**Estimating and Examining the Sensitivity of Di**ff**erent Vegetation Indices to Fractions of Vegetation Cover at Di**ff**erent Scaling Grids for Early Stage** *Acacia* **Plantation Forests Using a Fixed-Wing UAS**

**Kotaro Iizuka 1,\*, Tsuyoshi Kato <sup>2</sup> , Sisva Silsigia <sup>2</sup> , Alifia Yuni Soufiningrum <sup>2</sup> and Osamu Kozan 3,4**


Received: 12 July 2019; Accepted: 1 August 2019; Published: 3 August 2019

**Abstract:** Understanding the information on land conditions and especially green vegetation cover is important for monitoring ecosystem dynamics. The fraction of vegetation cover (FVC) is a key variable that can be used to observe vegetation cover trends. Conventionally, satellite data are utilized to compute these variables, although computations in regions such as the tropics can limit the amount of available observation information due to frequent cloud coverage. Unmanned aerial systems (UASs) have become increasingly prominent in recent research and can remotely sense using the same methods as satellites but at a lower altitude. UASs are not limited by clouds and have a much higher resolution. This study utilizes a UAS to determine the emerging trends for FVC estimates at an industrial plantation site in Indonesia, which utilizes fast-growing *Acacia* trees that can rapidly change the land conditions. First, the UAS was utilized to collect high-resolution RGB imagery and multispectral images for the study area. The data were used to develop general land use/land cover (LULC) information for the site. Multispectral data were converted to various vegetation indices, and within the determined resolution grid (5, 10, 30 and 60 m), the fraction of each LULC type was analyzed for its correlation between the different vegetation indices (Vis). Finally, a simple empirical model was developed to estimate the FVC from the UAS data. The results show the correlation between the FVC (acacias) and different Vis ranging from R<sup>2</sup> = 0.66–0.74, 0.76–0.8, 0.84–0.89 and 0.93–0.94 for 5, 10, 30 and 60 m grid resolutions, respectively. This study indicates that UAS-based FVC estimations can be used for observing fast-growing acacia trees at a fine scale resolution, which may assist current restoration programs in Indonesia.

**Keywords:** UAS; UAV; vegetation cover; multispectral; land cover; forest; *Acacia*; Indonesia; tropics

### **1. Introduction**

Quantitative assessments of the green vegetative covers of terrestrial environments are essential for understanding ecosystem dynamics. The functions of green environments (e.g., vegetation, forests) provide important benefits to ecosystems, such as controlling air quality through photosynthesis, generating an energy supply from woody biomass, preventing soil erosion, improving water quality and balancing the heat fluxes of the earth [1–5]. The worldwide terrestrial environment is currently showing rapid changes in particular regions from anthropogenic activities, causing land degradations that engulf the natural environment [6]. Some areas show transitions away from green areas, which results in substantial impacts on local to global ecosystems, sociocultural and economic impacts [7,8]. To mitigate these impacts, known cooperative agencies and organizations are planning actions for the recovery of green areas [9,10]. Consistent monitoring of the rapid environmental changes in green vegetative coverages is important for conservation and maintaining the sustainability of the natural environment.

Indonesia's landmass includes approximately 24 million hectares (Mha) of peatlands, which represents approximately 83% of the peatlands found in Southeast Asia. Peatlands in Indonesia are distributed mainly among the four large islands of Sumatera (9.2 Mha), Kalimantan (4.8 Mha), Sulawesi (0.06 Mha) and Papua (6.6 Mha) [11]. One of the common uses of peatlands in Indonesia is for Industrial Forest Plantations (IFP). The area of IFP concessions in Indonesia, which are located on peatlands, is 2 Mha [12], or 54.79% of the total IFP area in 2006, which reached 3.65 Mha [13], and *Acacia crassicarpa* is a fast-growing species that has been developed as a staple plant for most IFPs on peatlands [14].

Indonesia peatland forests provide important local and global benefits. However, their drainage and conversion into agricultural lands without well-planned management has caused considerable and irreversible environmental, social and economic damage. The catastrophic 2015 fires in Indonesia [15] drew national and international attention. That event reinforced the commitments of the Indonesian government to both reduce peatland deforestation and fires and to rehabilitate and restore degraded peatlands via reforestation. Strategic and operational approaches for monitoring the peat ecosystem together with the conditions of the green vegetation are crucial.

