© The Authors, 2023, Published by the Universidad del Zulia*Corresponding author: andres.estrada@unsaac.edu.pe
Keywords:
Carrying capacity
Ecosystem
NDVI
Unmanned aerial systems and passive remote sensors to classify microecosystems of high
Andean grasslands
Sistemas aéreos no tripulados y sensores remotos pasivos para clasicar microecosistemas de
pastizales alto andinos
Sistemas aéreos não tripulados e sensoriamento remoto passivo para classicar microecossistemas
de pastagens altas andinas
Andrés Corsino Estrada Zúñiga*
Dante Astete Canal
Jim Cárdenas Rodríguez
Juan Elmer Moscoso Muñoz
Rev. Fac. Agron. (LUZ). 2023, 40(4): e234036
ISSN 2477-9407
DOI: https://doi.org/10.47280/RevFacAgron(LUZ).v40.n4.05
Crop Production
Associate editor: Dra. Evelyn Peréz-Peréz
University of Zulia, Faculty of Agronomy
Bolivarian Republic of Venezuela
Abstract
Pastures are the fodder base for camelid and sheep production in the
southern Peruvian Andes, where 80 % of alpacas and 15 % of sheep live,
which requires better land management and grazing programs through the
classication of microecosystems. The objective of this study was to classify
the microecosystems based on the grasslands of the Kayra Agronomic Center
in the Region of Cusco using unmanned aerial vehicles and remote sensors.
To do this, traditional evaluation and estimation methods such as modied
Parker and quadrat sampling, were combined with biomass classication
and estimation methods supported by multispectral images. This was done
using 5 m RapidEye satellite images, and multispectral orthophotographs
acquired with a Micasense sensor transported by a Matrix 300 RTK Drone
with 10 cm pixels. Processing was performed by Pix 4D version 4.7.5
photogrammetry software, and ENVI and ArcGIS 10.3 image processing
software. An algorithm designed in the R programming language was used to
estimate the biomass. The results show three life zones, three climatic zones,
four ecosystems, and four plant communities with eleven dominant species.
The condition of the grasslands evaluated was regular with a tendency to
poor and a carrying capacity of 0.3 UV.ha
-1
.year
-1
; 0.83 UO.ha
-1
.year
-1
and
1.11 UA.ha
-1
.year
-1
. The use of remote sensors made it possible to classify
grasslands quickly and eciently.
Dunker Arturo Alvarez Medina
Juan Víctor Bejar Saya
Universidad Nacional de San Antonio Abab del Cusco, Perú.
Received: 30-08-2023
Accepted: 15-11-2023
Published: 05-12-2023
This scientic publication in digital format is a continuation of the Printed Review: Legal Deposit pp 196802ZU42, ISSN 0378-7818.
Rev. Fac. Agron. (LUZ). 2023, 40(4): e234036. October-December. ISSN 2477-9407.2-7 |
Resumen
Los pastos son la base forrajera de la producción de camélidos y
ovinos en el sur de los Andes peruanos, donde habitan el 80 % de las
alpacas y el 15 % de las ovejas, lo que exige una mejor gestión de
las tierras y programas de pastoreo, mediante la clasicación de los
microecosistemas. El objetivo del presente estudio fue clasicar los
microecosistemas considerando como base los pastizales del Centro
Agronómico Kayra de la Región Cusco, mediante vehículos aéreos
no tripulados y sensores remotos. Para ello se combinó métodos de
evaluación y estimación tradicionales, como el de Parker modicado
y el cuadrante de muestreo con métodos de clasicación y estimación
de biomasa apoyados con imágenes multiespectrales. Para ello
se utilizó imágenes satelitales RapidEye de 5 m y ortofotografías
multiespectrales adquiridas con un sensor Micasense transportado
por un Dron Matrix 300 RTK con pixeles de 10 cm. El procesamiento
se realizó en el software de fotogrametría Pix 4D versión 4.7.5 y
software de procesamiento de imágenes ENVI y ArcGIS 10.3. Para
estimar la biomasa se utilizó un algoritmo diseñado en el lenguaje de
programación R. Los resultados mostraron tres zonas de vida, tres
zonas climáticas, cuatro ecosistemas y cuatro comunidades vegetales
con once especies dominantes. La condición de los pastizales
evaluados fue de regular con tendencia a pobre y capacidad de carga
de 0,3 UV.ha
-1
.año
-1
; 0,83 UO.ha
-1
.año
-1
y 1,11 UA.ha
-1
.año
-1
. El uso
de sensores remotos permitió clasicar los pastizales de forma rápida
y eciente.
