds, Digital Surface Models (DSMs), and orthomosaics for each sampling year and camera model. The number of photographs and product characteristics obtained by Pix4D for each aerial survey are shown in Table 2.

Table 2

Products obtained using Pix4D for each flight with UAV in 2016 and 2019.
Flights	Number of photographs	Number of points in point clouds x 10⁶	Density (points/m²)	Orthomosaic resolution (cm)
RGB 2016	2346	290	42	6.5
NIR 2016	2846	316	45	6.5
RGB 2019	5219	600	108	7.5

The Canopy Height Model (CHM) and Digital Terrain Model (DTM) were calculated from point clouds using LAStools "Efficient LiDAR Processing Software" (version 141017, academic). Custom workflows were developed to segment point clouds into tiles, to filter, classify and calculate the CHM and DTMs. Classifying GCPs is key in this process. LAStools scripts are included as supplementary materials. The CHM is a high-resolution raster; it represents the canopy height of the forest over a continuous surface (Z = height of each pixel over the ground), where the topography relief has been removed. The DTM is a high-resolution raster representing the ground elevation without superficial elements (trees or buildings).

A Normalized Difference Vegetation Index (NDVI) filter > = 0.3, was applied to generate the CHM from the NIR point cloud. The result is a raster that represents only the green (live) trees. The CHMs generated from each point cloud have a 25 cm horizontal resolution and include heights between 0.1 and 24 m. The DSM and DTM were generated using RGB 2016-point cloud with a final resolution of 1 m each.

To estimate the cypress population size, 2D crown vectors, and tree top heights, we used R software version 3.6.1 (R core team 2019), using the tools from ForestTools library version 0.2.0 (Maintainer & Plowright, 2018). This package offers functions to analyze remotely sensed forest data generally derived from LiDAR or photogrammetric point clouds (Plowright, 2018).

We applied a workflow proposed by Plowright (2018) that uses minimum criteria on a CHM to calculate treetops and delineate tree crowns; these criteria are adjustable through a linear function, a minimum height (min-height) of treetops, and crowns. The linear function is used in the window filter algorithm to detect treetops. The linear function and min-height of trees and crowns can be established depending on the study area and features of trees that are the object of the study. In this study, we adjusted the minimum height to 2 m to account for young trees and 5 m for the mature trees. The win radius, a parameter to identify trees, was adjusted according to the patch location because of the difference in open and closed spaces (Ramírez-Serrato, 2014). The burned zones (BZ) and non-burned zones (NBZ) were processed separately, with different parameters. For the NBZ in the central patch, the win radius was set to 2.5 m, while for the NBZ in the south patch was set to 3.5 m.

Estimation of soil erosion using RUSLE

To calculate the annual rate of soil erosion by water we applied the RUSLE formula introduced by Renard et al., (1997) section 1.3, as:$A=R\times K\times LS\times P\times C$

For each factor in the RUSLE formula, a raster was created. The factor R was calculated according to the equation proposed by Wischmeier & Smith (1978) and modified by Renard & Freimund (1994). The data used to compute R comes from the weather station located in the cypress forest, including daily measurements from 2013 to 2019. The erodibility factor K was evaluated using the equation proposed by Renard et al. (1997); the values of organic matter; and percentage of silt, sand, and clay were taken from the work of Leathem & Zink (2003) and Hoeksema (2012). The LS factor was calculated applying the methodology proposed by Moore & Burch (1986), using the DTM obtained by photogrammetry, with a resolution of 1 m. Because no information related to management practices (factor P) was available, we assigned a value of 1 to factor P.

