In NUDEPU/GeoNUDEPU:

knitr::opts_chunk$set(echo = FALSE, fig.align = "center", out.width = "75%", 
                      warning = FALSE, message = FALSE)
knitr::knit_hooks$set(mysize = function(before, options, envir) {
  if (before) 
    return(options$size)
})
library(sf)
library(leaflet)
library(leafem)
library(lwgeom)
library(raster)
library(knitr)
library(kableExtra)
library(appraiseR)
library(units)
install_symbolic_unit(name = "Reais", warn = TRUE, dimensionless = TRUE)
library(geosphere)
library(GeoNUDEPU)

# 1. Digitação das Coordenadas
#
#                             E             N    
coords <- matrix(data = c(733314.8343, 6925664.5164, # VT-01
                          733309.8336, 6925642.9108, # VT-02
                          733306.6148, 6925619.5272, # VT-03
                          733306.3085, 6925608.1096, # VT-04
                          733306.3775, 6925595.2041, # VT-05
                          733306.6345, 6925582.8906, # VT-06
                          733307.1473, 6925571.2114, # VT-07
                          733348.1286, 6925551.4944, # VT-08
                          733351.9514, 6925547.3030, # VT-09
                          733427.3666, 6925508.8163, # VT-10
                          733428.0364, 6925510.1773, # VT-11
                          733429.2674, 6925509.7568, # VT-12
                          733430.9938, 6925508.2240, # VT-13
                          733432.4604, 6925506.3071, # VT-14
                          733457.5095, 6925494.7292, # VT-15
                          733463.9436, 6925561.4700, # VT-16
                          733465.1922, 6925582.9527, # VT-17
                          733467.3729, 6925594.7859, # VT-18
                          733439.9858, 6925607.6677, # VT-19
                          733314.8343, 6925664.5164),# VT-01
                 ncol = 2, byrow = TRUE)
#
#
# 2. Criação da feição  
#
# Atentar para a definição do crs apropriado
# 
#
# Exemplos:
# WGS 84 = 4326
# SAD 69 22S = 29192
# SIRGAS 2000 22S = 31982
#
#pl1 <- st_sf(geom = st_sfc(st_polygon(list(area1))))
#pl2 <- st_sf(geom = st_sfc(st_polygon(list(area2))))
#spl <- st_sfc(geom = st_combine(rbind(pl1, pl2)), crs = 29192)
spl <- st_sfc(geom = st_polygon(list(coords)), crs = 31982)
#
# 2. Leitura Polígonos da União para separar área de marinha de área alodial
#
data("polUniao")
#
# Transformar camada de polígonos da União para mesmo CRS dos dados em análise
#
polUniao <- st_transform(polUniao, 31982)

#
#
# 3. Criação dos metadados  
#
attr <- data.frame(
  destinacao = "cessao",
  tipo = "aquatica",
  rip = NA,
  interessado = "Prefeitura Municipal de Palhoça",
  area = st_area(spl),
  area_uniao = st_area(st_intersection(spl, st_geometry(polUniao))),
  inicio = NA,
  prazo = NA,
  vigencia = NA,
  cnpj = " 82.892.316/0001-08",
  nup = "10154.143536/2019-37",
  protocolo = "SC04008/2019",
  ref = 5010861,
  concedida = FALSE,
  municipio = 8185,
  logradouro = NA,
  trecho = NA,
  aval = NA,
  dataaval = NA,
  refaval = NA,
  onerosa = FALSE,
  geometriaAproximada = FALSE)
#
# 4. União dos atributos à geometria
#
#
spl_df <- st_sf(attr,
                geometry = spl) %>%
  st_transform(crs = 4326) # Transforma para WGS84.

# Leitura de dados da SPU-SC-NUDEM
polUniao <- polUniao %>% st_transform(4326)
data("lpmHomologada")
data("lpmDermarcada")
data("lpmPresumida")
data("lltmHomologada")
data("lltmDemarcada")
data("lltmPresumida")
#
# Leitura de dados das camadas existentes
ocupacoes <- st_read("https://raw.githubusercontent.com/geoSPU/SPU-SC-NUPRIV/master/ocupacoes.geojson")
autobras <- st_read("https://github.com/geoSPU/SPUData/raw/master/autobras.geojson")
cessoes <- st_read("https://raw.githubusercontent.com/geoSPU/SPUData/master/cessoes.geojson")
entregas <- st_read("https://raw.githubusercontent.com/geoSPU/SPUData/master/entregas.geojson")
certdisp <- st_read("https://github.com/geoSPU/SPUData/raw/master/certdisp.geojson")
ranchos <- st_read("https://raw.githubusercontent.com/geoSPU/SPUData/master/ranchos_de_pesca.geojson")
aeroportos <- st_read("https://github.com/geoSPU/SPUData/raw/master/aeroportos.geojson")
portos <- st_read("https://github.com/Cadastro-Marinho/BrasilData/raw/master/portos.geojson")

