Analisis Landscape Metrics: DAS Ciwulan 2022

Penulis

Afiliasi

Dr. Arif K Wijayanto

Laboratory of Environmental Analysis and Geospatial Modeling, Dept of Forest Resources Conservation and Ecotourism, IPB University

Diterbitkan

8 Juni 2026

1 Pendahuluan

Analisis ini menghitung berbagai landscape metrics pada kawasan DAS Ciwulan tahun 2022 menggunakan paket landscapemetrics di R. Landscape metrics digunakan untuk mengukur struktur, komposisi, dan konfigurasi lanskap — khususnya tutupan hutan alam yang tidak termasuk lahan pertanian.

Analisis dibagi menjadi dua bagian utama:

Kalkulasi metrik pada seluruh lanskap (landscape level & class level)
Kalkulasi metrik dalam grid 1 km² (sampling berbasis grid spasial)

2 Instalasi Paket

Jalankan perintah berikut satu kali sebelum memulai analisis (tidak perlu diulang setiap sesi):

install.packages("landscapemetrics")
install.packages("sf")
install.packages("tidyr")
install.packages("terra")
install.packages("raster")

3 Kalkulasi Metrik untuk Seluruh Lanskap

3.1 Load Library dan Data

library(landscapemetrics)
library(terra)

# Sesuaikan direktori kerja dengan lokasi file Anda
setwd("D:/1_Lecture/ARL/ARL604/ciwulan/LM")

# Import raster tutupan hutan alam (tanpa lahan pertanian)
forest <- rast('Refined_Forest_NoAgriculture_ciwulan_2022_noData_utm.tif')

3.2 Visualisasi Raster

plot(forest)

3.3 Pemeriksaan Data Raster

check_landscape(forest)

Fungsi check_landscape() memverifikasi bahwa raster memenuhi syarat untuk kalkulasi landscape metrics: proyeksi, tipe data, dan nilai piksel yang valid.

3.4 Kalkulasi Landscape Metrics

3.4.1 Number of Patches (NP)

Jumlah patch per kelas tutupan lahan:

x <- lsm_c_np(forest)
x

3.4.2 Patch Area

Luas masing-masing patch (dalam hektar):

y <- lsm_p_area(forest)
y

# Ekspor ke CSV
write.csv(y, "area.csv")

3.4.3 Kalkulasi Multi-Metrik

Menghitung beberapa metrik sekaligus dan mengekspornya:

a <- calculate_lsm(forest,
                   what = c("lsm_l_frac_mn",   # Mean fractal dimension index
                            "lsm_l_shape_mn",  # Mean shape index
                            "lsm_c_np",        # Number of patches
                            "lsm_p_frac"))     # Fractal dimension per patch
a

# Ekspor ke CSV
write.csv(a, "example.csv")

3.5 Ringkasan Metrik Lanskap

Tabel berikut merangkum semua metrik yang dihitung beserta fungsi, level, dan satuannya:

Tabel 1: Landscape Metrics yang Dihitung
Metrik	Fungsi R	Level	Satuan
Class Area (CA)	lsm_c_ca()	Class	Hektar
Number of Patches (NP)	lsm_c_np()	Class / Landscape	Jumlah
Patch Density (PD)	lsm_c_pd()	Class / Landscape	Patch / 100 ha
Largest Patch Index (LPI)	lsm_c_lpi()	Class	Persen
Total Edge (TE)	lsm_c_te()	Landscape	Meter
Edge Density (ED)	lsm_c_ed()	Class / Landscape	m / ha
Mean Patch Area (AREA_MN)	lsm_c_area_mn()	Patch / Class / Landscape	Hektar
Landscape Shape Index (LSI)	lsm_c_lsi()	Class / Landscape	Tanpa satuan

3.6 Eksekusi Metrik Individual

# Class Area (CA)
lsm_c_ca(forest, directions = 8)

# Number of Patches (NP)
lsm_c_np(forest)

# Patch Density (PD)
lsm_c_pd(forest, directions = 8)

# Largest Patch Index (LPI)
lsm_c_lpi(forest, directions = 8)

# Total Edge (TE)
lsm_c_te(forest, count_boundary = FALSE)

# Edge Density (ED)
lsm_c_ed(forest, count_boundary = FALSE, directions = 8)

# Mean Patch Area (AREA_MN)
lsm_c_area_mn(forest, directions = 8)

# Landscape Shape Index (LSI)
lsm_c_lsi(forest)

4 Kalkulasi Metrik dalam Grid 1 km²

Bagian ini menghitung landscape metrics pada setiap sel grid 1 km² yang melingkupi kawasan studi.