The cover factor C was calculated using a novel method with the CHM and DTM obtained by photogrammetry, according to the RUSLE handbook (Renard et al., 1997), and the works of Smith (2007) and González-Botello (2010), we used the next equation to calculate the spatial distribution of C raster:

$$C=0.25\left(1-p\times {e}^{-0.1\times h}\right)\left({e}^{-0.039\times g\left({\frac{24}{r}}^{0.08}\right)}\right)$$

Where p represents the percentage of air coverage, h is the height of vegetation (CHM), g is the percentage of plant coverage, and r is the rugosity. To compute p, we used the canopy coverage from the CHM, assigning the value of 1 to all cells with values higher or equal to 10 m (mature trees) and 0 for the other cells and calculating the sum in squares of 10 x 10 CHM cells, the value obtained is the percentage cover by the canopy in squares of 2.5 x 2.5 m. The h raster was obtained from the CHM, calculating the average height in boxes of 10 x 10 cells. The plant coverage factor g was estimated from the CHM, assigning the value of 1 to all the cells with values higher or equal to 0.2 m and 0 to the other cells and calculating the sum in squares of 10 x 10 cells, the result is the percentage of the area covered by vegetation in cells of 2.5 m. Finally, the rugosity raster is estimated from the standard deviation of DSM (Fig. 4).

Through the NDVI analysis of the Planet dry season image (2016-08-26), we delimited the four forest patches with different sizes and environmental conditions (Fig. 2). These patches are on the west facing slopes of the extinct volcano, adjacent to the rim of the collapsed crater. The north patch, the smallest, with 11.6 hectares comprising only young trees. This patch is the most exposed to wind, with rocky soils mostly, and an average slope of 17%. The central patch with 74 hectares; it has a non-burned zone (NBZ) with mature trees and a large burned zone (BZ) with young trees, the topography with an average slope of 12%, allows the accumulation of water. The central-south patch extending 9 hectares with only young individuals; it is a new zone of the natural expansion of the forest, with an average slope of 13%. The south patch, the largest (263 hectares) where a third of its extension is a NBZ, having mainly mature trees, and the rest is a BZ with mostly young trees, it has a steeper slope with an average of 18%.

On the 24 plots surveyed in 2019, we found 638 individuals taller than 2 m. Twelve of them were mature, and 626 were young; only the height of 575 trees were measured because the rest were too close to each other, making it impossible to distinguish the treetop of each one. The height range of young trees observed in the field was from 2 to 14 m: 74% less than four m, 18% between 5 and 7 m, and 8% between 8 and 14 m high. We could observe mature trees from 6 to 24 m in height. At least 68% of the counted trees had reproductive structures, in individuals from 1.5 m to 24 m.

The canopy height models cover all the patches, and the comparison between the two dates provides information about the changes in the vertical structure of the forest, allowing us to monitor the appearance, growth and loss of trees. Examples of these results are in Fig. 6.

The correlation between the number of individuals measured in the field and Forest tools results was R = 0.65, underestimating the number of young trees and overestimating the mature trees. Despite this, it shows a correlation between the field measurements and ForestTools results.

The correlation between tree heights measured in the field and from ForestTools was R = 0.92, showing that height values of the CHM are accurate to assess the forest and describe its vertical structure.

Table 3 shows the population of the cypress forest calculated using ForestTools, for the 2016 and 2019 surveys. The current densities of individuals by patches are shown in Table 4, considering the burned and non-burned zones in central and south patches (BZ and NBZ, respectively); the areas used to calculate these densities are the areas delineated for each polygon (Fig. 2). It is important to remember that the north and central-south patches have only BZ. The NBZs are covered mainly by mature trees with heights between 6 and 21 m. However, in these areas, some young trees grew after the fire due to the natural dynamic of the forest.

Table 3

The number of individuals calculated for each patch, according to the camera used and survey year.
Number of individuals
		Patches
		North	Central	Central-South	South	Total
2016 NIR	Young	2 459	33 929	286	25 899	62 576
	Mature	0	120	0	1684	1,804
	Total	*2459	*34,049	*286	*27,583	*64,380
2016 RGB	Young	5 759	47 348	565	39 878	93 550
	Mature	0	122	0	1675	1 797
	Total	*5759	*48 236	*565	*41 553	*95 374
2019 RGB	Young	3 986	38 425	419	22 558	65 388
	Mature	0	138	0	1814	1952
	Total	3 986	38 563	419	24 372	67 340

*Differences between the total number of trees reported by each type of camera in 2016. There was a difference of at least 30,000 trees between the RGB and NIR in Total results. NIR results include only live trees, while RGB include live and dead-standing trees.