Palaras Clave: capacidad de carga, ecosistema, NDVI.
Resumo
As pastagens são a base forrageira para a produção de
camelídeos e ovinos no sul dos Andes peruanos, onde vivem 80
% das alpacas e 15 % das ovelhas, o que exige uma melhor gestão
do território e programas de pastoreio, através da classicação
dos microecossistemas. O objetivo deste estudo foi classicar os
microecossistemas com base nas pastagens do Centro Agronómico
Kayra na região de Cusco, utilizando veículos aéreos não tripulados
e deteção remota. Para isso, métodos tradicionais de avaliação e
estimativa foram combinados, como o método de Parker modicado
e o quadrante amostral com métodos de classicação e estimativa de
biomassa suportados por imagens multiespectrais. Para isso, foram
utilizadas imagens de satélite RapidEye de 5 m, e ortofotograas
multiespectrais adquiridas com sensor Micasense transportadas por
um Drone Matrix 300 RTK com pixels de 10 cm. O processamento
foi realizado utilizando o software de fotogrametria Pix 4D versão
4.7.5 e os softwares de processamento de imagem ENVI e ArcGIS
10.3. Para estimar a biomassa, foi utilizado um algoritmo projetado
na linguagem de programação R. Os resultados mostram três zonas de
vida, três zonas climáticas, quatro ecossistemas e quatro comunidades
vegetais com onze espécies dominantes. A condição das pastagens
avaliadas era regular com tendência a ruim e tem capacidade de
suporte de 0,3 UV.ha
-1
.ano
-1
; 0,83 UA.ha
-1
.ano
-1
e 1,11 UA.ha
-1
.ano
-1
. O uso
do sensoriamento remoto permite que as pastagens sejam classicadas
de forma rápida e eciente.
Palabras chave: capacidade de suporte, ecossitema, NDVI.
Introduction
Grasslands are economically, ecologically, and socially important
because of the ecosystem services they provide to rural (puna
grasslands) and urban populations (Zorogasúa et al., 2012). These
resources, which are essential for the development of the Andes, are
increasingly threatened by the global processes of climate change,
desertication, and degradation of grazing lands, with the consequent
loss of biodiversity and productive capacity (Tomasi, 2013). This
accelerated grassland deterioration and biodiversity loss process is
strongly inuenced by anthropogenic actions such as inadequate
grazing, shifting cultivation, and land use change (Fuhlendorf et al.,
2012; Muñoz et al., 2018).
Peru is a tropical Andean-Amazonian country and due to the
presence of the Andean Mountain Range, it has a special characteristic
with several altitudinal levels and life zones with a diversity of plant
communities favoring livestock production (Comer et al., 2012;
Zaragoza et al., 2022; Zarria and Flores, 2015).
“The destructive or direct method consists of cutting the aerial
part of the plant to take to a laboratory” (Ramirez et al., 2006;
Yaranga, 2020), a method by which the estimation of primary
production and biomass availability requires more time and eort.
However, the information generated through the direct method can
be complemented and improved with indirect methods that show
greater accuracy, such as remote sensing with remote sensors and
microsensors on unmanned aerial vehicle platforms (Seo et al., 2014).
This new sensor technology makes it possible to conduct evaluations
in large territories in a short time and in some cases with the use of
drones in real time (Estrada and Ñaupari, 2021; Pizarro, 2017).