4.1 Load Library dan Data Grid

library(sf)
library(tidyr)
library(raster)
library(landscapemetrics)

r <- forest

# Load shapefile grid 1 km²
grid <- shapefile('grid_1km_ciwulan_clip.shp')

4.2 Kalkulasi Metrik per Grid

grid_lsm_class <- sample_lsm(
  r,
  y     = grid,
  shape = 'square',
  what  = c(
    "lsm_l_ai",        # Aggregation Index     — kecenderungan patch berkumpul
    "lsm_l_area_mn",   # Mean Patch Area       — rata-rata luas patch
    "lsm_l_area_sd",   # SD Patch Area         — keragaman ukuran patch
    "lsm_l_area_cv",   # CV Patch Area         — koefisien variasi luas patch
    "lsm_l_division",  # Division Index        — tingkat keterpecahan lanskap
    "lsm_l_lpi",       # Largest Patch Index   — dominansi patch terbesar
    "lsm_l_mesh",      # Effective Mesh Size   — ukuran kerapatan fragmen
    "lsm_l_np",        # Number of Patches     — jumlah patch
    "lsm_l_pd",        # Patch Density         — kerapatan patch per 100 ha
    "lsm_l_split",     # Splitting Index       — indeks pemisahan lanskap
    "lsm_l_ta"         # Total Landscape Area  — total luas lanskap
  ),
  return_raster = TRUE
)

4.3 Deskripsi Metrik Grid

4.3.1 Agregasi & Konektivitas

Metrik	Deskripsi
`lsm_l_ai`	Aggregation Index: Mengukur seberapa teragregasi (berkelompok) patch dalam lanskap. Nilai 100 = sangat teragregasi.
`lsm_l_np`	Number of Patches: Total jumlah patch dalam setiap sel grid.
`lsm_l_pd`	Patch Density: Jumlah patch per 100 hektar.

4.3.2 Ukuran & Distribusi

Metrik	Deskripsi
`lsm_l_area_mn`	Mean Patch Area: Rata-rata luas patch (hektar).
`lsm_l_area_sd`	SD Patch Area: Standar deviasi luas patch.
`lsm_l_area_cv`	CV Patch Area: Koefisien variasi luas patch (%).
`lsm_l_lpi`	Largest Patch Index: Proporsi area yang ditempati patch terbesar (%).
`lsm_l_mesh`	Effective Mesh Size: Ukuran efektif kerapatan fragmen (hektar).
`lsm_l_ta`	Total Area: Total luas lanskap (hektar).

4.3.3 Fragmentasi

Metrik	Deskripsi
`lsm_l_division`	Division Index: Probabilitas dua titik acak berada di patch berbeda (0–1).
`lsm_l_split`	Splitting Index: Jumlah patch berukuran sama yang mengisi total area lanskap.

4.4 Restrukturisasi dan Ekspor Data

# Ubah format long → wide (setiap metrik menjadi kolom tersendiri)
grid_lsm_w <- pivot_wider(
  grid_lsm_class,
  id_cols     = plot_id,
  names_from  = metric,
  values_from = value
)

# Gabungkan tabel atribut dengan shapefile grid
connect <- cbind(grid, grid_lsm_w)

# Simpan sebagai shapefile baru
shapefile(x        = connect,
          file     = "lsm_grid_1km_2022.shp",
          overwrite = TRUE)

Output: File lsm_grid_1km_2022.shp siap divisualisasikan di QGIS atau ArcGIS untuk analisis spasial lanjutan.

5 Informasi Sesi

sessionInfo()

R version 4.5.2 (2025-10-31 ucrt)
Platform: x86_64-w64-mingw32/x64
Running under: Windows 11 x64 (build 26200)

Matrix products: default
  LAPACK version 3.12.1

locale:
[1] LC_COLLATE=English_United States.utf8 
[2] LC_CTYPE=English_United States.utf8   
[3] LC_MONETARY=English_United States.utf8
[4] LC_NUMERIC=C                          
[5] LC_TIME=English_United States.utf8    

time zone: Asia/Jakarta
tzcode source: internal

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

loaded via a namespace (and not attached):
 [1] htmlwidgets_1.6.4 compiler_4.5.2    fastmap_1.2.0     cli_3.6.5        
 [5] tools_4.5.2       htmltools_0.5.9   rstudioapi_0.18.0 yaml_2.3.11      
 [9] rmarkdown_2.30    knitr_1.50        jsonlite_2.0.0    xfun_0.54        
[13] digest_0.6.39     rlang_1.1.6       evaluate_1.0.5

Dokumen ini dibuat dengan Quarto dan dapat direproduksi dengan menjalankan ulang seluruh chunk menggunakan data yang sesuai.