Table 4

Tree densities in each patch from the 2019 survey.
Patch	Individuals per hectare
	BZ	NBZ
North	344	-
Central	526	67
Central-South	40	-
South	165	24

Table 4 shows that central and north patches have a higher density than the south and central patch. Also, considering the number of individuals in each patch, we note that the central patch has the largest number of individuals, for both years and type of camera.

Height distribution was calculated, distinguishing between mature and youth. The results are in Table 5, showing the differences between BZ and NBZ.

Table 5

Height statistics for each patch, according to the camera used and survey year. Reporting height average, maximum and standard deviation (SD). All measures in m.
		Patches
		North	Central		Central-South	South
			NBZ	BZ		NBZ	BZ
2016 NIR	Average	2.7	12.58	2.98	4.67	7.37	3.16
	Max	15.76	21.96	14.93	9.36	23.47	21.15
	SD	0.98	5.11	0.9	1.64	4.9	2.19
2016 RGB	Average	2.87	12.14	3.32	4.4	7.68	3.66
	Max	15.76	22.18	20.1	9.9	24	23.69
	SD	0.98	5.51	1.5	1.76	4.8	2.32
2019 RGB2019	Average	2.96	11.85	3.34	4.95	8.4	3.99
	Max	17.2	21.53	19.17	10.5	23	22.21
	SD	1.09	5.6	1.14	1.76	4.6	2.34

According to Table 5, the average height in the NBZs is greater than the average height in the BZs. The maximum value of trees in BZ of Central Patch is reported with the RGB camera, it includes standing dead trees and branches, while the maximum value in the same zone with the NIR camera represents young living trees (NDVI > 0.3).

In the BZ of the south patch, the maximum value reported, is from a live tree, this fact was corroborated in the field and the images. We assumed that this tree, while in the burned polygon, was not affected by fire perhaps because of the larger open spaces in this patch reported by Ramirez Serrato (2016). The average height values of all patches and zones are mostly influenced by the large number of young trees between 2 and 3 m. This fact can be observed in Fig. 7, showing the tree height histograms for each patch.

Figure 7 shows the height distribution of the forest patches for each survey year and camera used. According to the number of individuals, the central patch has the largest number, and the central-south patch has the lowest number of trees. It is possible to observe that the trees with heights between 2 and 3 m are the most representative in all the patches, including burned and non-burned zones (NBZ). However, in NBZ, the tree population is between 11 and 24 m height and has a higher frequency than in BZs. We can observe a large difference in the number of individuals reported by each type of camera, the 2016 RGB data has the highest frequencies in each category compared with the others.

Result examples obtained by applying the RUSLE model are shown in Fig. 8, where it is possible to observe the high-resolution analysis obtained using photogrammetry. The modeled erosion rates vary from 0 to 2 Kg m^− 2 y^− 1 (Fig. 8 − 2) for areas with trees of different ages, from 4 to 5 Kg m^− 2 y^− 1 (Fig. 8 − 3) for areas with grass or shrubby vegetation, and from 15 Kg to 33 Kg m^− 2 y ^− 1 (Fig. 8 − 1) for bare soils and steep slopes areas.

The horizontal variation of a forest can be assessed by vegetation index (VI) using multispectral (high and medium resolution) imagery. In this research, we observed the recovery and expansion of the cypress forest on Guadalupe Island after the 2008 fire, by comparing the size of the polygons reported by Ramírez-Serrato (2014) and the polygons traced in this work based on 2019 Planetscope satellite images. The cypress forest showed a total increase of 134 hectares in its area, and new areas of expansion were identified like the south-central patch described in this work. This patch is a clear natural recovery zone, essential for the growth of the forest.