Nowadays, remote sensing has become a tool that provides up-
to-date and accurate information to identify plant communities and
estimate biomass production and animal carrying capacity per hectare
(Lussem et al., 2019; Yim et al., 2009).
In the dry puna of southern Peru, plant communities have been
identied and biomass production has been estimated with the
support of remote sensing (Quispe, 2016). Likewise, the researchers
reported that from orthophotographs with dierent spectra or
bands (blue, green, red, red edge, and near-infrared) and the use of
various vegetation indices, image classication processes have been
improved, as well as the estimation of biomass production, given the
quality of data provided by microsensors (Lussem et al., 2019; Xu
and Guo, 2015).
On the other hand, geographic information systems, with
the help of photogrammetry software and software for satellite
image processing such as ArcGIS or QGIS, allow supervised and
unsupervised classications to be made from multispectral images
(Chen et al., 2004; D’Oleire et al., 2012).
The Ministry of the Environment of Peru (MINAM) has
developed a set of tools for the analysis of the territory based on
microecosystems, whose institution denes them “.....as small
territorial spaces that present homogeneous characteristics of ora,
fauna, and geographical conguration”. The purpose of these tools
is to homogenize variables and criteria for the analysis of natural
resources, soil, water, and vegetation, with an emphasis on grasslands
(MINAM, 2016).
Environmental researchers, entities in charge of natural resource
management, academia (which requires new content for its teaching
and learning processes), public policymakers, and decision-makers
demand current, modern, and reliable tools for monitoring grasslands
and their plant communities in high Andean-Amazonian mountain
ecosystems. In addition, they must be simple to use and extremely
reliable in their temporal, spatial, and radiometric scales for eective
grassland monitoring (Chavez et al., 2017).
This scientic publication in digital format is a continuation of the Printed Review: Legal Deposit pp 196802ZU42, ISSN 0378-7818.
Estrada et al. Rev. Fac. Agron. (LUZ). 2023 40(4): e234036
3-7 |
On the other hand, the use of remote sensors in the classication
of grasslands and the determination of ecosystems requires the
production of tools that are accurate in land cover classication,
especially for high mountain territories (Melville et al., 2019).
The identication of objects and processes on the grassland
surface for the study of high mountain plant communities requires
knowledge of the reectivity of soil, vegetation, and water, concerning
the dierent wavelengths (Melville et al., 2019; Zarria and Flores,
2015). Each wavelength that gives the reectivity in percentage is
known as a “spectral signature” and constitutes a mark of aliation
of the objects. Knowledge of this facilitates and makes it possible
to distinguish between soil, water, and vegetation, and even between
dierent types of soil and vegetation (Meneses et al., 2015).
The objective of the present study was to classify the
microecosystems based on the grasslands of the Kayra Agronomic
Center using unmanned aerial vehicles and passive remote sensors.
Materials and methods
The eldwork was carried out during the rainy season (February
and March) and at the end of the dry season (October) of 2020
and 2021. During this time, samples were collected from three
representative areas (the upper part of pastures, the middle part of
forest plantations, and the lower part made up of agricultural and
urban areas) of the Kayra Agronomic Center. This zone has a total
area of 2,153.20 ha and is located in the Cusco Region of Peru (SL
13°33’29.43” WL 71°52’14.0”, between 3,200 and 4,600 meters
above sea level).
In the rst stage of the study, using the Google Earth Pro platform,
the recognition and identication of the sampling areas was performed
and their geographical location was determined, duly georeferenced
for the subsequent acquisition of RapidEye satellite images. Once
the study areas were selected, ight plans were prepared with the
following characteristics: height of 100 m, horizontal overlap of 75
%, vertical overlap of 70 %, speed of 8 m.s
-1
, and time interval of 3
s for each photograph. For this process, a ight autonomy of 25 to
30 min of the Matrix 300 RTK Drone, suitable for the conditions of
Cusco, was also considered.
In the sampling areas, 8 control points were placed, these control
points were 80 cm x 80 cm plywood and painted red, blue, and white.