Various researchers and institutions have performed related studies of quantitative analyzations of both local and global vegetation coverage. The products of MODIS Vegetation Continuous Fields or the fCover (fraction of vegetation cover, hereafter denoted as FVC) [16] were used, while other researchers utilized the MODIS reflectance data provided by Land Processes Distributed Active Archive Center (LP DAAC) for developing improved FVC data [17]. The remote sensing techniques for FVC development utilize the multispectral information observed from space and validates its product with ground truth information (e.g., field surveys). The estimation methods can vary depending on the model type used, including simple empirical models [18], linear spectral models [19], decision tree method [20], machine learning techniques [21], and so on. Although the input information is rather simple, remote sensing can use various spectral information or the computed vegetation indices (Vis) for its estimation, although correctly delineating the FVC for various regions of the world is still a challenge.

Many studies have indicated the capability of space-borne remotely sensed data for mapping and/or monitoring of regional to global vegetation cover. However, depending on the products or specific locations used, there can be constraints and challenges in processing or accurately estimating a fractional cover. One of the conventional issues observed is the cloud cover, which blocks otherwise available information for analyzing a terrestrial environment [22]. The missing information can be aided with a gap-filling technique [17] for including continuous land information. However, this technique can result in large differences in the spectral information of the area, and then the possibility of an incorrect FVC estimation rises. In tropical regions including Indonesia, obtaining sufficient land information from areas with lesser cloud cover can be challenging; even when cloud removal and gap filling are performed, correct land information for a certain period of time can be lacking. If a large area of peatlands in Indonesia is observed in IFPs with fast-growing trees, then even a small temporal gap of land information may show erroneous assumptions of the vegetation coverage (Figure 1). To accurately and effectively detect green areas, considering different platforms or sensors is an important step for effectively monitoring the tropical areas. This is especially true in Indonesia where rapid land transitions are occurring.

space-borne sensing.

*Remote Sens.* **2019**, *11*, x FOR PEER REVIEW 3 of 17

**Figure 1.** Example of temporal differences for fast-growing species. The left image shows the fastgrowing *Acacia* trees in its early stages in August, 2018, while the right image shows its rapid growth in October, 2018. Even with small temporal differences, the situation of the land area would change dramatically. **Figure 1.** Example of temporal differences for fast-growing species. The left image shows the fast-growing *Acacia* trees in its early stages in August, 2018, while the right image shows its rapid growth in October, 2018. Even with small temporal differences, the situation of the land area would change dramatically.

In recent years, many studies using unmanned aerial systems (UASs) were carried out. The UAS platform provides alternatives to space-borne platforms since optical data can be observed in a clearhigh spatial/temporal resolution for the region of interest [23]. This technique has been used in research on ecology [24], precision agriculture/forestry [25,26] and even analyses for estimating vegetation cover [27–29]. UAS was successful [27] in clearly estimating vegetation fractions and flower fractions in crop fields with the changing VIs, and work by Chen et al. [28] showed that utilizing UAS-captured imagery may clearly detect grassy vegetation covers due to its highresolution data. Riihimäkia et al. [29] showed that the UAV-derived information can be aided by satellite-observed information in FVC estimations. As Indonesia is exposed to high and frequent cloud coverage nationwide, obtaining clear satellite information is often difficult. Even if this information is collected, radiometric corrections for both atmospheric and topographic data are mandatory, which is a difficult task [30]. Riihimäkia et al. [29] recently showed an approach for estimating the FVC at arctic vegetation using UAS and satellite data through multiple spatial scaling's and different indices. They have expressed that there is a strong correlation between the UAS-based FVC for validation data that can be used to bridge with the satellite data and noted that the sensitivity of VIs was better when using Red-edge or Short Wave Infrared (SWIR) information. The prior study of Riihimäkia et al. [29] shows a relationship analysis between the VIs and FVC that is based on only a single class that classified the area into vegetation/non-vegetation. Depending on regions where heterogeneous land use/land cover (LULC) types are seen, there may be more classes requiring further analysis and how those classes affect the VI response. Minimal research has been conducted in rapid changing environments such as Indonesia for estimating the fractional cover of green vegetation by utilizing UASs, especially in rapidly growing industrial forest plantations (IFP). Higher spatial/temporal resolution imagery may have a high potential to analyze where the changes in green In recent years, many studies using unmanned aerial systems (UASs) were carried out. The UAS platform provides alternatives to space-borne platforms since optical data can be observed in a clear-high spatial/temporal resolution for the region of interest [23]. This technique has been used in research on ecology [24], precision agriculture/forestry [25,26] and even analyses for estimating vegetation cover [27–29]. UAS was successful [27] in clearly estimating vegetation fractions and flower fractions in crop fields with the changing VIs, and work by Chen et al. [28] showed that utilizing UAS-captured imagery may clearly detect grassy vegetation covers due to its high-resolution data. Riihimäkia et al. [29] showed that the UAV-derived information can be aided by satellite-observed information in FVC estimations. As Indonesia is exposed to high and frequent cloud coverage nationwide, obtaining clear satellite information is often difficult. Even if this information is collected, radiometric corrections for both atmospheric and topographic data are mandatory, which is a difficult task [30]. Riihimäkia et al. [29] recently showed an approach for estimating the FVC at arctic vegetation using UAS and satellite data through multiple spatial scaling's and different indices. They have expressed that there is a strong correlation between the UAS-based FVC for validation data that can be used to bridge with the satellite data and noted that the sensitivity of VIs was better when using Red-edge or Short Wave Infrared (SWIR) information. The prior study of Riihimäkia et al. [29] shows a relationship analysis between the VIs and FVC that is based on only a single class that classified the area into vegetation/non-vegetation. Depending on regions where heterogeneous land use/land cover (LULC) types are seen, there may be more classes requiring further analysis and how those classes affect the VI response. Minimal research has been conducted in rapid changing environments such as Indonesia for estimating the fractional cover of green vegetation by utilizing UASs, especially in rapidly growing industrial forest plantations (IFP). Higher spatial/temporal resolution imagery may have a high potential to analyze where the changes in green vegetative cover are occurring.