The estimate of the number of trees using photogrammetry and ForestTools correlated reasonably with the results obtained in the field (R = 0.65). However, it underestimated the number of young trees and overestimated the number of mature trees. Gallardo et al. (2020) used the same methodology (ForestTools algorithm) to count trees in a crop, and reported a higher correlation between field and ForestTools data. Ninety-five percent of the trees measured in the field were detected by the ForestTools algorithm, proving the efficiency of photogrammetry to identify and count trees. The difference in correlation results is because Gallardo (2020) analyzed a crop where the trees are regularly distributed and are the same age; unlike this study, which was performed in a forest affected by several factors and trees at different growth stages. Field observations allowed us to verify the high density of young trees in some areas of the cypress forest, where up to 30 individuals were registered in a square meter, making it difficult to measure the height of individuals in the field. The comparison of this work with Gallardo’s (2020) results allowed us to determine the reach of photogrammetry to model specific ecosystems and stimulated us to seek ways of improving the technique.

On the other side, this study’s correlation value of height obtained using photogrammetry was R = 0.92. Therefore, the CHM model derived from photogrammetry proved to be a good tool to assess the recovery and evolution of the canopy structure and horizontal distribution of the cypress forest. It was able to generate a plausible map of tree crown geometry for the different forest patches combined with tree height histograms.

The difference between both surveys exposes how photogrammetry results can change depending on the study area, and camera type. In the present work, the correlation was not too strong, but it was positive. Thus, current results are a reliable 3D description of the Guadalupe Island cypress forest, the first and only of this type with high resolution.

Compared with vegetation studies using satellite images, photogrammetry using UAV has some advantages. The results of canopy cover and the crown areas obtained in this work are compared with those of Rodríguez-Malagón (2007), who estimated the number of individuals using high-resolution satellite multispectral images, suggesting an average canopy coverage area of 98.86 m² and 132.38 m² for mature trees. By photogrammetry, results showed coverage values of 2 to 104 m² in trees from 13 to 24 m in height, and an average of 25 m² of cup coverage. This demonstrates variability and precision in photogrammetric products. Figure 9 shows the crown polygons generated by ForestTools using the photogrammetry-derived CHM, which delineates the canopy projection on the ground, with a more accurate approximation to the canopy cover area than the use of VI from multi-spectral images, where the vertical component is not included in the results. The CHM obtained by UAV photogrammetry considers the height of any type of vegetation, allowing the discrimination of the evergreen herbaceous vegetation as Calystegia macrostegia ssp. macrostegia. The full estimate of the number of individuals by Rodríguez-Malagón (2007) is not comparable with present results, because the first were obtained before the fire and only mature trees were considered. The resolution of satellite images has lately been improved. Multispectral images like Sentinel or even satellite photogrammetry are tools that nowadays enhance the temporal and spatial resolution; however, their high cost may hinder research.

Point clouds generated by photogrammetry, do not describe the vertical structure of the forest, because they register only one return from the surface, the highest. In contrast, analysis with laser scanners provides multiple returns from one pulse. Yépez-Rincón et al. (2021) used point clouds from a terrestrial laser scanner to measure tree species in specific areas of Guadalupe Island. In that study, they analyzed the cypress and pine trees and reconstructed the crown geometry to differentiate trees. Their results showed a high correlation between field measurements and LASER data: diameter at breast height R² = 0.94, crow diameter R² = 0.97 and tree heights R² = 0.97.

LiDAR aerial surveys and terrestrial laser scans are remote sensing techniques with capacities more suitable for a precise 3D reconstruction of the vertical structure of the forest, enabling the registration of multiple returns per pulse and the precise location of reflectors, although resources to acquire them are not always available. Terrestrial laser scans are limited to spatial coverage due to operational limitations. Satellite photogrammetry from high-resolution stereo pairs or tri-stereos is an option for large areas, although the spatial resolution of reconstruction may not be appropriate for the construction of the CHM.

Comparing present results from NIR and RGB images of 2016, we found a large difference between the number of individuals counted. The differences correspond to standing dead trees located in BZ in each patch. Results showed the advantage of using an NIR camera to apply an NDVI filter on the resulting point cloud. With this filter, the live and dead trees within a zone may be detected.