The control point was placed at the start and end point of the transects,
which was used to make geometric corrections.
In the second stage, the acquisition of 5 m RapidEye four-band
(blue, green, red and NIR) multispectral images of the Planet Scope
platform was performed (Estrada and Ñaupari, 2021).
Field sample collection was carried out using the modied Parker
method with three 100 m transects (georeferenced with dierential
GPS, 2 cm approximation). These transects, in turn, served as control
points and lines for the geographic correction of the RapidEye satellite
images and the orthophotographs acquired with the drone.
In the transects, in addition to identifying the species and
determining their frequency of occurrence, samples were taken to
estimate biomass production in 0.5 m x 0.5 m quadrats. For this
purpose, the aerial biomass was cut with pruning shears at a height of
2 cm from the soil and in 30 quadrats per selected zone (10 samples
per transect) with a distance of 10 meters between samples.
The third stage of the study included the processing of samples
to estimate the production of biomass in green matter (GM) and dry
matter (DM), in the pasture processing room of the Laboratory of
Animal Science and Climate Change of the Professional School of
Zootechnics of the National University of Saint Anthony the Abbot in
Cusco (UNSAAC), Cusco – Peru.
In the remote sensing cabinet of the same laboratory, the
RapidEye images were processed using ENVI and ArcGIS software,
with the corresponding geometric corrections. With the Pix 4D
photogrammetry software, the photographs obtained by the drone
were processed, and multispectral orthophotographs with dierent
spectra (blue, red, green, NIR) and an orthophotography of normalized
dierence vegetation indices (NDVI) with 10 cm pixels and high
resolution were generated.
Using as reference (ROIs) the orthophotographs of the drone,
the supervised classication of the RapidEye images was carried out
with the ArcToolbok’s spatial command tool analysis of the ArcGIS,
then maps of the variables required to obtain the ecosystems map
were generated, these being: 1) slope map, 2) altitudinal zonation
map, 3) plant community map, and 4) vegetation cover. Finally, the
map of microecosystems of the Kayra Agronomic Center based on
grasslands was obtained.
For the estimation of biomass production by plant community, the
algorithm developed by Estrada et al. (2022) to classify and estimate
biomass in high Andean plant communities was used. This algorithm
uses the libraries “Caret”, “Performance Analytics”, “Corrplot”,
“RandomForest”, “Rgdal”, “Raster”, “Sp” in the R programming
language and requires as input information orthophotographs with the
5 spectra (blue, green, red; red edge and NIR), NDVI orthophotographs
and the biomass production data in the green matter (GM, DM).
The output products of the algorithm are dierent land cover or
classication maps, with the estimation of biomass production per
pixel. From this, the biomass production per ecosystem was estimated
and nally, the animal carrying capacity for the identied grassland
microecosystems was calculated. This procedure took into account
the production of biomass per pixel and the animal requirement based
on dry matter, considering that an animal requires 10 % of its weight
in GM and 3 % of its weight in DM.
Results and discussion
Identication of natural grass species
In the plant communities studied, 11 species of grasses were
identied, the dominant ones being: Stipa ichu, Festuca dollicophill,
and Festuca ortophylla. The low number of species was due to the
grassland and forest res that occurred during the years 2020 and
2021. However, it was observed that grasslands, still in the process
of degradation, maintained their capacity for grazing animals that
consume tall grasses (table 1).
Identication of grassland-based ecosystems with the
assistance of passive remote sensors
Applying Holdrigde’s life zone classication methodology
(1978), the Kayra Agronomic Center has three life zones: Subtropical
Montane Moist Forest (bh_MS), Subtropical Low Montane Dry
Forest (bs_MBS), and the Wet Sub-Andean Subtropical Paramo zone
(pmsh_SaS) (gure 1a).
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Table 1. Floristic composition of grasslands.