--- title: "Analisis Landscape Metrics: DAS Ciwulan 2022" author: - name: "Dr. Arif K Wijayanto" affiliation: "Laboratory of Environmental Analysis and Geospatial Modeling, Dept of Forest Resources Conservation and Ecotourism, IPB University" email: "" date: today lang: id format: html: theme: cosmo toc: true toc-depth: 3 toc-title: "Daftar Isi" toc-location: left number-sections: true number-depth: 3 code-fold: false code-tools: true code-copy: true code-line-numbers: true highlight-style: github df-print: kable fig-align: center fig-cap-location: bottom tbl-cap-location: top embed-resources: true smooth-scroll: true page-layout: full execute: echo: true warning: false message: false eval: false --- # Pendahuluan Analisis ini menghitung berbagai **landscape metrics** pada kawasan DAS Ciwulan tahun 2022 menggunakan paket `landscapemetrics` di R. Landscape metrics digunakan untuk mengukur struktur, komposisi, dan konfigurasi lanskap — khususnya tutupan hutan alam yang tidak termasuk lahan pertanian. Analisis dibagi menjadi dua bagian utama: 1. **Kalkulasi metrik pada seluruh lanskap** (landscape level & class level) 2. **Kalkulasi metrik dalam grid 1 km²** (sampling berbasis grid spasial) --- # Instalasi Paket Jalankan perintah berikut **satu kali** sebelum memulai analisis (tidak perlu diulang setiap sesi): ```{r} #| label: install #| eval: false install.packages("landscapemetrics") install.packages("sf") install.packages("tidyr") install.packages("terra") install.packages("raster") ``` --- # Kalkulasi Metrik untuk Seluruh Lanskap ## Load Library dan Data ```{r} #| label: load-library library(landscapemetrics) library(terra) ``` ```{r} #| label: load-raster # Sesuaikan direktori kerja dengan lokasi file Anda setwd("D:/1_Lecture/ARL/ARL604/ciwulan/LM") # Import raster tutupan hutan alam (tanpa lahan pertanian) forest <- rast('Refined_Forest_NoAgriculture_ciwulan_2022_noData_utm.tif') ``` ## Visualisasi Raster ```{r} #| label: fig-raster #| fig-cap: "Peta tutupan hutan alam DAS Ciwulan 2022 (tanpa lahan pertanian)" plot(forest) ``` ## Pemeriksaan Data Raster ```{r} #| label: check-landscape check_landscape(forest) ``` Fungsi `check_landscape()` memverifikasi bahwa raster memenuhi syarat untuk kalkulasi landscape metrics: proyeksi, tipe data, dan nilai piksel yang valid. --- ## Kalkulasi Landscape Metrics ### Number of Patches (NP) Jumlah patch per kelas tutupan lahan: ```{r} #| label: np x <- lsm_c_np(forest) x ``` ### Patch Area Luas masing-masing patch (dalam hektar): ```{r} #| label: patch-area y <- lsm_p_area(forest) y # Ekspor ke CSV write.csv(y, "area.csv") ``` ### Kalkulasi Multi-Metrik Menghitung beberapa metrik sekaligus dan mengekspornya: ```{r} #| label: multi-metric a <- calculate_lsm(forest, what = c("lsm_l_frac_mn", # Mean fractal dimension index "lsm_l_shape_mn", # Mean shape index "lsm_c_np", # Number of patches "lsm_p_frac")) # Fractal dimension per patch a # Ekspor ke CSV write.csv(a, "example.csv") ``` --- ## Ringkasan Metrik Lanskap Tabel berikut merangkum semua metrik yang dihitung beserta fungsi, level, dan satuannya: ```{r} #| label: tbl-metrics #| tbl-cap: "Landscape Metrics yang Dihitung" #| echo: false #| eval: true metrics_df <- data.frame( Metrik = c("Class Area (CA)", "Number of Patches (NP)", "Patch Density (PD)", "Largest Patch Index (LPI)", "Total Edge (TE)", "Edge Density (ED)", "Mean Patch Area (AREA_MN)", "Landscape Shape Index (LSI)"), `Fungsi R` = c("lsm_c_ca()", "lsm_c_np()", "lsm_c_pd()", "lsm_c_lpi()", "lsm_c_te()", "lsm_c_ed()", "lsm_c_area_mn()", "lsm_c_lsi()"), Level = c("Class", "Class / Landscape", "Class / Landscape", "Class", "Landscape", "Class / Landscape", "Patch / Class / Landscape", "Class / Landscape"), Satuan = c("Hektar", "Jumlah", "Patch / 100 ha", "Persen", "Meter", "m / ha", "Hektar", "Tanpa satuan"), check.names = FALSE ) knitr::kable(metrics_df, align = c("l", "l", "c", "c")) ``` ## Eksekusi Metrik Individual ```{r} #| label: individual-metrics # Class Area (CA) lsm_c_ca(forest, directions = 8) # Number of Patches (NP) lsm_c_np(forest) # Patch Density (PD) lsm_c_pd(forest, directions = 8) # Largest Patch Index (LPI) lsm_c_lpi(forest, directions = 8) # Total Edge (TE) lsm_c_te(forest, count_boundary = FALSE) # Edge Density (ED) lsm_c_ed(forest, count_boundary = FALSE, directions = 8) # Mean Patch Area (AREA_MN) lsm_c_area_mn(forest, directions = 8) # Landscape Shape Index (LSI) lsm_c_lsi(forest) ``` --- # Kalkulasi Metrik dalam Grid 1 km² Bagian ini menghitung landscape metrics pada setiap sel grid 1 km² yang melingkupi kawasan studi. ## Load Library dan Data Grid ```{r} #| label: load-library-grid library(sf) library(tidyr) library(raster) library(landscapemetrics) ``` ```{r} #| label: load-grid r <- forest # Load shapefile grid 1 km² grid <- shapefile('grid_1km_ciwulan_clip.shp') ``` ## Kalkulasi Metrik per Grid ```{r} #| label: sample-lsm grid_lsm_class <- sample_lsm( r, y = grid, shape = 'square', what = c( "lsm_l_ai", # Aggregation Index — kecenderungan patch berkumpul "lsm_l_area_mn", # Mean Patch Area — rata-rata luas patch "lsm_l_area_sd", # SD Patch Area — keragaman ukuran patch "lsm_l_area_cv", # CV Patch Area — koefisien variasi luas patch "lsm_l_division", # Division Index — tingkat keterpecahan lanskap "lsm_l_lpi", # Largest Patch Index — dominansi patch terbesar "lsm_l_mesh", # Effective Mesh Size — ukuran kerapatan fragmen "lsm_l_np", # Number of Patches — jumlah patch "lsm_l_pd", # Patch Density — kerapatan patch per 100 ha "lsm_l_split", # Splitting Index — indeks pemisahan lanskap "lsm_l_ta" # Total Landscape Area — total luas lanskap ), return_raster = TRUE ) ``` ## Deskripsi Metrik Grid ### Agregasi & Konektivitas | Metrik | Deskripsi | |---|---| | `lsm_l_ai` | **Aggregation Index**: Mengukur seberapa teragregasi (berkelompok) patch dalam lanskap. Nilai 100 = sangat teragregasi. | | `lsm_l_np` | **Number of Patches**: Total jumlah patch dalam setiap sel grid. | | `lsm_l_pd` | **Patch Density**: Jumlah patch per 100 hektar. | ### Ukuran & Distribusi | Metrik | Deskripsi | |---|---| | `lsm_l_area_mn` | **Mean Patch Area**: Rata-rata luas patch (hektar). | | `lsm_l_area_sd` | **SD Patch Area**: Standar deviasi luas patch. | | `lsm_l_area_cv` | **CV Patch Area**: Koefisien variasi luas patch (%). | | `lsm_l_lpi` | **Largest Patch Index**: Proporsi area yang ditempati patch terbesar (%). | | `lsm_l_mesh` | **Effective Mesh Size**: Ukuran efektif kerapatan fragmen (hektar). | | `lsm_l_ta` | **Total Area**: Total luas lanskap (hektar). | ### Fragmentasi | Metrik | Deskripsi | |---|---| | `lsm_l_division` | **Division Index**: Probabilitas dua titik acak berada di patch berbeda (0–1). | | `lsm_l_split` | **Splitting Index**: Jumlah patch berukuran sama yang mengisi total area lanskap. | ## Restrukturisasi dan Ekspor Data ```{r} #| label: pivot-export # Ubah format long → wide (setiap metrik menjadi kolom tersendiri) grid_lsm_w <- pivot_wider( grid_lsm_class, id_cols = plot_id, names_from = metric, values_from = value ) # Gabungkan tabel atribut dengan shapefile grid connect <- cbind(grid, grid_lsm_w) # Simpan sebagai shapefile baru shapefile(x = connect, file = "lsm_grid_1km_2022.shp", overwrite = TRUE) ``` > **Output:** File `lsm_grid_1km_2022.shp` siap divisualisasikan di QGIS atau ArcGIS untuk analisis spasial lanjutan. --- # Informasi Sesi ```{r} #| label: session-info #| eval: true sessionInfo() ``` --- *Dokumen ini dibuat dengan [Quarto](https://quarto.org/) dan dapat direproduksi dengan menjalankan ulang seluruh chunk menggunakan data yang sesuai.*