The difference between the number counted in 2016 and 2019 using RGB cameras (at least 30,000 individuals) may be due to the removal of dry material (dead-standing trees and their fallen branches), either by natural factors or human activity. Natural factors involve the removal of dry matter by wind and humidity; for example, in the northern patch, where there is no human activity, we recorded the fall of dry trees of up to 17 m. On the other hand, human activity is evident in the south patch, where management practices are applied to prevent fires by removing fuel material (knocking down, cutting, and removing dead trees). The difference between dry and live vegetation in 2016 is in Table 1, where the number of individuals counted with NIR image data (live trees) is similar to the count in 2019.

Although the loss of dead trees or large branches may explain the significant difference between 2016 and 2019, the loss of green vegetation must also be considered, especially for mature individuals. Due to their large size and weight, in addition to their shallow roots, mature individuals can be knocked down by strong winds, which are common in winter.

The number and density of trees vary between patches; the central patch accommodated the highest number of individuals and density; the north patch showed the second largest density despite its extent and substrate (mainly rock); the south patch density was at least three times lower than the central patch despite having a greater extension, and the central-south patch displayed the lowest density and number of individuals. The differences in distribution and growth of trees may be due to the environmental conditions at each patch, determined by topography and exposure to ocean moisture. The north patch is the most exposed to humidity, and thus has enough water for the trees; however, soils and exposure to winds are limiting conditions for tree growth. The central patch receives and retains the highest humidity due to its topography. Although it is exposed to winds, this patch can sustain a continuous distribution of trees. The central-south and south patches have the largest space to accommodate new trees; however, topography makes them the most exposed to soil erosion (Ramos-Franco, 2007), generating many runoff areas due to their proximity to the inactive crater rim. This also allows the growth of the tallest individuals due to the open spaces and was the reason why a great number of trees survived the fire in 2008 (Ramírez, 2014), even with their low density.

The height distribution in each patch, in burned or non-burned zones, shows the forest strata and evidences its recovery. The highest stratum is represented by mature trees, with heights between 6 and 23 m (from field observations). The most representative height frequency in this stratum is between 13 and 17 m. Table 5 shows that tree height is more variable in the areas not affected by fire (NBZs), independently of the camera used and year of survey. The NBZs contain mostly mature trees. There are two groups of young trees, those that grew immediately after the fire (2008–2009 cohort), and those that grew in the following years. The first group had heights between 4 and 12 m in 2019; their growth is determined by the abiotic conditions (topography, humidity, or wind), and the competition with other trees. The second group is represented mainly by trees between 2 and 3 m. These are trees of different generations, probably due to the seed bank that did not germinate immediately after the fire as well as the seed production of the new trees. In this work, we did not estimate the age of each stratum, but by the presence of reproductive structures in trees of 2 m height, we can assume they are a new generation. The existence of the second group of young trees is evidence of the continuous natural regeneration of the forest.

According to RUSLE analysis results, the most susceptible areas to water-borne erosion are those with the steepest slopes or bare surfaces, while the areas covered with trees, shrubs, or grass are more resistant to soil loss. The vertical projection and distribution of vegetation is an important feature included in the analysis that determines the effect of water on soil erosion. Canopy cover reduces the kinetic energy of falling raindrops, and the surface cover (shrubs and grass) reduces the transport capacity of water, allowing the accumulation of humidity and preventing the formation of gullies (Renard et al., 1997). Integrating vegetation height into the RUSLE model to assess water-borne erosion is an advantage over models that only consider the horizontal distribution of plants, because it considers the heterogeneity of vegetation structure and its different effects on the water dynamics and soil retention.

Smith (2007) and Puente (2011) developed studies in areas with similar environmental conditions to Guadalupe Island; their methodologies were used to incorporate the CHM in the C factor of the RUSLE model. These studies are based on multispectral images and vegetation index to evaluate the C factor; the resolution and variability of their results depend on satellite image resolution (30–60 m) and the estimated parameters for each vegetal species identified in the area, omitting the variability of the vegetation structure. Compared with those methodologies, photogrammetric analysis (using unmanned aerial vehicles) presents an advantage over multispectral analysis, because it incorporates surface variability in the CHM. Besides, the resolution of products can be adjusted depending on the camera and altitude of flights.