Family Gender Species Key Local Name
Asteraceae Hypochaeris
Hyta Pill amarillo
Cyperaceae Carex
Carsp Ccaran ccaran
Fabaceae Trifolium
Triam Layo
Poaceae Paspalum
Papy Sara sara
Poaceae Festuca
Fedo Chillihua, coya
Poaceae Festuca
Feor Iru ichu
Poaceae Poa
Posp Llachu, chili
Poaceae Stipa
Stich Ichu
Geraniaceae Geranium
Gese Ojotillo
Rosaceae Alchemilla
Alpi Sillu sillu
Asteraceae Baccharis
Basp Mullaca
The study identied three climatic zones: Semi-dry climate, dry
autumn, dry winter, semi-warm C(o,i)B’; Semi-dry climate, semi-dry,
dry autumn, winter, cold C(o,i)C’ and rainy, dry autumn, winter, cold
B(o,i)C’ (gure 1b).
According to the methodology for classifying microecosystems
at the Kayra Agronomic Center, four ecosystems were determined:
Ecosystems of hillside pastures with moderate slope in mountainous
Figure 1. (a) Map of Kayra life zones (b) Climate and (c) slopes.
areas, with moderate precipitation and with capacity animal grazing
(gure 2b); ecosystems of relict forests and plantations in low-slope
massifs with moderate precipitation with the presence of grass
species for animal grazing (gure 2d); agricultural ecosystems in at
and plateau areas, with moderate precipitation and intensive land use
(gure 2c).
Hypochaeris taraxacoides
Carex
sp.
Trifolium amabille
Paspallum pigmaeum
Festuca dollicophylla
Fescue orthophylla
Poa
sp.
Stipa ichu
Geranium sessiorum
Alchemilla pinnata
Baccharis
sp.
This scientic publication in digital format is a continuation of the Printed Review: Legal Deposit pp 196802ZU42, ISSN 0378-7818.
Estrada et al. Rev. Fac. Agron. (LUZ). 2023 40(4): e234036
5-7 |
The classication methodology implemented made it possible
to determine the existence of 1,165 ha of grassland with potential
for grazing animals [cattle (UV), sheep (UO) and alpacas (UA)] and
621 ha of forest ecosystem (natural and plantations in massifs) whose
main potential is the production of wood for fuel or other uses and
with possibilities for cattle and sheep grazing (table 2).
The Kayra Agronomic Center has an agricultural ecosystem,
with Andean crops and a predominance of corn (Zea mays L.). This
ecosystem also provides food for livestock in the form of stubble.
Finally, the constructed area or infrastructure area was recorded (table 2).
Table 2. Ecosystems area of the Kayra Agronomic Center.
Microecosystem Hectares
Grassland 1.165,00
Forest 621,00
Agricultural 327,30
Constructed 39,80
Total 2.153,20
Forest: natural and plantations in massif.
The classication of grasslands by ecosystems is a methodology
that allows for analyzing grasslands in a holistic way (Zarria and Flores,
2015) and determining their carrying capacity (Estrada et al., 2022).
The study carried out at the Kayra Agronomic Center made it possible
to identify three large ecosystems that can become management units
as proposed by Zorogasúa et al. (2012) and corroborated by Pizarro
(2017) in the study of degradation and vulnerability to climate change
in high Andean grasslands.
Identied grassland plant communities
Three plant communities were identied as hillside grassland,
shrublands with shrub species, and forests with tree species and
grasses, as well as a crop area (gure 3).
Figure 2. (a) Map of micro-ecosystems of the Kayra Agronomic Centre, (b) Grasslands, (c) Agriculture, (d) Forests, (e) Infrastructure.
Figure 3. Normalized Dierence Vegetation Index map (NDVI).
The study identied 840 ha of the hillside grassland plant
community with an NDVI ranging from 0.020 to 0.20; the second
plant community was that of scrub grassland with 325 ha and NDVI
from 0.20 to 0.30; as well as a grassland forest plant community
with an area of 621 ha and an NDVI from 0.30 to 0.40. The NDVI
classication showed that agricultural crops for the sampling period
had a range from 0.40 to 0.44 and higher, and 39.80 ha of cultivars
(gure 3).