vegetative cover are occurring. The objective of this study is to develop a method for retrieving the FVC by utilizing UAS and multispectral sensors for the fast-growing *Acacia* plantation forests in Indonesia. Several VIs are computed using the raw band information to compare the sensitivity of the VIs to FVC, moreover, the result is also compared at different spatial resolutions and with other LULC types. The developed product is compared with the existing method for computing FVC by using satellite imagery, and it examines how the UAS observed product can compensate for conventional space-borne products. This work mainly focus on if UAV-based FVC can be obtained in the forested area, while it is out of the scope at the moment for generalizing the result which could be utilized for global estimations. This study may present advances in UAS research in developing FVC estimations and the possibility of utilizing the platform for collecting ground truth information to bridge airborne sensing with The objective of this study is to develop a method for retrieving the FVC by utilizing UAS and multispectral sensors for the fast-growing *Acacia* plantation forests in Indonesia. Several VIs are computed using the raw band information to compare the sensitivity of the VIs to FVC, moreover, the result is also compared at different spatial resolutions and with other LULC types. The developed product is compared with the existing method for computing FVC by using satellite imagery, and it examines how the UAS observed product can compensate for conventional space-borne products. This work mainly focus on if UAV-based FVC can be obtained in the forested area, while it is out of the scope at the moment for generalizing the result which could be utilized for global estimations. This study may present advances in UAS research in developing FVC estimations and the possibility of utilizing the platform for collecting ground truth information to bridge airborne sensing with space-borne sensing.

#### **2. Study Area 2. Study Area**  The study site is located in West Kalimantan and is the same area used in Iizuka et al. [31] (Figure *Remote Sens.* **2019**, *11*, x FOR PEER REVIEW 4 of 17

The study site is located in West Kalimantan and is the same area used in Iizuka et al. [31] (Figure 2). The large plantation area is managed by an industrial plantation company. The area was planted in January 2017 with *Acacia crassicarpa* as the main commercial species, which is one of the fast-growing species that can grow from saplings to up to a few meters in one year. Usually, the plantation site has a cycle of planting to logging in four-year intervals, which is a dramatic change rate. One section of the compartment site is considered for the test. Brief details of the area are explained in Iizuka et al. [31]. 2). The large plantation area is managed by an industrial plantation company. The area was planted in January 2017 with *Acacia crassicarpa* as the main commercial species, which is one of the fastgrowing species that can grow from saplings to up to a few meters in one year. Usually, the plantation site has a cycle of planting to logging in four-year intervals, which is a dramatic change rate. One section of the compartment site is considered for the test. Brief details of the area are explained in Iizuka et al. [31]. **2. Study Area**  The study site is located in West Kalimantan and is the same area used in Iizuka et al. [31] (Figure 2). The large plantation area is managed by an industrial plantation company. The area was planted in January 2017 with *Acacia crassicarpa* as the main commercial species, which is one of the fastgrowing species that can grow from saplings to up to a few meters in one year. Usually, the plantation site has a cycle of planting to logging in four-year intervals, which is a dramatic change rate. One

**Figure 2.** Location of the study site at West Kalimantan. **Figure 2.** Location of the study site at West Kalimantan. **Figure 2.** Location of the study site at West Kalimantan.