Polígono Total do Imóvel

ext <- extent(spl_df)
m <- spl_df %>% 
  leaflet() %>% 
  # Adiciona ortofotos do Estado de SC (SIG-SC) como base
  addWMSTiles(baseUrl = "http://sigsc.sc.gov.br/sigserver/SIGSC/wms", 
              layers = list("OrtoRGB-Landsat-2012")) %>%
  # Adiciona como camada de base Fotos de satélite da ESRI
  # addProviderTiles(providers$Esri.WorldImagery) %>%
  # Adiciona o(s) polígono(s) em análise
  addPolygons(stroke = TRUE, weight = 2, color = "blue", 
              group = "Em análise") %>%
  # Adiciona outras camadas existentes
  # addPolygons(data = cessoes, color = "red") %>% 
  # addPolygons(data = certdisp, color = "white", fillColor = "blue", 
  #             dashArray = "10 10") %>% 
  # addPolygons(data = autobras, color = "white", fillColor = "grey", 
  #             dashArray = "10 10") %>% 
  # addPolygons(data = entregas, color = "white", fillColor = "white", 
  #             dashArray = "10 10") %>% 
  # Adiciona polígonos da União
  addPolygons(data = polUniao, color = "yellow", group = "Terras de Marinha") %>% 
  # Adiciona Linhas de Marinha
  addPolylines(data = lltmHomologada, color = "red") %>% 
  addPolylines(data = lpmHomologada, color = "blue") %>% 
  # addPolylines(data = lltmDemarcada, color = "red", dashArray = "10 10") %>%
  # addPolylines(data = lpmDemarcada, color = "blue", dashArray = "10 10") %>%
  # addPolylines(data = lltmPresumida, color = "red", dashArray = "10 10",
  #              opacity = .2, weight = 3) %>%
  # addPolylines(data = lpmPresumida, color = "blue", dashArray = "10 10",
  #              opacity = .2, weight = 3) %>%
  # Zoom no imóvel em análise
  fitBounds(lng1 = ext[1], lat1 = ext[3], lng2 = ext[2], lat2 = ext[4]) %>% 
  # Adiciona Legendas
  addLegend(labels = "Terras de Marinha", colors = "blue", 
            group = "Terras de Marinha", position = "topright") %>% 
  addLegend(labels = "Em análise", colors = "red", 
            group = "Em análise", position = "topright") %>% 
  # Adiciona Minimapa
  addMiniMap(position = "topleft", 
             tiles = providers$Esri.WorldGrayCanvas) %>% 
  addMouseCoordinates() %>%
  addLogo("SPU.jpg", src = "local", position = "bottomleft", 
                  offset.x = 15, offset.y = 15, 
                  width = 75, height = 75)
name <- paste(st_geohash(spl_df, 10), ".png", sep = "")
mapview::mapshot(m, file = name, remove_url = FALSE)
knitr::include_graphics(name)

Polígono de terras da União

spl_df_Uniao <- st_intersection(spl_df, polUniao)
spl_df_Uniao$area <- st_area(spl_df_Uniao)
spl_df_Uniao$perimetro <- st_length(spl_df_Uniao)

ext <- extent(spl_df_Uniao)
m <- spl_df_Uniao %>% 
  leaflet() %>% 
  # Adiciona ortofotos do Estado de SC (SIG-SC) como base
  addWMSTiles(baseUrl = "http://sigsc.sc.gov.br/sigserver/SIGSC/wms", 
              layers = list("OrtoRGB-Landsat-2012")) %>%
  # Adiciona como camada de base Fotos de satélite da ESRI
  # addProviderTiles(providers$Esri.WorldImagery) %>%
  # Adiciona o(s) polígono(s) em análise
  addPolygons(stroke = TRUE, weight = 2, color = "blue", 
              group = "Em análise") %>%
  # Adiciona outras camadas existentes
  # addPolygons(data = cessoes, color = "red") %>% 
  # addPolygons(data = certdisp, color = "white", fillColor = "blue", 
  #             dashArray = "10 10") %>% 
  # addPolygons(data = autobras, color = "white", fillColor = "grey", 
  #             dashArray = "10 10") %>% 
  # addPolygons(data = entregas, color = "white", fillColor = "white", 
  #             dashArray = "10 10") %>% 
  # Adiciona polígonos da União
  addPolygons(data = polUniao, color = "yellow", group = "Terras de Marinha") %>% 
  # Adiciona Linhas de Marinha
  addPolylines(data = lltmHomologada, color = "red") %>% 
  addPolylines(data = lpmHomologada, color = "blue") %>% 
  # addPolylines(data = lltmDemarcada, color = "red", dashArray = "10 10") %>%
  # addPolylines(data = lpmDemarcada, color = "blue", dashArray = "10 10") %>%
  # addPolylines(data = lltmPresumida, color = "red", dashArray = "10 10",
  #              opacity = .2, weight = 3) %>%
  # addPolylines(data = lpmPresumida, color = "blue", dashArray = "10 10",
  #              opacity = .2, weight = 3) %>%
  # Zoom no imóvel em análise
  fitBounds(lng1 = ext[1], lat1 = ext[3], lng2 = ext[2], lat2 = ext[4]) %>% 
  # Adiciona Legendas
  addLegend(labels = "Terras de Marinha", colors = "blue", 
            group = "Terras de Marinha", position = "topright") %>% 
  addLegend(labels = "Em análise", colors = "red", 
            group = "Em análise", position = "topright") %>% 
  # Adiciona Minimapa
  addMiniMap(position = "topleft", 
             tiles = providers$Esri.WorldGrayCanvas) %>% 
  addMouseCoordinates() %>%
  addLogo("SPU.jpg", src = "local", position = "bottomleft", 
                  offset.x = 15, offset.y = 15, 
                  width = 75, height = 75)
name <- paste("Uniao-", st_geohash(spl_df, 10), ".png", sep = "")
mapview::mapshot(m, file = name, remove_url = FALSE)
knitr::include_graphics(name)