Photogrammetry is a useful technique and a low-cost alternative to model the vertical structure of the forest canopy. It allows the analysis of the temporal dynamics in a forest. The orthomosaics derived from this process have no equivalent with other satellite options due to their high spatial resolution (centimeter), sharpness, and detail, which makes them a valuable input for forest monitoring.

The reconstruction of the forest using an infrared camera (NIR) presents advantages over the use of only a natural color (RGB) camera, giving us more information on the dynamics of the forest, especially considering live and dead trees.

The estimation of the number of individuals using photogrammetry has limitations due to the conditions of natural environments that do not usually conform to ideal models. However, the CHM is representative of tree heights, as the two-dimensional grid of canopy geometry.

The Guadalupe Island cypress forest has had a significant recovery since the 2008 fire, with a total horizontal expansion of 134 hectares. Around 67,340 individuals over 2 m high were counted using ForestTools, 90% are young trees between 2 and 3 m. These young individuals are distributed mainly in the areas affected by the fire and represent different generations in addition to the ones that grew immediately after the fire. The height and distribution of trees in each patch of the cypress forest are determined by biotic and abiotic conditions, being different for each patch and if they are in a burned or non-burned zone. Wind activity is an important environmental factor that affects the distribution of the trees in the cypress forest, causing the fall of live and dead trees. This phenomenon can be seen in the three years observation window (2016–2019) using photogrammetry.

The use of the DTM and the CHM to evaluate the C factor of the RUSLE model has an advantage over multispectral analysis since it describes the horizontal and vertical distribution of vegetation and allows to generate a highly accurate model. Applying this technique, the estimates of water-borne erosion for the cypress forest were 0 to 2 kg m^− 2 y^− 1 over surfaces with aerial coverage, 3 to 15 kg m^− 2 y^− 1 in areas with shrubs and herbaceous plants, and more than 15 kg m^− 2 y^− 1 in areas without vegetation or with the steepest slopes.

Present results may be used to validate and compared with the data from the Global Ecosystem Dynamics Investigation (GEDI) project, a high-resolution laser ranging of Earth’s forests and topography from the International Space Station (ISS) (Tang et al., 2019; Wake et al., 2019) and complement initiatives by NASA (Donneland et al., 2021).

Author Declarations

Ethics approval: All ethical practices have been followed in relation to the development, writing, and publication of the article.

Consent to participate: All authors consent to participate in the submission of this manuscript to the Environmental Monitoring and Assessment Springer Journal

Consent for publication: All authors consent to publish this manuscript to the Environmental Monitoring and Assessment Springer Journal.

Availability of data and materials: Point clouds of 2016 and 2019 photogrammetric reconstructions, orthomosaics, digital surface (DSMs) and canopy height models (CHMs) will be available in the https://opentopography.org/ portal. DOI to be defined.

Competing interests: Authors have no competing interests as defined by Springer, or other interests that might be perceived to influence the results and/or discussion reported in this paper.

Funding: Consejo Nacional de Humanidades Ciencia y Tecnología (CONAHCYT) from México for a scholarship to Laura Abigail Vera-Ortega during her master’s in science studies at CICESE. NGO Grupo de Ecología y Conservación de Islas (GECI) financed operations expenses during 2016 and 2019 field campaigns, also aerial transportation of part of the crew from the island to land.

Acknowledgements: We would like to acknowledge Eng. Jorge Raul Ochoa-Abundis for technical support and advice during the 2016 GPS ground survey and fixed wing drone survey. To NGO Grupo de Ecología y Conservación de Islas (GECI) for their support and financing of field campaigns at Guadalupe Island, and for aerial transportation of part of the crew from the island to land. To Secretaria de Marina for the transportation of crew and equipment to and from the island on their ships. To María Isabel Pérez-Montfort for reviewing and correcting the English version of manuscript.

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No competing interests reported.

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Using photogrammetry to assess the recovery of a cypress forest and its impact on water-borne erosion. Case study: Guadalupe Island.

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