NDVI from 0.020 to 0.44 indicates low NDVI and grasslands that
are reaching senescence or have been disturbed by res. Estrada and
Ñaupari (2021), established that the NDVI for hillside grasslands in
This scientic publication in digital format is a continuation of the Printed Review: Legal Deposit pp 196802ZU42, ISSN 0378-7818.
Estrada et al. Rev. Fac. Agron. (LUZ). 2023 40(4): e234036
6-7 |
the dry season ranges from 0.10 to 0.70 and it can be elucidated that
the NDVI range found for the grasslands of the Kayra Agronomic
Center is lower, despite being located in a humid zone. These results
are also lower than those found by Paredes (2019) for the grasslands
of the central grasslands of Peru, with the assistance of the Modis
Terra sensor (table 3).
Table 3. Plant Community area and NDVI.
Plant community Area (ha) NDVI
Hillside grassland 840.00 0.02 – 0.20
Shrub grassland 325.00 0.20 – 0.30
Grassland forest 621.00 0.30 – 0.40
Agricultural crops 39.80 0.40 – 0.44
NDVI: Normalized dierence vegetation index
Animal carrying capacity per community, plant ecosystem,
and study area
Condition and animal carrying capacity were estimated from
grazing areas used at the time of assessment. Each of the three
grazing zones was composed of the ecosystems of hillside grassland,
natural forest and plantations in massifs, agricultural ecosystem, and
constructed landscape.
The study determined that the condition of the pasture (table 4) in
the Kayra Agronomic Center, considering cattle, sheep, and alpacas,
is regular, showing that the Perolpuquio area has poor grasslands and
the Fierroccata and Chequicocha areas have regular condition. The
poor condition of the Perolpuquio area was mainly due to the res
that occurred in 2020 and 2021.
Table 4. Condition and animal carrying capacity per study area.
Study area
Pasture Condition
Animal Carrying
Capacity
Cattle Sheep Alpaca
Cattle
(UV.year
-1
)
Sheep
(UO.year
-1
)
Alpaca
(AU.year
-1
)
Perolpuquio Poor Poor Poor 0.13 0.5 0.33
Fierroccta Regular Regular Regular 0.38 1 1.5
Chequilccocha Regular Regular Regular 0.38 1 1.5
KAC Regular Regular Regular 0.30 0.83 1.11
KAC: Kayra Agronomic Center
The condition of the pasture in the Kayra Agronomic Center
for the study period was 0.30 UV.year
-1
, 0.83 UO.year
-1
and 1.11
UA.year
-1
, while for the Perolpuquio area, the carrying capacity for
cattle was 0.13
UV.year
-1
; 0.50 UO.year
-1
and 0.33
UA.year
-1.
In the
areas of Fierroccata and Chequiccocha it was 0.38 UV.year
-1,
1.11
UO.year
-1,
and 0.33 UA.year
-1
for cattle.
It was determined that the grasslands of the Kayra Agronomic
Center have a carrying capacity with a tendency from regular to low
or poor (table 4). Considering the ecosystem and climate map, it can
be noted that these grasslands are below the parameters established
by MINAM (2016) and show the eects of disturbances such as
grassland burning, res, and overgrazing (Chavez et al., 2017;
Pizarro, 2017; Zorogasúa et al., 2012).
Conclusions
The use of satellite images and high-resolution orthophotographs
from the Kayra Agronomic Center, taken with unmanned aerial
vehicles, made it possible to qualify ecosystems and develop land
cover maps with high precision.
The study has identied four microecosystems that can be used as
a basis for soil management at the Kayra Agronomic Center.
Acknowledgment
Our special thanks to the Laboratory of Animal Science and
Climate Change, remote sensing area, and unmanned aerial vehicles of
the Professional School of Zootechnics of the Faculty of Agricultural
Sciences of the National University of Saint Anthony the Abbot in
Cusco.
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