Tabela de coordenadas

df <- st_coordinates(spl_df_Uniao)[, c(1, 2)]
df_SIRGAS <- st_coordinates(st_transform(spl_df_Uniao, crs = 31982))[, c(1, 2)]
vertices <- paste("VT", seq_len(dim(df_SIRGAS)[1] - 1), sep = "")
row.names(df_SIRGAS) <- make.names(c(vertices, vertices[1]), unique = TRUE)
# kable(df,
#       format.args = list(nsmall = 2),
#       col.names = c("E", "N"))
#df_SIRGAS
azimutes <- vector(mode = "numeric", length = dim(df)[1]-1)
distances <- vector(mode = "numeric", length = dim(df)[1]-1) # Curso é o azimute de 0 a 360

for (i in seq_len(dim(df)[1])) {
  if (i != dim(df)[1]){

      azimutes[[i]] <- st_geod_azimuth(st_sfc(st_point(df[i, ]), 
                                          st_point(df[i + 1, ]), crs = 4326))

      distances[[i]] <- st_distance(st_point(df_SIRGAS[i, ]),
                               st_point(df_SIRGAS[i + 1, ]))
  }
}

azimutes <- set_units(azimutes, rad)
azimutes_deg <- set_units(azimutes, degrees)
course <- (as.vector(azimutes_deg) + 360) %% 360
conv_merid <- -1.06277192089
planar_course <- course - conv_merid

df_SIRGAS <- cbind(df_SIRGAS, 
                   AzReal = c(course, NA),
                   AzPlano = c(planar_course, NA),
                   Dist = c(distances, NA))
df_SIRGAS <- as.data.frame(df_SIRGAS)

# Para grau, minutos e segundos
#sp::dd2dms(as.vector(course[c(7, 8, 1, 2, 3, 4, 5, 6)]))

kable(df_SIRGAS, digits = 2,
      format.args = list(nsmall = 2, decimal.mark = ",", big.mark = "."),
      col.names =  c("E", "N", "Real", "Plano", "(m)"),
      longtable = TRUE, 
      booktabs = TRUE) %>%
  add_header_above(c("Vértice", "Coordenadas" = 2, "Azimutes" = 2, "Distância")) %>% 
  kable_styling(font_size = 3)

Tabela de atributos

kable(t(st_drop_geometry(spl_df_Uniao)), col.names = NULL, booktabs = TRUE)%>% 
  kable_styling(font_size = 6)

Memorial

writeLines("Inicia-se este memorial pelo vertice ", 
           con = 'memorial.md', sep = "")
for (i in seq_len(nrow(df_SIRGAS))) {
  cat("**", rownames(df_SIRGAS)[i], "** com coordenadas **N ", 
  brf(df_SIRGAS[i, 2]), "** e **E ", brf(df_SIRGAS[i, 1]), 
  "**. Deste segue com azimute ", brf(df_SIRGAS[i, 4]), " graus e distancia ",
  brf(df_SIRGAS[i, 5]), "m ate o vertice ", 
  sep = "", file = 'memorial.md', append = TRUE)
}

readLines('memorial.md')

Exportar

# Escrever no disco em formato geojson
st_write(spl_df, "10154.143536.2019-37.geojson", delete_dsn = TRUE)
# Escrever em kml
#st_write(spl_df, "PMPalhoca.kml", delete_dsn = TRUE)

NUDEPU/GeoNUDEPU documentation built on Jan. 17, 2022, 5:18 p.m.